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The Most Important Research Skills (With Examples)

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Research skills are the ability to find out accurate information on a topic. They include being able to determine the data you need, find and interpret those findings, and then explain that to others. Being able to do effective research is a beneficial skill in any profession, as data and research inform how businesses operate. Whether you’re unsure of your research skills or are looking for ways to further improve them, then this article will cover important research skills and how to become even better at research. Key Takeaways Having strong research skills can help you understand your competitors, develop new processes, and build your professional skills in addition to aiding you in finding new customers and saving your company money. Some of the most valuable research skills you can have include goal setting, data collection, and analyzing information from multiple sources. You can and should put your research skills on your resume and highlight them in your job interviews. In This Article    Skip to section What are research skills? Why are research skills important? 12 of the most important research skills How to improve your research skills Highlighting your research skills in a job interview How to include research skills on your resume Resume examples showcasing research skills Research skills FAQs References Sign Up For More Advice and Jobs Show More What are research skills?

Research skills are the necessary tools to be able to find, compile, and interpret information in order to answer a question. Of course, there are several aspects to this. Researchers typically have to decide how to go about researching a problem — which for most people is internet research.

In addition, you need to be able to interpret the reliability of a source, put the information you find together in an organized and logical way, and be able to present your findings to others. That means that they’re comprised of both hard skills — knowing your subject and what’s true and what isn’t — and soft skills. You need to be able to interpret sources and communicate clearly.

Why are research skills important?

Research skills are useful in any industry, and have applications in innovation, product development, competitor research, and many other areas. In addition, the skills used in researching aren’t only useful for research. Being able to interpret information is a necessary skill, as is being able to clearly explain your reasoning.

Research skills are used to:

Do competitor research. Knowing what your biggest competitors are up to is an essential part of any business. Researching what works for your competitors, what they’re doing better than you, and where you can improve your standing with the lowest resource expenditure are all essential if a company wants to remain functional.

Develop new processes and products. You don’t have to be involved in research and development to make improvements in how your team gets things done. Researching new processes that make your job (and those of your team) more efficient will be valued by any sensible employer.

Foster self-improvement. Folks who have a knack and passion for research are never content with doing things the same way they’ve always been done. Organizations need independent thinkers who will seek out their own answers and improve their skills as a matter of course. These employees will also pick up new technologies more easily.

Manage customer relationships. Being able to conduct research on your customer base is positively vital in virtually every industry. It’s hard to move products or sell services if you don’t know what people are interested in. Researching your customer base’s interests, needs, and pain points is a valuable responsibility.

Save money. Whether your company is launching a new product or just looking for ways to scale back its current spending, research is crucial for finding wasted resources and redirecting them to more deserving ends. Anyone who proactively researches ways that the company can save money will be highly appreciated by their employer.

Solve problems. Problem solving is a major part of a lot of careers, and research skills are instrumental in making sure your solution is effective. Finding out the cause of the problem and determining an effective solution both require accurate information, and research is the best way to obtain that — be it via the internet or by observation.

Determine reliable information. Being able to tell whether or not the information you receive seems accurate is a very valuable skill. While research skills won’t always guarantee that you’ll be able to tell the reliability of the information at first glance, it’ll prevent you from being too trusting. And it’ll give the tools to double-check .

12 of the most important research skills

Experienced researchers know that worthwhile investigation involves a variety of skills. Consider which research skills come naturally to you, and which you could work on more.

Data collection . When thinking about the research process, data collection is often the first thing that comes to mind. It is the nuts and bolts of research. How data is collected can be flexible.

For some purposes, simply gathering facts and information on the internet can fulfill your need. Others may require more direct and crowd-sourced research. Having experience in various methods of data collection can make your resume more impressive to recruiters.

Data collection methods include: Observation Interviews Questionnaires Experimentation Conducting focus groups

Analysis of information from different sources. Putting all your eggs in one source basket usually results in error and disappointment. One of the skills that good researchers always incorporate into their process is an abundance of sources. It’s also best practice to consider the reliability of these sources.

Are you reading about U.S. history on a conspiracy theorist’s blog post? Taking facts for a presentation from an anonymous Twitter account?

If you can’t determine the validity of the sources you’re using, it can compromise all of your research. That doesn’t mean just disregard anything on the internet but double-check your findings. In fact, quadruple-check. You can make your research even stronger by turning to references outside of the internet.

Examples of reliable information sources include: Published books Encyclopedias Magazines Databases Scholarly journals Newspapers Library catalogs

Finding information on the internet. While it can be beneficial to consulate alternative sources, strong internet research skills drive modern-day research.

One of the great things about the internet is how much information it contains, however, this comes with digging through a lot of garbage to get to the facts you need. The ability to efficiently use the vast database of knowledge that is on the internet without getting lost in the junk is very valuable to employers.

Internet research skills include: Source checking Searching relevant questions Exploring deeper than the first options Avoiding distraction Giving credit Organizing findings

Interviewing. Some research endeavors may require a more hands-on approach than just consulting internet sources. Being prepared with strong interviewing skills can be very helpful in the research process.

Interviews can be a useful research tactic to gain first-hand information and being able to manage a successful interview can greatly improve your research skills.

Interviewing skills involves: A plan of action Specific, pointed questions Respectfulness Considering the interview setting Actively Listening Taking notes Gratitude for participation

Report writing. Possessing skills in report writing can assist you in job and scholarly research. The overall purpose of a report in any context is to convey particular information to its audience.

Effective report writing is largely dependent on communication. Your boss, professor , or general reader should walk away completely understanding your findings and conclusions.

Report writing skills involve: Proper format Including a summary Focusing on your initial goal Creating an outline Proofreading Directness

Critical thinking. Critical thinking skills can aid you greatly throughout the research process, and as an employee in general. Critical thinking refers to your data analysis skills. When you’re in the throes of research, you need to be able to analyze your results and make logical decisions about your findings.

Critical thinking skills involve: Observation Analysis Assessing issues Problem-solving Creativity Communication

Planning and scheduling. Research is a work project like any other, and that means it requires a little forethought before starting. Creating a detailed outline map for the points you want to touch on in your research produces more organized results.

It also makes it much easier to manage your time. Planning and scheduling skills are important to employers because they indicate a prepared employee.

Planning and scheduling skills include: Setting objectives Identifying tasks Prioritizing Delegating if needed Vision Communication Clarity Time-management

Note-taking. Research involves sifting through and taking in lots of information. Taking exhaustive notes ensures that you will not neglect any findings later and allows you to communicate these results to your co-workers. Being able to take good notes helps summarize research.

Examples of note-taking skills include: Focus Organization Using short-hand Keeping your objective in mind Neatness Highlighting important points Reviewing notes afterward

Communication skills. Effective research requires being able to understand and process the information you receive, either written or spoken. That means that you need strong reading comprehension and writing skills — two major aspects of communication — as well as excellent listening skills.

Most research also involves showcasing your findings. This can be via a presentation. , report, chart, or Q&A. Whatever the case, you need to be able to communicate your findings in a way that educates your audience.

Communication skills include: Reading comprehension Writing Listening skills Presenting to an audience Creating graphs or charts Explaining in layman’s terms

Time management. We’re, unfortunately, only given 24 measly hours in a day. The ability to effectively manage this time is extremely powerful in a professional context. Hiring managers seek candidates who can accomplish goals in a given timeframe.

Strong time management skills mean that you can organize a plan for how to break down larger tasks in a project and complete them by a deadline. Developing your time management skills can greatly improve the productivity of your research.

Time management skills include: Scheduling Creating task outlines Strategic thinking Stress-management Delegation Communication Utilizing resources Setting realistic expectations Meeting deadlines

Using your network. While this doesn’t seem immediately relevant to research skills, remember that there are a lot of experts out there. Knowing what people’s areas of expertise and asking for help can be tremendously beneficial — especially if it’s a subject you’re unfamiliar with.

Your coworkers are going to have different areas of expertise than you do, and your network of people will as well. You may even know someone who knows someone who’s knowledgeable in the area you’re researching. Most people are happy to share their expertise, as it’s usually also an area of interest to them.

Networking involves: Remembering people’s areas of expertise Being willing to ask for help Communication Returning favors Making use of advice Asking for specific assistance

Attention to detail. Research is inherently precise. That means that you need to be attentive to the details, both in terms of the information you’re gathering, but also in where you got it from. Making errors in statistics can have a major impact on the interpretation of the data, not to mention that it’ll reflect poorly on you.

There are proper procedures for citing sources that you should follow. That means that your sources will be properly credited, preventing accusations of plagiarism. In addition, it means that others can make use of your research by returning to the original sources.

Attention to detail includes: Double checking statistics Taking notes Keeping track of your sources Staying organized Making sure graphs are accurate and representative Properly citing sources

How to improve your research skills

As with many professional skills, research skills serve us in our day to day life. Any time you search for information on the internet, you’re doing research. That means that you’re practicing it outside of work as well. If you want to continue improving your research skills, both for professional and personal use, here are some tips to try.

Differentiate between source quality. A researcher is only as good as their worst source. Start paying attention to the quality of the sources you use, and be suspicious of everything your read until you check out the attributions and works cited.

Be critical and ask yourself about the author’s bias, where the author’s research aligns with the larger body of verified research in the field, and what publication sponsored or published the research.

Use multiple resources. When you can verify information from a multitude of sources, it becomes more and more credible. To bolster your faith in one source, see if you can find another source that agrees with it.

Don’t fall victim to confirmation bias. Confirmation bias is when a researcher expects a certain outcome and then goes to find data that supports this hypothesis. It can even go so far as disregarding anything that challenges the researcher’s initial hunch. Be prepared for surprising answers and keep an open mind.

Be open to the idea that you might not find a definitive answer. It’s best to be honest and say that you found no definitive answer instead of just confirming what you think your boss or coworkers expect or want to hear. Experts and good researchers are willing to say that they don’t know.

Stay organized. Being able to cite sources accurately and present all your findings is just as important as conducting the research itself. Start practicing good organizational skills , both on your devices and for any physical products you’re using.

Get specific as you go. There’s nothing wrong with starting your research in a general way. After all, it’s important to become familiar with the terminology and basic gist of the researcher’s findings before you dig down into all the minutia.

Highlighting your research skills in a job interview

A job interview is itself a test of your research skills. You can expect questions on what you know about the company, the role, and your field or industry more generally. In order to give expert answers on all these topics, research is crucial.

Start by researching the company . Look into how they communicate with the public through social media, what their mission statement is, and how they describe their culture.

Pay close attention to the tone of their website. Is it hyper professional or more casual and fun-loving? All of these elements will help decide how best to sell yourself at the interview.

Next, research the role. Go beyond the job description and reach out to current employees working at your desired company and in your potential department. If you can find out what specific problems your future team is or will be facing, you’re sure to impress hiring managers and recruiters with your ability to research all the facts.

Finally, take time to research the job responsibilities you’re not as comfortable with. If you’re applying for a job that represents increased difficulty or entirely new tasks, it helps to come into the interview with at least a basic knowledge of what you’ll need to learn.

How to include research skills on your resume

Research projects require dedication. Being committed is a valuable skill for hiring managers. Whether you’ve had research experience throughout education or a former job, including it properly can boost the success of your resume .

Consider how extensive your research background is. If you’ve worked on multiple, in-depth research projects, it might be best to include it as its own section. If you have less research experience, include it in the skills section .

Focus on your specific role in the research, as opposed to just the research itself. Try to quantify accomplishments to the best of your abilities. If you were put in charge of competitor research, for example, list that as one of the tasks you had in your career.

If it was a particular project, such as tracking the sale of women’s clothing at a tee-shirt company, you can say that you “directed analysis into women’s clothing sales statistics for a market research project.”

Ascertain how directly research skills relate to the job you’re applying for. How strongly you highlight your research skills should depend on the nature of the job the resume is for. If research looks to be a strong component of it, then showcase all of your experience.

If research looks to be tangential, then be sure to mention it — it’s a valuable skill — but don’t put it front and center.

Resume examples showcasing research skills

Example #1: Academic Research

Simon Marks 767 Brighton Blvd. | Brooklyn, NY, 27368 | (683)-262-8883 | [email protected] Diligent and hardworking recent graduate seeking a position to develop professional experience and utilize research skills. B.A. in Biological Sciences from New York University. PROFESSIONAL EXPERIENCE Lixus Publishing , Brooklyn, NY Office Assistant- September 2018-present Scheduling and updating meetings Managing emails and phone calls Reading entries Worked on a science fiction campaign by researching target demographic Organizing calendars Promoted to office assistant after one year internship Mitch’s Burgers and Fries , Brooklyn, NY Restaurant Manager , June 2014-June 2018 Managed a team of five employees Responsible for coordinating the weekly schedule Hired and trained two employees Kept track of inventory Dealt with vendors Provided customer service Promoted to restaurant manager after two years as a waiter Awarded a $2.00/hr wage increase SKILLS Writing Scientific Research Data analysis Critical thinking Planning Communication RESEARCH Worked on an ecosystem biology project with responsibilities for algae collection and research (2019) Lead a group of freshmen in a research project looking into cell biology (2018) EDUCATION New York University Bachelors in Biological Sciences, September 2016-May 2020

Example #2: Professional Research

Angela Nichols 1111 Keller Dr. | San Francisco, CA | (663)-124-8827 |[email protected] Experienced and enthusiastic marketer with 7 years of professional experience. Seeking a position to apply my marketing and research knowledge. Skills in working on a team and flexibility. EXPERIENCE Apples amp; Oranges Marketing, San Francisco, CA Associate Marketer – April 2017-May 2020 Discuss marketing goals with clients Provide customer service Lead campaigns associated with women’s health Coordinating with a marketing team Quickly solving issues in service and managing conflict Awarded with two raises totaling $10,000 over three years Prestigious Marketing Company, San Francisco, CA Marketer – May 2014-April 2017 Working directly with clients Conducting market research into television streaming preferences Developing marketing campaigns related to television streaming services Report writing Analyzing campaign success statistics Promoted to Marketer from Junior Marketer after the first year Timberlake Public Relations, San Francisco, CA Public Relations Intern – September 2013–May 2014 Working cohesively with a large group of co-workers and supervisors Note-taking during meetings Running errands Managing email accounts Assisting in brainstorming Meeting work deadlines EDUCATION Golden Gate University, San Francisco, CA Bachelor of Arts in Marketing with a minor in Communications – September 2009 – May 2013 SKILLS Marketing Market research Record-keeping Teamwork Presentation. Flexibility

Research skills FAQs

What research skills are important?

Goal-setting and data collection are important research skills. Additional important research skills include:

Using different sources to analyze information.

Finding information on the internet.

Interviewing sources.

Writing reports.

Critical thinking.

Planning and scheduling.

Note-taking.

Managing time.

How do you develop good research skills?

You develop good research skills by learning how to find information from multiple high-quality sources, by being wary of confirmation bias, and by starting broad and getting more specific as you go.

When you learn how to tell a reliable source from an unreliable one and get in the habit of finding multiple sources that back up a claim, you’ll have better quality research.

In addition, when you learn how to keep an open mind about what you’ll find, you’ll avoid falling into the trap of confirmation bias, and by staying organized and narrowing your focus as you go (rather than before you start), you’ll be able to gather quality information more efficiently.

What is the importance of research?

The importance of research is that it informs most decisions and strategies in a business. Whether it’s deciding which products to offer or creating a marketing strategy, research should be used in every part of a company.

Because of this, employers want employees who have strong research skills. They know that you’ll be able to put them to work bettering yourself and the organization as a whole.

Should you put research skills on your resume?

Yes, you should include research skills on your resume as they are an important professional skill. Where you include your research skills on your resume will depend on whether you have a lot of experience in research from a previous job or as part of getting your degree, or if you’ve just cultivated them on your own.

If your research skills are based on experience, you could put them down under the tasks you were expected to perform at the job in question. If not, then you should likely list it in your skills section.

University of the People – The Best Research Skills for Success

Association of Internet Research Specialists — What are Research Skills and Why Are They Important?

MasterClass — How to Improve Your Research Skills: 6 Research Tips

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Sky Ariella is a professional freelance writer, originally from New York. She has been featured on websites and online magazines covering topics in career, travel, and lifestyle. She received her BA in psychology from Hunter College.

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What are research skills?

Last updated

26 April 2023

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Broadly, it includes a range of talents required to:

Find useful information

Perform critical analysis

Form hypotheses

Solve problems

It also includes processes such as time management, communication, and reporting skills to achieve those ends.

Research requires a blend of conceptual and detail-oriented modes of thinking. It tests one's ability to transition between subjective motivations and objective assessments to ensure only correct data fits into a meaningfully useful framework.

As countless fields increasingly rely on data management and analysis, polishing your research skills is an important, near-universal way to improve your potential of getting hired and advancing in your career.

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

What are basic research skills?

Almost any research involves some proportion of the following fundamental skills:

Organization

Decision-making

Investigation and analysis

Creative thinking

What are primary research skills?

The following are some of the most universally important research skills that will help you in a wide range of positions:

Time management — From planning and organization to task prioritization and deadline management, time-management skills are highly in-demand workplace skills.

Problem-solving — Identifying issues, their causes, and key solutions are another essential suite of research skills.

Critical thinking — The ability to make connections between data points with clear reasoning is essential to navigate data and extract what's useful towards the original objective.

Communication — In any collaborative environment, team-building and active listening will help researchers convey findings more effectively through data summarizations and report writing.

What are the most important skills in research?

Detail-oriented procedures are essential to research, which allow researchers and their audience to probe deeper into a subject and make connections they otherwise may have missed with generic overviews.

Maintaining priorities is also essential so that details fit within an overarching strategy. Lastly, decision-making is crucial because that's the only way research is translated into meaningful action.

• Why are research skills important?

Good research skills are crucial to learning more about a subject, then using that knowledge to improve an organization's capabilities. Synthesizing that research and conveying it clearly is also important, as employees seek to share useful insights and inspire effective actions.

Effective research skills are essential for those seeking to:

Analyze their target market

Investigate industry trends

Identify customer needs

Detect obstacles

Find solutions to those obstacles

Develop new products or services

Develop new, adaptive ways to meet demands

Discover more efficient ways of acquiring or using resources

Why do we need research skills?

Businesses and individuals alike need research skills to clarify their role in the marketplace, which of course, requires clarity on the market in which they function in. High-quality research helps people stay better prepared for challenges by identifying key factors involved in their day-to-day operations, along with those that might play a significant role in future goals.

• Benefits of having research skills

Research skills increase the effectiveness of any role that's dependent on information. Both individually and organization-wide, good research simplifies what can otherwise be unwieldy amounts of data. It can help maintain order by organizing information and improving efficiency, both of which set the stage for improved revenue growth.

Those with highly effective research skills can help reveal both:

Opportunities for improvement

Brand-new or previously unseen opportunities

Research skills can then help identify how to best take advantage of available opportunities. With today's increasingly data-driven economy, it will also increase your potential of getting hired and help position organizations as thought leaders in their marketplace.

• Research skills examples

Being necessarily broad, research skills encompass many sub-categories of skillsets required to extrapolate meaning and direction from dense informational resources. Identifying, interpreting, and applying research are several such subcategories—but to be specific, workplaces of almost any type have some need of:

Searching for information

Attention to detail

Taking notes

Problem-solving

Communicating results

Time management

• How to improve your research skills

Whether your research goals are to learn more about a subject or enhance workflows, you can improve research skills with this failsafe, four-step strategy:

Make an outline, and set your intention(s)

Know your sources

Learn to use advanced search techniques

Practice, practice, practice (and don't be afraid to adjust your approach)

These steps could manifest themselves in many ways, but what's most important is that it results in measurable progress toward the original goals that compelled you to research a subject.

• Using research skills at work

Different research skills will be emphasized over others, depending on the nature of your trade. To use research most effectively, concentrate on improving research skills most relevant to your position—or, if working solo, the skills most likely have the strongest impact on your goals.

You might divide the necessary research skills into categories for short, medium, and long-term goals or according to each activity your position requires. That way, when a challenge arises in your workflow, it's clearer which specific research skill requires dedicated attention.

How can I learn research skills?

Learning research skills can be done with a simple three-point framework:

Clarify the objective — Before delving into potentially overwhelming amounts of data, take a moment to define the purpose of your research. If at any point you lose sight of the original objective, take another moment to ask how you could adjust your approach to better fit the original objective.

Scrutinize sources — Cross-reference data with other sources, paying close attention to each author's credentials and motivations.

Organize research — Establish and continually refine a data-organization system that works for you. This could be an index of resources or compiling data under different categories designed for easy access.

Which careers require research skills?

Especially in today's world, most careers require some, if not extensive, research. Developers, marketers, and others dealing in primarily digital properties especially require extensive research skills—but it's just as important in building and manufacturing industries, where research is crucial to construct products correctly and safely.

Engineering, legal, medical, and literally any other specialized field will require excellent research skills. Truly, almost any career path will involve some level of research skills; and even those requiring only minimal research skills will at least require research to find and compare open positions in the first place.

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Home › Study Tips › Research Skills: What They Are and How They Benefit You

Research Skills: What They Are and How They Benefit You

• Published May 23, 2024

Research skills give you the ability to gather relevant information from different sources and analyse it critically in order to develop a comprehensive understanding of a subject. Thus, research skills are fundamental to academic success.

Developing these skills will improve your studies, helping you understand subjects better and positioning you for academic success.

That said, how can you develop important research skills? This will explore what research skills are, identify the core ones, and explain how you can develop them.

What Are Research Skills?

Research skills are a set of abilities that allow individuals to find and gather reliable information and then evaluate the information to find answers to questions.

Good research skills are important in academic settings, as finding and critically evaluating relevant information can help you gain a deeper understanding of a subject.

These skills are also important in professional and personal settings. When you graduate and are working in a professional capacity, you’ll often need to analyse sets of data to identify issues and determine how to solve them.

In personal contexts, you’ll always need to assess relevant information to make an informed decision. Whether you’re deciding on a major purchase, choosing a healthcare provider, or planning to make an investment, you’ll need to evaluate options to ensure better decision outcomes.

Different Types of Research Skills

Research skills are categorised into different sub-skills. The most common types are:

Quantitative Skills

Quantitative skills refer to the ability to work with numerical data and perform mathematical and statistical analyses to extract meaningful insights and draw conclusions. 

When you have quantitative skills, you’ll be able to apply mathematical concepts and operations in research design and data analysis. 

You’ll also be proficient in using statistical methods to analyse data and interpreting numerical data to draw meaningful conclusions. 

Analytical Skills

Analytical skills refer to the ability to gather data, evaluate it, and draw sound conclusions. When you have analytical skills, you’ll be able to systematically analyse information to reach a reasonable conclusion. 

Analytical skills are important in problem-solving. They help you to break down complex problems into more manageable components, think critically about the information at hand, analyse root causes, and develop effective solutions.

Qualitative Skills

Qualitative skills refer to the ability to collect, analyse, and interpret non-numerical data. When you have qualitative skills, you’ll be proficient in observation, interviewing, and other methods for collecting qualitative research data. 

You’ll also be able to analyse non-numerical data, such as documents and images, to identify themes, patterns, and meanings.

Research Skills Examples

The core research skills you need for success in academic, professional, and personal contexts include:

Data Collection

Data is at the centre of every research, as data is what you assess to find the answers you seek. Thus, research starts with collecting relevant data.

Depending on the research, there are two broad categories of data you can collect: primary and secondary.

Primary data is generated by the researcher, like data from interviews, observations, or experiments. Secondary data is pre-existing data obtained from different existing databases, like published literature, government reports, etc. 

Thus, data collection is more than gathering information from the Internet. Depending on the research, it can require more advanced skills for conducting experiments to generate your own data.

Source Evaluation

When doing research on any subject (especially when using the Internet), you’ll be amazed at the volume of information you’ll find. And a lot is pure garbage that can compromise your research work.

Thus, an important research skill is being able to dig through the garbage to get to the real facts. This is where source evaluation comes in!

Good research skills call for being able to identify biases, assess the authority of the author, and determine the accuracy of information before using it.

Time Management Skills

Have you ever felt that there is not enough time in a day for all that you need to do? When you already have so much to do, adding research can be overwhelming.

Good time management skills can help you find the time to do all you need to do, including relevant research work, making it an essential research skill.

Time management allows you to plan and manage your research project effectively. It includes breaking down research tasks into more manageable parts, setting priorities, and allocating time to the different stages of the research.

Communication Skills

Communication is an important aspect of every research, as it aids in data collection and sharing research findings. 

Important communication skills needed in research include active listening, active speaking, interviewing, report writing, data visualisation, and presentation, etc.

For example, when research involves collecting primary data via interviews, you must have sound speaking and listening skills. 

When you conclude the research and need to share findings, you’ll need to write a research report and present key findings in easy-to-understand formats like charts. 

Attention to Detail

Attention to detail is the ability to achieve thoroughness and accuracy when doing something. It requires focusing on every aspect of the tasks, even small ones. 

Anything you miss during your research will affect the quality of your research findings. Thus, the ability to pay close attention to details is an important research skill.

You need attention to detail at every stage of the research process. During data collection, it helps you ensure reliable data. 

During analysis, it reduces the risk of error to ensure your results are trustworthy. It also helps you express findings precisely to minimise ambiguity and facilitate understanding.

Note-Taking

Note-taking is exactly what it sounds like—writing down key information during the research process.

Remember that research involves sifting through and taking in a lot of information. It’s impossible to take in all the information and recall it from memory. This is where note-taking comes in!

Note-taking helps you capture key information, making it easier to remember and utilise for the research later. It also involves writing down where to look for important information.

Critical Thinking

Critical thinking is the ability to think rationally and synthesise information in a thoughtful way. It is an important skill needed in virtually all stages of the research process.

