literature review about academic success

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

Defining and Measuring Academic Success

2015, Practical Assessment, Research and Evaluation

Despite, and perhaps because of its amorphous nature, the term ‘academic success’ is one of the most widely used constructs in educational research and assessment within higher education. This paper conducts an analytic literature review to examine the use and operationalization of the term in multiple academic fields. Dominant definitions of the term are conceptually evaluated using Astin’s I-E-O model resulting in the proposition of a revised definition and new conceptual model of academic success. Measurements of academic success found throughout the literature are presented in accordance with the presented model of academic success. These measurements are provided with details in a user-friendly table (Appendix B). Results also indicate that grades and GPA are the most commonly used measure of academic success. Finally, recommendations are given for future research and practice to increase effective assessment of academic success.

Related Papers

Zin Eddine Dadach

The final goals of this study is to investigate the impact of using a Programmable Logic Controller (PLC) as a research based learning tool on the performance and motivation of each student in an industrial automation course. For this purpose, ten PLC’s lab experiments are used to visualize the theories covered in the learning outcomes (CLO’S) of the course and investigate their applications in industrial automation. The success of the PLC as research-based tool are demonstrated by the fact the performance of 40 students (73%) is higher in this course than their corresponding average performance in the college. Moreover, using the Dadach Motivation Factor (DMF) as tool to measure motivation of students, our results also indicate that thirty-five students (63.63%) were motivated during the course. In overall, this investigation shows that the utilization of ten PLC lab experiments for the visualization of the opaque theory of industrial automation is successful.

Knowledge and Performance Management

Mercy Ogbeta

Academic resilience and emotional intelligence are considered important personal resources for furthering students’ academic performance. However, many educational organizations seem to trivialize the performance implications of these constructs in teachings and curriculum. Consequently, it can decrease not just their academic performance but also their employability, as they lack the generic competencies to adapt and survive in a stressful context. Even so, empirical evidence on integrating academic resilience, emotional intelligence, and academic performance remains unexplored in the Nigerian university context. Therefore, the study aimed to investigate the linkages between academic resilience, emotional intelligence, and academic performance in Nigeria. The partial least square (PLS) modeling method was utilized for testing the stated hypotheses with data collected from 179 final year undergraduate students in the regular B.Sc. Business Administration and B.Sc. Marketing program ...

International Journal of Higher Education

margie roma

Higher educational institutions (HEIs) play a substantial role in the development of knowledge and skills that can cope with the demands of industries in the fourth industrial revolution (4IR). This study examined the alignment between the current assessment practices used by HEIs and the competencies demanded by the hospitality and tourism industry. It also aimed to develop an assessment strategy typology that could specifically target the competencies required by the industry. In addition, the study was able to determine the three most and the three least preferred assessment methods as perceived by the hotel and restaurant management students in a private university in Mandaluyong City, Philippines. The findings revealed the common assessment methods employed by teachers in Hospitality and Tourism Managemnt (HTM) major courses. The study argues that the use of these identified assessment methods likewise contribute in developing the emerging skills in the 4IR such as sense-maki...

College & Research Libraries

Angela Rockwell

Practical Assessment, Research and Evaluation

Donna H Ziegenfuss

Turkish Journal of Computer and Mathematics Education

Saumya Kumar

The significance accorded to the amorphous concept of Academic Performance of students is immense, owing to the mere reason that better performances academically set the prospect for the better future of any nation. Likewise, understanding the integrities of the concept holds a great connotation. The current paper aims to present a review of the various definitions of "academic performance" along with the factors perceived to have an effect on the academic performance of students belonging to Higher Education Institutions (HEIs). Additionally, an effort has also been made to gain an insight into the reliability of "Grade Point Average" (GPA) as a method for measuring academic performance.

Wanda Robinson

Educating a sufficient nursing workforce to provide high quality, compassionate, and ethical care to an increasingly diverse population is an ongoing challenge and opportunity for nurse educators. Current literature highlights the importance of engaging students in learning to strengthen student achievements. Fostering student engagement within nursing courses is particularly important. Grit (consistency of interest and perseverance of effort) is a factor that may be associated with student course engagement. Demographic characteristics of age, gender, race/ethnicity, prior education, degree program, and self-reported grade point average (GPA) also may be factors associated with student course engagement. Guided by a conceptual model derived from the literature, the purpose of this study was to determine whether grit and demographic characteristics were associated with student course engagement (skills, emotion, participation/interaction, and performance) within a nursing course. Us...

Health and Academic Achievement

Marcela Verešová

Marilee Bresciani Ludvik

The clash of whether higher education should serve the public good or economic stimulation seems more alive than ever to some, and to others, it has come to an end. Not agreeing on the purpose of American higher education certainly makes it difficult to know whether educators are being responsible for delivering what is expected of them. Rather than reviewing the important debate that has already taken place, this chapter seeks to merge the two seemingly juxtaposed disagreements and discuss how bringing the two purposes together may influence how we examine accountability. As such, an inquiry model, including ways to gather and interpret institutional performance indicators for accountability is posited. Practical suggestions for implementation of this methodology are provided.

RELATED PAPERS

Community College Journal of Research and Practice

Juliann McBrayer

Lee O Farrell

Higher education's response to exponential societal shifts

Jillian Kinzie

Higher Education Studies

karma El Hassan

Matthew Gaertner

Monica Burnette

Ann Popkess

The International Review of Research in Open and Distributed Learning

Lynette Bryan

Liz Chinlund

Journal of College Student Development

Fred Newton

WHY SUPERVISORS MATTER FOR COLLEGE STUDENTS: THE RELATIONSHIP BETWEEN TYPE OF EMPLOYMENT AND STUDENT OUTCOMES

Dr. Justin Hultman

Heather Friesen

Meltem Ozmutlu

ANIMA Indonesian Psychological Journal

titik kristiyani

Shafeeka Dockrat

mohamed deen

International Journal of Learning, Teaching and Educational Research

Nahla Moussa

Richard Okundia

The Journal of Campus Activities, Practice, and Scholarship

Christy Moran Craft

MIRDEC-10th, International Academic Conference Global and Contemporary Trends in Social Science (Global Meeting of Social Science Community) 06-08 November 2018, Barcelona, Spain. ISBN: 978-605-81247-2-1. https://www.mirdec.com/barca2018proceedings.