For example, when collecting data, you need critical thinking to assess the quality and relevance of data. It can help you identify gaps in data to formulate your research question and hypothesis. 

It can also help you to identify patterns and make reasonable connections when interpreting research findings.

Data Analysis

Data may not mean anything until you analyse it qualitatively or quantitatively (using techniques like Excel or SPSS). For this reason, data analysis analysis is an important research skill.

Researchers need to be able to build hypotheses and test these using appropriate research techniques. This helps to draw meaningful conclusions and gain a comprehensive understanding of research data.

Problem-Solving Skills

Research often involves addressing specific questions and solving problems. For this reason, problem-solving skills are important skills when conducting research. 

Problem-solving skills refer to the ability to identify, analyse, and solve problems effectively. 

With problem-solving skills, you’ll be able to assess a situation, consider various solutions, and choose the most appropriate course of action toward finding a solution.

Benefits of Research Skills

Research skills have many benefits, including:

Enhances Critical Thinking

Research skills and critical thinking are intertwined such that developing one enhances the other.

Research requires people to question assumptions, evaluate evidence, analyse information, and draw conclusions. These activities require you to think critically about the information at hand. Hence, engaging in research enhances critical thinking.

Develops Problem-Solving Skills

Research helps you acquire a set of critical skills that are directly transferable to problem-solving. 

For example, research fosters creative thinking, as it often requires synthesising data from different sources and connecting different concepts. After developing creative thinking via research, you can apply the skill to generate innovative solutions in problem-solving situations. 

Helps in Knowledge Acquisition

Engaging in research is a powerful way to acquire knowledge. Research involves exploring new ideas, and this helps you expand your breadth of knowledge.

It also involves applying research methods and methodologies. So, you’ll acquire knowledge about research methods, enhancing your ability to design and conduct studies in your higher education or professional life.

Why Are Research Skills Important?

Strong research skills offer numerous benefits, especially for students’ academic learning and development. 

When you develop good research skills, you’ll reap great academic rewards that include:

In-Depth Understanding

Conducting research allows you to delve deep into specific topics, helping you gain a thorough understanding of the subject matter beyond what is covered in standard coursework.

Critical Thinking Development

Research involves critical evaluation of information and making informed decisions. This builds your ability to think critically.

This skill will not only help you solve academic problems better, but it’s also crucial to your personal and professional growth.

Encouragement of Independent Learning

Research encourages independent learning. When you engage in research, you seek answers independently. You take the initiative to find, retrieve, and evaluate information relevant to your research.

That helps you develop self-directed study habits. You’ll be able to take ownership of your education and actively seek out information for a better understanding of the subject matter.

Intellectual Curiosity Development

Research skills encourage intellectual curiosity and a love of learning, as they’ll make you explore topics you find intriguing or important. Thus, you’ll be more motivated to explore topics beyond the scope of your coursework.

Enhanced Communication Skills

Research helps you build better interpersonal skills as well as report-writing skills.

Research helps you sharpen your communication skills when you interact with research subjects during data collection. Communicating research findings to an audience also helps sharpen your presentation skills or report writing skills.

Assistance in Career Preparation 

Many professions find people with good research skills. Whether you’ll pursue a career in academia, business, healthcare, or IT, being able to conduct research will make you a valuable asset.

So, researching skills for students prepares you for a successful career when you graduate.

Contribution to Personal Growth

Research also contributes to your personal growth. Know that research projects often come with setbacks, unexpected challenges, and moments of uncertainty. Navigating these difficulties helps you build resilience and confidence.

Acquisition of Time Management Skills

Research projects often come with deadlines. Such research projects force you to set goals, prioritise tasks, and manage your time effectively.

That helps you acquire important time management skills that you can use in other areas of academic life and your professional life when you graduate.

Ways to Improve Research Skills

The ways to improve your research skills involve a combination of learning and practice. 

You should consider enrolling in research-related programmes, learning to use data analysis tools, practising summarising and synthesising information from multiple sources, collaborating with more experienced researchers, and more. 

Looking to improve your research skills? Read our 11 ways to improve research skills article.

How Can I Learn Research Skills?

You can learn research skills using these simple three-point framework:

Clarifying the Objective

Start by articulating the purpose of your research. Identify the specific question you are trying to answer or the problem you are aiming to solve.

Then, determine the scope of your research to help you stay focused and avoid going after irrelevant information.

Cross-Referencing Sources

The next step is to search for existing research on the topic. Use academic databases, journals, books, and reputable online sources.

It’s important to compare information from multiple sources, taking note of consensus among studies and any conflicting findings. 

Also, check the credibility of each source by looking at the author’s expertise, information recency, and reputation of the publication’s outlet.

Organise the Research

Develop a note-taking system to document key findings as you search for existing research. Create a research outline, then arrange your ideas logically, ensuring that each section aligns with your research objective.

As you progress, be adaptable. Be open to refining your research plan as new understanding evolves.

Enrolling in online research programmes can also help you build strong research skills. These programmes combine subject study with academic research project development to help you hone the skills you need to succeed in higher education.

Immerse Education is a foremost provider of online research programmes.

Acquire Research Skills with Immerse Education 

Research skills are essential to academic success. They help you gain an in-depth understanding of subjects, enhance your critical thinking and problem-solving skills, improve your time management skills, and more. 

In addition to boosting you academically, they contribute to your personal growth and prepare you for a successful professional career.

Thankfully, you can learn research skills and reap these benefits. There are different ways to improve research skills, including enrolling in research-based programmes. This is why you need Immerse Education!

Immerse Education provides participants aged 13-18 with unparalleled educational experience. All our programmes are designed by tutors from top global universities and help prepare participants for future success.

Our online research programme expertly combines subject study with academic research projects to help you gain subject matter knowledge and the important research skills you need to succeed in higher education.  With one-on-one tutoring or group sessions from an expert academic from Oxford or Cambridge University and a flexible delivery mode, the programme is designed for you to succeed. Subsequently, enrolling in our accredited Online Research Programme will award students with 8 UCAS points upon completion.

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10 Research Skills and How To Develop Them

• Updated December 25, 2023
• Published August 8, 2023

Are you looking to learn more about Research skills? In this article, we discuss Research skills in more detail and give you tips about how you can develop and improve them.

What are Research skills?

Research skills refer to the ability to effectively and efficiently gather, analyze, and synthesize information to answer questions, solve problems, or contribute to a body of knowledge. These skills are essential for various fields and disciplines, ranging from academic and scientific research to business, journalism, and beyond. Effective research skills involve several key components:

Information Retrieval

Source evaluation.

• Critical Thinking

Data Analysis

Problem formulation, organization and note-taking, synthesis and writing, ethical considerations, time management.

• Adaptability

Top 10 Research Skills

Below we discuss the top 10 Research skills. Each skill is discussed in more detail, and we will also give you tips on improving them.

Information Retrieval is all about mastering the art of finding relevant and credible sources of information to support your research goals. This skill involves using various online and offline tools to locate the data, articles, studies, and materials that are most pertinent to your research topic. It’s like being a detective for knowledge – you’re trying to uncover valuable insights that will contribute to your research project.

To excel in Information Retrieval, you must become adept at effectively using search engines, databases, libraries, and other resources. It’s not just about typing keywords into a search bar; it’s about understanding how to refine your searches, use advanced search operators, and explore different databases and sources.

You’ll need to evaluate the quality and reliability of sources to ensure that the information you gather is trustworthy and accurate. This skill also requires critical thinking, as you’ll need to assess the relevance of sources to your research objectives.

How to Improve Information Retrieval

Improving your Information Retrieval skills involves a combination of practice, strategy, and awareness. Start by familiarizing yourself with different research databases and libraries relevant to your field. Experiment with various search terms and use advanced search operators to narrow down results. Take the time to evaluate the credibility of sources – look for peer-reviewed articles, authoritative authors, and reliable institutions. Keep track of your searches and results to refine your strategies over time.

Stay updated with the latest developments in search technology and research databases to optimize your information retrieval process. Remember, the more you practice and fine-tune your approach, the better you’ll become at uncovering valuable gems of information for your research endeavors.

Source Evaluation is about becoming a discerning judge of the information you encounter during your research journey. It involves assessing the credibility, reliability, and relevance of the sources you come across, ensuring that you’re building your work on a foundation of trustworthy and accurate information. Think of yourself as a gatekeeper, using only the most reliable and relevant sources to support your research.

You need to develop a critical eye to enhance your Source Evaluation skills. Begin by examining the authorship – who wrote the source, and what are their credentials? Peer-reviewed articles from established researchers are more reliable than anonymous blog posts. Consider the publication source – is it a reputable journal or website in your field?

Next, look for citations and references within the source – a well-researched work will often cite other credible sources. Additionally, evaluate the publication date – while older sources can provide historical context, ensure you’re using recent information for up-to-date insights.

How to Improve Source Evaluation

Improving your Source Evaluation skills requires a combination of awareness and practice. As you encounter new sources, ask questions about their credibility and relevance. Do evidence and references support the information? Does the author have any potential biases? Take advantage of critical thinking to analyze the source’s overall quality.

To further refine your skills, seek guidance from mentors, professors, or librarians who can provide valuable insights into evaluating sources. The more you engage with this skill, the better you’ll become at building a solid foundation for your research with credible and reliable materials.

Critical Thinking is the intellectual toolset that empowers you to analyze information objectively, discern patterns, and draw well-informed conclusions based on evidence. It’s like being a detective for ideas – you sift through data, identify biases, and unravel complexities to make informed judgments that drive your research forward with clarity and precision.

To hone your Critical Thinking skills, you need to cultivate a curious and analytical mindset. Start by questioning assumptions and biases in both your own thinking and the information you encounter.

When evaluating sources, consider multiple viewpoints and sources of evidence before forming conclusions. Develop the ability to identify logical fallacies or weak arguments that may distort the validity of your findings. Embrace open-mindedness and be willing to adapt your ideas when faced with compelling evidence that challenges your initial perspective.

How to Improve Critical Thinking

Improving your Critical Thinking skills requires practice and deliberate effort. Engage in discussions and debates within your field and beyond to expose yourself to diverse perspectives and sharpen your ability to analyze complex issues. Regularly challenge yourself to critically evaluate information, whether it’s a news article, a research paper, or a colleague’s argument.

Seek feedback from mentors or peers to refine your critical thinking process and identify areas for improvement. Remember, Critical Thinking is an ongoing journey that can be developed over time – the more you engage with it, the more adept you’ll become at navigating the intricate landscape of ideas in your research endeavors.

Related :  Critical Thinking Interview Questions & Answers

Data Analysis is the art of processing, interpreting, and extracting meaningful insights from the raw information you’ve collected during your research journey. Think of it as deciphering a puzzle – you’re transforming numbers, observations, or qualitative data into a coherent narrative that answers your research questions and adds value to your work.

To excel in Data Analysis, you need to develop both quantitative and qualitative skills. For quantitative data, embrace statistical tools and techniques that help you identify trends, correlations, and patterns in your data sets. Practice using software like Excel, SPSS, or specialized tools for your field to perform statistical tests and visualize results effectively. For qualitative data, immerse yourself in the details, coding and categorizing themes to distill rich insights from textual or visual sources.

How to Improve Data Analysis

Improving your Data Analysis skills involves a combination of practice, learning, and refining your techniques. Start by immersing yourself in the basics of statistics and data analysis methodologies relevant to your research field. Engage in tutorials and online courses to familiarize yourself with various tools and software. As you analyze data, maintain clear documentation of your process and decisions, which will be crucial when presenting your findings.

Collaborate with peers or mentors who are experienced in data analysis to gain insights and feedback on your techniques. Remember, Data Analysis is about transforming data into knowledge – the more you engage with this skill, the better you’ll become at uncovering valuable insights that contribute to the depth and impact of your research.

Related :  Research Interview Questions & Answers

Problem Formulation is like setting the compass for your research journey – it involves defining clear and focused research questions or hypotheses that guide your entire investigation. Consider it the foundation of your work, as it shapes your approach, methods, and the ultimate impact of your research.

To master Problem Formulation, you need to become skilled in asking the right questions. Begin by thoroughly understanding the topic you’re exploring. What gaps or uncertainties do you notice in the existing knowledge? What specific aspect of the topic piques your interest? Craft research questions that are specific, measurable, achievable, relevant, and time-bound (SMART).

If you’re developing hypotheses, ensure they are testable and grounded in existing theories or observations. Your skills in Problem Formulation also extend to identifying the scope and boundaries of your research – understanding what you’re including and excluding from your study.

How to Improve Problem Formulation

Improving your Problem Formulation skills requires practice and iterative refinement. Start by conducting a comprehensive literature review to understand the existing research landscape in your area. This will help you identify potential gaps and formulate questions that build upon existing knowledge.

Discuss with peers, mentors, or experts in your field to gain different perspectives and insights into potential research problems. As you develop your skills, be open to revising and refining your research questions based on new information or insights. Remember, Problem Formulation is the compass that guides your research journey – the more you invest in crafting clear and well-defined questions, the more impactful and focused your research will be.

Related :  10 Fact Finding Skills and How to Develop Them

Imagine these skills as your research toolkit for maintaining order amidst the vast sea of information you encounter. Organization involves structuring and managing your research materials, while Note-Taking ensures you capture valuable insights and details for future reference. Together, they help you stay on track and prevent valuable information from slipping through the cracks.

To excel in Organization and Note-Taking, you need to develop strategies that work best for you. Start by creating a systematic folder structure on your computer to store digital documents, articles, and data sets. For physical materials, consider using labeled folders or binders. As you gather information, employ tools like reference management software to keep track of your sources and generate citations efficiently.

Simultaneously, practice effective Note-Taking during your readings and research. Jot down key points, ideas, and relevant quotes in a structured format, whether you’re using a physical notebook or a digital note-taking app.

How to Improve Organization and Note-Taking

Improving your Organization and Note-Taking skills requires a mix of discipline and adaptability. Establish consistent routines for organizing research materials, updating folders, and managing citations. Regularly review and reorganize your notes to keep them relevant and accessible. Experiment with different note-taking techniques, such as outlining, summarizing, or mind mapping, to find the approach that aligns with your learning style.

Remember, Organization and Note-Taking are your allies in navigating the sea of information – the more you refine these skills, the smoother your research journey will become and the more confident you’ll be in tackling complex topics.

Synthesis and Writing are your means of weaving together the threads of information and insights you’ve collected into a coherent and impactful narrative. Think of it as crafting a masterpiece from the puzzle pieces of your research – you’re presenting your findings, analysis, and conclusions in a way that informs and engages your audience.

To excel in Synthesis and Writing, you must become a data and idea storyteller. Begin by outlining your research paper or report. Organize your findings logically, building a structured framework that guides your reader through your research journey. Ensure each section flows smoothly, connecting the dots between concepts and evidence. While writing, focus on clarity and conciseness – avoid jargon and convoluted language that may confuse your readers. Use effective transitions to guide them from one point to the next.

How to Improve Synthesis and Writing

Improving your Synthesis and Writing skills requires both practice and revision. Start by breaking down the writing process into manageable steps – drafting, revising, and editing. Give yourself time between drafting and revising to approach your work with fresh eyes. Critically evaluate your writing for clarity, coherence, and accuracy during revision.

Consider seeking feedback from peers, mentors, or writing centers to gain insights into improving your writing style. Study well-written papers in your field to observe how experienced researchers present their ideas effectively. Remember, Synthesis and Writing are your tools for communicating your research’s impact – the more you refine these skills, the more effectively you’ll share your discoveries and contribute to the body of knowledge in your field.

Ethical Considerations encompass the principles and guidelines that ensure your research is conducted with integrity, respect for participants’ rights, and a commitment to transparency. Think of it as the moral compass that guides your research journey, ensuring that your work upholds ethical standards and contributes positively to society.

To excel in Ethical Considerations, you need to become a guardian of ethical integrity in your research. Begin by understanding the ethical guidelines and regulations specific to your field and your research type. This involves respecting participants’ autonomy by obtaining informed consent, protecting their privacy and confidentiality, and ensuring they’re treated with dignity. Additionally, uphold intellectual honesty by properly attributing sources, avoiding plagiarism, and disclosing any potential conflicts of interest.

How to Improve Ethical Considerations

Improving your Ethical Considerations skills involves a combination of awareness and vigilance. Regularly educate yourself on the ethical codes and regulations relevant to your field and research methods. When designing your research, carefully plan how you will address ethical concerns and potential risks.

As you conduct your research, stay attuned to any ethical dilemmas that may arise and be prepared to address them appropriately. Remember, Ethical Considerations are at the heart of responsible research – the more you cultivate these skills, the more your work will contribute positively to both your field and society as a whole.

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Time Management involves the art of effectively allocating your time to different research tasks, ensuring that you meet deadlines, stay on track, and maintain a balanced workflow. Think of it as your compass for navigating the often-intricate landscape of research – it helps you stay organized, productive, and in control of your research journey.

To excel in Time Management, you need to become a master of planning and prioritization. Start by breaking down your research project into manageable tasks and setting realistic goals for each stage. Create a schedule that allocates research, data collection, analysis, writing, and revision time. Be mindful of your energy levels – tackle complex tasks during your most productive hours. Embrace tools like to-do lists, calendars, and time-tracking apps to keep yourself accountable and stay aware of your progress.

How to Improve Time Management

Improving your Time Management skills requires consistent practice and self-awareness. Continuously assess your progress against your planned schedule, adjusting as needed to accommodate unexpected challenges or new insights. Develop the skill of saying no to distractions and non-essential tasks that can derail your focus.

Break larger tasks into smaller, more manageable chunks to prevent feeling overwhelmed. Regularly reflect on your time allocation and efficiency – what strategies are working well, and where can you improve? Remember, Time Management is a skill that can significantly impact your research journey – the more you refine it, the more you’ll find yourself navigating your work with greater ease and achieving your research goals with greater success.

Related :  10 Coordinating Skills and How to Develop Them

Adaptability is the ability to flex and evolve in response to changing circumstances, unexpected findings, and new information that arise during your research journey. Think of it as your compass for navigating the dynamic and ever-changing landscape of research – it empowers you to embrace uncertainty and adjust your course to ensure the best outcomes for your work.

To excel in Adaptability, you need to cultivate a mindset that embraces change and seeks opportunities within challenges. Start by acknowledging that research is often full of surprises and plans might need to shift. Develop a sense of resilience by staying open to revising your research questions, altering methodologies, or exploring unanticipated angles.

Being adaptable also means being resourceful – finding alternative approaches when things don’t go as planned. Embrace feedback from peers, mentors, or unexpected results, and be ready to integrate this feedback to improve the quality of your research.

How to Improve Adaptability

Improving your Adaptability skills involves practicing flexibility and embracing a growth mindset. Regularly reassess your research plan and objectives in light of new information or developments. Embrace failures and setbacks as opportunities for learning and growth rather than roadblocks. Seek out interdisciplinary perspectives and engage with new ideas that challenge your assumptions.

As you navigate through unexpected turns, continuously reflect on what you’ve learned and how you’ve adapted, so you can refine your approach in the future. Remember, Adaptability is the key to thriving in the dynamic landscape of research – the more you foster this skill, the better equipped you’ll be to tackle unforeseen challenges and emerge stronger from your research journey.

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Research Skills Conclusion

In the pursuit of knowledge and discovery, honing research skills is the linchpin that sets the stage for success. Throughout this exploration of various research skills and how to nurture them, one thing becomes evident: deliberate practice and continuous improvement are the bedrock of growth. Developing research skills is not merely a checkbox to mark; it’s a journey that empowers you to excel in your field, make meaningful contributions, and amplify the impact of your work.

Improving these skills isn’t just an option – it’s a necessity in today’s job market. The ability to gather information effectively, critically evaluate sources, analyze data, formulate problems, synthesize findings, and more, transforms the research process from a mere task into a dynamic and transformative experience. These skills serve as the pillars that uphold the credibility and validity of your work, ensuring that your contributions stand the test of scrutiny and time.

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Research Skills: What they are and Benefits

Research skills play a vital role in the success of any research project, enabling individuals to navigate the vast sea of information, analyze data critically, and draw meaningful conclusions. Whether conducting academic research, professional investigations, or personal inquiries, strong research skills are essential for obtaining accurate and reliable results.

LEARN ABOUT:   Research Process Steps

By understanding and developing these skills, individuals can embark on their research endeavors with confidence, integrity, and the capability to make meaningful contributions in their chosen fields. This article will explore the importance of research skills and discuss critical competencies necessary for conducting a research project effectively.

Content Index

What are Research Skills?

Important research skills for research project, benefits of research skills.

• Improving your Research Skills

Talk to Experts to Improve Skills

Research skills are the capability a person carries to create new concepts and understand the use of data collection. These skills include techniques, documentation, and interpretation of the collected data. Research is conducted to evaluate hypotheses and share the findings most appropriately. Research skills improve as we gain experience.

To conduct efficient research, specific research skills are essential. These skills are necessary for companies to develop new products and services or enhance existing products. To develop good research skills is important for both the individual as well as the company.

When undertaking a research project, one must possess specific important skills to ensure the project’s success and accuracy. Here are some essential research skills that are crucial for conducting a project effectively:

Time Management Skills:

Time management is an essential research skill; it helps you break down your project into parts and enables you to manage it easier. One can create a dead-line oriented plan for the research project and assign time for each task. Time management skills include setting goals for the project, planning and organizing functions as per their priority, and efficiently delegating these tasks.

Communication Skills:

These skills help you understand and receive important information and also allow you to share your findings with others in an effective manner. Active listening and speaking are critical skills for solid communication. A researcher must have good communication skills.

Problem-Solving:  

The ability to handle complex situations and business challenges and come up with solutions for them is termed problem-solving. To problem-solve, you should be able to fully understand the extent of the problem and then break it down into smaller parts. Once segregated into smaller chunks, you can start thinking about each element and analyze it to find a solution.

Information gathering and attention to detail:

Relevant information is the key to good research design . Searching for credible resources and collecting information from there will help you strengthen your research proposal and drive you to solutions faster. Once you have access to information, paying close attention to all the details and drawing conclusions based on the findings is essential.

Research Design and Methodology :

Understanding research design and methodology is essential for planning and conducting a project. Depending on the research question and objectives, researchers must select appropriate research methods, such as surveys, experiments, interviews, or case studies. Proficiency in designing research protocols, data collection instruments, and sampling strategies is crucial for obtaining reliable and valid results.

Data Collection and Analysis :

Researchers should be skilled in collecting and analyzing data accurately. It involves designing data collection instruments, collecting data through various methods, such as surveys or observations, and organizing and analyzing the collected data using appropriate statistical or qualitative analysis techniques. Proficiency in using software tools like SPSS, Excel, or qualitative analysis software can be beneficial.

By developing and strengthening these research skills, researchers can enhance the quality and impact of their research process, contributing to good research skills in their respective fields.

Research skills are invaluable assets that can benefit individuals in various aspects of their lives. Here are some key benefits of developing and honing research skills:

Boosts Curiosity :

Curiosity is a strong desire to know things and a powerful learning driver. Curious researchers will naturally ask questions that demand answers and will stop in the search for answers. Interested people are better listeners and are open to listening to other people’s ideas and perspectives, not just their own.

Cultivates Self-awareness :

As well as being aware of other people’s subjective opinions, one must develop the importance of research skills and be mindful of the benefits of awareness research; we are exposed to many things while researching. Once we start doing research, the benefit from it reflects on the beliefs and attitudes and encourages them to open their minds to other perspectives and ways of looking at things.

Effective Communication:

Research skills contribute to practical communication skills by enhancing one’s ability to articulate ideas, opinions, and findings clearly and coherently. Through research, individuals learn to organize their thoughts, present evidence-based arguments, and effectively convey complex information to different audiences. These skills are crucial in academic research settings, professional environments, and personal interactions.

Personal and Professional Growth :

Developing research skills fosters personal and professional growth by instilling a sense of curiosity, intellectual independence, and a lifelong learning mindset. Research encourages individuals to seek knowledge, challenge assumptions, and embrace intellectual growth. These skills also enhance adaptability as individuals become adept at navigating and assimilating new information, staying updated with the latest developments, and adjusting their perspectives and strategies accordingly.

Academic Success:

Research skills are essential for academic research success. They enable students to conduct thorough literature reviews, gather evidence to support their arguments, and critically evaluate existing research. By honing their research skills, students can produce well-structured, evidence-based essays, projects, and dissertations demonstrating high academic research rigor and analytical thinking.

Professional Advancement:

Research skills are highly valued in the professional world. They are crucial for conducting market research, analyzing trends, identifying opportunities, and making data-driven decisions. Employers appreciate individuals who can effectively gather and analyze information, solve complex problems, and provide evidence-based recommendations. Research skills also enable professionals to stay updated with advancements in their field, positioning themselves as knowledgeable and competent experts.

Developing and nurturing research skills can significantly benefit individuals in numerous aspects of their lives, enabling them to thrive in an increasingly information-driven world.

Improving Your Research Skills

There are many things you can do to improve your research skills and utilize them in your research or day job. Here are some examples:

• Develop Information Literacy: Strengthening your information literacy skills is crucial for conducting thorough research. It involves identifying reliable sources, evaluating the credibility of information, and navigating different research databases.
• Enhance Critical Thinking: Critical thinking is an essential skill for effective research. It involves analyzing information, questioning assumptions, and evaluating arguments. Practice critical analysis by analyzing thoughtfully, identifying biases, and considering alternative perspectives.
• Master Research Methodologies: Familiarize yourself with different research methodologies relevant to your field. Whether it’s qualitative, quantitative, or mixed methods research, realizing the strengths and limitations of each approach is crucial.
• Practice Effective Time Management: Research requires dedicated time and effort. Develop good time management skills to ensure that you allocate sufficient time for each stage of the research process, including planning, data collection, analysis, and writing.
• Embrace Collaboration: Collaborating with peers and colleagues can provide a fresh perspective and enrich your research experience. Engage in discussions, share ideas, and seek feedback from others. Collaborative projects allow for exchanging knowledge and skills.
• Continuously Update Your Knowledge: Stay informed about your field’s latest developments and advancements. Regularly read scholarly articles, attend conferences, and follow reputable sources of information to stay up to date with current research trends.

There is plenty of information available on the internet about every topic; hence, learning skills to know which information is relevant and credible is very important. Today most search engines have the feature of advanced search, and you can customize the search as per your preference. Once you learn this skill, it will help you find information. 

Experts possess a wealth of knowledge, experience, and insights that can significantly enhance your understanding and abilities in conducting research. Experts have often encountered numerous challenges and hurdles throughout their research journey and have developed effective problem-solving techniques. Engaging with experts is a highly effective approach to improving research skills.

Moreover, experts can provide valuable feedback and constructive criticism on your research work. They can offer fresh perspectives, identify areas for improvement, and help you refine your research questions, methodology, and analysis.

At QuestionPro, we can help you with the necessary tools to carry out your projects, and we have created the following free resources to help you in your professional growth:

• Survey Templates

Research skills are invaluable assets that empower individuals to navigate the ever-expanding realm of information, make informed decisions, and contribute to advancing knowledge. With advanced research tools and technologies like QuestionPro Survey Software, researchers have potent resources to conduct comprehensive surveys, gather data, and analyze results efficiently.

Where data-driven decision-making is crucial, research skills supported by advanced tools like QuestionPro are essential for researchers to stay ahead and make impactful contributions to their fields. By embracing these research skills and leveraging the capabilities of powerful survey software, researchers can unlock new possibilities, gain deeper insights, and pave the way for meaningful discoveries.

Authors : Gargi Ghamandi & Sandeep Kokane

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Research skills: Examples + how to improve them

No matter what career path you choose to take, research skills are one of the key graduate career skills that will help you impress employers in applications and support you throughout your entire working life. 

Research skills are essential in problem-solving; learning how to improve research skills is therefore a great way to prepare for the workplace and improve your overall skill set in your early career. In this article, you’ll find out what research skills are, how to improve your research skills and much more. 

• What are research skills?
• Examples of research skills
• Jobs that require research skills
• How to improve research skills

How to use research skills at your workplace

How to include research skills in a cv, how to include research skills in a cover letter.

• How to demonstrate your research skills at a job interview

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What are research skills? 

Research skills refer to an individual’s ability to source information about a certain topic, and effectively extract and evaluate the information in order to answer questions or solve problems. 

Research skills are soft skills that are highly sought after by employers as they show a candidate’s ability to understand and analyse a variety of materials and sources. Whether you’re studying or already in the workplace, research skills are important transferable skills to have in any role or sector that you choose.

These skills can be constantly improved, and this is a great way to develop in your early career and prepare for the workplace. For example, your manager might ask you to conduct research or analysis for various projects, where these skills will be essential for your success. 

Learn how to develop your entire transferable skillset with this free online learning course. You'll also get a certificate once you complete the course that you can display on your CV and LinkedIn profile.

Examples of research skills 

During your time at school and university, you will have used a variety of research skills to complete projects and assignments. If you’re not sure what research skills look like in practice, here are some examples: 

Data collection 

Data collection is the process of systematically gathering information in order to solve problems, answer questions and better understand a particular topic. The information or data that you are collecting can be quantitative or qualitative; it can be collected through using surveys, interviews, reviewing existing materials and more to solve a particular problem.

At university, you would need to read broadly on a certain topic or conduct a literature review for a certain project. This is all data collection, and you can develop and use these experiences in your future role too. 

Critical thinking

Critical thinking is the ability to interpret and analyse information in order to form a particular judgement or evaluation. Someone who is a great critical thinker will be able to apply their knowledge (informed by evidence from, for example, data collection) to think rationally and come to a conclusion. Critical thinking is key in the workplace as it means you can analyse and evaluate strategically, to come to a judgement that will inform a particular action or idea.

Detail orientation 

Another key example of a research skill is detail orientation, or the ability to focus on small details. Someone who is detail-oriented will be able to notice small mistakes and will be able to deliver high-quality and accurate work. When solving problems, this is essential, as the ability to extract and evaluate information with accuracy is important for the validity of your research and will help drive high-quality results. 

Time management 

Time management is the ability to organise your time when planning different activities and projects. Effective time management means you’re able to balance your workload and ensure all tasks are completed within an allotted time. This is important for your research skills, as it means you are able to effectively delegate your time between data collection, analysis and evaluation.

Jobs that require research skills 

• External auditors have great attention to detail to investigate organisations. In an external auditor role, you will need to research policies and regulations, analyse data provided by the organisation and draw conclusions for a report.
• A strategist in the financial sector looks at an organisation’s finances to come up with plans for the future. You need great analytical and evaluative skills in order to understand the best options for your clients and turn a rational judgement into action. 
• A role in the Civil Service involves researching, developing and maintaining policy in the UK. Being able to inform your decisions with evidence, and manage your time effectively, is key. 
• In the role of a data scientist , you will need to conduct research to understand why a client or company needs a data scientist, and be able to analyse effectively to see big patterns in large amounts of data. 
• Clinical scientists must carefully analyse and process large amounts of data, requiring strong research skills and detail orientation.

Not quite sure about the type of career you should pursue? Take our Career Path Test and get matched with the career paths and sectors that meet your interests. 

How to improve research skills 

• Practise your time management and organisation skills: Whether you’re at university or in your early career, it’s important to start learning how to balance your time effectively to complete a number of tasks. For your next project, try setting out clear activities that need to be completed, how long you need to spend on each, and a timeline for when each task will be started and completed. 
• Learn how to write reports: In any research process or project, you will need to summarise and evaluate your findings in a written report in a clear and concise way. Make sure to include the objective of your research, a summary of your findings, and the judgements you have made from the evidence you found. 
• Read more widely: One of the core aspects of research and analysis is the ability to extract information from a variety of materials. Reading more widely will improve your data collection skills and will give you experience with forming judgements from a range of sources and on a number of topics.
• Plan . Before you start a project at work, make sure you’ve taken time to plan what tasks you need to do, and how long each will take, to understand the timelines of the project. This allows you to set aside dedicated time for the research phase, for example, before analysing data or putting ideas into action.
• Read about the topic . Whatever sector you’re in, and whatever project you’re working on, reading about your subject area is key to understanding your field ahead of any decisions being made. This will help you solve problems and answer any questions you need to be answered at the offset.
• Compare your results . Following any research or data collection, it’s a good idea to compare your findings with colleagues to ensure consistency across the team. This will lead to greater accuracy for the project as a whole.
• Present . Practising your presentation and communication skills is an essential part of developing your research skills. At the end of any research you’ve conducted, get into the habit of presenting your findings in a written report, and try presenting this to your line manager and wider team.

Once you’ve developed your research skills, it’s important that you know how to convey these effectively in applications – starting with your CV.

Read: How to write a CV | Advice & templates

Your CV is usually the first thing an employer sees of you, so you need to impress them from the offset. Highlighting your research skills, and how you’ve used them in your experience so far, is a great way to do this and will show your organisation, attention to detail and critical thinking.

Research skills should be included under the ‘skills and achievements section of your CV. This is where you include your technical and personal skills that relate to the role you’re applying for.

When talking about your research skills, remember to highlight how you’ve developed these in a concise way. For example, you might have developed research skills by writing a number of literature reviews at university. This might be phrased as “developed effective research skills through data collection and analysis when writing literature reviews for university projects.”

Another way to convey your research skills on your application and impress employers is through the cover letter. If an employer asks for one, it’s important to know how to structure a cover letter so that you can convey your skillset and interest in the role clearly and succinctly.

Your cover letter needs to be no more than one page and should highlight your competency for the role you’re applying for. Approach your application from the basis of ‘what I can do for you’ rather than ‘what you can do for me’. As research skills are transferable, this is a great chance to highlight how you can benefit the organisation and team you’re applying for, as it shows your ability to collect data, think critically, organise your time, analyse and more. Remember to apply these soft and transferable skills to what the job description says will be expected of you.

How to demonstrate your research skills at a job interview 

Interviews are another opportunity to impress employers with your skill set - including how you have developed strong research skills which you can use in the role you’re applying to. 

Ahead of your interview, you should be using your research skills to look into the company you’ve applied for. Get familiar with what they do, their company values and what they’re looking for in a candidate for your chosen role. 

You can also get prepared by practising to answer potential research skills questions like “give me an example of a time when you solved a problem using your research skills.” To answer this, make sure you’re identifying the specific research skills you have used, and explain a real example of when you have solved problems using them. Think about the impact using those research skills had in order to highlight how you have developed these skills effectively in practice. 

Research skills are essential for success in many different roles and fields. By learning how to improve your research skills, you are setting yourself up to impress employers at application and become an asset to a team when you enter the workplace. 

Research skills are soft skills that employers value, are essential for developing your problem-solving skills and are one of the key graduate career skills that recruiters look for. By adding ‘research skills’ to your CV, and highlighting your research capabilities at interviews, you are increasing your employability and chances for success.

Browse thousands of available graduate jobs, schemes and more and demonstrate to employers that you're able to use your research skills to succeed at interview and in your early career. 

Mastering Research Skills: A Cornerstone For Success In Science

Explore the importance of research skills in science. Learn critical thinking, reliable sources, search engine tips, and time management.

Research Skills are a cornerstone of success in science, encompassing the abilities necessary to navigate knowledge acquisition and extensive research. These skills not only facilitate the discovery of new information but also contribute to them being thoroughly analyzed and implemented. Let’s delve into critical research skills and see how they form the backbone of a thriving scientific career.

Understanding The Essence Of Research Skills

Research skills encompass the methodologies and competencies employed to gather, assess, and synthesize information effectively. These skills go beyond mere data collection; they involve critical thinking, reliable source identification, active listening, and proficient time management.

Related article: Mastering Critical Reading: Uncover The Art Of Analyzing Texts

Research skills constitute the backbone of any successful inquiry process, serving as a comprehensive toolkit that extends beyond the superficial gathering of information. Beyond the initial data accumulation phase, these skills embody a multidimensional approach. 

Research skills further encompass active listening — an often underestimated yet pivotal aspect — enabling researchers to glean valuable insights from conversations, lectures, or expert discussions. This skill ensures a holistic understanding of the subject matter, enriching the depth of information gathered and enhancing the overall research process.

The Merits Of Proficient Research Skills

The benefits of cultivating strong research skills extend beyond the individual researcher; they reverberate throughout academic and professional realms. One of the primary advantages is the empowerment to make informed decisions based on a thorough understanding of the available data. Researchers with honed skills can navigate through vast datasets with efficiency, identifying patterns and correlations that might elude those with less developed research acumen.

Moreover, these skills are instrumental in fostering innovation. The ability to synthesize information from various sources, coupled with critical thinking, allows researchers to envision new possibilities and alternative approaches to longstanding problems. This capacity for creative thinking is a hallmark of individuals with advanced research skills and is often the driving force behind groundbreaking discoveries and advancements in scientific knowledge.

In the professional sphere, individuals with strong research skills are valuable assets to organizations. Their ability to gather, evaluate, and apply information contributes to effective problem-solving and decision-making. In industries driven by technological advancements and data-driven insights, research skills are increasingly becoming a sought-after and indispensable trait in employees.

Cultivating Critical Thinking In Research Endeavors

Critical thinking is the cornerstone upon which the edifice of scientific inquiry stands. Beyond merely gathering information, it involves a systematic approach to analyzing, evaluating, and interpreting data. This skill equips researchers with the ability to discern patterns, spot anomalies, and derive meaningful conclusions from complex datasets. Moreover, critical thinking in research facilitates the identification of potential biases, ensuring objectivity and rigor in the investigative process. It enables scientists to challenge established theories, fostering an environment of intellectual curiosity and continual exploration.

Also read: How To Avoid Bias In Research: Navigating Scientific Objectivity

Furthermore, honing critical thinking skills is essential in adapting to the rapidly evolving scientific landscape. As new information emerges and paradigms shift, researchers equipped with strong critical thinking abilities are better poised to adapt to changes, integrate new knowledge, and refine their approaches.

Why Critical Thinking Is Paramount In Research

In the realm of scientific inquiry, critical thinking serves as the linchpin for discerning between reliable and fallacious information. It is the foundation for formulating hypotheses, design experiments and draw conclusions. By employing critical thinking skills, researchers can evaluate the validity of claims, weigh evidence objectively, and arrive at conclusions rooted in evidence-based reasoning. This approach safeguards against premature conclusions and ensures that scientific findings are based on robust analysis and thorough scrutiny.

Moreover, critical thinking promotes intellectual humility, encouraging researchers to remain open to alternative viewpoints and possibilities. It fosters a culture of constructive skepticism, wherein scientific claims are subject to continuous scrutiny and refinement. This aspect of critical thinking is pivotal in mitigating the influence of personal biases and fostering a collective pursuit of objective truth within the scientific community.

Related article: Thesis Conclusion: Making Your Research Paper Outstanding

Sharpening Critical Thinking Abilities

Improving critical thinking skills is an ongoing endeavor that involves deliberate practice and exposure to diverse perspectives. Engaging in activities that challenge assumptions, such as analyzing conflicting viewpoints or participating in debates, can sharpen critical thinking abilities. Additionally, fostering a habit of continuous learning and staying abreast of developments in various scientific domains can broaden perspectives, enhancing the ability to approach problems from different angles.

Moreover, encouraging interdisciplinary collaboration and engaging in discussions with peers from varied scientific backgrounds can stimulate critical thinking. Exposure to alternative methodologies and problem-solving approaches cultivates adaptability and a more nuanced understanding of complex scientific issues.

Navigating Reliable Sources In Scientific Inquiry

The art of identifying reliable sources is a skill that requires meticulous evaluation and discrimination. Apart from traditional peer-reviewed journals and reputable publications, researchers must discern the authenticity of online sources, considering factors like author expertise, publication credibility, and potential biases. Furthermore, acknowledging the significance of preprint articles while discerning their limitations is pivotal in staying updated with the latest scientific developments.

Criteria For Reliable Sources

Developing a keen eye for distinguishing credible sources from unreliable ones is crucial. Encouragingly, initiatives promoting media literacy and critical evaluation of sources are gaining traction, empowering individuals to navigate the digital landscape more discerningly.

Reliable sources in scientific research adhere to stringent criteria, embodying credibility, accuracy, objectivity, and relevance. Peer-reviewed journals, known for their rigorous editorial and review processes, serve as gold standards in academic research. Additionally, reputable institutions and recognized experts within specific scientific fields contribute to the authenticity of information.

Understanding the underlying criteria for determining source reliability allows researchers to make informed decisions about the veracity and applicability of the information they encounter. This discernment, crucial in an era flooded with information, is fundamental to upholding the integrity of scientific inquiry.

Discerning And Assessing Reliable Sources

Developing the ability to discern credible sources involves a multifaceted approach. Understanding the context in which the information is and cross-referencing it with multiple reliable sources adds layers of validation, ensuring the reliability and accuracy of the information gathered.

Furthermore, distinguishing between primary and secondary sources aids in the assessment process. While primary sources offer firsthand information or original research findings, secondary sources interpret and analyze primary data. Recognizing the nuances between these sources is pivotal in grasping the depth and breadth of scientific information.

Harnessing The Power Of Search Engines In Research

In the digital age, search engines serve as gateways to an expansive pool of information. Leveraging these tools efficiently necessitates familiarity with advanced search techniques. Utilizing specific search operators, employing filters, and refining search queries enable researchers to access targeted and relevant information swiftly. Moreover, being mindful of the credibility of the websites accessed via search engines ensures the reliability of the gathered data.

Despite the convenience offered by search engines, researchers must approach the results with a critical lens. Verifying the sources’ credibility and cross-referencing information from various reputable sources remains imperative. Search engines, while valuable, are tools that require adept navigation to extract credible and pertinent information effectively.

Maximizing Search Engine Efficiency

Understanding the nuances of different search engines tailored for scientific research, such as PubMed, Google Scholar, and Scopus, can significantly enhance the efficiency of the research process. Each search engine has unique functionalities and focuses, catering to specific scientific disciplines or types of information. Familiarizing oneself with these platforms and their features empowers researchers to optimize their search strategies and access specialized resources pertinent to their field of study.

Related article: The Importance of Scholarly Sources: How to Find and Evaluate

Moreover, utilizing advanced search parameters, such as Boolean operators or specific filters, allows researchers to refine their searches and access highly targeted information. Learning and applying these techniques enhance the precision and relevance of the obtained results, streamlining the research process and saving valuable time.

Active Listening: A Crucial Research Skill

Active listening extends beyond the conventional perception of listening; it involves a focused and deliberate effort to comprehend and assimilate information effectively. During the research process, active listening plays a pivotal role, particularly when gathering insights from expert discussions, interviews, or academic lectures. It demands undivided attention, keen observation, and an open-minded approach to absorb the nuances and key concepts communicated by the speaker.

Furthermore, active listening isn’t solely about hearing words; it encompasses deciphering underlying meanings, interpreting tone and context, and probing for additional information. This skill enhances the depth and quality of information garnered, ensuring a more holistic understanding of the subject matter.

Engaging In Active Listening During Research

Researchers can refine active listening skills by actively engaging in discussions, seeking clarification when necessary, and taking comprehensive notes. Encouraging dialogue and posing pertinent questions fosters a deeper engagement in the information communicated. Additionally, employing techniques such as summarizing key points or paraphrasing to confirm understanding promotes effective communication and comprehension.

Moreover, employing active listening skills aids researchers in identifying underlying implications and nuances in conversations. It contributes to the extraction of invaluable insights and perspectives, enriching the research process and broadening the scope of information gathered.

Time Management: A Prerequisite For Effective Research

Time management skills are indispensable for researchers seeking to optimize their productivity and efficiency. Successful research requires careful planning and allocation of time for various research phases, including data collection, analysis, experimentation, and documentation.

Efficient time management involves setting realistic goals, establishing priorities, and adhering to structured timelines. Breaking down larger research tasks into manageable segments not only prevents overwhelm but also ensures a systematic approach towards achieving milestones.

Also read: Time Management for Researchers: A Comprehensive Toolkit

Efficient Time Management Strategies

Adopting strategies like creating timelines, setting deadlines for specific research milestones, and maintaining a structured schedule helps researchers stay focused and organized. Furthermore, allocating dedicated time slots for research activities prevents procrastination and promotes consistent progress.

Moreover, embracing tools and techniques that aid in time management, such as calendar applications, task management software, or productivity frameworks like the Pomodoro Technique , can significantly enhance efficiency in research endeavors.

Create Scientifically Precise Infographics Effortlessly

For scientists seeking to enhance the presentation of their research, it’s crucial to go beyond just the depth of data. Readers often gravitate towards visual content, allowing them to grasp concepts more quickly. With Mind the Graph , you can access a vast library of scientifically accurate figures spanning various topics. Sign up and create compelling infographics in minutes, elevating the quality of your research papers.

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Research skills

On this page:.

• Introduction
• 1.1 Research seems to have been extremely high-impact historically
• 1.2 There are good theoretical reasons to think that research will be high-impact
• 1.3 Research skills seem extremely useful to the problems we think are most pressing
• 1.4 If you’re a good fit, you can have much more impact than the average
• 1.5 Depending on which subject you focus on, you may have good backup options
• 2.1 Academic research
• 2.2 Practical but big picture research
• 2.3 Applied research
• 2.4 Stages of progression through building and using research skills
• 3.1 How much do researchers differ in productivity?
• 3.2 What does this mean for building research skills?
• 4.1 How to predict your fit in advance
• 4.2 How to tell if you’re on track
• 5.1 Choosing a research field
• 6.1 Which research topics are the highest-impact?
• 6.2 Find jobs that use a research skills
• 7 Career paths we’ve reviewed that use these skills
• 8 Learn more about research

Norman Borlaug was an agricultural scientist. Through years of research, he developed new, high-yielding, disease-resistant varieties of wheat.

It might not sound like much, but as a result of Borlaug’s research, wheat production in India and Pakistan almost doubled between 1965 and 1970, and formerly famine-stricken countries across the world were suddenly able to produce enough food for their entire populations. These developments have been credited with saving up to a billion people from famine, 1 and in 1970, Borlaug was awarded the Nobel Peace Prize.

Many of the highest-impact people in history , whether well-known or completely obscure, have been researchers.

Table of Contents

In a nutshell: Talented researchers are a key bottleneck facing many of the world’s most pressing problems . That doesn’t mean you need to become an academic. While that’s one option (and academia is often a good place to start), lots of the most valuable research happens elsewhere. It’s often cheap to try out developing research skills while at university, and if it’s a good fit for you, research could be your highest impact option.

Key facts on fit

Why are research skills valuable.

Not everyone can be a Norman Borlaug, and not every discovery gets adopted. Nevertheless, we think research can often be one of the most valuable skill sets to build — if you’re a good fit.

We’ll argue that:

Research seems to have been extremely high-impact historically

There are good theoretical reasons to think that research will be high-impact, research skills seem extremely useful to the problems we think are most pressing, if you’re a good fit, you can have much more impact than the average.

• And, depending on which subject you focus on, you may have good backup options .

Together, this suggests that research skills could be particularly useful for having an impact.

Later, we’ll look at:

• How to evaluate your fit for building research skills

How to get started building research skills

• How you can use these skills to have an impact once you’ve started

If we think about what has most improved the modern world, much can be traced back to research: advances in medicine such as the development of vaccines against infectious diseases, developments in physics and chemistry that led to steam power and the industrial revolution , and the invention of the modern computer, an idea which was first proposed by Alan Turing in his seminal 1936 paper On Computable Numbers . 2

Many of these ideas were discovered by a relatively small number of researchers — but they changed all of society. This suggests that these researchers may have had particularly large individual impacts.

That said, research today is probably lower-impact than in the past. Research is much less neglected than it used to be: there are nearly 25 times as many researchers today as there were in 1930. 3 It also turns out that more and more effort is required to discover new ideas, so each additional researcher probably has less impact than those that came before. 4

However, even today, a relatively small fraction of people are engaged in research. As an approximation, only 0.1% of the population are academics, 5 and only about 2.5% of GDP is spent on research and development . If a small number of people account for a large fraction of progress, then on average each person’s efforts are significant.

Moreover, we still think there’s a good case to be made for research being impactful on average today, which we cover in the next two sections.

There’s little commercial incentive to focus on the most socially valuable research. And most researchers don’t get rich, even if their discoveries are extremely valuable. Alan Turing made no money from the discovery of the computer, and today it’s a multibillion-dollar industry. This is because the benefits of research often come a long time in the future and can’t usually be protected by patents. This means if you care more about social impact than profit, then it’s a good opportunity to have an edge.

Research is also a route to leverage. When new ideas are discovered, they can be spread incredibly cheaply, so it’s a way that a single person can change a field. And innovations are cumulative — once an idea has been discovered, it’s added to our stock of knowledge and, in the ideal case, becomes available to everyone. Even ideas that become outdated often speed up the important future discoveries that supersede it.

When you look at our list of the world’s most pressing problems — like preventing future pandemics or reducing risks from AI systems — expert researchers seem like a key bottleneck.

For example, to reduce the risk posed by engineered pandemics , we need people who are talented at research to identify the biggest biosecurity risks and to develop better vaccines and treatments.

To ensure that developments in AI are implemented safely and for the benefit of humanity, we need technical experts thinking hard about how to design machine learning systems safely and policy researchers to think about how governments and other institutions should respond. (See this list of relevant research questions .)

And to decide which global priorities we should spend our limited resources on, we need economists, mathematicians, and philosophers to do global priorities research . For example, see the research agenda of the Global Priorities Institute at Oxford .

We’re not sure why so many of the most promising ways to make progress on the problems we think are most pressing involve research, but it may well be due to the reasons in the section above — research offers huge opportunities for leverage, so if you take a hits-based approach to finding the best solutions to social problems, it’ll often be most attractive.

In addition, our focus on neglected problems often means we focus on smaller and less developed areas, and it’s often unclear what the best solutions are in these areas. This means that research is required to figure this out.

For more examples, and to get a sense of what you might be able to work on in different fields, see this list of potentially high-impact research questions, organised by discipline .

The sections above give reasons why research can be expected to be impactful in general . But as we’ll show below , the productivity of individual researchers probably varies a great deal (and more than in most other careers). This means that if you have reason to think your degree of fit is better than average, your expected impact could be much higher than the average.

Depending on which subject you focus on, you may have good backup options

Pursuing research helps you develop deep expertise on a topic, problem-solving, and writing skills. These can be useful in many other career paths. For example:

• Many research areas can lead to opportunities in policymaking , since relevant technical expertise is valued in some of these positions. You might also have opportunities to advise policymakers and the public as an expert.
• The expertise and credibility you can develop by focusing on research (especially in academia) can put you in a good position to switch your focus to communicating important ideas , especially those related to your speciality, either to the general public, policymakers, or your students.
• If you specialise in an applied quantitative subject, it can open up certain high-paying jobs, such as quantitative trading or data science , which offer good opportunities for earning to give .

Some research areas will have much better backup options than others — lots of jobs value applied quantitative skills, so if your research is quantitative you may be able to transition into work in effective nonprofits or government. A history academic, by contrast, has many fewer clear backup options outside of academia.

What does building research skills typically involve?

By ‘research skills’ we broadly mean the ability to make progress solving difficult intellectual problems.

We find it especially useful to roughly divide research skills into three forms:

• Academic research

Building academic research skills is the most predefined route. The focus is on answering relatively fundamental questions which are considered valuable by a specific academic discipline. This can be impactful either through generally advancing a field of research that’s valuable to society or finding opportunities to work on socially important questions within that field.