Nazmi Xhomara

Journal of Hispanic Higher Education

Glai Martinez

The problem of college readiness, edited by William G. Tierney and Julia C. Duncheon

Julia Duncheon

Mohamed Tejan Tarawally Deen

Kristopher A Oliveira

David Edens

The Professional Counselor

Jeffrey Warren

Jefferson Chavez

International Journal of Knowledge and learning

David Cheng

Oscar Cervantes

Journal of Multidisciplinary Research

Cristina Lopez Vergara

Azucena Montoya

Journal for the Advancement of Educational Research

Dr. Justin Hultman , Dr. Daniel Eadens

Proceedings of the 2010 …

Hala Bayoumy

putri hazarianti

Academic Achievement: Influences of University Students’ Self-Management and Perceived Self-Efficacy

Mohammed hasan ali al-abyadh.

1 Mental Health Department, College of Education, Prince Sattam bin Abdulaziz University, Alkharj 16273, Saudi Arabia

2 College of Education, Thamar University, Thamar 87246, Yemen

Hani Abdel Hafeez Abdel Azeem

3 Quality Unit at the Higher Institute of Administrative Sciences, Janaklis, Al Buhayrah 22732, Egypt

Associated Data

Not applicable.

Successful students are more than just those who have more effective and efficient learning techniques for acquiring and applying information. They can also motivate, evaluate, and adjust their behavior if they are not learning properly. Thus, the objective of this study was to investigate the influence of university students’ self-management during their learning experience and their self-efficacy on their academic achievement. Additionally, the study investigated the differences between the Egyptian and Saudi students’ perceptions of self-management skills and self-efficacy in their academic achievement within the two countries. A total of 889 students from two different Arab countries took part in the study (Egypt and the Kingdom of Saudi Arabia). The sample was given an online questionnaire to evaluate their self-management abilities, perceived self-efficacy, and academic achievement. A quantitative approach using SmartPLS-SEM was deployed. The findings demonstrate that self-management and self-efficacy have positive influences on students’ academic achievement in both countries. Further, self-management skills have been proven to influence self-efficacy, which in turn highly influences academic achievement. Moreover, the findings of the Multi-Group Analysis (MGA) did not report significant differences between the Egyptian and Saudi students in terms of their perception of self-management, self-efficacy, and academic achievement.

1. Introduction

In an effort to build the nation’s workforce for future rapid growth, the university education stage plays a vital role. According to Dev ( 2016 ), students’ learning outcomes, particularly at the university level, are a barometer of education’s success or ineffectiveness and a key predictor of youths’ and the nation’s future. Therefore, higher education should focus on the student’s whole development in terms of social, economic, and political environments, and it should be more than merely obtaining a certificate ( Harris 2001 ). Successful students are not only those who have more efficient and effective learning techniques for acquiring and applying their information. They can also encourage themselves and assess and adjust their behavior if they are not learning appropriately ( Kadiyono and Hafiar 2017 ). In this regard, Dembo ( 2004 ) identified six elements that students should manage to be good learners. These include self-motivation, learning techniques, social and physical environments, and time management. These elements serve as the foundation for structuring and integrating the essential skills to fulfill the academic expectations of university students learning. This concentration allows for the integration of both skill and academic-performance techniques. In addition, much self-motivation and self-discipline is required to achieve academic excellence ( Kadiyono and Hafiar 2017 ).

Recently, researchers, families, policymakers, and planners have focused on student academic achievement ( Dev 2016 ). Institutions should train students’ academic and life skills to ensure they can function at an appropriate learning level, according to previous research on comprehensive student development ( Wood and Olivier 2004 ). This has inspired various studies into more effective methods of increasing academic standards, and it has been discovered that proper self-management for students of higher education, among other criteria, improves learning and academic accomplishment ( Sikhwari 2014 ). Individuals with effective management skills, according to Kadiyono and Hafiar ( 2017 ), know where to place goals; how to solve problems effectively, think optimistically when presented with academic problems, utilize resources, manage their surroundings to meet their objectives; and may reflect on the causes of failure and establish objectives for future growth. Self-management is described as the ability to work efficiently toward significant goals while being adaptable in the face of difficulties ( Agolla and Ongori 2009 ). According to Stan ( 2021 ), self-management is a multidimensional umbrella concept that combines the personal qualities of the individual on which it can work through a behavioral transformation process. In this essence, Agolla and Ongori ( 2009 ) claimed that students with higher levels of self-reported behavioral self-management report better levels of self-reported academic success and adaptability to change.

In the same line, previous studies have found that willingness to attempt and tenacity are some of the characteristics of students with a good level of self-efficacy ( Ahmad and Safaria 2013 ). Students who have a good sense of self-efficacy will be able to pay close attention to, organize, and elaborate on content successfully due to their cognitive abilities ( Heslin and Klehe 2006 ). Such students work consistently; if they are unable to follow the course, they devise efficient ways to overcome obstacles to reaching their goals. Self-efficacy, or belief in one’s talents and capacities for performance and learning, is an important characteristic of university students’ success ( Hill 2002 ). Students who believe they can learn or complete an activity are more likely to accomplish the implementation of academic self-efficacy, study harder, persevere longer when faced with problems, and succeed at a better level than students who question their ability ( Schunk and Pajares 2002 ). According to Bandura ( 1997 ), self-efficacy beliefs determine task selection, effort, perseverance, resilience, and accomplishment.