Turing was an academic. He didn’t just invent the computer — during World War II he developed code-breaking machines that allowed the Allies to be far more effective against Nazi U-boats. Some historians estimate this enabled D-Day to happen a year earlier than it would have otherwise. 6 Since World War II resulted in 10 million deaths per year, Turing may have saved about 10 million lives.

We’re particularly excited about academic research in subfields of machine learning relevant to reducing risks from AI , subfields of biology relevant to preventing catastrophic pandemics , and economics — we discuss which fields you should enter below .

Academic careers are also excellent for developing credibility, leading to many of the backup options we looked at above, especially options in communicating important ideas or policymaking .

Academia is relatively unique in how flexibly you can use your time. This can be a big advantage — you really get time to think deeply and carefully about things — but can be a hindrance, depending on your work style.

See more about what academia involves in our career review on academia .

Practical but big picture research

Academia rewards a focus on questions that can be decisively answered with the methods of the field. However, the most important questions can rarely be answered rigorously — the best we can do is look at many weak forms of evidence and come to a reasonable overall judgement. which means while some of this research happens in academia, it can be hard to do that.

Instead, this kind of research is often done in nonprofit research institutes, e.g. the Centre for the Governance of AI or Our World in Data , or independently.

Your focus should be on answering the questions that seem most important (given your view of which global problems most matter) through whatever means are most effective.

Some examples of questions in this category that we’re especially interested in include:

• How likely is a pandemic worse than COVID-19 in the next 10 years?
• How difficult is the AI alignment problem going to be to solve?
• Which global problems are most pressing?
• Is the world getting better or worse over time?
• What can we learn from the history of philanthropy about which forms of philanthropy might be most effective?

You can see a longer list of ideas in this article .

Someone we know who’s had a big impact with research skills is Ajeya Cotra. Ajeya initially studied electrical engineering and computer science at UC Berkeley. In 2016, she joined Open Philanthropy as a grantmaker. 7 Since then she’s worked on a framework for estimating when transformative AI might be developed , how worldview diversification could be applied to allocating philanthropic budgets, and how we might accidentally teach AI models to deceive us .

Applied research

Then there’s applied research. This is often done within companies or nonprofits, like think tanks (although again, there’s also plenty of applied research happening in academia). Here the focus is on solving a more immediate practical problem (and if pursued by a company, where it might be possible to make profit from the solution) — and there’s lots of overlap with engineering skills . For example:

• Developing new vaccines
• Creating new types of solar cells or nuclear reactors
• Developing meat substitutes

Neel was doing an undergraduate degree in maths when he decided that he wanted to work in AI safety . Our team was able to introduce Neel to researchers in the field and helped him secure internships in academic and industry research groups. Neel didn’t feel like he was a great fit for academia — he hates writing papers — so he applied to roles in commercial AI research labs. He’s now a research engineer at DeepMind. He works on mechanistic interpretability research which he thinks could be used in the future to help identify potentially dangerous AI systems before they can cause harm.

We also see “policy research” — which aims to develop better ideas for public policy — as a form of applied research.

Stages of progression through building and using research skills

These different forms of research blur into each other, and it’s often possible to switch between them during a career. In particular, it’s common to begin in academic research and then switch to more applied research later.

However, while the skill sets contain a common core, someone who can excel in intellectual academic research might not be well-suited to big picture practical or applied research.

The typical stages in an academic career involve the following steps:

• Pick a field. This should be heavily based on personal fit (where you expect to be most successful and enjoy your work the most), though it’s also useful to think about which fields offer the best opportunities to help tackle the problems you think are most pressing, give you expertise that’s especially useful given these problems, and use that at least as a tie-breaker. (Read more about choosing a field .)
• Earn a PhD.
• Learn your craft and establish your career — find somewhere you can get great mentorship and publish a lot of impressive papers. This usually means finding a postdoc with a good group and then temporary academic positions.
• Secure tenure.
• Focus on the research you think is most socially valuable (or otherwise move your focus towards communicating ideas or policy).

Academia is usually seen as the most prestigious path…within academia. But non-academic positions can be just as impactful — and often more so since you can avoid some of the dysfunctions and distractions of academia, such as racing to get publications.

At any point after your PhD (and sometimes with only a master’s), it’s usually possible to switch to applied research in industry, policy, nonprofits, and so on, though typically you’ll still focus on getting mentorship and learning for at least a couple of years. And you may also need to take some steps to establish your career enough to turn your attention to topics that seem more impactful.

Note that from within academia, the incentives to continue with academia are strong, so people often continue longer than they should!

If you’re focused on practical big picture research, then there’s less of an established pathway, and a PhD isn’t required.

Besides academia, you could attempt to build these skills in any job that involves making difficult, messy intellectual judgement calls, such as investigative journalism, certain forms of consulting, buy-side research in finance, think tanks, or any form of forecasting.

Personal fit is perhaps more important for research than other skills

The most talented researchers seem to differ hugely in their impact compared to typical researchers across a wide variety of metrics and according to the opinions of other researchers.

For instance, when we surveyed biomedical researchers, they said that very good researchers were rare, and they’d be willing to turn down large amounts of money if they could get a good researcher for their lab. 8 Professor John Todd, who works on medical genetics at Cambridge, told us :

The best people are the biggest struggle. The funding isn’t a problem. It’s getting really special people[…] One good person can cover the ground of five, and I’m not exaggerating.

This makes sense if you think the distribution of research output is very wide — that the very best researchers have a much greater output than the average researcher.

How much do researchers differ in productivity?

It’s hard to know exactly how spread out the distribution is, but there are several strands of evidence that suggest the variability is very high.

Firstly, most academic papers get very few citations, while a few get hundreds or even thousands. An analysis of citation counts in science journals found that ~47% of papers had never been cited, more than 80% had been cited 10 times or less, but the top 0.1% had been cited more than 1,000 times. A similar pattern seems to hold across individual researchers , meaning that only a few dominate — at least in terms of the recognition their papers receive.

Citation count is a highly imperfect measure of research quality, so these figures shouldn’t be taken at face-value. For instance, which papers get cited the most may depend at least partly on random factors, academic fashions, and “winner takes all” effects — papers that get noticed early end up being cited by everyone to back up a certain claim, even if they don’t actually represent the research that most advanced the field.

However, there are other reasons to think the distribution of output is highly skewed.

William Shockley, who won the Nobel Prize for the invention of the transistor, gathered statistics on all the research employees in national labs, university departments, and other research units, and found that productivity (as measured by total number of publications, rate of publication, and number of patents) was highly skewed , following a log-normal distribution.

Shockley suggests that researcher output is the product of several (normally distributed) random variables — such as the ability to think of a good question to ask, figure out how to tackle the question, recognize when a worthwhile result has been found, write adequately, respond well to feedback, and so on. This would explain the skewed distribution: if research output depends on eight different factors and their contribution is multiplicative, then a person who is 50% above average in each of the eight areas will in expectation be 26 times more productive than average. 9

When we looked at up-to-date data on how productivity differs across many different areas , we found very similar results. The bottom line is that research seems to perhaps be the area where we have the best evidence for output being heavy-tailed.

Interestingly, while there’s a huge spread in productivity, the most productive academic researchers are rarely paid 10 times more than the median, since they’re on fixed university pay-scales. This means that the most productive researchers yield a large “excess” value to their field. For instance, if a productive researcher adds 10 times more value to the field than average, but is paid the same as average, they will be producing at least nine times as much net benefit to society. This suggests that top researchers are underpaid relative to their contribution, discouraging them from pursuing research and making research skills undersupplied compared to what would be ideal.

Can you predict these differences in advance?

Practically, the important question isn’t how big the spread is, but whether you could — early on in your career — identify whether or not you’ll be among the very best researchers.

There’s good news here! At least in scientific research, these differences also seem to be at least somewhat predictable ahead of time, which means the people entering research with the best fit could have many times more expected impact.

In a study , two IMF economists looked at maths professors’ scores in the International Mathematical Olympiad — a prestigious maths competition for high school students. They concluded that each additional point scored on the International Mathematics Olympiad “is associated with a 2.6 percent increase in mathematics publications and a 4.5 percent increase in mathematics citations.”

We looked at a range of data on how predictable productivity differences are in various areas and found that they’re much more predictable in research.

What does this mean for building research skills?

The large spread in productivity makes building strong research skills a lot more promising if you’re a better fit than average. And if you’re a great fit, research can easily become your best option.

And while these differences in output are not fully predictable at the start of a career, the spread is so large that it’s likely still possible to predict differences in productivity with some reliability.

This also means you should mainly be evaluating your long-term expected impact in terms of your chances of having a really big success.

That said, don’t rule yourself out too early. Firstly, many people systematically underestimate their skills . (Though others overestimate them!) Also, the impact of research can be so large that it’s often worth trying it out, even if you don’t expect you’ll succeed . This is especially true because the early steps of a research career often give you good career capital for many other paths.

How to evaluate your fit

How to predict your fit in advance.

It’s hard to predict success in advance, so we encourage an empirical approach: see if you can try it out and look at your track record.

You probably have some track record in research: many of our readers have some experience in academia from doing a degree, whether or not they intended to go into academic research. Standard academic success can also point towards being a good fit (though is nowhere near sufficient!):

• Did you get top grades at undergraduate level (a 1st in the UK or a GPA over 3.5 in the US)?
• If you do a graduate degree, what’s your class rank (if you can find that out)? If you do a PhD, did you manage to author an article in a top journal (although note that this is easier in some disciplines than others)?

Ultimately, though, your academic track record isn’t going to tell you anywhere near as much as actually trying out research. So it’s worth looking for ways to cheaply try out research (which can be easy if you’re at college). For example, try doing a summer research project and see how it goes.

Some of the key traits that suggest you might be a good fit for a research skills seem to be:

• Intelligence (Read more about whether intelligence is important for research .)
• The potential to become obsessed with a topic ( Becoming an expert in anything can take decades of focused practice , so you need to be able to stick with it.)
• Relatedly, high levels of grit, self-motivation, and — especially for independent big picture research, but also for research in academia — the ability to learn and work productively without a traditional manager or many externally imposed deadlines
• Openness to new ideas and intellectual curiosity
• Good research taste, i.e. noticing when a research question matters a lot for solving a pressing problem

There are a number of other cheap ways you might try to test your fit.

Something you can do at any stage is practice research and research-based writing. One way to get started is to try learning by writing .

You could also try:

• Finding out what the prerequisites/normal backgrounds of people who go into a research area are to compare your skills and experience to them
• Reading key research in your area, trying to contribute to discussions with other researchers (e.g. via a blog or twitter), and getting feedback on your ideas
• Talking to successful researchers in a field and asking what they look for in new researchers

How to tell if you’re on track

Here are some broad milestones you could aim for while becoming a researcher:

• You’re successfully devoting time to building your research skills and communicating your findings to others. (This can often be the hardest milestone to hit for many — it can be hard to simply sustain motivation and productivity given how self-directed research often needs to be.)
• In your own judgement, you feel you have made and explained multiple novel, valid, nontrivially important (though not necessarily earth-shattering) points about important topics in your area.
• You’ve had enough feedback (comments, formal reviews, personal communication) to feel that at least several other people (whose judgement you respect and who have put serious time into thinking about your area) agree, and (as a result) feel they’ve learned something from your work. For example, lots of this feedback could come from an academic supervisor. Make sure you’re asking people in a way that gives them affordance to say you’re not doing well.
• You’re making meaningful connections with others interested in your area — connections that seem likely to lead to further funding and/or job opportunities. This could be from the organisations most devoted to your topics of interest; but, there could also be a “dissident” dynamic in which these organisations seem uninterested and/or defensive, but others are noticing this and offering help.

If you’re finding it hard to make progress in a research environment, it’s very possible that this is the result of that particular environment, rather than the research itself. So it can be worth testing out multiple different research jobs before deciding this skill set isn’t for you.

Within academic research

Academia has clearly defined stages, so you can see how you’re performing at each of these.

Very roughly, you can try asking “How quickly and impressively is my career advancing, by the standards of my institution and field?” (Be careful to consider the field as a whole, rather than just your immediate peers, who might be very different from average.) Academics with more experience than you may be able to help give you a clear idea of how things are going.

We go through this in detail in our review of academic research careers .

Within independent research

As a very rough guideline, people who are an excellent fit for independent research can often reach the broad milestones above with a year of full-time effort purely focusing on building a research skill set, or 2–3 years of 20%-time independent effort (i.e. one day per week).

Within research in industry or policy

The stages here can look more like an organisation-building career , and you can also assess your fit by looking at your rate of progression through the organisation.

As we mentioned above , if you’ve done an undergraduate degree, one obvious pathway into research is to go to graduate school ( read our advice on choosing a graduate programme ) and then attempt to enter academia before deciding whether to continue or pursue positions outside of academia later in your career.

If you take the academic path, then the next steps are relatively clear. You’ll want to try to get excellent grades in undergraduate and in your master’s, ideally gain some kind of research experience in your summers, and then enter the best PhD programme you can. From there, focus on learning your craft by working under the best researcher you can find as a mentor and working in a top hub for your field. Try to publish as many papers as possible since that’s required to land an academic position.

It’s also not necessary to go to graduate school to become a great researcher (though this depends a lot on the field), especially if you’re very talented. For instance, we interviewed Chris Olah , who is working on AI research without even an undergraduate degree.

You can enter many non-academic research jobs without a background in academia. So one starting point for building up research skills would be getting a job at an organisation specifically focused on the type of question you’re interested in. For examples, take a look at our list of recommended organisations , many of which conduct non-academic research in areas relevant to pressing problems .

More generally, you can learn research skills in any job that heavily features making difficult intellectual judgement calls and bets, preferably on topics that are related to the questions you’re interested in researching. These might include jobs in finance, political analysis, or even nonprofits.

Another common route — depending on your field — is to develop software and tech skills and then apply them at research organisations. For instance, here’s a guide to how to transition from software engineering into AI safety research .

If you’re interested in doing practical big-picture research (especially outside academia), it’s also possible to establish your career through self-study and independent work — during your free time or on scholarships designed for this (such as EA Long-Term Future Fund grants and Open Philanthropy support for individuals working on relevant topics ).

Some example approaches you might take to self-study:

• Closely and critically review some pieces of writing and argumentation on relevant topics. Explain the parts you agree with as clearly as you can and/or explain one or more of your key disagreements.
• Pick a relevant question and write up your current view and reasoning on it. Alternatively, write up your current view and reasoning on some sub-question that comes up as you’re thinking about it.
• Then get feedback, ideally from professional researchers or those who use similar kinds of research in their jobs.

It could also be beneficial to start with some easier versions of this sort of exercise, such as:

• Explaining or critiquing interesting arguments made on any topic you find motivating to write about
• Writing fact posts
• Reviewing the academic literature on any topic of interest and trying to reach and explain a bottom-line conclusion

In general, it’s not necessary to obsess over being “original” or having some new insight at the beginning. You can learn a lot just by trying to write up your current understanding.

Choosing a research field

When you’re getting started building research skills, there are three factors to consider in choosing a field:

• Personal fit — what are your chances of being a top researcher in the area? Even if you work on an important question, you won’t make much difference if you’re not particularly good at it or motivated to work on the problem.
• Impact — how likely is it that research in your field will contribute to solving pressing problems?
• Back-up options — how will the skills you build open up other options if you decide to change fields (or leave research altogether)?

One way to go about making a decision is to roughly narrow down fields by relevance and back-up options and then pick among your shortlist based on personal fit.

We’ve found that, especially when they’re getting started building research skills, people sometimes think too narrowly about what they can be good at and enjoy. Instead, they end up pigeonholing themselves in a specific area (for example being restricted by the field of their undergraduate degree). This can be harmful because it means people who could contribute to highly important research don’t even consider it. This increases the importance of writing a broad list of possible areas to research.

Given our list of the world’s most pressing problems , we think some of the most promising fields to do research within are as follows:

• Fields relevant to artificial intelligence, especially machine learning , but also computer science more broadly. This is mainly to work on AI safety directly, though there are also many opportunities to apply machine learning to other problems (as well as many back-up options).
• Biology, particularly synthetic biology, virology, public health, and epidemiology. This is mainly for biosecurity .
• Economics . This is for global priorities research , development economics, or policy research relevant to any cause area, especially global catastrophic risks.
• Engineering — read about developing and using engineering skills to have an impact .
• International relations/political science, including security studies and public policy — these enable you to do research into policy approaches to mitigating catastrophic risks and are also a good route into careers in government and policy more broadly.
• Mathematics, including applied maths or statistics (or even physics). This may be a good choice if you’re very uncertain, as it teaches you skills that can be applied to a whole range of different problems — and lets you move into most of the other fields we list. It’s relatively easy to move from a mathematical PhD into machine learning, economics, biology, or political science, and there are opportunities to apply quantitative methods to a wide range of other fields. They also offer good back-up options outside of research.
• There are many important topics in philosophy and history, but these fields are unusually hard to advance within, and don’t have as good back-up options. (We do know lots of people with philosophy PhDs who have gone on to do other great, non-philosophy work!)

However, many different kinds of research skills can play a role in tackling pressing global problems.

Choosing a sub-field can sometimes be almost as important as choosing a field. For example, in some sciences the particular lab you join will determine your research agenda — and this can shape your entire career.

And as we’ve covered, personal fit is especially important in research. This can mean it’s easily worth going into a field that seems less relevant on average if you are an excellent fit. (This is due both to the value of the research you might produce and the excellent career capital that comes from becoming top of an academic field.)

For instance, while we most often recommend the fields above, we’d be excited to see some of our readers go into history , psychology, neuroscience, and a whole number of other fields. And if you have a different view of global priorities from us, there might be many other highly relevant fields.

Once you have these skills, how can you best apply them to have an impact?

Richard Hamming used to annoy his colleagues by asking them “What’s the most important question in your field?”, and then after they’d explained, following up with “And why aren’t you working on it?”

You don’t always need to work on the very most important question in your field, but Hamming has a point. Researchers often drift into a narrow speciality and can get detached from the questions that really matter.

Now let’s suppose you’ve chosen a field, learned your craft, and are established enough that you have some freedom about where to focus. Which research questions should you focus on?

Which research topics are the highest-impact?

Charles Darwin travelled the oceans to carefully document different species of birds on a small collection of islands — documentation which later became fuel for the theory of evolution. This illustrates how hard it is to predict which research will be most impactful.

What’s more, we can’t know what we’re going to discover until we’ve discovered it, so research has an inherent degree of unpredictability. There’s certainly an argument for curiosity-driven research without a clear agenda.

That said, we think it’s also possible to increase your chances of working on something relevant, and the best approach is to try to find topics that both personally motivate you and seem more likely than average to matter. Here are some approaches to doing that.

Using the problem framework

One approach is to ask yourself which global problems you think are most pressing , and then try to identify research questions that are:

• Important to making progress on those problems (i.e. if this question were answered, it would lead to more progress on these problems)
• Neglected by other researchers (e.g. because they’re at the intersection of two fields, unpopular for bad reasons, or new)
• Tractable (i.e. you can see a path to making progress)

The best research questions will score at least moderately well on all parts of this framework. Building a perpetual motion machine is extremely important — if we could do it, then we’d solve our energy problems — but we have good reason to think it’s impossible, so it’s not worth working on. Similarly, a problem can be important but already have the attention of many extremely talented researchers, meaning your extra efforts won’t go very far.

Finding these questions, however, is difficult. Often, the only way to identify a particularly promising research question is to be an expert in that field! That’s because (when researchers are doing their jobs), they will be taking the most obvious opportunities already.

However, the incentives within research rarely perfectly line up with the questions that most matter (especially if you have unusual values, like more concern for future generations or animals). This means that some questions often get unfairly neglected. If you’re someone who does care a lot about positive impact and have some slack, you can have a greater-than-average impact by looking for them.

Below are some more ways of finding those questions (which you can use in addition to directly applying the framework above).

Rules of thumb for finding unfairly neglected questions

• There’s little money in answering the question. This can be because the problem mostly affects poorer people, people who are in the future , or non-humans, or because it involves public goods . This means there’s little incentive for businesses to do research on this question.
• The political incentives to answer the question are missing. This can happen when the problem hurts poorer or otherwise marginalised people, people who tend not to organise politically, people in countries outside the one where the research is most likely to get done, people who are in the future , or non-humans. This means there’s no incentive for governments or other public actors to research this question.
• It’s new, doesn’t already have an established discipline, or is at the intersection of two disciplines. The first researchers in an area tend to take any low hanging fruit, and it gets harder and harder from there to make big discoveries. For example, the rate of progress within machine learning is far higher than the rate of progress within theoretical physics. At the same time, the structure of academia means most researchers stay stuck within the field they start in, and it can be hard to get funding to branch out into other areas. This means that new fields or questions at the intersection of two disciplines often get unfairly neglected and therefore provide opportunities for outsized impact.
• There is some aspect of human irrationality that means people don’t correctly prioritise the issue. For instance, some issues are easy to visualise, which makes them more motivating to work on. People are scope blind which means they’re likely to neglect the issues with the very biggest scale. They’re also bad at reasoning about issues with low probability, which can make them either over-invest or under-invest in them.
• Working on the question is low status. In academia, research that’s intellectually interesting and fits the research standards of the discipline are high status. Also, mathematical and theoretical work tends to be seen as higher status (and therefore helps to progress your career). But these don’t correlate that well with the social value of the question.
• You’re bringing new skills or a new perspective to an established area. Progress often comes in science from bringing the techniques and insights of one field into another. For instance, Kahneman started a revolution in economics by applying findings from psychology. Cross-over is an obvious approach but is rarely used because researchers tend to be immersed in their own particular subject.

If you think you’ve found a research question that’s short on talent, it’s worth checking whether the question is answerable. People might be avoiding the question because it’s just extremely difficult to find an answer. Or perhaps progress isn’t possible at all. Ask yourself, “If there were progress on this question, how would we know?”

Finally, as we’ve discussed, personal fit is particularly important in research . So position yourself to work on questions where you maximise your chances of producing top work.

Find jobs that use a research skills

If you have these skills already or are developing it and you’re ready to start looking at job opportunities that are currently accepting applications, see our curated list of opportunities for this skill set:

View all opportunities

Career paths we’ve reviewed that use these skills

• AI safety technical research and engineering
• AI governance and coordination
• Biorisk research
• China-related AI safety and governance paths
• Grantmaker focused on pressing world problems
• Research into global priorities
• Forecasting and related research and implementation
• Historian of large societal trends, inflection points, progress or collapse
• Expert in AI hardware

Specialist in emerging global powers

• Investigate a potentially pressing but unexplored global issue
• Research management
• Think tank research
• Research and advocacy promoting impactful climate solutions
• Improving China-Western coordination on global catastrophic risks
• Engineering
• Economics PhDs
• Machine learning PhDs
• Biomedical research
• Computer science PhDs
• Data science
• Philosophy academia

Learn more about research

• High Impact Science by Carl Shulman
• How to succeed as an early-stage researcher: the “lean startup” approach
• Podcast: Luisa and Robert Long on how to make independent research more fun
• A list of potentially high-impact research questions, organised by discipline

See all our articles and podcasts on research careers .

Read next:  Explore other useful skills

Want to learn more about the most useful skills for solving global problems, according to our research? See our list.

Plus, join our newsletter and we’ll mail you a free book

Join our newsletter and we’ll send you a free copy of The Precipice — a book by philosopher Toby Ord about how to tackle the greatest threats facing humanity. T&Cs here .

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Notes and references

• “Green Revolution technology saved an estimated one billion people from famine and produced more than enough food for a world population that doubled from three to six billion between 1960 and 2000.” Archived link , retrieved 5-Nov-2018. ↩
• Turing, A. M. (1937). “ On Computable Numbers, with an Application to the Entscheidungsproblem “. Proceedings of the London Mathematical Society. 2. 42 (1): 230–265. ↩
• See Figure 1 of Bloom et al, (2017) ↩
• “We present a wide range of evidence from various industries, products and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore’s law. The number of researchers required today to achieve the famous doubling every two years of the density of computer chips is more than 18 times larger than the number required in the early 1970s.” Bloom, N., Jones, C. I., Van Reenen, J., & Webb, M. (2017). Are ideas getting harder to find? National Bureau of Economic Research. ↩
• The number of academics and graduate students in the world ↩

If Turing and his group had not weakened the U-boats’ hold on the North Atlantic, the 1944 Allied invasion of Europe — the D-Day landings — could have been delayed, perhaps by about a year or even longer, since the North Atlantic was the route that ammunition, fuel, food and troops had to travel in order to reach Britain from America. ↩

• Open Philanthropy is 80,000 Hours’ largest funder, as of 2023 ↩

Sir Andrew McMichael, leading HIV vaccine researcher

For the good person whose CV you just described, would you prefer their CV landing on your desk or an extra grant?

“It’s not a simple choice. If they’re that good, they’ll probably get their own funding at some point. You can take them on without huge risk. I would always take the person.” How about if you could have half a million pound grant?

“It’s hard to turn down half a million pounds. I wouldn’t know many groups who would. You could buy another machine or do another project that would be too expensive otherwise. It depends on how much money I’ve got there already. It’s fantastic to get good people though, no question.”

Can good researchers always get funding?

“Yes, reasonably easily. Everyone can get bad patches. It’s unusual to always be on top of everything. For instance, you can get a dip at the end of a line of work, while you’re getting ready to start something else. But on the whole they can.”

John Todd, a Professor of Medical Genetics at Cambridge

Would you prefer £100,000 per year or [a good person] working for you?

“Definitely the guy”

How about £0.5mn per year?

“I’d still take the person at £0.5mn. By £5mn, I’d prefer the money! There’s a cut off somewhere between the two.”

Why would you pay so much?

“It’s very difficult to find brilliant people who have the true grit to get things done, even if it takes a long time. Most of them end up in the city.”