In summary, students’ ideas about their skills and the outcomes of their efforts have an important impact on how they behave. As a result, it is not surprising that a large body of research indicates that student skills impact learning and achievement ( Meral et al. 2012 ). However, Novo and Calixto ( 2009 ) asserted that researchers do not provide deep and experimentally proven insights into the structure that lies at the foundation of learning processes and shape their growth, but rather about the challenges of the learning process. For example, several research studies in the United States ( Kuhfeld et al. 2020 ), the Netherlands ( Meeter 2021 ), Belgium ( Maldonado and de Witte 2022 ), and Germany ( Meeter 2021 ) have examined the difficulties imposed by COVID-19 on academic success ( Schult et al. 2022 ). The majority of these studies looked at student standardized test scores before and after the spring 2020 lockdown and showed slight but substantial drops. Academic achievement is frequently related to successful students’ particular talents and abilities. According to Díaz-Morales and Escribano ( 2015 ), academic achievement is the result of the complex interplay of the psychological, economic, and social factors that contribute to students’ optimal growth. One of the most important indicators of a student’s performance is their academic achievement; hence, research into the elements influencing academic achievement has long been highly regarded ( Rivkin et al. 2005 ). However, there is still a scarcity of studies on academic accomplishment and what factors should be developed ( Kadiyono and Hafiar 2017 ), which is surprising given that the goal of learning (education) is to assist each student in achieving their desired level of growth.

Within the context of the above-mentioned introductory framework, this study (1) investigates the influence of university students’ self-management during their learning experiences and their self-efficacy on their academic achievement in two different countries (Egypt and KSA), all of which appear to be key aspects of the learning process. Moreover, the study is considered pioneer research that (2) investigates the differences between the Egyptian and Saudi students’ perceptions of self-management skills and self-efficacy in their academic achievement. However, of the massive research studies that investigated each variable of the current framework with another, the current framework is considered novel due to studying the current three variables together within two different contexts in two different countries on different continents. This study also offers valuable advice to students on self-concept and soft skills, as well as acts as a roadmap for future research by potential researchers. In actuality, improving educational achievements necessitates the development of soft skills to promote human capacities, which is required to encourage the individual’s growth ( Levasseur 2013 ). Therefore, the interest in studying the aspects (skills) involved in academic performance stems primarily from the phenomenon’s complexity, the long-term impacts of which aim for high employability chances and good professional adaption.

2. Literature Review and Hypotheses Development

It is common knowledge that developing personal qualities during university education impacts a student’s later career and personal life since they are easily transferable. Subsequently, identifying individuals’ distinctive academic factors that contribute to achievement is critical since it aids academic success in higher education and potential career possibilities ( Sanchez-Ruiz et al. 2016 ). Academic success is influenced by a wide range of factors. The “Coleman Report”, a report on academic achievement from a large-scale study, was published in the 1960s ( Cheng et al. 2019 ), and numerous applications were produced based on this study as a result, which is essential for academic achievement difficulties. The elements influencing academic accomplishment can be loosely characterized as follows: psychological perceptions, student skills, and environmental perspectives ( Dijkstra and Peschar 2003 ). Moreover, some researchers think that four elements influence academic achievement: individual, family, educational institution, and the environment; the factors involved in individual factors can be further divided into cognitive functioning, learning mindset, motivation, and self-aspiration ( Hammouri 2004 ). Learning outcomes have become a phenomenon that everyone is interested in, which is why researchers have been working hard to uncover aspects that promote high academic achievement ( Aremu and Sokan 2003 ). As a result, we present a theoretical background on this triangular relationship among self-management abilities, self-efficacy, and academic-achievement motivation among university students in this section.

2.1. The Role of Student Self-Management in Increasing Student Self-Efficacy

In a wide sense, self-efficacy is described as a person’s belief in his/her abilities to plan and carry out the steps required to achieve specific objectives ( Bandura 1997 ). Bandura ( 2001 ) observed that students’ conduct is frequently best predicted by their ideas about their skills. Bandura ( 1997 ) proposed that self-efficacy influenced how students felt, thought, and acted. Self-efficacy, according to self-efficacy theory, is one’s belief in their capacity to plan and carry out a certain course of conduct to find a solution or complete a task ( Eccles and Wigfield 2002 ). Thus, a student’s self-efficacy refers to an individual’s belief in the ability to learn and perform behavior at a particular level. In addition, a high level of students’ self-efficacy promotes skill development, capacity building, and resilience by promoting task motivation and commitment, hard-working spirit, longer endurance, and resilience, especially when faced with difficulties ( Vermeiren et al. 2022 ).

In their conceptualization, Sharma and Nasa ( 2014 ) claimed that students’ abilities provide a method for explaining and predicting one’s feelings, thoughts, and behaviors, as well as organizing and carrying out courses of conduct to achieve certain goals. In this regard, self-management is described as the act of personally directing the dispositions, behavior, and recognition of persons toward achieving goals or tasks ( Amini and Noroozi 2018 ). Self-management is an important tool for all types of learning, including materials and academic courses, as well as other curriculum areas and abilities. It refers to the tactics, procedures, and methods that we use to successfully direct the actions and behaviors of students during their studies ( Jasim 2020 ). Self-management teaches students how to regulate their emotions, create objectives, and arrange themselves so that they may be powerful self-motivators ( Amini and Noroozi 2018 ). This concept has a significant meaning, in that self-management affects one’s level of ability and the amount of tenacity required to achieve a tough goal ( Bandura 2001 ). Therefore, self-management assists students in becoming effective students. Self-management enables students to stick to their strategies for completing tasks while remaining focused in the classroom ( Jasim 2020 ). As a result, the researchers present the hypothesis below.

Students with high self-management are more likely to achieve a higher academic self-efficacy.

2.2. The Role of Student Self-Management in Increasing Student Academic Achievement

In a determinate sense, self-management encompasses, among other things, self-discipline, self-control, self-regulation, willpower, ego strength, and effortful control ( Duckworth and Kern 2011 ). Along the same line, self-management, according to CASEL ( 2018 ), is defined as the capacity to control an individual’s emotions, ideas, gratification, and actions to motivate oneself and strive toward academic and personal objectives. On the other hand, the approaches used to describe student achievement vary with the concept’s complexity and breadth. It refers to a student’s acquisitions in a structured academic setting, as evidenced by the value placed on academic performance expressed in grades, standardized test results, or teachers’ recognitions in evaluations ( Erhuvwu and Adeyemi 2019 ). Academic achievement, operationally, indicates the set of learned knowledge, the degree of growth of capacities, and skills in the academic setting ( Jeynes 2008 ). Most studies in this field emphasize the relationships between student skills and academic achievement ( Di Fabio and Palazzeschi 2009 ) and occupational status ( Deary et al. 2007 ). Sanchez-Ruiz et al. ( 2016 ) developed another argument for comparing and generalizing the findings of studies on the influence of students’ ability on academic accomplishment that refers to personality characteristics as indicators of academic achievement. Robbins et al. ( 2004 ) suggest a composite social model that includes individual skills, social engagement, and academic-related abilities to explain the mechanism of academic achievement. According to previous research, students who utilize self-regulation tactics (such as self-regulated learning, time management, goal planning, and metacognition) perform better in class ( Stan 2021 ). In this essence, Claro and Loeb ( 2019 ) refer to self-management as the capacity to control an individual’s thoughts, emotions, and behaviors in a variety of settings. According to Balica et al. ( 2016 ) and Deming ( 2015 ), self-management is a powerful indicator of academic success, decision-making abilities, and competence in behavior modification. As a result, the following hypothesis is developed.

Students with high self-management are more likely to secure a higher academic achievement.

2.3. The Role of Self-Efficacy in Enhancing Student Academic Achievement

Academic achievement was originally regarded as the most essential consequence of the formal academic experience ( Kell et al. 2013 ); although there is little dispute about the importance of such achievements in student experience and later life, they are no longer the most important outcome ( Colmar et al. 2019 ; Martinez et al. 2019 ).

Students’ views on their capacity to master new abilities and activities, frequently in a particular academic topic, are referred to as self-efficacy ( Nasiriyan et al. 2011 ). In other words, Gardner ( 1983 ) defines a self-efficacious student as someone who believes in their ability to plan and carry out the steps necessary to achieve certain goals. According to Bandura ( 1997 ), perceived self-efficacy indicates people’s beliefs in their ability to achieve specific goals. Kryshko et al. ( 2022 ) argued that investigating the effect of self-efficacy on motivational adjustments to academic performance may be useful empirically. Thus, researchers pay little attention to this type of belief in effectiveness and its role in academic performance. Self-efficacy is a key element of Bandura ’s ( 2001 ) social-cognitive theory, which asserts that self-influence profoundly influences behavior. It increases grit when faced with problems, promotes purposeful behaviors, supports long-term vision and develops self-regulation and allows for self-correction when required within the context of social-cognitive theory. Previous research has identified cognitive skills and academic self-efficacy as well-established determinants of academic performance ( Köseoğlu 2015 ). According to Abouserie ( 1995 ), failure or success may be associated with weak or strong self-efficacy, and these links might influence university students’ performance. In previous research studies, belief in self-efficacy in various domains, along with various indicators of motivation and academic achievement, has emerged as an important determinant of students’ effective use of self-regulation skills and strategies ( Kim et al. 2021 ; Kryshko et al. 2022 ). Several studies have demonstrated that self-efficacy is a reliable predictor of motivation and academic performance that is unaffected by time, place, or community ( Duckworth et al. 2007 ). It is the motivational aspect of self-efficacy that appears to generate academic achievement ( Ashwin 2006 ). According to Miller and Brickman ( 2004 ), excellent educational success is related to improved confidence in one’s abilities, which encourages students to accept more responsibilities for the effective completion of assignments and projects. As a result, strong self-efficacy is widely acknowledged as an essential predictor of work-related achievements. More specifically, Honicke and Broadbent ( 2016 ) examined 59 self-efficacy studies conducted at universities and discovered a modest relationship between academic achievement and self-efficacy. In a similar vein, Schunk and Zimmerman ( Meral et al. 2012 ) identified a connection between academic achievement and self-efficacy, indicating that students’ academic achievement increases when they are taught to have stronger self-efficacy beliefs. As a consequence, we formulate the a hypotheses below.

Students who have a high level of self-efficacy are more likely to achieve higher academic achievement.

Perceived self-efficacy positively mediates the relationship between perceived self-management and students’ academic achievement.

Trautwein et al. ( 2006 ) suggested an academic achievement model in which a variety of factors impact the completion of certain academic tasks. In addition to class and social characteristics, they looked at personal and intellectual qualities such as IQ, consciousness, knowledge, and attitude. In our study, we focused on prioritizing the role of personal and intellectual ability in terms of self-management and self-efficacy to better represent the factors facing college students ( Figure 1 ). This adjustment is justified, since the involvement of student qualities (IQ, consciousness, knowledge) is expected to be equal at the same stage of education, especially if they are studying the same subject, even in different countries. This study was conducted on university students in two different countries (i.e., Egypt and the Kingdom of Saudi Arabia), to investigate the current research framework and to illustrate the differences between Egyptian and Saudi students, if applicable. Thus, we propose the following hypothesis.

An external file that holds a picture, illustration, etc.
Object name is jintelligence-10-00055-g001.jpg

The research conceptual framework and hypotheses.

There are no differences between Egyptian and Saudi students’ perceptions in terms of the direct and indirect relationships between self-management, self-efficacy, and academic achievement.

3. Materials and Methods

3.1. sampling and data collection.