“The best people are the biggest struggle. The funding isn’t a problem. It’s getting really special people. I call them the one percenters…If you have a good person, it’s easy to get the grants for them. I don’t think there’s a really good researcher out there who couldn’t get funding from the MRC or Wellcome Trust.”

“One good guy can cover the ground of five, and I’m not exaggerating”

Katie Ewer, a cellular immunologist

Is your impression that it’s harder to find good researchers or additional funding?

“In order for research to progress, you need lots of different types of people within an organisation. You need people who are very methodical in what they do and are capable of doing large volumes of high through-put work, and then you need a few people at the top with the creativity to pull ideas out of the sky that nobody else would ever think of and convince Bill Gates to give you £1 million. I guess if you have somebody like that in your institution who is that creative and has that amazing ability and insight, then you can probably convince people to give you £1 million. But funding is always limited. We could proceed our field more quickly if we had as much funding as the HIV field.”

“If you are uniquely gifted in scientific research, then you should probably be a scientific researcher. But for the other 99.9% of the population, they’re probably best going and earning £1 million elsewhere and funding research.” ↩

• “Differences in rates of scientific production are much bigger than differences in the rates of performing simpler acts, such as the rate of running the mile, or the number of words a man can speak per minute… a large number of factors are involved so that small changes in each, all in the same direction, may result in a very large change in output. For example, the number of ideas a scientist can bring into awareness at one time may control his ability to make an invention and his rate of invention may increase very rapidly with this number.” Shockley, W. (1957) On the statistics of individual variations of productivity in research laboratories . Proceedings of the IRE, 45(3), 279-290. ↩

What are Research Skills? How to Improve Your Skills in Research

Learn strategies and techniques to improve your research skills. Avoid common mistakes and implement proven methods for efficient research. This article offers practical tips to enhance your ability to find and evaluate high-quality information.

Are you struggling to find relevant and reliable information for your research? Do you want to avoid getting lost in a sea of sources and needing help knowing where to start? Improving your research skills is essential for academic success and professional growth.

In today's information age, effectively conducting research has become more important than ever. Whether you are a student, a professional, or simply someone who wants to stay informed, knowing how to find and evaluate information is crucial.

Fortunately, some strategies and techniques can help you improve your research skills and become a more efficient and effective researcher. By avoiding common mistakes and implementing proven methods, you can enhance your ability to find high-quality information and make the most of your research endeavors. This article will explore some practical tips and tricks to help you improve your research skills and achieve better results.

What is Research?

Research is a critical part of learning, problem-solving, and decision-making. It is an essential process used in every field for both the individual and collective’s mutual benefit and success. Research involves systematically gathering data from primary or secondary sources, analyzing it, interpreting it, and communicating its findings to researchers and other interested parties.

Research can be divided into two main categories: quantitative research, which uses numerical data to describe phenomena, and qualitative research, which seeks to understand people's beliefs, opinions, values, or behaviors. Quantitative research often involves applying model-based approaches that can predict outcomes based on observations. It is one of the most powerful methods of discovering information about the world, as it allows for testing hypotheses in a systematic manner. Qualitative research is more exploratory in nature by focusing on understanding the motivations behind what people do or think rather than developing models or producing statistics in order to conclude behavior and relationships between variables. This type of research usually relies more on observation and engagement with people instead of using statistical models.

What are Research Skills?

Research skills are the abilities and talents required to focus on an objective, gather the relevant data linked to it, analyze it using appropriate methods, and accurately communicate the results. Taking part in research indicates that you have acquired knowledge of your subject matter, have digested that knowledge, and processed, evaluated, and analyzed it until you can resolve a problem or answer a query. It is highly beneficial for employers to hire people with strong research skills since they can provide valuable insights and add value to the company’s performance. Therefore, researching effectively has become crucial to securing a job in most industries.

Why Do Research Skills Matter?

Research skills are essential if one intends to succeed in today's competitive world. With technology ever-evolving and a need to stay ahead of the competition, employees who possess research skills can prove invaluable to their employers. These skills include researching, analyzing, and interpreting data and making informed decisions based on that information.

Employers value workers who can quickly develop a thorough understanding of any changes or trends in their field of work through accurate research. Knowing how to assess customer needs, recognize competition, write reports, improve productivity, and advise on investments can also benefit any business. With the help of research skills, companies can uncover ways to adapt their services or products that better serve their customers’ needs while helping them save money at the same time. This makes overall operations more efficient as well as helps a company remain ahead of its competitors.

Essential Research Skills :

Here is a list of essential research skills:

Data Collection

Data collection is an important part of comprehending a certain topic and ensuring reliable information is collected while striving to answer complex questions. Every situation differs, but data collection typically includes surveys, interviews, observations, and existing document reviews. The data collected can be quantitative or qualitative, depending on the nature of the problem at hand. As students advance through university and other educational institutions, they will need to read extensively into a particular field and may even need to undertake comprehensive literature reviews to answer fundamental questions.

The skills acquired through data collection during university are invaluable for future roles and jobs. Gaining experience in understanding complex topics, reading widely on a given subject matter, collecting relevant data, and analyzing findings - all these activities are integral when dealing with any type of project within the corporate sector. Therefore, embarking on various research projects enhances a person's education level and brings about significant professional experience.

Goal-Setting

Setting goals is an important skill for any successful research project. It allows you to stay focused and motivated throughout the process. Goals are also essential in helping with direction: they provide a path to organize our thoughts, narrow our focus, and prioritize the tasks we need to undertake to achieve our desired result. The concept of goal-setting is inherent in most research processes, as everything needs to have something to strive for — whether that’s gaining knowledge about a particular topic or testing a theory.

When it comes to creating and setting goals during the research process, you must have clear and specific objectives in mind from the outset. Writing down your thoughts helps define these objectives, which can inform the data collection process; moreover, thinking about short-term and long-term goals can help you create manageable steps toward achieving them. Learning how to break up larger projects into smaller “mini-goals effectively” can make all the difference when tackling complex investigations — allowing researchers to monitor their progress more easily and culminate results further down the line.

Critical Thinking

Critical thinking is an integral part of the modern workplace. To succeed, one must be able to look at a situation objectively and make decisions based on evidence. The information examined needs to come from various sources, such as data collection, personal observation, or analysis. The goal should then be to take all this information and form a logical judgment that informs an action plan or idea.

Someone who displays strong critical thinking skills will not just accept proposed ideas at face value but instead can understand how these ideas can be applied and challenged. Accepting something without consideration means making the wrong decision due to a lack of thought. Critical thinkers understand how brainstorming works, assessing all elements before forming any decision. From negotiating with colleagues or customers in adversarial scenarios to analyzing complex documents such as legal contracts in order to review business agreements - critical dedicated apply their knowledge effectively and are able to back up their evaluation with evidence collected from multiple sources.

Observation Skills

Observation skills are necessary for conducting any form of research, whether it be in the workplace or as part of an investigative process. It is important to be able to pick up on the details that might otherwise pass unnoticed, such as inconsistencies in data or irregularities in how something is presented, and to pay careful attention to regulations and procedures that govern the company or environment. This can help researchers to ensure their processes are accurate and reliable.

As well as analyzing what we see around us directly, many research methodologies often involve calculated statistical analyses and calculations. For this reason, it’s important to develop strong observation skills so that the legitimacy of information can be confirmed and checked before conclusions are formed. Improving this skill requires dedication and practice, which could include keeping a journal reflecting on experiences, posing yourself questions about what you have observed, and seeking out opportunities in unfamiliar settings to test your observations.

Detail Orientation

Detail orientation is an important research skill for any scientific endeavor. It allows one to assess a situation or problem in minute detail and make appropriate judgments based on the information gathered. A detail-oriented thinker can easily spot errors, inconsistencies, and vital pieces of evidence, which can help lead to accurate conclusions from the research. Additionally, this skill allows someone to evaluate the quality and accuracy of data recorded during an experiment or project more efficiently to ensure validity.

Spotting small mistakes that may otherwise have been overlooked is a crucial part of conducting detailed research that must be perfected. Individuals aiming for superior outcomes should strive to develop their skill at detecting details by practicing critical analysis techniques, such as breaking down large bodies of information into smaller tasks to identify finer points quickly. Moreover, encouragement should also be made for elaborate comparison and analysis between different pieces of information when solving a complex problem, as it can help provide better insights into problems accurately.

Investigative Skills

Investigative skills are an essential component when it comes to gathering and analyzing data. In a professional setting, it is important to determine the accuracy and validity of different sources of information before making any decisions or articulating ideas. Generally, effective investigation requires collecting different sets of reliable data, such as surveys and interviews with stakeholders, employees, customers, etc. For example, if a company internally assesses possible challenges within its business operations environment, it would need to conduct more profound research involving talking to relevant stakeholders who could provide critical perspectives about the situation.

Data-gathering techniques such as comparison shopping and regulatory reviews have become more commonplace in the industry as people strive for greater transparency and more accurate results. Knowing how to identify reliable sources of information can give individuals a competitive advantage and allow them to make sound decisions based on accurate data. Investing time in learning different investigative skills can help recruiters spot applicants dedicated to acquiring knowledge in this field. Developing these investigative skills is also valuable for those looking for executive positions or starting their own business. By familiarizing themselves with their application process, people can become adept at collecting high-quality data they may use in their research endeavors.

Time Management

Time management is a key skill for any researcher. It's essential to be able to allocate time between different activities so you can effectively plan and structure your research projects. Without good time management, you may find yourself hastily completing tasks or feeling stressed out as you rush to complete an analysis. Ultimately, managing your time allows you to stay productive and ensure that each project is completed with the highest results.

Good time management requires various skills such as planning ahead, prioritizing tasks, breaking down large projects into smaller steps, and even delegating some activities when possible. It also means setting realistic goals for yourself in terms of the amount of research that can be achieved in certain timestamps and learning how to adjust these goals when needed. Becoming mindful of how you spend the same hours each day will propel your productivity and see positive results from your efforts. Time management becomes especially relevant regarding data collection and analysis – it is crucial to understand precisely what kind of resources are needed for each task before diving into the research itself. Knowing how much time should be dedicated to each step is essential for meeting deadlines while still retaining accuracy in the final outcomes of one’s study.

Tips on How to Improve Your Research Skills

Below are some tips that can help in improving your skills in research:

Initiate your project with a structured outline

When embarking on any research project, creating an outline and scope document must first ensure that you remain on the right track. An outline sets expectations for your project by forming a detailed strategy for researching the topic and gathering the necessary data to conclude. It will help you stay organized and break down large projects into more manageable parts. This can help prevent procrastination as each part of the project has its own timeline, making it easier to prioritize tasks accordingly.

Using an outline and scope document also allows for better structure when conducting research or interviews, as it guides which sources are most relevant, what questions need to be answered, and how information should be collected or presented. This ensures that all information received through research or interviews stays within the confines of the chosen topic of investigation. Additionally, it ensures that no important details are overlooked while minimizing the chance that extraneous information gets included in your results. Taking this time upfront prevents potential problems during analysis or reporting of findings later.

Acquire expertise in advanced data collection methods

When it comes to collecting data for research purposes, a range of advanced data collection techniques can be used to maximize your efficiency and accuracy. One such technique is customizing your online search results with advanced search settings. By adding quotation marks and wildcard characters to the terms you are searching for, you are more likely to find the information you need from reliable sources. This can be especially useful if, for instance, you are looking for exact quotes or phrases. Different search engines require different advanced techniques and tactics, so learning these can help you get more specific results from your research endeavors.

Aside from using online searches, another standard methodology when conducting research is accessing primary information through libraries or other public sources. A specific classification system will likely be in place that can help researchers locate the materials needed quickly and easily. Knowing and understanding this system allows one to access information much more efficiently while also giving them ample opportunity to increase their knowledge of various topics by browsing related content in the same category groups. Thus, by learning about advanced data collection techniques for both online and offline sources, researchers can make substantial progress in their studies more efficiently.

Validate and examine the reliability of your data sources

Collecting reliable information for research can be a challenge, especially when relying on online sources. It is essential to remember that not all sources are created equal, and some sites may contain false or inaccurate data. It is, therefore important to verify and analyze the data before using it as part of your research.

One way to start verifying and analyzing your sources is to cross-reference material from one source with another. This may help you determine if particular facts or claims are accurate and, therefore, more valid than others. Additionally, trace where the data is coming from by looking at the author or organization behind it so that you can assess their expertise in a particular field and authority on the topic at hand. Once these steps have been completed, you can confidently use this trusted information for your project.

Structure your research materials

Organizing your research materials is an integral part of any research process. When you’re conducting a project or study and trying to find the most relevant information, you can become overwhelmed with all the data available. It’s important to separate valid from invalid materials and to categorize research materials by subject for easy access later on. Bookmarking websites on a computer or using a digital asset management tool are two effective methods for organizing research information.

When researching, it’s critical to remember that some sources have limited value and may be outside the scope of your topic. Recognizing reliable material versus trustworthy resources can be complex in this sea of information. However, sorting data into appropriate categories can help narrow down what is necessary for producing valid conclusions. This method of classifying information helps ensure that vital documents aren't overlooked during the organization process as they are placed in folders shortcutted for quick access within one centralized source whenever needed. Separating valuable sources also makes it easier to reference later on when writing reports or giving presentations - material won't get lost among irrelevant data, and conclusions will be backed by sound evidence.

Enhance your research and communication capabilities

Developing research and communication skills is essential for succeeding academically and professionally in the modern world. The key to improving these skills lies in rigorous practice, which can begin with small projects such as resolving common issues or completing a research task that can be made into a personal project. One way to do this is to volunteer for research projects at work and gain experience under the guidance of experienced researchers. This will improve your research skills and help you develop communication skills when working with others on the project. Another option is to turn a personal project into a research task. For example, if you plan on taking a holiday soon, you could create an objective method to select the best destination by conducting online research on destinations and making informed decisions based on thorough analysis. Practicing in this way enables you to complete any research task confidently and communicate efficiently with ease.

How to Articulate Research Skills on Your Resume

Research projects require commitment and perseverance, making it an important skill to include on a resume. Even if you have had limited research experience throughout your education or previous job, including this in your resume assesses these qualities to potential employers. It's important to consider the extent of your research experience when deciding how to add this part of your background to your resume. If you have been involved with multiple in-depth research projects, it might be best to highlight this by including it as its own section. On the other hand, if the amount of research you have completed is more limited, then try including it in the skills section instead.

When adding research experience and accomplishments into either section of your resume, be sure to emphasize any specific roles or contributions you made during the process instead of just describing the project itself. Furthermore, remember to quantify any successes where possible - this showcases both communication and technical proficiency strengths, which can help make your resume stand out even more. By properly articulating research skills within a resume, employers will likely be more interested in what job seekers have accomplished in their careers.

How to Apply Research Skills Effectively in Your Workplace

Research skills are an invaluable set of abilities to bring to your workplace. To make sure you use them properly, a good place to start is by taking time to plan the project you have been assigned. Whether it’s writing a report or analyzing data, mapping out what tasks you need to do and how long they should take helps to understand the project timeline better. This also makes setting aside dedicated time for research easier too.

To ensure that the decisions made are sound and informed, reading up on the subject area related to the project remains one of the premier ways of doing this. This will help to ensure that any problems arising can be solved quickly and effectively, as well as provide answers before any decisions are actually put into practice. By arming yourself with knowledge gathered through reading about a particular topic, it can give you more confidence when formulating plans or strategies in which direction to take your work in.

Final Thoughts

Research skills are increasingly important in the modern world, and gaining proficiency in this area can significantly benefit a person's career. Research skills are essential for success in many different roles and fields, including those within business and industry, education, science, and medicine. Developing a deep understanding of research allows us to identify problems better and critically evaluate potential solutions. It also bolsters our problem-solving abilities as we work to find creative solutions that meet our efforts' objectives.

By improving your research capabilities, you can impress employers during an application process or when joining a team at work. Research skills are considered soft skills by potential employers since they signal that you have attention to detail while simultaneously demonstrating your ability to learn new things quickly. Employers regard these skills highly, making them one of the key graduate career skills recruiters seek. Furthermore, being able to add ‘research skills’ to your CV will be looked upon favorably by employers and help drive up your employability significantly. Demonstrating that you possess these sought-after traits makes it easier for recruiters to give you the opportunity you've been looking for, so it's worth investing the time into developing these life-long learning tools today.

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Empowering students to develop research skills

February 8, 2021

This post is republished from   Into Practice ,  a biweekly communication of Harvard’s  Office of the Vice Provost for Advances in Learning

Terence D. Capellini, Richard B Wolf Associate Professor of Human Evolutionary Biology, empowers students to grow as researchers in his Building the Human Body course through a comprehensive, course-long collaborative project that works to understand the changes in the genome that make the human skeleton unique. For instance, of the many types of projects, some focus on the genetic basis of why human beings walk on two legs. This integrative “Evo-Devo” project demands high levels of understanding of biology and genetics that students gain in the first half of class, which is then applied hands-on in the second half of class. Students work in teams of 2-3 to collect their own morphology data by measuring skeletons at the Harvard Museum of Natural History and leverage statistics to understand patterns in their data. They then collect and analyze DNA sequences from humans and other animals to identify the DNA changes that may encode morphology. Throughout this course, students go from sometimes having “limited experience in genetics and/or morphology” to conducting their own independent research. This project culminates in a team presentation and a final research paper.

The benefits: Students develop the methodological skills required to collect and analyze morphological data. Using the UCSC Genome browser  and other tools, students sharpen their analytical skills to visualize genomics data and pinpoint meaningful genetic changes. Conducting this work in teams means students develop collaborative skills that model academic biology labs outside class, and some student projects have contributed to published papers in the field. “Every year, I have one student, if not two, join my lab to work on projects developed from class to try to get them published.”

“The beauty of this class is that the students are asking a question that’s never been asked before and they’re actually collecting data to get at an answer.”

The challenges:  Capellini observes that the most common challenge faced by students in the course is when “they have a really terrific question they want to explore, but the necessary background information is simply lacking. It is simply amazing how little we do know about human development, despite its hundreds of years of study.” Sometimes, for instance, students want to learn about the evolution, development, and genetics of a certain body part, but it is still somewhat a mystery to the field. In these cases, the teaching team (including co-instructor Dr. Neil Roach) tries to find datasets that are maximally relevant to the questions the students want to explore. Capellini also notes that the work in his class is demanding and hard, just by the nature of the work, but students “always step up and perform” and the teaching team does their best to “make it fun” and ensure they nurture students’ curiosities and questions.

Takeaways and best practices

• Incorporate previous students’ work into the course. Capellini intentionally discusses findings from previous student groups in lectures. “They’re developing real findings and we share that when we explain the project for the next groups.” Capellini also invites students to share their own progress and findings as part of class discussion, which helps them participate as independent researchers and receive feedback from their peers.
• Assign groups intentionally.  Maintaining flexibility allows the teaching team to be more responsive to students’ various needs and interests. Capellini will often place graduate students by themselves to enhance their workload and give them training directly relevant to their future thesis work. Undergraduates are able to self-select into groups or can be assigned based on shared interests. “If two people are enthusiastic about examining the knee, for instance, we’ll match them together.”
• Consider using multiple types of assessments.  Capellini notes that exams and quizzes are administered in the first half of the course and scaffolded so that students can practice the skills they need to successfully apply course material in the final project. “Lots of the initial examples are hypothetical,” he explains, even grounded in fiction and pop culture references, “but [students] have to eventually apply the skills they learned in addressing the hypothetical example to their own real example and the data they generate” for the Evo-Devo project. This is coupled with a paper and a presentation treated like a conference talk.

Bottom line:  Capellini’s top advice for professors looking to help their own students grow as researchers is to ensure research projects are designed with intentionality and fully integrated into the syllabus. “You can’t simply tack it on at the end,” he underscores. “If you want this research project to be a substantive learning opportunity, it has to happen from Day 1.” That includes carving out time in class for students to work on it and make the connections they need to conduct research. “Listen to your students and learn about them personally” so you can tap into what they’re excited about. Have some fun in the course, and they’ll be motivated to do the work.

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Research Skills: How to Find the Right Answers

Most jobs require some form of problem-solving. You might encounter an obstacle and come up with a question that you will need to answer to move forward. To answer this question, chances are you will need to have research skills to do some investigating. This kind of investigation is known as research.

What Is Research?

Research is the investigation of sources or facts to establish or draw conclusions. In an academic context, people often think of research in the sciences and the social sciences. But really, you will need to conduct some kind of research in any academic subject or while performing any job.

Find your bootcamp match

In fact, nearly every profession or job requires some amount of research and research skills. As long as you come across a question, which is a natural occurrence in almost everything, you should come across an opportunity to research. And when there is a call to research, strong research skills definitely come in handy.

What Are Research Skills?

Research skills mean that you are able to identify the answer to a question or a set of questions. Research questioning can lead to many different kinds of research. You might get started by using search engines to find reliable sources. You can evaluate information by scanning search results to embark on your research project. 

What Is the Purpose of Research?

Research can serve a few different purposes, depending on the kind of research you are doing. The three main kinds of research are exploratory, descriptive, and explanatory. 

Exploratory research seeks to explore a general question and possible answers without necessarily seeking one singular, specific answer. Descriptive research is often data-driven and seeks to describe research findings in great detail. Explanatory research is often more qualitative and does seek explanations to substantiate it and its findings. 

Below is a deep dive into the kinds of general research skills you will need to excel in any field, especially tech.

Different Kinds of Research Skills

Below are a few basic types of research skills that might help you get a better sense of what research is and why you need to master research skills.

Searching for Information

In its most basic sense, research is the search for information. This can take on many different forms. Though in 2020, we are all used to using Google as one of our primary research methods. 

Older generations remember having to go to the library any time they had a question about the way the world works or any time they needed to search for information.

Attention to Detail

By paying close attention to detail, you can conduct better research on a micro-level, noticing details and storing them away for future reference. During job training, an information session, or a webinar, for example, you can conduct research just by paying close attention to detail. This can also involve taking notes so you don’t end up forgetting all of this detail.

Time Management

You will likely never come across a question or a research question with absolutely no time limit. Research almost always requires time management skills to make sure you can get everything done on time. 

Depending on the kind of research you’re conducting, you may have to manage your time between one kind of research, interviews, for example, and another kind of research, such as online web searches. 

Problem-Solving

Research is all about problem-solving. Without problem-solving, research would just be looking for information. But research is about searching and then identifying information that provides a potential answer to a question or a solution to a problem. 

Communicating Results

Research results are useless if you don’t know what to do with them. Ideally, you will have the resources and ability to apply your research findings to your question or your problem. 

If you’re working on a team, you should be able to describe your research, your research methods, and your research results to your teammates. The goal is to get others on board by communicating your results. 

Online Research Skills

In a time when the Internet is overloaded with so much information, it’s hard to know what to trust. Though online research is by far the most accessible, it can also be the most difficult. 

Internet users using the web for research, including simple search engine searches, should understand how search engine results work and how to discern the reliable from the unreliable sources.

Below are a few tips for conducting discerning online research responsibly. 

Ask the Right Questions

Remember that all research starts with at least one question. The question you are asking absolutely makes a difference in the kind of research you will want to be conducting. It also makes a difference in how fruitful your online research might be. 

Ask the right question by considering how you are phrasing the question and what words and terms you are including in the question. To do this, try to be as specific as possible to get to the root of the question you are asking.

Check Your Sources

Always do some research on your source pages. Is the domain something you’ve never heard of? Does it look very outdated and low-budget? If the answer to these questions is yes, you might want to find more reliable sources. You will also need to evaluate the actual information you find from your sources, which might even require a bit of fact-checking.

Never Plagiarize, Always Interpet

Even if you find exactly what you’re looking for in an Internet search, you will need to interpret what you find. Never take anything for granted and always reinterpret information in your own words.

Conclusion: Start Your Research

Whatever your question, all you have to do to develop research skills is get started. Like with anything else in life, practice makes perfect. Good luck and check out our other Career Karma resources as you embark on your research projects.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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• Research Skills

50 Mini-Lessons For Teaching Students Research Skills

Please note, I am no longer blogging and this post hasn’t updated since April 2020.

For a number of years, Seth Godin has been talking about the need to “ connect the dots” rather than “collect the dots” . That is, rather than memorising information, students must be able to learn how to solve new problems, see patterns, and combine multiple perspectives.

Solid research skills underpin this. Having the fluency to find and use information successfully is an essential skill for life and work.

Today’s students have more information at their fingertips than ever before and this means the role of the teacher as a guide is more important than ever.

You might be wondering how you can fit teaching research skills into a busy curriculum? There aren’t enough hours in the day! The good news is, there are so many mini-lessons you can do to build students’ skills over time.

This post outlines 50 ideas for activities that could be done in just a few minutes (or stretched out to a longer lesson if you have the time!).

Learn More About The Research Process

I have a popular post called Teach Students How To Research Online In 5 Steps. It outlines a five-step approach to break down the research process into manageable chunks.

This post shares ideas for mini-lessons that could be carried out in the classroom throughout the year to help build students’ skills in the five areas of: clarify, search, delve, evaluate , and cite . It also includes ideas for learning about staying organised throughout the research process.

Notes about the 50 research activities:

• These ideas can be adapted for different age groups from middle primary/elementary to senior high school.
• Many of these ideas can be repeated throughout the year.
• Depending on the age of your students, you can decide whether the activity will be more teacher or student led. Some activities suggest coming up with a list of words, questions, or phrases. Teachers of younger students could generate these themselves.
• Depending on how much time you have, many of the activities can be either quickly modelled by the teacher, or extended to an hour-long lesson.
• Some of the activities could fit into more than one category.
• Looking for simple articles for younger students for some of the activities? Try DOGO News or Time for Kids . Newsela is also a great resource but you do need to sign up for free account.
• Why not try a few activities in a staff meeting? Everyone can always brush up on their own research skills!