University students in Egypt and the Kingdom of Saudi Arabia (KSA) are the participants of the current study to align with the research objectives. Egyptian and Saudi universities were chosen for the field study due to the development of the education sector in both countries to achieve their visions for 2030. Additionally, the well-recognized economic development in all different sectors in both countries, (i.e., service and industrial) encourages students to build their academic careers to hunt for job opportunities after graduation. Finally, the authors of the current paper are faculty members in these countries. Thus, an online survey was established through Google docs targeting only 1600 students virtually representing Prince Sattam Bin Abdulaziz and King Saud University students in the KSA and University of Sadat City and Menoufia University in Egypt. We contacted the information technology unit of each university to disseminate the questionnaire to students after obtaining official approvals. The online survey link was sent to students via their academic emails. Of the 1600 respondents who received the online survey, we received 1005 surveys with a response rate of 62.8%, only 889 (KSA = 419; Egypt = 470) were eventually usable for the statistical analysis. About 116 surveys were excluded due to incomplete responses. Table 1 presents the demographics of the study’s participants.

Sociodemographic characteristics of the students.

3.2. Measurements

We deployed a quantitative approach to investigate the research hypotheses. Thus, the questionnaire was built based on a thorough revision of related research studies. Consequently, the questionnaire includes four categories: self-management, perceived self-efficacy, academic achievement, and respondents’ profiles. First, self-management was measured by 10 items adapted from ( Öberg et al. 2019 ). Second, the perceived self-efficacy was measured by ten items retrieved from ( Sukmak et al. 2001 ). Third, twenty items adapted from Turner ( 2007 ) were used to measure the motivations of students for academic achievement. Finally, the fourth section contains the students’ demographics. Additionally, all of the items in the questionnaire were assessed using five-point Likert scales ranging from “strongly disagree = 1” to “strongly agree = 5. The questionnaire was translated from English to the Arabic language to fit all students and to guarantee a full understanding of the questionnaire statements. To confirm the context validity of the questionnaire items before disseminating, the Arabic version of the questionnaire was retranslated into English. We conducted a pilot study on one hundred students in both countries to check the validity and reliability of the questionnaire. The findings of the refined draft of the questionnaire showed slight modifications to some Arabic words.

3.3. Data Analysis and Hypotheses Testing

The SmartPLS-SEM software, version 3.2.8 (Oststeinbek, Germany), was run to analyze the research data and test the hypotheses. The PLS technique has been extensively operationalized in all research disciplines for several reasons ( Alsetoohy et al. 2019 , 2021 ; Alsetoohy and Ayoun 2018 ). PLS is more suitable for small sample sizes, predictions, and the development of theories in research studies. Additionally, PLS is non-sensitive to the normality of data distribution. Finally, the PLS technique works well with models that have a large number of indicators. A two-step process (i.e., the measurement model and the structural model) was deployed to test the research hypotheses using Smart PLS-SEM software, version 3.2.8 (Oststeinbek, Germany) ( Hair et al. 2012 ).

3.4. The Measurement Model

The validity and reliability of all latent variables of the study were assessed and checked to validate the research model relationships. To verify the internal reliability of the constructs, the Composite Reliability (CR) and Cronbach’s alpha were checked. The convergent validity of the model was assessed by the item loadings of the indicators, CR, and the average variance extracted (AVE). Furthermore, the Heterotrait–Monotrait (HTMT) ratio of correlation and the AVE were utilized to establish the discriminant validity. Finally, the variance inflation factor (VIF) was calculated to assess the collinearity of the constructions.

Table 2 illustrates that the Composite Reliability (CR) and Cronbach’s alpha values for all latent variables in the models were above the floor of .7 ( Hair et al. 2012 ). Thus, the internal consistency of the research models was achieved. Additionally, the item loadings were above .60 ( Hair et al. 2010 ). Two indicators (AA9 and AA10) were removed as their loadings were less than .60. The CR values were greater than .7 ( Hair et al. 2012 ), and the AVE values were above the value of .5 ( Fornell and Larcker 1981 ), which establishes the convergent validity. Likewise, the HTMT values ranged from .736 to .858, less than the floor of −.90 ( Hair et al. 2012 ) (see Table 3 ). Therefore, discriminant validity was established for all models. Finally, the highest value of VIF is 4.331, which is lower than 5, confirming that there are no multicollinearity issues between the models’ constructs ( Ringle et al. 2015 ).

Assessment results of the measurement model.

NB. AA9 and AA10 in italic were dropped.

Heterotrait–Monotrait Ratio (HTMT).

3.5. Multigroup Analysis

After all the research models passed the robustness check using the measurement models’ assessment, we applied a non-parametric structural equation-modeling approach to analyze the differences between the Egyptian and Saudi students using Henseler’s MGA and the permutation test ( Garson 2016 ; Henseler et al. 2016 ). Thus, the MICOM technique was run before the final step of the data analysis to test the invariance assessment to ensure the heterogeneity of the groups ( Henseler et al. 2016 ). This technique was used to confirm that the same indicators were used for each measurement model and an acceptable reliability of each construct was obtained for both groups. Hence, two groups of students were created: Egyptians ( n = 470) and Saudis ( n = 419). Table 1 displays the assessment results of the measurement model between the two datasets of Egyptians ( n = 470) and Saudis ( n = 419) along with the total students’ model ( n = 889). In step one, the assessment of configural invariance was achieved. Table 4 shows the results of the measurement invariance testing. The results of the compositional invariance assessment for Step two were established as none of the correlation (c) values are significantly different from 1. In Step 3, the composites’ equality of mean values and variances across the group was assessed. The results indicate that the confidence intervals of differences in mean values and variances partially include zero, which means the composite mean values and variances are partially equal. As such, achieving the establishment of the three steps of the MICOM procedure supports the partial measurement invariance of the two groups ( Garson 2016 ; Henseler et al. 2016 ). This indicates that the pooled data for each group meets the requirement for comparing and interpreting any differences in structural relationships. Thus, further analysis for comparing and interpreting the MGA group-specific differences of PLS-SEM can be performed.

3.6. Testing the Research Hypotheses and Results

To assess the structural model of the current research study, we checked the R 2 values, the p values, and the significance of the path coefficient (β) see Figure 2 , Figure 3 and Figure 4 . The results show that the R 2 values achieved ranged between 56.8% to 67% for the dependent variable, which represents the substantial explanatory power of the current models ( Chin 2010 ). The p values and the path coefficients refer to the statistical significances between the research variables. In general, the results of the research study show that perceived self-management has the strongest positive influence on the academic self-efficacy (β all = .804, β eg = .818, β sa = .794; p = .000) of all students. This supports hypothesis 1 (H1). Moreover, the findings of the current study reveal that perceived self-management has positive effects on students’ academic achievement (β all = .294, β eg = .279, β sa = .286; p = .000) in both countries. Thus, hypothesis 2 (H2) is supported. In the same context, the results of this study indicate that perceived self-efficacy is positively correlated with students’ academic achievement (β all = .516, β eg = .507, β sa = .286; p = .000). Thus, hypothesis 3 (H3) is further supported.

An external file that holds a picture, illustration, etc.
Object name is jintelligence-10-00055-g002.jpg

Results of the structural model with data from all students.

An external file that holds a picture, illustration, etc.
Object name is jintelligence-10-00055-g003.jpg

Results of the structural model with data from the Egyptian students.

An external file that holds a picture, illustration, etc.
Object name is jintelligence-10-00055-g004.jpg

Results of the structural model with data from the Saudi students.

To assess the significance/insignificance of the indirect effects of the current research model, bootstrapping tests with 5000 samples in SmartPLS-SEM were conducted to calculate the Bias-Corrected-Confidence Interval (BCCI), T-statistics, component weights, and observed significance values in the path coefficients to check the mediating effects of self-efficacy on the students’ academic achievement. The findings of the current study revealed a positive indirect significant relationship between perceived self-management (IV) and students’ academic achievement (DV) through perceived self-efficacy. Moreover, BBCI does not straddle zero between identified significant mediations, as shown in Table 5 . The results report that perceived self-efficacy (β all = .415, β eg = .415, β sa = .455; p = .000) positively mediates the relationship between self-management and students’ academic achievement, which supports hypothesis 4 (H4).

Results of hypotheses.

** p < 0.01; *** p < 0.001.

Results of invariance measurement testing using permutation.

As a prior step, the MGA was conducted using the Egyptian and Saudis datasets after completing the MICOM tests. In general, the MGA results showed non-significant differences between Egyptian and Saudis students for both direct relationships and indirect relationships of the research model, see Table 4 . This supports hypothesis 5 (H5). Thus, the results of the total participant students in the current study (Egyptian and Saudi students) can be generalized.

4. Discussion

The current research sought to measure the relative impact of the self-management concept on modeling students’ academic achievement via self-efficacy.

On the one hand, for students of developed countries, there is a clear path from academic self-management, self-efficacy, student dedication, patience, and goal setting to ultimate academic performance ( Bandura et al. 2001 ; Honicke and Broadbent 2016 ). Thus, the current research study examines the influence of self-management and self-efficacy on student academic achievement among students in two different developing countries. We attempted to overcome the shortcomings of previous studies in this area by (1) considering several theoretical and empirically distinct foundations of student achievement, (2) students’ self-management and self-efficacy, and (3) investigating predictors in two different domains, namely Egypt and the Kingdom of Saudi Arabia.

However, although the MGA results did not show significant differences between the Egyptian students (see Figure 2 ) and the Saudi students (see Figure 3 ), the results of Figure 1 (i.e., the total model) can be used to generalize this research results. The interpretation of the non-significant differences between the Saudi and Egyptian students may be due to both countries being in different regions and students speaking the same language (Arabic) and sharing the same traditions and customs. Additionally, a large number of Egyptian faculty members teach in Saudi universities, which in turn may lead to similar influences on students’ academic consciousnesses, knowledge, and academic accomplishments. These factors may contribute to diminishing the differences between students in both countries in terms of self-management, self-efficacy, and academic achievement. This finding is contrary to previous research studies ( Oettingen 1997 ; Scholz et al. 2002 ), which confirmed that there was a cultural variation in how people felt about their abilities.

Among the predictor factors, students’ self-efficacy explained the most variance in academic achievement. It is considered that students’ self-efficacy assessments have a significant impact on their learning-process success. Students’ self-efficacy contributed significantly to the variation in the criteria in our study. It was revealed that students who are self-assured and more confident are more likely to achieve higher academic achievements, confirming that self-efficacy beliefs play an essential role in explaining academic achievement. The relative superiority of students’ self-efficacy in this investigation is consistent with the literature on the subject (e.g., Affuso et al. 2017 ; Honicke and Broadbent 2016 ; Köseoğlu 2015 ; Meral et al. 2012 ; Travis et al. 2020 ) and with several studies that have looked at the antecedents that influence academic accomplishment (e.g., Ashwin 2006 ; Hennig-Thurau et al. 2001 ). Crain ( 2005 ) claims that, when students have doubts about their abilities, they are less active and more likely to have no problems.

Students develop academic self-efficacy by evaluating and interpreting their task performance, which represents a self-judgment of competence ( Bandura et al. 2001 ; Usher and Pajares 2009 ). Additionally, Ansong et al. ( 2019 ) argued that students’ self-efficacy is more likely to increase when students believe their academic abilities and efforts are successful and, conversely, are likely to diminish when they feel their efforts are insufficient. As a result, students with a high level of self-efficacy mastered their objectives, which included challenges and new information; performance quality, which included good grades; and outperforming peers. When they feel they are good at something, they work hard at it and stick with it despite failures ( Crain 2005 ).

Moreover, self-management was also found to have a key impact on self-efficacy. According to our findings, the degree of self-efficacy determines a high percentage of the variation in the self-efficacy criteria, which is consistent with other studies (e.g., Di Fabio and Palazzeschi 2009 ; Stan 2021 ). Self-management is a broad concept that encompasses qualities such as self-efficacy. Self-management is widely recognized as one of the required abilities that drive students toward becoming more self-determined youths who can responsibly and proactively manage the elements of their lives, both in and out of educational contexts, according to King-Sears ( 2006 ). As a result, our study’s perspective is that students who can create objectives and employ various self-management tactics have better self-efficacy.