• Choose a topic (e.g. koalas, basketball, Mount Everest) . Write as many questions as you can think of relating to that topic.
• Make a mindmap of a topic you’re currently learning about. This could be either on paper or using an online tool like Bubbl.us .
• Read a short book or article. Make a list of 5 words from the text that you don’t totally understand. Look up the meaning of the words in a dictionary (online or paper).
• Look at a printed or digital copy of a short article with the title removed. Come up with as many different titles as possible that would fit the article.
• Come up with a list of 5 different questions you could type into Google (e.g. Which country in Asia has the largest population?) Circle the keywords in each question.
• Write down 10 words to describe a person, place, or topic. Come up with synonyms for these words using a tool like  Thesaurus.com .
• Write pairs of synonyms on post-it notes (this could be done by the teacher or students). Each student in the class has one post-it note and walks around the classroom to find the person with the synonym to their word.

• Explore how to search Google using your voice (i.e. click/tap on the microphone in the Google search box or on your phone/tablet keyboard) . List the pros and cons of using voice and text to search.
• Open two different search engines in your browser such as Google and Bing. Type in a query and compare the results. Do all search engines work exactly the same?
• Have students work in pairs to try out a different search engine (there are 11 listed here ). Report back to the class on the pros and cons.
• Think of something you’re curious about, (e.g. What endangered animals live in the Amazon Rainforest?). Open Google in two tabs. In one search, type in one or two keywords ( e.g. Amazon Rainforest) . In the other search type in multiple relevant keywords (e.g. endangered animals Amazon rainforest).  Compare the results. Discuss the importance of being specific.
• Similar to above, try two different searches where one phrase is in quotation marks and the other is not. For example, Origin of “raining cats and dogs” and Origin of raining cats and dogs . Discuss the difference that using quotation marks makes (It tells Google to search for the precise keywords in order.)
• Try writing a question in Google with a few minor spelling mistakes. What happens? What happens if you add or leave out punctuation ?
• Try the AGoogleADay.com daily search challenges from Google. The questions help older students learn about choosing keywords, deconstructing questions, and altering keywords.
• Explore how Google uses autocomplete to suggest searches quickly. Try it out by typing in various queries (e.g. How to draw… or What is the tallest…). Discuss how these suggestions come about, how to use them, and whether they’re usually helpful.
• Watch this video  from Code.org to learn more about how search works .
• Take a look at  20 Instant Google Searches your Students Need to Know  by Eric Curts to learn about “ instant searches ”. Try one to try out. Perhaps each student could be assigned one to try and share with the class.
• Experiment with typing some questions into Google that have a clear answer (e.g. “What is a parallelogram?” or “What is the highest mountain in the world?” or “What is the population of Australia?”). Look at the different ways the answers are displayed instantly within the search results — dictionary definitions, image cards, graphs etc.

• Watch the video How Does Google Know Everything About Me?  by Scientific American. Discuss the PageRank algorithm and how Google uses your data to customise search results.
• Brainstorm a list of popular domains   (e.g. .com, .com.au, or your country’s domain) . Discuss if any domains might be more reliable than others and why (e.g. .gov or .edu) .
• Discuss (or research) ways to open Google search results in a new tab to save your original search results  (i.e. right-click > open link in new tab or press control/command and click the link).
• Try out a few Google searches (perhaps start with things like “car service” “cat food” or “fresh flowers”). A re there advertisements within the results? Discuss where these appear and how to spot them.
• Look at ways to filter search results by using the tabs at the top of the page in Google (i.e. news, images, shopping, maps, videos etc.). Do the same filters appear for all Google searches? Try out a few different searches and see.
• Type a question into Google and look for the “People also ask” and “Searches related to…” sections. Discuss how these could be useful. When should you use them or ignore them so you don’t go off on an irrelevant tangent? Is the information in the drop-down section under “People also ask” always the best?
• Often, more current search results are more useful. Click on “tools” under the Google search box and then “any time” and your time frame of choice such as “Past month” or “Past year”.
• Have students annotate their own “anatomy of a search result” example like the one I made below. Explore the different ways search results display; some have more details like sitelinks and some do not.

• Find two articles on a news topic from different publications. Or find a news article and an opinion piece on the same topic. Make a Venn diagram comparing the similarities and differences.
• Choose a graph, map, or chart from The New York Times’ What’s Going On In This Graph series . Have a whole class or small group discussion about the data.
• Look at images stripped of their captions on What’s Going On In This Picture? by The New York Times. Discuss the images in pairs or small groups. What can you tell?
• Explore a website together as a class or in pairs — perhaps a news website. Identify all the advertisements .
• Have a look at a fake website either as a whole class or in pairs/small groups. See if students can spot that these sites are not real. Discuss the fact that you can’t believe everything that’s online. Get started with these four examples of fake websites from Eric Curts.
• Give students a copy of my website evaluation flowchart to analyse and then discuss as a class. Read more about the flowchart in this post.
• As a class, look at a prompt from Mike Caulfield’s Four Moves . Either together or in small groups, have students fact check the prompts on the site. This resource explains more about the fact checking process. Note: some of these prompts are not suitable for younger students.
• Practice skim reading — give students one minute to read a short article. Ask them to discuss what stood out to them. Headings? Bold words? Quotes? Then give students ten minutes to read the same article and discuss deep reading.

All students can benefit from learning about plagiarism, copyright, how to write information in their own words, and how to acknowledge the source. However, the formality of this process will depend on your students’ age and your curriculum guidelines.

• Watch the video Citation for Beginners for an introduction to citation. Discuss the key points to remember.
• Look up the definition of plagiarism using a variety of sources (dictionary, video, Wikipedia etc.). Create a definition as a class.
• Find an interesting video on YouTube (perhaps a “life hack” video) and write a brief summary in your own words.
• Have students pair up and tell each other about their weekend. Then have the listener try to verbalise or write their friend’s recount in their own words. Discuss how accurate this was.
• Read the class a copy of a well known fairy tale. Have them write a short summary in their own words. Compare the versions that different students come up with.
• Try out MyBib — a handy free online tool without ads that helps you create citations quickly and easily.
• Give primary/elementary students a copy of Kathy Schrock’s Guide to Citation that matches their grade level (the guide covers grades 1 to 6). Choose one form of citation and create some examples as a class (e.g. a website or a book).
• Make a list of things that are okay and not okay to do when researching, e.g. copy text from a website, use any image from Google images, paraphrase in your own words and cite your source, add a short quote and cite the source. 
• Have students read a short article and then come up with a summary that would be considered plagiarism and one that would not be considered plagiarism. These could be shared with the class and the students asked to decide which one shows an example of plagiarism .
• Older students could investigate the difference between paraphrasing and summarising . They could create a Venn diagram that compares the two.
• Write a list of statements on the board that might be true or false ( e.g. The 1956 Olympics were held in Melbourne, Australia. The rhinoceros is the largest land animal in the world. The current marathon world record is 2 hours, 7 minutes). Have students research these statements and decide whether they’re true or false by sharing their citations.

Staying Organised

• Make a list of different ways you can take notes while researching — Google Docs, Google Keep, pen and paper etc. Discuss the pros and cons of each method.
• Learn the keyboard shortcuts to help manage tabs (e.g. open new tab, reopen closed tab, go to next tab etc.). Perhaps students could all try out the shortcuts and share their favourite one with the class.
• Find a collection of resources on a topic and add them to a Wakelet .
• Listen to a short podcast or watch a brief video on a certain topic and sketchnote ideas. Sylvia Duckworth has some great tips about live sketchnoting
• Learn how to use split screen to have one window open with your research, and another open with your notes (e.g. a Google spreadsheet, Google Doc, Microsoft Word or OneNote etc.) .

All teachers know it’s important to teach students to research well. Investing time in this process will also pay off throughout the year and the years to come. Students will be able to focus on analysing and synthesizing information, rather than the mechanics of the research process.

By trying out as many of these mini-lessons as possible throughout the year, you’ll be really helping your students to thrive in all areas of school, work, and life.

Also remember to model your own searches explicitly during class time. Talk out loud as you look things up and ask students for input. Learning together is the way to go!

You Might Also Enjoy Reading:

How To Evaluate Websites: A Guide For Teachers And Students

Five Tips for Teaching Students How to Research and Filter Information

Typing Tips: The How and Why of Teaching Students Keyboarding Skills

8 Ways Teachers And Schools Can Communicate With Parents

10 Replies to “50 Mini-Lessons For Teaching Students Research Skills”

Loving these ideas, thank you

This list is amazing. Thank you so much!

So glad it’s helpful, Alex! 🙂

Hi I am a student who really needed some help on how to reasearch thanks for the help.

So glad it helped! 🙂

seriously seriously grateful for your post. 🙂

So glad it’s helpful! Makes my day 🙂

How do you get the 50 mini lessons. I got the free one but am interested in the full version.

Hi Tracey, The link to the PDF with the 50 mini lessons is in the post. Here it is . Check out this post if you need more advice on teaching students how to research online. Hope that helps! Kathleen

Best wishes to you as you face your health battler. Hoping you’ve come out stronger and healthier from it. Your website is so helpful.

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Fostering students’ motivation towards learning research skills: the role of autonomy, competence and relatedness support

Louise maddens.

1 Centre for Instructional Psychology and Technology, Faculty of Psychology and Educational Sciences, KU Leuven and KU Leuven Campus Kulak Kortrijk, Etienne Sabbelaan 51 – bus 7800, 8500 Kortrijk, Belgium

2 Itec, imec Research Group at KU Leuven, imec, Leuven, Belgium

3 Vives University of Applied Sciences, Kortrijk, Belgium

Fien Depaepe

Annelies raes.

In order to design learning environments that foster students’ research skills, one can draw on instructional design models for complex learning, such as the 4C/ID model (in: van Merriënboer and Kirschner, Ten steps to complex learning, Routledge, London, 2018). However, few attempts have been undertaken to foster students’ motivation towards learning complex skills in environments based on the 4C/ID model. This study explores the effects of providing autonomy, competence and relatedness support (in Deci and Ryan, Psychol Inquiry 11(4): 227–268, https://doi.org/10.1207/S15327965PLI1104_01, 2000) in a 4C/ID based online learning environment on upper secondary school behavioral sciences students’ cognitive and motivational outcomes. Students’ cognitive outcomes are measured by means of a research skills test consisting of short multiple choice and short answer items (in order to assess research skills in a broad way), and a research skills task in which students are asked to integrate their skills in writing a research proposal (in order to assess research skills in an integrative manner). Students’ motivational outcomes are measured by means of students’ autonomous and controlled motivation, and students’ amotivation. A pretest-intervention-posttest design was set up in order to compare 233 upper secondary school behavioral sciences students’ outcomes among (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. Both learning environments proved equally effective in improving students’ scores on the research skills test. Students in the need supportive condition scored higher on the research skills task compared to their peers in the baseline condition. Students’ autonomous and controlled motivation were not affected by the intervention. Although, unexpectedly, students’ amotivation increased in both conditions, students’ amotivation was lower in the need supportive condition compared to students in the baseline condition. Theoretical relationships were established between students’ need satisfaction, students’ motivation (autonomous, controlled, and amotivation), and students’ cognitive outcomes. These findings are discussed taking into account the COVID-19 affected setting in which the study took place.

Introduction

Several scholars have argued that the process of learning research skills is often obstructed by motivational problems (Lehti & Lehtinen, 2005 ; Murtonen, 2005 ). Some even describe these issues as students having an aversion towards research (Pietersen, 2002 ). Examples of motivational problems are that students experience research courses as boring, inaccessible, or irrelevant to their daily lives (Braguglia & Jackson, 2012 ). In a research synthesis on teaching and learning research methods, Earley ( 2014 ) argues that students fail to see the relevance of research methods courses, are anxious or nervous about the course, are uninterested and unmotivated to learn the material, and have poor attitudes towards learning research skills. It should be mentioned that the studies mentioned above focused on the field of higher university education. In upper secondary education, to date, students’ motivation towards learning research skills has rarely been studied. As difficulties while learning research seem to relate to problems involving students’ previous experiences regarding learning research skills (Murtonen, 2005 ), we argue that fostering students’ motivation from secondary education onwards is a promising area of research.

The current study combines insights from instructional design theory and self-determination theory (SDT, Deci & Ryan, 2000 ), in order to investigate the cognitive and motivational effects of providing psychological need support (support for the need for autonomy, competence and relatedness) in a 4C/ID based (van Merriënboer & Kirschner, 2018 ) online learning environment fostering upper secondary schools students’ research skills. In the following section, we elaborate on the definition of research skills in the understudied domain of behavioral sciences; on 4C/ID (van Merriënboer & Kirschner, 2018 ) as an instructional design model for complex learning; and on self-determination theory and its related need theory (Deci & Ryan, 2000 ). In addition, the research questions addressed in the current study are outlined.

Conceptual framework

Research skills.

As described by Fischer et al., ( 2014 , p. 29), we define research skills 1 as a broad set of skills used “to understand how scientific knowledge is generated in different scientific disciplines, to evaluate the validity of science-related claims, to assess the relevance of new scientific concepts, methods, and findings, and to generate new knowledge using these concepts and methods”. Furthermore, eight scientific activities learners engage in while performing research are distinguished, namely: (1) problem identification, (2) questioning, (3) hypothesis generation, (4) construction and redesign of artefacts, (5) evidence generation, (6) evidence evaluation, (7) drawing conclusions, and (8) communicating and scrutinizing (Fischer et al., 2014 ). Fischer et al. ( 2014 ) argue that both the nature of, and the weights attributed to each of these activities, differ between domains. Intervention studies aiming to foster research skills are almost exclusively situated in natural sciences domains (Engelmann et al., 2016 ), leaving behavioral sciences domains largely understudied. The current study focuses on research skills in the understudied domain of behavioral sciences. We refer to the domain of behavioral sciences as the study of questions related to how people behave, and why they do so. Human behavior is understood in its broadest sense, and is the study of object in fields of psychology, educational sciences, cultural and social sciences.

The design of the learning environments used in this study is based on an existing instructional design model, namely the 4C/ID model (van Merriënboer & Kirschner, 2018 ). The 4C/ID model has been proven repeatedly effective in fostering complex skills (Costa et al., 2021 ), and thus drew our attention for the case of research skills, as research skills can be considered complex skills (it requires learners to integrate knowledge, skills and attitudes while performing complex learning tasks). Since the 4C/ID model focusses on supporting students’ cognitive outcomes, it might not be considered as relevant from a motivational point of view. However, since we argue that a deliberately designed learning environment from a cognitive point of view is an important prerequisite to provide qualitative motivational support, we briefly sketch the 4C/ID model and its characteristics. The 4C/ID model has a comprehensive character, integrating insights from different theories and models (Merrill, 2002 ), and highlights the relevance of four crucial components: learning tasks, supportive information, part task-practice, and just-in-time information. Central characteristics of these four components are that (a) high variability in authentic learning tasks is needed in order to deal with the complexity of the task; (b) supportive information is provided to the students in order to help them build mental models and strategies for solving the task under study (Cook & McDonald, 2008 ); (c) part-task practice is provided for recurrent skills that need to be automated; and (d) just-in-time (procedural) information is provided for recurrent skills.

Taking into account students’ cognitive struggles regarding research skills, and the existing research on the role of support in fostering research skills (see for example de Jong & van Joolingen, 1998 ), the 4C/ID model was found suitable to design a learning environment for research skills. This is partly because of its inclusion of (almost) all of the support found effective in the literature on research skills, such as providing direct access to domain information at the appropriate moment, providing learners with assignments, including model progression, the importance of students’ involvement in authentic activities, and so on (Chi, 2009 ; de Jong, 2006 ; de Jong & van Joolingen, 1998 ; Engelmann et al., 2016 ). While mainly implemented in vocational oriented programs, the 4C/ID model has been proposed as a good model to design learning environments aiming to foster research skills as well (Bastiaens et al., 2017 ; Maddens et al., 2020b ). Indeed, acquiring research skills requires complex learning processes (such as coordinating different constituent skills). Overall, the 4C/ID model can be considered to be highly suitable for designing learning environments aiming to foster research skills. Given its holistic design approach, it helps “to deal with complexity without losing sight of the interrelationships between the elements taught” (van Merriënboer & Kirschner, 2018 , p. 5).

Although the 4C/ID model has been used widely to construct learning environments enhancing students’ cognitive outcomes (see for example Fischer, 2018 ), research focusing on students’ motivational outcomes related to the 4C/ID model is scarce (van Merriënboer & Kirschner, 2018 ). Van Merriënboer and Kirschner ( 2018 ) suggest self-determination theory (SDT; Deci & Ryan, 2000 ) and its related need theory as a sound theoretical framework to investigate motivation in relation to 4C/ID.

Self-determination theory

Self-determination theory (SDT; Deci & Ryan, 2000 ) provides a broad framework for the study of motivation and distinguishes three types of motivation: amotivation (a lacking ability to self-regulate with respect to a behaviour), extrinsic motivation (extrinsically motivated behaviours, be they self-determined versus controlled), and intrinsic motivation (the ‘highest form’ of self-determined behaviour) (Deci & Ryan, 2000 ). According to Deci and Ryan ( 2000 , p. 237), intrinsic motivation can be considered “a standard against which the qualities of an extrinsically motivated behavior can be compared to determine its degree of self-determination”. Moreover, the authors (Deci & Ryan, 2000 , p. 237) argue that “extrinsic motivation does not typically become intrinsic motivation”. As the current study focuses on research skills in an academic context in which students did not voluntary chose to learn research skills, and thus learning research skills can be considered instrumental (directed to attaining a goal), the current study focuses on students’ amotivation, and students’ extrinsic motivation, realistically striving for the most self-determined types of extrinsic motivation.

Four types of extrinsic motivation are distinguished by SDT (external regulation, introjection, identification, and integration). These types can be categorized in two overarching types of motivation (autonomous and controlled motivation). Autonomous motivation contains the integrated and identified regulation towards a task (be it because the task is considered interesting, or because the task is considered personally relevant respectively). Controlled motivation refers to the external and introjected regulation towards the task (as a consequence of external or internal pressure respectively) (Vansteenkiste et al., 2009 ). More autonomous types of motivation have been found to be related to more positive cognitive and motivational outcomes (Deci & Ryan, 2000 ).

SDT further maintains that one should consider three innate psychological needs related to students’ motivation. These needs are the need for autonomy, the need for competence, and the need for relatedness. The need for autonomy can be described as the need to experience activities as being “concordant with one’s integrated sense of self” (Deci & Ryan, 2000 , p. 231). The need for competence refers to the need to feel effective when dealing with the environment (Deci & Ryan, 2000 ). The need for relatedness contains the need to have close relationships with others, including peers and teachers (Deci & Ryan, 2000 ). The satisfaction of these needs is hypothesized to be related to more internalization, and thus to more autonomous types of motivation (Deci & Ryan, 2000 ). This relationship has been studied frequently (for a recent overview, see Vansteenkiste et al., 2020 ). Indeed, research established the positive relationships between perceived autonomy (see for example Deci et al., 1996 ), perceived competence (see for example Vallerand & Reid, 1984 ), and perceived relatedness (see for example Ryan & Grolnick, 1986 for a self-report based study) with students’ more positive motivational outcomes. Apart from students’ need satisfaction, several scholars also aim to investigate need frustration as a different notion, as “it involves an active threat of the psychological needs (rather than a mere absence of need satisfaction)” (Vansteenkiste et al., 2020 , p. 9). In what follows, possible operationalizations are defined for the three needs.

Possible operationalizations of autonomy need support found in the literature are: teachers accepting irritation or negative feelings related to aspects of a task perceived as “uninteresting” (Reeve, 2006 ; Reeve & Jang, 2006 ; Reeve et al., 2002 ); providing a meaningful rationale in order to explain the value/usefulness of a certain task and stressing why involving in the task is important or why a rule exists (Deci & Ryan, 2000 ); using autonomy-supportive, inviting language (Deci et al., 1996 ); and allowing learners to regulate their own learning and to work at their own pace (Martin et al., 2018 ). Related to competence support, possible operationalizations are: providing a clear task rationale and providing structure (Reeve, 2006 ; Vansteenkiste et al., 2012 ); providing informational positive feedback after a learning activity (Deci et al., 1996 ; Martin et al., 2018 ; Vansteenkiste et al., 2012 ); providing an indication of progress and dividing content into manageable blocks (Martin et al., 2018 ; Schunk, 2003 ); and evaluating performance by means of previously introduced criteria (Ringeisen & Bürgermeister, 2015 ). Possible operationalizations concerning relatedness support are: teacher’s relational supports (Ringeisen & Bürgermeister, 2015 ); encouraging interaction between course participants and providing opportunities for learners to connect with each other (Butz & Stupnisky, 2017 ; van Merriënboer & Kirschner, 2018 ); using a warm and friendly approach or welcoming learners personally into a course (Martin et al., 2018 ); and offering a platform for learners to share ideas and to connect (Butz & Stupnisky, 2017 ; Martin et al., 2018 ).

In the current research, SDT is selected as a theoretical framework to investigate students’ motivation towards learning research skills, as, in contrast to other more purely goal-directed theories, it includes the concept of innate psychological needs or the Basic Psychological Need Theory (Deci & Ryan, 2000 ; Ryan, 1995 ; Vansteenkiste et al., 2020 ), and it describes the relation between these perceived needs and students’ autonomous motivation: higher levels of perceived needs relate to more autonomous forms of motivation. The inclusion of this need theory is considered an advantage in the case of research skills because research revealed problems of students with respect to both their feelings of competence in relation to research skills (Murtonen, 2005 ), as their feelings of autonomy in relation to research skills (Martin et al., 2018 ), as was indicated in the introduction. As such, fostering students’ psychological needs while learning research skills seems a promising way of fostering students’ motivation towards learning research skills.

4C/ID and SDT

One study (Bastiaens et al., 2017 ) was found to implement need support in 4C/ID based learning environments, comparing a traditional module, a 4C/ID based module and an autonomy supportive 4C/ID based module in a vocational undergraduate education context. Autonomy support was operationalized by means of providing choice to the learners. No main effect of the conditions was found on students’ motivation. Surprisingly, providing autonomy support did also not lead to an increase in students’ autonomy satisfaction. Similarly, no effects were found on students’ relatedness and competence satisfaction. Remarkably, students did qualitatively report positive experiences towards the need support, but this did not reflect in their quantitatively reported need experiences. In a previous study performed in the current research trajectory, Maddens et al. ( under review ) investigated the motivational effects of providing autonomy support in a 4C/ID based online learning environment fostering students’ research skills, compared to a learning environment not providing such support. Autonomy support was operationalized as stressing task meaningfulness to the students. Based on insights from self-determination theory, it was hypothesized that students in the autonomy condition would show more positive motivational outcomes compared to students in the baseline condition. However, results showed that students’ motivational outcomes appeared to be unaffected by the autonomy support. One possible explanation for this unexpected finding was that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support (Deci & Ryan, 2000 ; Niemiec & Ryan, 2009 ), and thus, that the intervention was insufficiently powerful for effects to occur. Autonomy support has often been manipulated in experimental research (Deci et al., 1994 ; Reeve et al., 2002 ; Sheldon & Filak, 2008 ). However, the three needs are rarely simultaneously manipulated (Sheldon & Filak, 2008 ).

Integrated need support

Although not making use of 4C/ID based learning environments, some scholars have focused on the impact of integrated (autonomy, competence and relatedness) need support on learners’ motivation. For example, Raes and Schellens ( 2015 ) found differential effects of a need supportive inquiry environment on upper secondary school students’ motivation: positive effects on autonomous motivation were only found in students in a general track, and not in students in a science track. This indicates that motivational effects of need-supportive environments might differ between tracks and disciplines. However, Raes and Schellens ( 2015 ) did not experimentally manipulate need support, as the learning environment was assumed to be need-supportive and was not compared to a non-need supportive learning environment. Pioneers in manipulating competence, relatedness and autonomy support in one study are Sheldon and Filak ( 2008 ), predicting need satisfaction and motivation based on a game-learning experience with introductory psychology students. Relatedness support (mainly operationalized by emphasizing interest in participants’ experiences in a caring way) had a significant effect on intrinsic motivation. Competence support (mainly operationalized by means of explicating positive expectations) had a marginal significant effect on intrinsic motivation. No main effects on intrinsic motivation were found regarding autonomy support (mainly operationalized by means of emphasizing choice, self-direction and participants’ perspective upon the task). However, as is often the case in motivational research based on SDT, the task at hand was quite straight forward (a timed task in which students try to form as many words as possible from a 4 × 4 letter grid), and thus, the applicability of the findings for providing need support in 4C/ID based learning environments for complex learning might be limited.

In the preceding section, several operationalizations of need support were discussed. Deci and Ryan ( 2000 ) argue that optimal circumstances for positive motivational outcomes are those that allow satisfaction of autonomy, competence, ánd relatedness support. However, such integrated need support has rarely been empirically studied (Sheldon & Filak, 2008 ). In addition, research investigating how need support can be implemented in learning environments based on the 4C/ID model is particularly scarce (van Merriënboer & Kirschner, 2018 ). This study aims to combine insights from instructional design theory for complex learning (van Merriënboer & Kirschner, 2018 ) and self-determination theory (Deci & Ryan, 2000 ) in order to investigate the motivational effects of providing need support in a 4C/ID based learning environment for students’ research skills. A pretest-intervention-posttest design is set up in order to compare 233 upper secondary school behavioral sciences students’ cognitive and motivational outcomes among two conditions: (1) a 4C/ID based online learning environment condition, and (2) an identical condition additively providing support for students’ need satisfaction. The following research questions are answered based on a combination of quantitative and qualitative data (see ‘method’): (1) Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task? ; ( 2) What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes (i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)? ; (3) What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)? ; (4) How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment? .