Furthermore, this study demonstrates that self-efficacy is a mediating factor in the relationship between self-management and academic achievement. Although analyses of the specialized literature confirm that self-management predicts student success (because the relationship with self-management is stronger than any other component of self-efficacy) ( Stan 2021 ), our research results indicate that, without self-efficacy (mastery of skills and activities), academic achievement is relative. It might be claimed that academic self-efficacy is frequently used to prepare and carry out the procedures required to accomplish certain goals. Perceived self-efficacy, according to Bandura ( 1997 ), relates to students’ beliefs in their capacity to attain specified goals. So, the role of self-efficacy in explaining variation in academic achievement across students is a central theme in our study.

Furthermore, our research shows that students’ self-management has a modest influence on academic achievement. This outcome is consistent with the arguments of Kadiyono and Hafiar ( 2017 ), who believe that academic self-management may be utilized to motivate students to enhance their academic achievement, so that they can build a solid foundation to go forward and construct their futures. Nonetheless, given a well-established research background supporting self-management as an intervention, it appears that its usage among students must be encouraged by their instructors’ actions. Thus, when students are confident in their academic ability, they can set educational goals that drive them to academic excellence. On the other hand, students with little or no confidence in their abilities and capacities may be less likely to pursue higher levels of academic performance that require a higher level of effort, abilities, and skills; this confirms the findings of Ansong et al. ( 2019 ). In this regard, King-Sears ( 2006 ) argued that teachers play a critical role in enhancing students’ abilities to practice self-management.

5. Conclusions

The conclusions of this study have a variety of ramifications for educators, counselors, and students. This study attempted to investigate whether students’ self-management and self-efficacy produce excellent academic achievement when adopted by students working around a range of academic variables. The current study confirmed the significant relationships between self-management, self-efficacy, and academic achievement in two different domains (i.e., Egypt and KSA) through three models with identical significant results. Thus, academia and practitioners can use this research framework to guide their students to effective academic accomplishments. Additionally, our results did not show differences between students in terms of self-management, self-efficacy, and academic achievement according to country. This supports a fundamental conceptualization that students with different skills and motives can direct these positively toward their academic achievement regardless of their geographical domain and culture. Thus, the current study is considered a pioneer study that investigates the relationships between self-management, self-efficacy, and academic achievement among university students all in one model. This could be a guide for both students and educators who are seeking to optimize their (students’) academic achievements through self-management and efficacy. Additionally, this model was tested twice in two different countries which, in turn, helps generalize the results among all university students.

Due to the lack of orientation, self-management provides a fair to good degree of academic accomplishment, highlighting the need for treatments aimed at assisting students in developing a meaningful understanding of their self-management about their current views. The findings of this study confirm that self-management helps students control their impulses, set goals, organize themselves, and become strong self-motivators. Hence, students who can coordinate emotions and control and manage impulsivity stress are more likely to recognize goals and achieve them consistently. Additionally, students need to be aware of the purpose, the breadth, and the depth of self-management research and how expanding this skill can alleviate current problems. As a result, the current study elicits the role of educators, mentors, and counselors to empower and direct students’ motives, skills, and abilities to achieve both academic and life goals through facing and overcoming daily problems. Moreover, these findings affirmed that self-management is a powerful indicator of academic success, decision-making abilities, and competence in the behavior modification among students. This helps educators and students to modify students’ behaviors in a positive manner to establish academic achievement in both the short and long term. Nonetheless, the foundation of self-management plays a significant part in attaining students’ self-efficacy, due to its critical function in organizing all sorts of learning, including materials and academic courses. Such a finding is very noticeable in the overall evaluation of university students’ achievements. The results reveal that self-efficacy is a positive predictor of students’ academic achievement. Self-efficacy and academic achievement are reciprocally associated and mutually reinforcing, according to the mutual-effects model used in this study. Educators and university educators must create and use treatments that target self-management, self-efficacy, and academic achievement to put the model into effect. Finally, the positive relationship between the triangle-connection modeling could be used as a base for policymakers when establishing new curricula targeting efficient outcomes for students, educators, and the community.

Some limitations must be considered when evaluating the current study’s conclusions. Two distinct students’ behaviors were evaluated in this study, with different instructors adopting different teaching strategies. Future studies should aim to evaluate the triangle-connection modeling individually to obtain benchmark findings in each situation. The current study does not allow for a thorough conclusion about the underlying causes of the reciprocal impact of self-management, self-efficacy, and academic achievement. Further research should put to the test theoretically relevant antecedent models that might explain the relationships between self-management, self-efficacy, and academic achievement in greater depth. For example, engagement in supportive institutional–student connections in terms of teaching staff, teaching style, etc., can impact self-management, self-efficacy, and academic achievement all at the same time.

Acknowledgments

The authors would like to thank the University of Prince Sattam bin Abdulaziz for supporting the research.

Funding Statement

This project was supported by the Deanship of Scientific Research at the Prince Sattam bin Abdulaziz University under the research project 18820/02/2021.

Author Contributions

Conceptualization, M.H.A.A.-A. and H.A.H.A.A.; methodology, M.H.A.A.-A. and H.A.H.A.A.; software, M.H.A.A.-A. and H.A.H.A.A.; validation, M.H.A.A.-A. and H.A.H.A.A.; formal analysis, M.H.A.A.-A.; investigation, M.H.A.A.-A. and H.A.H.A.A.; resources, M.H.A.A.-A. and H.A.H.A.A.; data curation, M.H.A.A.-A.; writing—original draft preparation, M.H.A.A.-A. and H.A.H.A.A.; writing—review and editing, M.H.A.A.-A. and H.A.H.A.A.; visualization, M.H.A.A.-A. and H.A.H.A.A.; supervision, M.H.A.A.-A. and H.A.H.A.A.; project administration, M.H.A.A.-A. and H.A.H.A.A.; funding acquisition, M.H.A.A.-A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and Ethics Committee) of both the university of Prince Sattam bin Abdulaziz, KSA and the Higher Institute of Administrative Sciences, Janaklis, Al Buhayrah, Egypt.