The first three questions are answered by means of quantitative data. Since the learning environment is constructed in line with existing instructional design principles for complex learning, we hypothesize that both learning environments will succeed in improving students’ research skills (RQ1). Relying on insights from self-determination theory (Deci & Ryan, 2000 ), we hypothesize that providing need support will enhance students’ autonomous motivation (RQ2). In addition, we hypothesize students’ need satisfaction to be positively related to students’ autonomous motivation (RQ3). These hypotheses on the relationship between students’ needs and students’ motivation rely on Vallerands’ ( 1997 ) finding that changes in motivation can be largely explained by students’ perceived competence, autonomy and relatedness (as psychological mediators). More specifically, Vallerand ( 1997 ) argues that environmental factors (in this case the characteristics of a learning environment) influence students’ perceptions of competence, autonomy, and relatedness, which, in turn, influence students’ motivation and other affective outcomes. In addition, based on the self-determination literature (Deci & Ryan, 2000 ), we expect students’ motivation to be positively related to students’ cognitive outcomes. In order to answer the fourth research question, qualitative data (students’ qualitative feedback on the learning environments) is analysed and categorized based on the need satisfaction and need frustration concepts (RQ4) in order to thoroughly capture the meaning of the quantitative results collected in light of RQ1–3. No hypotheses are formulated in this respect.

Methodology

Participants.

The study took place in authentic classroom settings in upper secondary behavioral sciences classes. In total, 233 students from 12 classes from eight schools in Flanders participated in the study. All participants are 11th or 12th grade students in a behavioral sciences track 2 in general upper secondary education in Flanders (Belgium). Classes were randomly assigned to one out of two experimental conditions. Of all 233 students, 105 students (with a mean age of 16.32, SD 0.90) worked in the baseline condition (of which 62% 11th grade students, 36% 12th grade students, and 2% not determined; and of which 31% male, 68% female, and 1% ‘other’), and 128 students (with a mean age of 16.02, SD 0.59) worked in the need supportive condition (of which 80% 11th grade students, and 20% 12th grade students; and of which 19% male, and 81% female). As the current study did not randomly assign students within classes to one out of the two conditions, this study should be considered quasi-experimental. Full randomization was considered but was not feasible as students worked in the learning environments in class, and would potentially notice the experimental differences when observing their peers working in the learning environment. As such, we argued that this would potentially cause bias in the study. By taking into account students’ pretest scores on the relevant variables (cognitive and motivational outcomes) as covariates, we aimed to adjust for inter-conditional differences. No such differences were found for students’ autonomous motivation t (226) =  − 0.115, p  < 0.909, d  = 0.015, and students’ amotivation t (226) =  − 0.658, p  < 0.511, d  =  − 0.088. However, differences were observed for students’ controlled motivation t (226) =  − 2.385, p  < 0.018, d  =  − 0.318, and students’ scores on the LRST pretest t (225) = − 5.200, p  < 0.001, d  =  − 0.695.

Study design and procedure

In a pretest session of maximum two lesson hours, the Leuven Research Skills Test (LRST, Maddens et al., 2020a ), the Academic Self-Regulation Scale (ASRS, Vansteenkiste et al., 2009 ), and four items related to students’ amotivation (Aydin et al., 2014 ) were administered in class via an online questionnaire, under supervision of the teacher. In the subsequent eight weeks, participants worked in the online learning environment, one hour a week. Out of the 233 participating students, 105 students studied in a baseline online learning environment. The baseline online learning environment 3 is systematically designed using existing instructional design principles for complex learning based on the 4C/ID model (van Merriënboer & Kirschner, 2018 ). All four components of the 4C/ID model were taken into account in the design process: regarding the first component, the learning tasks included real-life, authentic cases. More specifically, tasks were selected from the domains of psychology, educational sciences and sociology. As such, there was a large variety in the cases used in the learning tasks. This large variety in learning tasks is expected to facilitate transfer of learners’ research skills in a wide range of contexts. Furthermore, the tasks were ill-structured and required learners to make judgments, in order to provoke deep learning processes. Regarding the second component, supportive information was provided for complex tasks in the learning environment, such as formulating a research question, where students can consult general information on what constitutes a good research question, can consult examples or demonstrations of this general information, and can receive cognitive feedback on their answers (for example by means of example answers). Examples of the implementation of the third component (procedural information) are the provision of information on how to recognize a dependent and an independent variable by means of on-demand (just-in-time) presentation by means of pop-ups; information on how to use Boolean operators; and information on how to read a graph. To avoid split attention, this kind of information was integrated with the task environment itself (van Merriënboer & Kirschner, 2018 ). Finally, the fourth component, part-task-practice (by means of short tests) was implemented for routine aspects of research skills that should be automated, for example the formulation of a search query.

The remaining participating students ( n  = 128) completed an adapted version of the baseline online learning environment, in which autonomy, relatedness and competence support are provided. In total, need support consisted of 12 implementations (four implementations for each need), based on existing research on need support. An overview of these adaptations can be found in Tables ​ Tables1 1 and ​ and2. 2 . Although, ideally, students would work in class, under supervision of their teacher, this was not possible for all classes, due to the COVID-19 restrictions. 4 As a consequence, some students completed the learning environment partly at home. All students were supervised by their teachers (be it virtually or in class), and the researcher kept track of students’ overall activities in order to be able to contact students who did not complete the main activities. During the last two sessions of the intervention, participants submitted a two-pages long research proposal (“two-pager”). One week after the intervention, the LRST (Maddens et al., 2020a ), the ASRS (Vansteenkiste et al., 2009 ), four items related to students’ amotivation (Aydin et al., 2014 ), the value/usefulness scale (Ryan, 1982 ) and the Basic Psychological Need Satisfaction and Frustration Scale (BPNSNF, Chen et al., 2015 ) were administered in a posttest session of maximum two hours. Although most classes succeeded in organizing this posttest session in class, for some classes this posttest was administered at home. However, all classes were supervised by the teacher (be it virtually or in class). These contextual differences at the test moments will be reflected upon in the discussion section.

Adaptations online learning environment

Overview instruments

a When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively

Instruments

In this section, we elaborate on the tests used during the pretest and the posttest. Example items for each scale are presented in Appendix 1.

Motivational outcomes

In the current study, two groups of motivational outcomes are assessed: (1) students’ need satisfaction and frustration, and students’ experiences of value/usefulness; and (2) students’ level of autonomous motivation, controlled motivation, and amotivation. When administered at both pretest and posttest level (see ‘procedure’), the internal consistency values are reported respectively.

The BPNSNF-training scale (The Basic Psychological Need Satisfaction and Frustration Scale, Chen et al., 2015 ; translated version Aelterman et al., 2016 5 ) measured students’ need satisfaction and need frustration while working in the learning environment, and consists of 24 items (four items per scale): (autonomy satisfaction, α  = 0.67; ω = 0.67; autonomy frustration, α  = 0.76; ω = 0.76; relatedness satisfaction, α  = 0.79; ω = 0.79; relatedness frustration, α  = 0.60; ω = 0.61; competence satisfaction, α  = 0.72; ω = 0.73; competence frustration, α  = 0.68; ω = 0.67). The items are Likert-type items ranging from one (not at all true) to five (entirely true). Although the current study focusses mainly on students’ need satisfaction, the scales regarding students’ need frustration are included in order to be able to also detect students’ potential ill-being and in order to detect potential critical issues regarding students’ needs. In addition to the BPNSNF, by means of seven Likert-type items ranging from one (not at all true) to seven (entirely true), the (for the purpose of this research translated) value/usefulness scale of the Intrinsic Motivation Inventory (IMI, Ryan, 1982 ) measured to what extent students valued the activities of the online learning environment ( α  = 0.92; ω = 0.92). Since in the research skills literature problems have been observed related to students’ perceived value/usefulness of research skills (Earley, 2014 ; Murtonen, 2005 ), and this concept is not sufficiently stressed in the BPNSNF-scale, we found it useful to include this value/usefulness scale to the study. The difference in the range of the answer possibilities (one to five vs one to seven) exists because we wanted to keep the range as initially prescribed by the authors of each instrument. All motivational measures are calculated by adding the scores on every item, and dividing this sum score by the number of items on a scale, leading to continuous outcomes. Although the IMI and the BPNSNF targeted students’ experiences while completing the online learning environment, these measures were administered during the posttest. Thus, students had to think retrospectively about their experiences. In order to prevent cognitive overload while completing the online learning environment, these measures were not administered during the intervention itself.

Students’ autonomous and controlled motivation towards learning research skills was measured by means of the Dutch version of the Academic Self-Regulation Scale (ASRS; Vansteenkiste et al., 2009 ), adapted to ‘ research skills ’. The ASRS consists of Likert-type items ranging from one (do not agree at all) to five (totally agree), and contains eight items per subscale (autonomous and controlled motivation). In the autonomous motivation scale, four items are related to identified regulation, and four items are related to intrinsic motivation. 6 In the controlled motivation scale, four items are related to external regulation, and four items are related to introjected regulation. Both scales (autonomous motivation and controlled motivation) indicated good internal consistency for the study’s data (autonomous motivation: α  = 0.91; 0.92; ω = 0.90; 0.92; controlled motivation: α  = 0.83; 0.86; ω = 0.82; 0.85). The items were adapted to the domain under study (motivation to learn about research skills). Based on students’ motivational issues related to research skills, we found it useful to also include a scale to assess students’ amotivation. This was measured with (for the purpose of the current research translated) four items related to students’ amotivation regarding learning research skills, adapted from Academic Motivation Scale for Learning Biology (Aydin et al., 2014 ) ( α  = 0.80; 0.75; ω = 0.81; 0.75). Also this measure consist of Likert-type items ranging from one (do not agree at all) to five (totally agree).

Cognitive outcomes

Students’ research skills proficiency was measured by means of a research skills test (Maddens et al., 2020a ) and a research skills task.

The research skills test used in this study is the LRST (Maddens et al., 2020a ) consisting of a combination of 37 open ended and close ended items ( α  = 0.79; 0.82; ω = 0.78; ω = 0.80 for this data set), administered via an online questionnaire. Each item of the LRST is related to one of the eight epistemic activities regarding research skills as mentioned in the introduction (Fischer et al., 2014 ), and is scored as 0 or 1. The total score on the LRST is calculated by adding the mean subscale scores (related to the eight epistemic activities), and dividing them by eight (the number of scales). In a previous study (Maddens et al., 2020a ), the LRST was checked and found suitable in light of interrater reliability ( κ  = 0.89). As the same researchers assessed the same test with a similar cohort in the current study, the interrater reliability was not calculated for this study.

In the research skills task (“two pager task”), students were asked to write a research proposal of maximum two pages long. The concrete instructions for this research proposal are given in Appendix 1. In this research proposal, students were asked to formulate a research question and its relevance; to explain how they would tackle this research question (method and participants); to explain their hypotheses or expectations; and to explain how they would communicate their results. The two-pager task was analyzed using a pairwise comparison technique, in which four evaluators (i.e. the four authors of this paper) made comparative judgements by comparing two two-pagers at a time, and indicating which two-pager they think is best. All four evaluators are researchers in educational sciences and are familiar with the research project and with assessing students’ texts. This shared understanding and expertise is a prerequisite for obtaining reliable results (Lesterhuis et al., 2018 ). The comparison technique is performed by means of the Comproved tool ( https://comproved.com ). As described by Lesterhuis et al. ( 2018 , p. 18), “the comparative judgement method involves assessing a text on its overall quality. However, instead of requiring an assessor to assign an absolute score to a single text, comparative judgement simplifies the process to a decision about which of two texts is better”. In total, 1635 comparisons were made (each evaluator made 545 comparisons), and this led to a (interrater)reliability score of 0.79. In a next step, these comparative judgements were used to rank the 218 products (15 students did not submit a two-pager) on their quality; and the products were graded based on their ranking. This method was used to grade the two-pagers because it facilitates the holistic evaluation of the tasks, based on the judgement of multiple experts (interrater reliability).

Qualitative feedback

Students’ experiences with the online learning environment were investigated in the online learning environment itself. After completing the learning environment, students were asked how they experienced the tasks, the theory, the opportunity to post answers in the forum and to ask questions via the chat, what they liked or disliked in the online learning environment, and what they disliked in the online learning environment (Fig.  1 ).

Study overview

The first research question (” Does a deliberately designed (4C/ID-based) learning environment improve students’ research skills, as measured by a research skills test and a research skills task?” ) is answered by means of a paired samples t -test in order to look for overall improvements in order to detect potential general trends, followed by a full factorial MANCOVA, as this allows us to investigate the effectiveness for both conditions taking into account students’ pretest scores. Hence, the condition is included as an experimental factor, and students’ scores on the LRST and the two-pager task are included as continuous outcome variables. Students’ pretest scores on the LRST are included as a covariate. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariate.

The second research question (“ What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID-based) learning environment fostering students’ research skills, on students’ motivational outcomes, i.e. students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?”) ;) is answered by means of a full factorial MANCOVA. The condition (need satisfaction condition versus baseline condition) is included as an experimental factor, and students’ responses on the value/usefulness, autonomous and controlled motivation, amotivation, and need satisfaction scales are included as continuous outcome variables. ASRS pretest scores (autonomous and controlled motivation) are included as covariates in order to test the differences between group means, adjusted for students’ a priori motivation. Prior to the analysis, a MANCOVA model is defined taking into account possible interaction effects between the experimental factor and the covariates, and assumptions to be met to perform a MANCOVA are checked. 7

The third research question ( “ What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?” ), is initially answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students’ value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables. The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores, and (5) scores on the two-pager task as dependent variables. As a follow-up analysis (see ‘ results ’) two additional regression analyses are performed to look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST (6) and two-pager (7)). As the goal of this analysis is to investigate the relationships between variables as described in SDT research, this analysis focuses on the full sample, rather than distinguishing between the two conditions. An ‘Enter’ method (Field, 2013 ) is used in order to enter the independent variables simultaneously (in line with Sheldon et al., 2008 ).

The fourth research question (“ How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID-based) learning environment?” ) is analyzed by means of the knowledge management tool Citavi. Based on the theoretical framework, students’ experiences are labeled by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. For example, students’ quotes referring to the value/usefulness of the learning environment, are labeled as ‘autonomy satisfaction’ or ‘autonomy frustration’. Students’ references towards their feelings of mastery of the learning content are labeled as ‘competence satisfaction’ or ‘competence frustration’. Students’ quotes regarding their relationships with peers and teachers are labeled as ‘relatedness satisfaction’ or ‘relatedness frustration’ (Fig.  2 ).

Overview variables

Does the deliberately designed (4C/ID based) learning environments improve students’ research skills, as measured by a research skills test and a research skills task?

Paired samples t -test. A paired samples t -test reveals that, in general, students ( n  = 210) improved on the LRST-posttest ( M  = 0.57, SD  = 0.16) compared to the pretest ( M  = 0.51, SD  = 0.15) (range 0–1). The difference between the posttest and the pretest is significant t (209) =  − 8.215, p  < 0.001, d 8  =  − 0.567. The correlation between the LRST pretest and posttest is 0.70 ( p  < 0.010).

MANCOVA. A MANCOVA model ( n  = 196) was defined checking for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariate LRST pretest did not show significant interaction effects for the two outcome variables LRST post ( p  = 0.259) and the two-pager task ( p  = 0.702). The correlation between the outcome variables (LRST post and two-pager), is 0.28 ( p  < 0.050).

Of all 233 students, 36 students were excluded from the main analysis because of missing data (for example, because they were absent during a pretest or posttest moment). These students were excluded by means of a listwise deletion method because we found it important to use a complete dataset, since, in a lot of cases, students who did not complete the pretest or posttest, did also not complete the entire learning environment. Including partial data for these students could bias the results. The baseline condition counted 86 students, and the need satisfaction condition counted 111 students. Using Pillai’s Trace [ V  = 0.070, F (2,193) = 7.285, p  ≤ 0.001], there was a significant effect of the condition on the cognitive outcome variables, taking into account students’ LRST pretest scores. Separate univariate ANOVAs on the outcome variables revealed no significant effect of the condition on the LRST posttest measure, F (1,194) = 2.45, p  = 0.120. However, a significant effect of condition was found on the two-pager scores, F (1,194) = 13.69, p  < 0.001 (in the baseline group, the mean score was 6,6/20; in the need condition group, the mean score was 7,6/20). It should be mentioned that both scores are rather low.

What is the effect of providing autonomy, competence and relatedness support in a deliberately designed (4C/ID based) learning environment fostering students’ research skills, on students’ motivational outcomes (students’ amotivation, autonomous motivation, controlled motivation, students’ perceived value/usefulness, and students’ perceived needs of competence, relatedness and autonomy)?

Paired samples t -tests. The correlations between students’ pretest and posttestscores for the motivational measures are 0.67 ( p  < 0.010) for autonomous motivation; 0.44 ( p  < 0.010) for controlled motivation, and 0.38 for amotivation ( p  < 0.010). Regarding the differences in students’ motivation, three unexpected findings were observed. Overall, students’ ( n  = 215) amotivation was higher on the posttest ( M  = 2.26, SD  = 0.89) compared to the pretest ( M  = 1.77, SD  = 0.79) (based on a score between 1 and 5). The difference between the posttest and the pretest is significant t (214) =  − 7.69, p  < 0.001, d  =  − 0.524. Further analyses learn that the amotivation means in the baseline group increased with 0.65, and the amotivation in the need support group increased with 0.37. In addition, students’ ( n  = 215) autonomous motivation was higher on the pretest ( M  = 2.81, SD  = 0.81) compared to the posttest ( M  = 2.64, SD  = 0.82). The difference between the posttest and the pretest is significant t (214) = 3.72, p  < 0.001, d  = 0.254. Students’ mean scores on autonomous motivation in the baseline condition decreased with 0.19, and students’ autonomous motivation in the need support condition decreased with 0.15. Students’ ( n  = 215) controlled motivation was higher on the posttest ( M  = 2.33, SD  = 0.75) compared to the pretest ( M  = 1.93, SD  = 0.67). The difference between the posttest and the pretest is significant t (214) =  − 07.72, p  < 0.001, d  =  − 0.527. Students’ controlled motivation in the baseline group increased with 0.36, and students’ controlled motivation in the need support group increased with 0.43. However, overall, all mean scores are and stay below neutral score (below 3), indicating robust low autonomous, controlled and amotivation scores (see Table ​ Table3). 3 ). An independent samples T -test on the mean differences between these measures shows that the increases/decreases on autonomous motivation [ t (213) =  − 0.506, p  = 0.613, d  =  − 0.069] and controlled motivation [ t (213) =  − 0.656, p  = 0.513, d  =  − 0.090] did not differ between the two groups. However, the increases in amotivation [ t (213) = 2.196, p  = 0.029, d  = 0.301] does differ significantly between the two conditions. More specifically, the increase was lower in the need supportive condition compared to the baseline condition.

Mean scores and standard deviations motivational variables

a Overall, students’ ( n  = 215) autonomous motivation was significantly higher on the pretest compared to the posttest ( t (214) 3.72, p  ≤ 0.001, d  = 0.254

b Students’ (n = 215) controlled motivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 7.72, p  ≤ 0.001, d  =  − 0.527

c Students’ ( n  = 215) amotivation was significantly higher on the posttest compared to the pretest ( t (214) =  − 07,69, p  ≤ 0.001, d  =  − 0.534)

MANCOVA. Of all 233 students, 18 students were excluded from the analysis because of missing data (for example, because they were absent during a pretest or posttest moment). Compared to the cognitive analyses, the amount of missing data is lower concerning motivational outcomes since, concerning the cognitive outcomes, some students did not complete the two-pager task. However, we found it important to use all relevant data and chose to report this is in a clear way. In total, the baseline condition counted 97 students, and the experimental condition counted 118 students. Similar to the analysis for the cognitive outcomes, a MANCOVA model was defined to check for possible interaction effects between the experimental factor and the covariate in order to control for the assumption of ‘independence of the covariate and treatment effect’ (Field, 2013 ). The covariates did not show significant interaction effects for the outcome variables. 9

Using Pillai’s Trace [ V  = 0.113, F (10,201) = 2.558, p  = 0.006], there was a significant effect of condition on the motivational variables, taking into account students’ autonomous and controlled pretest scores, and students’ a priori amotivation. Separate univariate ANOVAs on the outcome variables revealed a significant effect of the condition on the outcome variables amotivation, F (1,210) = 3.98, p  = 0.047; and relatedness satisfaction F (1,210) = 6.41, p  = 0.012. As was hypothesized, students in the need satisfaction group reported less amotivation ( M  = 2.38), compared to students in the baseline group ( M  = 2.18). In contrast to what was hypothesized, students in the need satisfaction group reported less relatedness satisfaction ( M  = 2.43) compared to students in the baseline group ( M  = 2.73), and no significant effects of condition were found on the outcome variables autonomous motivation post, controlled motivation post, value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, and relatedness frustration. Table ​ Table4 4 shows the correlations between the motivational outcome variables.

Correlations motivational outcome variables

AM autonomous motivation, CM controlled motivation, AMOT amotivation, VU value/usefulness, AS autonomy satisfaction, AF autonomy frustration, CS competence satisfaction, CF competence frustration, RS relatedness satisfaction, RF relatedness frustration

**Correlation is significant at the 0.010 level (2-tailed)

*Correlation is significant at the 0.050 level (2-tailed)

What are the relationships between students’ need satisfaction, students’ need frustration, students’ autonomous and controlled motivation and students’ cognitive outcomes (research skills test and research skills task)?

The third research question (investigating the relationships between students’ need satisfaction, students’ motivation and students’ cognitive outcomes), is answered by means of five multiple regression analyses. The first three regressions include the need satisfaction and frustration scales, and students value/usefulness as independent variables, and students’ (1) autonomous motivation, (2) controlled motivation, and (3) amotivation as dependent variables ( n  = 219). The fourth and fifth regressions include students’ autonomous motivation, controlled motivation, and amotivation as independent variables, and students’ (4) LRST scores ( n  = 215), and (5) scores on the two-pager task as dependent variables ( n  = 206). Table ​ Table4 4 depicts the correlations for the first three analyses. Table ​ Table5 5 depicts the correlations for the last two analyses.

Correlations motivational and cognitive outcome variables

AM  autonomous motivation, CM  controlled motivation, AMOT  amotivation, LRST  score on LRST, Twopager  score on Twopager

In Table ​ Table3, 3 , we can see that students in both conditions experience average competence and autonomy satisfaction. However, students’ relatedness satisfaction seems low in both conditions. This finding will be further discussed in the discussion section. For autonomous motivation, a significant regression equation was found F (7,211) = 37.453, p  < 0.001. The regression analysis (see Table ​ Table5) 5 ) further reveals that all three satisfaction scores (competence satisfaction, relatedness satisfaction and autonomy satisfaction) contribute positively to students’ autonomous motivation, as does students’ experienced value/usefulness. Also for students’ controlled motivation a significant regression equation was found F (7,211) = 8.236, p  < 0.001, with students’ autonomy frustration and students’ relatedness satisfaction contributing to students’ controlled motivation. The aforementioned relationships are in line with the expectations. However, we noticed that relatedness satisfaction contributed to students’ controlled motivation in the opposite direction of what was expected (the higher students’ relatedness satisfaction, the lower students’ controlled motivation). This finding will be reflected upon in the discussion section. Also for students’ amotivation, a significant regression equation was found F (7,211) = 7.913, p  < 0.001. Students’ autonomy frustration, competence frustration and students’ value/usefulness contributed to students’ amotivation in an expected way. Also for cognitive outcomes related to the research skills test, a significant regression equation was found F (3,211) = 8.351, p  < 0.001. In line with the expectations, the regression analysis revealed that the higher students’ amotivation, the lower students’ scores on the research skills test. No significant regression equation was found for the outcome variable related to the research skills task F (3,202) = 0.954, p  < 0.416. For all regression equations, the R 2 and the exact regression weights are presented in Table ​ Table6 6 .

Linear model of predictors of autonomous motivation, controlled motivation, amotivation, LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

*Significant at .050 level

As a follow-up analysis and in order to better understand the outcomes, we decided to also look into the direct relationships between students’ perceived needs and students’ experienced value/usefulness, with students’ cognitive outcomes (LRST and two-pager) by means of two additional regression analyses. The motivation behind this decision relates to possible issues regarding the motivational measures used, which might complicate the investigation of indirect relationships (see discussion). The results are provided in Table ​ Table7, 7 , and show that both for the LRST and the two-pager, respectively, a significant [ F (7,207) = 4.252, p  < 0.001] and marginally significant regression weight [ F (7,199) = 2.029, p  = 0.053] was found. More specifically, students’ relatedness satisfaction and students’ perceived value/usefulness contribute to students’ scores on the two-pager and on the research skills test. As one would expect, we see that the higher students’ value/usefulness, the higher students’ scores on both cognitive outcomes. In contrast to one would expect, we found that the higher students’ relatedness satisfaction, the lower students’ scores on the cognitive outcomes. These findings are reflected upon in the discussion section.

Linear model of predictors of LRST scores, and two-pager scores with beta values, standard errors, standardized beta values and significance values

How do students experience need satisfaction and need frustration in a deliberately designed (4C/ID based) learning environment?

As was mentioned in the method section, the fourth research question was analysed by labelling students’ qualitative feedback by the codes ‘autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration, relatedness satisfaction, and relatedness frustration’. By means of this approach, we could analyse students’ need experiences in a fine grained manner. When students’ quotes were applicable to more than one code, they were labelled with different codes. In what follows, students’ quotes are indicated with the codes “BC” (baseline condition) or “NSC” (need satisfaction condition) in order to indicate which learning environment the student completed. Of all 233 students, 124 students provided qualitative feedback (44 in BC and 80 in NSC). In total, 266 quotes were labeled. Autonomy satisfaction was coded 40 times BC and 41 times in NSC; autonomy frustration was coded 13 times in BC and four times in NSC; competence satisfaction was coded 28 times in BC and 34 times in NSC; competence frustration was coded 31 times in BC and 27 times in NSC; relatedness satisfaction was coded 10 times in BC and 16 times in NSC; and relatedness frustration was coded five times in BC and 17 times in NSC. Several observations could be drawn from the qualitative data.

Related to autonomy satisfaction , in both conditions, several students explicitly mentioned the personal value and usefulness of what they had learned in the learning environment. While in the baseline condition, these references were often vague (“Now I know what people expect from me next year ”; “I think I might use this information in the future ”); some references appeared to be more specific in the need support condition (“I want to study psychology and I think I can use this information!”; “This is a good preparation for higher education and university ”; “I can use this information to write an essay ”; “I think the theory was interesting, because you are sure you will need it once. I don’t always have that feeling during a normal lesson in school”). In addition, students in both conditions mentioned that they found the material interesting, and that they appreciated the online format: “It’s different then just listening to a teacher, I kept interested because of the large variety in exercises and overall, I found it fun” (NSC).

Several comments were coded as ‘ autonomy frustration’ in both conditions. Some students indicated that they found the material “useless” (BC), or that “they did not remember that much” (BC). Others found the material “uninteresting” (BC), “heavy and boring” (NSC) or “not fun” (BC). In addition, some students “did not like to complete the assignments” (NSC), or “prefer a book to learn theory” (NSC).

Related to competence satisfaction , students in both conditions found the material “clear” (BC, NSC). In addition, students’ appreciated the example answers, the difficulty rate (“Some exercises were hard, but that is good. That’s a sign you’re learning something new” (NSC)), and the fact that the theory was segmented in several parts. In addition, students recognized that the material required complex skills: “I learned a lot, you had to think deeper or gain insights in order to solve the exercises” (NSC), “you really had to think to complete the exercises” (NSC). In the need satisfaction group, several quotes were labelled related to the specific need support provided. For example, students indicated that they appreciated the forum option: “If something was not clear, you could check your peer’s answers” (NSC). Students also valued the fact that they could work at their own pace: “I found it very good that we could solve everything at our own pace” (NSC); “good that you could choose your own pace, and if something was not clear to you, you could reread it at your own pace” (NSC). In addition, students appreciated the immediate feedback provided by the researcher “I found it very good that we received personal feedback from xxx (name researcher). That way, I knew whether I understood the theory correctly” (NSC); and the fact that they could indicate their progress “It was good that you could see how far you proceeded in the learning environment” (NSC).

In both the baseline and the need supportive condition, there were also several comments related to competence frustration . For example, students found exercises vague, unclear or too difficult. While students, overall, understood the theory provided, applying the theory to an integrative assignment appears to be very difficult: “I did understand the several parts of the learning environment, but I did not succeed in writing a research proposal myself” (NSC). “I just found it hard to respond to questions. When I had to write my two-pager research proposal, I really struggled. I really felt like I was doing it entirely wrong” (NSC)). In addition, a lot comments related to the fact that the theory was a lot to process in a short time frame, and therefore, students indicated that it was hard to remember all the theory provided. In addition, this led pressure in some students: “Sometimes, I experiences pressure. When you see that your peers are finished, you automatically start working faster.” (BC).

Concerning relatedness satisfaction , in the baseline condition, students appreciated the chat function “you could help each other and it was interesting to hear each other’s opinions about the topics we were working on” (BC). However, most students indicated that they did not make use of the chat or forum options. In the need satisfaction condition, students appreciated the forum and the chat function: “You knew you could always ask questions. This helped to process the learning material” (NSC), “My peers’ answers inspired me” (NSC), “Thanks to the chat function, I felt more connected to my peers” (NSC). In addition, students in the need satisfaction condition appreciated the fact that they could contact the researcher any time.

Several students made comments related to relatedness frustration . In both groups, students missed the ‘live teaching’: “I tried my best, but sometimes I did not like it, because you do not receive the information in ‘real time’, but through videos” (BC). In addition, students missed their peers: “We had to complete the environment individually” (BC). While some students appreciated the opportunity of a forum, other students found this possibility stressful: “I think the forum is very scary. I posted everything I had to, but I found it very scary that everyone can see what you post” (NSC). Others did not like the fact that they needed to work individually: “Sometimes I lost my attention because no one was watching my screen with me” (NSC); “I found it hard because this was new information and we could not discuss it with each other” (NSC); “I felt lonely” (NSC); “It is hard to complete exercises without the help of a teacher. In the future this will happen more often, so I guess I will have to get used to it” (NSC); “When I see the teacher physically, I feel less reluctant to ask questions” (NSC).

The current intervention study aimed at exploring the motivational and cognitive effects of providing need support in an online learning environment fostering upper secondary school students’ research skills. More specifically, we investigated the impact of autonomy, competence and relatedness support in an online learning environment on students’ scores on a research skills test, a research skills task, students’ autonomous motivation, controlled motivation, amotivation, need satisfaction, need frustration, and experienced value/usefulness. Adopting a pretest-intervention-posttest design approach, 233 upper secondary school behavioral sciences students’ motivational outcomes were compared among two conditions: (1) a 4C/ID inspired online learning environment condition (baseline condition), and (2) a condition with an identical online learning environment additively providing support for students’ autonomy, relatedness and competence need satisfaction (need supportive condition). This study aims to contribute to the literature by exploring the integration of need support for all three needs (the need for competence, relatedness and autonomy) in an ecologically valid setting. In what follows, the findings are discussed taking into account the COVID-19 affected circumstances in which the study took place.

As was hypothesized based on existing research (Costa et al., 2021 ), results showed significant learning gains on the LRST cognitive measure in both conditions, pointing out that the learning environments in general succeeded in improving students’ research skills. The current study did not find any significant differences in these learning gains between both conditions. Controlling for a priori differences between the conditions on the LRST pretest measure, students in the need support condition did exceed students in the baseline condition on the two-pager task. However, overall, the scores on the research skills task were quite low, pointing to the fact that students still seem to struggle in writing a research proposal. This task can be considered more complex (van Merriënboer & Kirschner, 2018 ) than the research skills test, as students are required to combine their conceptual and procedural knowledge in one assignment. Indeed, in the qualitative feedback, students indicate that they understand the theory and are able to apply the theory in basic exercises, but that they struggle in integrating their knowledge in a research proposal. Future research could set up more extensive interventions explicitly targeting students’ progress while writing a research proposal, for example using development portfolios (van Merriënboer et al., 2006 ).

The effect of the intervention on the motivational outcome measures was investigated. Since we experimentally manipulated need support, this study hypothesized that students in the need supportive condition would show higher scores for autonomous motivation, value/usefulness and need satisfaction; and lower scores for controlled motivation, amotivation and need frustration compared to students in the baseline condition (Deci & Ryan, 2000 ). However, the analyses showed that students in the conditions did not differ on the value/usefulness, autonomy satisfaction, autonomy frustration, competence satisfaction, competence frustration and relatedness frustration measures. In contrast to what was hypothesized, students’ in the baseline condition reported higher relatedness satisfaction compared to students in the need supportive condition. No differences were found in students’ autonomous motivation and controlled motivation. However, as was expected, students in the need supportive conditions did report lower levels of amotivation compared to students in the baseline condition. Still, for the current study, one could question the role of the need support in this respect, as the current intervention did not succeed in manipulating students’ need experiences. In what follows, possible explanations for these findings are outlined in light of the existing literature.

Need experiences

A first observation based on the findings as described above is that the intervention did not succeed in manipulating students’ need satisfaction, need frustration and value/usefulness in an expected way. One effect was found of condition on relatedness satisfaction, but in the opposite direction of what was expected. We did not find a conclusive explanation for this unanticipated finding, but we do argue that the COVID-19 related measures at play during the intervention could have impacted this result. This will be reflected upon later in this discussion (limitations). In both conditions, students seem to be averagely satisfied regarding autonomy and competence in the 4C/ID based learning environments. This might be explained by the fact that 4C/ID based learning environments inherently foster students’ perceived competence because of the attention for structure and guidance, and the fact that the use of authentic tasks can be considered autonomy supportive (Bastiaens & Martens, 2007). However, we see that students experience low relatedness satisfaction in both conditions. The fact that the learning environment was organized entirely online might have influenced this result. While one might also partly address this low relatedness satisfaction to the COVID-19 circumstances at play during the study, this hypothetical explanation does not hold entirely since also in a previous non COVID-affected study in this research trajectory (Maddens et al., under review ), students’ relatedness satisfaction was found to be low. This finding, combined with findings from students’ qualitative feedback clearly indicating relatedness frustration, we argue that future research could focus on the question as how to provide need for relatedness support in 4C/ID based learning environments. On a more general level, this raises the question how opportunities for discussions and collaboration can be included in 4C/ID based learning environments. For example, organizing ‘real classroom interactions’ or performing assignments in groups (see also the suggestion of van Merriënboer & Kirschner, 2018 ), might be important in fostering students’ relatedness satisfaction (Salomon, 2002 ) . As argued by Wang et al. ( 2019 ), relatedness support is clearly understudied, for a long time often even ignored, in the SDT literature. Recently, relatedness is beginning to receive more attention, and has been found a strong predictor of autonomous motivation in the classroom (Wang et al., 2019 ).

Possibly, the need support provided in the learning environment was insufficient or inadequate to foster students’ need experiences. However, as the implementations were based on the existing literature (Deci & Ryan, 2000 ), this finding can be considered surprising. In addition, we derive from the qualitative feedback that students seem to value the need support provided in the learning environment. These contradictory observations are in line with previous research (Bastiaens et al., 2017 ), and call for further investigation.

Autonomous motivation, controlled motivation, amotivation

A second observation is that, in both conditions, students seem to hold low autonomous motivation and low controlled motivation towards learning research. On average, also students’ amotivation is low. The fact that students are not amotivated to learn about research can be considered reassuring. However, the fact that students experience low autonomous motivation causes concerns, as we know this might negatively impact their learning behavior and intentions to learn (Deci & Ryan, 2000 ; Wang et al., 2019 ). However, this result is based on mean scores. Future research might look at these results at student level, in order to identify individual motivational profiles (Vansteenkiste et al., 2009 ) and their prevalence in upper secondary behavioral sciences education.

A third observation is that students’ autonomous and controlled motivation were not affected by the intervention. Since the intervention did not succeed in manipulating students’ need experiences, this finding is not surprising. In addition, this is in line with Bastiaens et al.’ ( 2017 ) study, not finding motivational effects of providing need support in 4C/ID based learning environments. However, the current study did confirm that—although still higher than at pretest level, see below—students in the need supportive condition reported lower amotivation compared to students in the baseline condition. As no amotivational differences were observed at pretest level, this might indicate that students’ self-reported motivation (autonomous and controlled motivation) and/or needs do not align with students’ experienced motivation and needs. As was mentioned, this calls for further research.

Theoretical relationships

In line with previous research (Wang et al., 2019 ), multiple regression analyses revealed that students’ need satisfaction (on all three measures) contributed positively to students’ autonomous motivation. In addition, also students’ perceived value/usefulness contributed positively to students’ autonomous motivation. Students’ competence frustration and autonomy frustration contributed positively to students’ amotivation, and students’ value/usefulness contributed negatively to students’ amotivation. Students’ autonomy frustration contributed positively to students’ controlled motivation. While all the aforementioned relationships are in line with the expectations (Deci & Ryan, 2000 ; Wang et al., 2019 ), an unexpected finding is that students’ relatedness satisfaction contributed positively to students’ controlled motivation. This contradicts previous research (Wang et al., 2019 ), reporting that relatedness contributes to controlled motivation negatively. However, previous research (Wang et al., 2019 ) did find controlled motivation to be positively related to pressure . Although we did not find a conclusive explanation for this unanticipated finding, one possible reason thus is that students who contacted their peers in the online learning environment (and thus felt more related to their peers), might have experienced pressure because they felt like their peers worked faster or in a different way. Indeed, in the qualitative feedback, we noticed that some students indicated they ‘rushed’ through the online learning environment because they noticed a peer working faster. This finding calls for further research.

Overall, the results indicate that the observed need variables contributed most to students’ autonomous motivation, compared to (reversed relationships in) students’ amotivation and students’ controlled motivation. As such, when targeting students’ motivation, fostering students’ autonomous motivation based on students’ need experiences seems most promising. This is in line with previous research (Wang et al., 2019 ) reporting high correlations between students’ needs and students’ autonomous motivation, compared to students’ controlled motivation. We also investigated the relationships between students’ motivation and students’ cognitive outcomes. In line with a previously conducted study in this research trajectory (Maddens et al., under review ), but in contrast to what was hypothesized based on the existing literature (Deci & Ryan, 2000 ; Grolnick et al., 1991 ; Reeve, 2006 ) we found that nor students’ autonomous motivation, nor students’ controlled motivation contributed to students’ scores on the research skills test. However, we did find that students’ amotivation contributed negatively to students’ LRST scores. As such, when targeting students’ cognitive outcomes in educational programs, one might pay explicit attention to preventing amotivation. This is in line with previous research conducted in other domains, reporting that amotivation plays an important role in predicting mathematics achievement (Leroy & Bressoux, 2016 ), while this relationship was not found in other motivation types. Related to research skills, the current research suggests that preventing competence frustration and autonomy frustration, and fostering students’ experiences of value/usefulness might be especially promising to reach this goal.

Initially, we did not plan any analyses investigating the direct relationships between students’ needs and students’ cognitive outcomes, partly because previous research (Vallerand & Losier, 1999 ) suggests that the relationships between need satisfaction and (cognitive) outcomes are mediated by the types of motivation. To this end, we investigated the relationships between students’ needs and students’ motivation, separately from the relationships between students’ motivation and students’ cognitive outcomes. However, because of potential issues with the motivational measures (see earlier), which possibly hampers the interpretation of the relationships between students’ needs, students’ motivation, and students’ cognitive outcomes, we decided to also directly assess the regression weights of students’ needs and students’ perceived value/usefulness, on students’ cognitive outcomes. Results revealed that, in line with the expectations, students’ perceived value/usefulness contributed positively to students’ LRST scores and two-pager scores, which potentially stresses the importance of value/usefulness, not only for motivational purposes, but also for cognitive purposes. This is in line with previous research (Assor et al., 2002 ), establishing relationships between fostering relevance and students’ behavioral and cognitive engagement (which potentially leads to better cognitive outcomes). In contrast to the expectations, students’ relatedness satisfaction was found to be negatively related to students’ scores on the LRST and the two-pager. However, again, this surprising finding is best interpreted in light of the COVID-10 pandemic (see earlier).

Limitations

This study faced some reliability issues given the time frame in which the study took place. Due to the COVID-19-restrictions at play at the time of study, the study plan needed to be revised several times in collaboration with teachers in order to be able to complete the interventions. In addition, it is very likely that students’ motivation (and relatedness satisfaction) was influenced by the COVID 19-restrictions. For example, due to the restrictions, in the last phase of the intervention, students could only be present at school halftime, and therefore, some students worked from home while others worked in the classroom. In the qualitative feedback, students reported several COVID-19 related frustrations (it was too cold in class because teachers were obligated to open the windows; students needed to frequently disinfect their computers…). Also the teachers mentioned that students suffered from low well-being during the COVID-19 time frame (see further), and as such, this affected their motivation. Although all efforts were undertaken in order for the study to take place as controlled as possible, results should be interpreted in light of this time frame. The impact of the COVID-19 pandemic on students’ self-reported motivation has been established in recent research (Daniels et al., 2021 ). Overall, one could question to what extent we can expect an intervention at microlevel (manipulating need support in learning environments) to work, when the study takes place in a time frame where students’ need experiences are seriously threatened by the circumstances.

Decreasing motivation

Students’ motivation evolved in a non-desirable way in both conditions. This unexpected finding (decreasing motivation) might be explained by four possible reasons: a first explanation is that asking students to fill out the same questionnaire at posttest and pretest level might lead to frustration and lower reported motivation (Kosovich et al., 2017 ). Indeed, students spent a lot of time working in the online learning environment, so filling out another motivational questionnaire on top of the intervention might have added to the frustration (Kosovich et al., 2017 ). A second explanation is that students’ motivation naturally declines over time (which is a common finding in the motivational literature, Kosovich et al., 2017 ). A third explanation is that students, indeed, felt less motivated towards research skills after having completed the online learning environment. For example, the qualitative data indicated that a lot of students acknowledged the fact that the learning environment was useful, but that personally, they were not interested in learning the material. In addition, students indicated that the learning material was a lot to process in a short time frame, and was new to them, which might have negatively impacted their motivation. The latter (students indicating that the learning material was extensive) might indicate that students experienced high cognitive load (Paas & van Merriënboer, 1994; Sweller et al., 1994 ) while completing the learning environment. A fourth explanation is that, due to the COVID19-restrictions, students lost motivation during the learning process. A post-intervention survey in which we asked teachers about the impact of the COVID-19 restrictions on students’ motivation indicated that some students experienced low well-being during the COVID-19 pandemic, and thus, this might have hampered their motivation to learn. In addition, a teacher mentioned that COVID-19 in general was very demotivating for the students, and that students had troubles concentrating due to the fact they felt isolated. As was mentioned, the impact of COVID-19 on students’ motivation has been well described in the literature (Daniels et al., 2021 ). Although, in the current study, we cannot prove the impact of these measures on students’ motivation specifically towards learning research skills, it is important to take this context into account when interpreting the results.

Students’ learning behavior

Based on students’ qualitative feedback, we have reasons to believe that students did not always work in the learning environment as we would want them to do. Thus, students did not interact with the need support in the intended way (‘instructional disobedient behavior’: Elen, 2020 ). For example, several students reported that they did not always read all the material, did not make use of the forum, or did not notice certain messages from the researcher. However, the current research did not specifically look into students’ learning behavior in the learning environment. In learning environments organized online, future researchers might want to investigate students’ online behavior in order to gain insights in students’ interactions with the learning environment.

This study aims to contribute to theory and practice. Firstly, this study defines the 4C/ID model (van Merriënboer & Kirschner, 2018 ) as a good theoretical framework in order to design learning environments aiming to foster students’ research skills. However, this study also points to students’ struggling in writing a research proposal, which might lead to more specific intervention studies especially focussing on monitoring students’ progress while performing such tasks. Secondly, this study clearly elaborates on the operationalizations of need support used, and as such, might inform instructional designers in order to implement need support in an integrated manner (including competence, relatedness and autonomy support). Future interventions might want to track and monitor students’ learning behavior in order for students to interact with the learning environment as expected (Elen, 2020 ). Thirdly, this study established theoretical relationships between students’ needs, motivation and cognitive outcomes, which might be useful information for researchers aiming to investigate students’ motivation towards learning research skills in the future. Based on the findings, future researchers might especially involve in research fostering students’ autonomous motivation by means of providing need support; and avoiding students’ amotivation in order to enhance students’ cognitive outcomes. Suggestions are made based on the need support and frustration measures relating to these motivational and cognitive outcomes. For example, fostering students’ value/usefulness seems promising for both cognitive and motivational outcomes. Fourthly, although we did not succeed in manipulating students’ need experiences, we did gain insights in students’ experiences with the need support by means of the qualitative data. For example, the irreplaceable role of teachers in motivating students has been exposed. This study can be considered innovative because of its aim to inspect both students’ cognitive and motivational outcomes after completing a 4C/ID based educational program (van Merriënboer & Kirschner, 2018 ). In addition, this study implements integrated need support rather than focusing on a single need (Deci & Ryan, 2000 ; Sheldon & Filak, 2008 ).

Acknowledgements

This study was carried out within imec’s Smart Education research programme, with support from the Flemish government.

Appendix: Overview test instruments

• Instructions 2-pager (Maddens, Depaepe, Raes, & Elen, under review)

Write a research proposal for a fictional study.

In a Word-document of maximum two pages…

• You describe a research question and the importance of this research question
• You explain how you would answer this research question (manner of data collection and target group)
• You explain what your expectations are, and how you will report your results.

To do so, you receive 2 hours.

Post your research proposal here.

Good luck and thank you for your activity in the RISSC-environment!

Declarations

The authors declare that they have no conflict of interest.

All ethical and GDPR-related guidelines were followed as required for conducting human research and were approved by SMEC (Social and Societal Ethics Committee).

1 Fischer et al. ( 2014 ) refer to these research skills as scientific reasoning skills.

2 In Flanders, during the time of study, four different types of education are offered from the second stage of secondary education onwards (EACEA, 2018) (general secondary education, technical secondary education, secondary education in the arts and vocational secondary education). Behavioral sciences is a track in general secondary education.

3 For a complete overview on the design and the evaluation of this learning environment, see Maddens et al ( 2020b ).

4 During the time of study, the COVID-19 restrictions became more strict: students in upper secondary education could only come to school half of the time. Therefore, some students completed the last modules of the learning environment at home.

5 The BPNSNF-training scale is initially constructed to evaluate motivation related to workshops. The phrasing was adjusted slightly in order for the suitability for the current study. For example, we changed the wording ‘during the past workshop…’ to ‘while completing the online learning environment…’.

6 In the current study, we would label the items categorized as ‘intrinsic motivation’ in ASRS (finding something interesting, fun, fascinating or a pleasant activity) as ‘integration’. In SDT (Deci & Ryan, 2000 ; Deci et al., 2017 ), integration is described as being “fully volitional”, or “wholeheartedly engaged”, and it is argued that fully internalized extrinsic motivation does not typically become intrinsic motivation, but rather remains extrinsic even though fully volitional (because it is still instrumental). In the context of the current study, in which students learn about research skills because this is instructed (thus, out of instrumental motivations), we think that the term integration is more applicable than pure intrinsic motivation in self-initiated contexts (which can be observed for example in children’s play or in sports).

7 Levene’s test for homogeneity of variances was significant for the outcome “two-pager”. However, we continued with the analyses since the treatment group sizes are roughly equal, and thus, the assumption of homogeneity of variances does not need to be considered (Field, 2013 ). Levene’s test for homogeneity of variances was non-significant for all the other outcome measures.

8 Cohen’s D is calculated in SPSS by means of the formula: D = M 1 - M 2 Sp

Condition x autonomous motivation pretest Value/usefulness: p  = 0.251; autonomous motivation: p  = 0.269; controlled motivation: p  = 0.457; amotivation: p  = 0.219; autonomy satisfaction: p  = 0.794; autonomy frustration: p  = 0.096; competence satisfaction: p  = 0.682; competence frustration: p  = 0.699; relatedness satisfaction: p  = 0.943; relatedness frustration: p  = 0.870.

Condition x controlled motivation pretest Value/usefulness: p  = 0.882; autonomous motivation: p  = 0.270; controlled motivation: p  = 0.782; amotivation: p  = 0.940; autonomy satisfaction: p  = 0.815; autonomy frustration: p  = 0.737; competence satisfaction: p  = 0.649; competence frustration: p  = 0.505; relatedness satisfaction: p  = 0.625; relatedness frustration: p  = 0.741.

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Research Skills Framework

Patterns of practice and a set of tools for researchers and their teams to grow—, 1. learn skills & practices.

2. Build Projects & Playbooks

3. Map Progress & Goals

4. New Frontiers & Exploration

Driven by volunteers in the ReOps community, and organizers and participants around the world

Program overview

The fourth round of the mRNA Victoria Research Acceleration Fund program (mVRAF Program) is part of the Victorian Government’s investment to grow mRNA capability in the state. The mVRAF Program is designed to capitalise on Victoria’s comparative advantages in research, increase the RNA candidate pipeline of next generation vaccines and therapies and enhance the Victorian economy by growing the RNA ecosystem.

This fourth round of the mVRAF Program fund will provide $1.7 million to support and accelerate mRNA- based therapeutics research through the provision of one-off grants to successful applicants.

The mVRAF Program is designed to complement the Victorian Government’s existing Victorian Medical Research Acceleration Fund by providing a dedicated funding stream for mRNA-based therapeutics projects. Projects submitted to the mVRAF Program will not be eligible to be submitted to the Victorian Medical Research Acceleration Fund.

mRNA Victoria and the Department of Jobs, Skills, Industry and Regions (DJSIR) will administer this program.

Please make sure to read the program guidelines and frequently asked questions (FAQs) before beginning your application.

Who can apply?

• Eligibility Requirement 1 – hold an active ABN, lead and perform the majority of research activity in Victoria. See program guidelines for more information.
• Eligibility Requirement 2 – co-contribution required - 1:1 matched co-contribution required (either financial or in-kind). Grant funding under this fourth round of the mVRAF Program will be provided on the basis that Applicants provide a 1:1 matched co-contribution in the form of either a financial or in-kind contribution (Co-contribution). See program guidelines for eligible co-contributions.
• Eligibility Requirement 3 – collaborative partner requirement - an Applicant is required to collaborate with at least one other organisation . Organisations based outside of Australia are eligible to be collaborative partners, providing the majority of the research activities will be led from and conducted in Victoria. See program guidelines for more information.

What you get

The fourth round of the mVRAF Program will provide $1.7 million (excluding GST) in total funding to successful mRNA projects.

Up to $500,000 (excluding GST) of funding is available for each project.

How to apply

Applicants will need to complete the application form, including providing a detailed project budget and responses against the assessment criteria, compile all necessary supporting documents and complete all required attestations.

Other information

Documents: for accessibility, please provide documents in PDF versions.

mRNA Victoria Research Acceleration Fund Program Guidelines

Applications selected for funding will need to enter into a DJSIR Standard Grant Agreement in order to receive the grant payment. A blank copy of the DJSIR Standard Grant Agreement Template is available here for reference.