Informed Consent Statement

Written informed consent was obtained from the participant(s) to publish this paper.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abouserie Reda. Self-esteem and achievement motivation as determinants of students’ approaches to studying. Studies in Higher Education. 1995; 20 :19–26. doi: 10.1080/03075079512331381770. [ CrossRef ] [ Google Scholar ]
Affuso Gaetana, Bacchini Dario, Miranda Maria Concetta. The contribution of school-related parental monitoring, self-determination, and self-efficacy to academic achievement. The Journal of Educational Research. 2017; 110 :565–74. doi: 10.1080/00220671.2016.1149795. [ CrossRef ] [ Google Scholar ]
Agolla Joseph E., Ongori Henry. An assessment of academic stress among undergraduate students: The case of the University of Botswana. Botswana: University of Botswana. Educational Research and Reviews. 2009; 4 :63–70. [ Google Scholar ]
Ahmad Alay, Safaria Triantoro. Effects of Self-Efficacy on Students’ Academic Performance. Journal of Educational, Health, and Community Psychology. 2013; 2 :22–29. [ Google Scholar ]
Alsetoohy Omar, Ayoun Baker. Intelligent agent technology: The relationships with hotel food procurement practices and performance. Journal of Hospitality and Tourism Technology. 2018:109–124. doi: 10.1108/JHTT-04-2017-0028. [ CrossRef ] [ Google Scholar ]
Alsetoohy Omar, Ayoun Baker, Arous Saleh, Megahed Farida, Nabil Gihan. Intelligent agent technology: What affects its adoption in hotel food supply chain management? Journal of Hospitality and Tourism Technology. 2019; 10 :286–310. doi: 10.1108/JHTT-01-2018-0005. [ CrossRef ] [ Google Scholar ]
Alsetoohy Omar, Ayoun Baker, Abou-Kamar Mahmoud. COVID-19 Pandemic Is a Wake-Up Call for Sustainable Local Food Supply Chains: Evidence from Green Restaurants in the USA. Sustainability. 2021; 13 :9234. doi: 10.3390/su13169234. [ CrossRef ] [ Google Scholar ]
Amini Mohammad Taghi, Noroozi Reza. Relationship Between Self-Management Strategy and Self-Efficacy Among Staff of Ardabil Disaster and Emergency Medical Management Centers. Health in Emergencies and Disasters Quarterly. 2018; 3 :85–90. doi: 10.29252/nrip.hdq.3.2.85. [ CrossRef ] [ Google Scholar ]
Ansong David, Eisensmith Sarah R., Okumu Moses, Chowa Gina A. The importance of self-efficacy and educational aspirations for academic achievement in resource-limited countries: Evidence from Ghana. Journal of Adolescence. 2019; 70 :13–23. doi: 10.1016/j.adolescence.2018.11.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Aremu Oyesoji, Sokan Bo. A Multi-Causal Evaluation of the Academic Performance of Nigerian Learners: Issues and Implications for National Development. Department of Guidance and Counselling, University of Ibadan; Ibadan: 2003. [ Google Scholar ]
Ashwin Paul. Changing Higher Education. Routledge; Hoboken: 2006. [ Google Scholar ]
Balica Magdalena, Benga Oana, Caragea Violeta Maria, David-Crisbăşanu Simona, Goia Delia, Horga Irina, Iftode Oana. Non-Cognitive Skill Development in Adolescents in Romania. UNICEF Romania; Bucharest: 2016. [ Google Scholar ]
Bandura Albert. Self-Efficacy: The Exercise of Control. Freeman; New York: 1997. [ CrossRef ] [ Google Scholar ]
Bandura Albert. Social cognitive theory: An agentive perspective. Annual Review of Psychology. 2001; 52 :1–26. doi: 10.1146/annurev.psych.52.1.1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Bandura Albert, Barbaranelli Claudio, Caprara Gian Vittorio, Pastorelli Concetta. Self-efficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development. 2001; 72 :187–206. doi: 10.1111/1467-8624.00273. [ PubMed ] [ CrossRef ] [ Google Scholar ]
CASEL Core SEL Competencies. 2018. [(accessed on 8 December 2019)]. Available online: https://casel.org/core-competencies/
Cheng Ching-Hsue, Wang Yun-Chun, Liu Wei-Xiang. Exploring the Related Factors in Students’ Academic Achievement for the Sustainable Education of Rural Areas. Sustainability. 2019; 11 :5974. doi: 10.3390/su11215974. [ CrossRef ] [ Google Scholar ]
Chin Wynne W. Springer Handbooks of Computational Statistics. Springer; Berlin: 2010. How to write up and report PLS Analyses. Handbook of partial least squares: Concepts, methods, and applications. [ Google Scholar ]
Claro Susana, Loeb Susanna. Self-Management Skills and Student Achievement Gains: Evidence from California’s CORE Districts [Working Paper]. Policy Analysis for California Education. 2019. [(accessed on 20 December 2020)]. Available online: https://edpolicyinca.org/publications/self-management-skills-and-student-achievement-gains-evidence-california-core-districts
Colmar Susan, Liem Gregory Arief D., Connor Julie, Martin Andrew J. Exploring the relationships between academic buoyancy, academic self-concept, and academic performance: A study of mathematics and reading among primary school students. Educational Psychology. 2019; 39 :1068–89. doi: 10.1080/01443410.2019.1617409. [ CrossRef ] [ Google Scholar ]
Crain William. Theories of Development: Concepts and Applications. 5th ed. Prentice-Hall; Upper Saddle River: 2005. [ Google Scholar ]
Deary Ian J., Strand Steve, Smith Pauline, Fernandes Cres. Intelligence and educational achievement. Intelligence. 2007; 35 :13–21. doi: 10.1016/j.intell.2006.02.001. [ CrossRef ] [ Google Scholar ]
Dembo Myron H. Motivation and Learning Strategies for College Success: A Self-Management Approach. Lawrence Erlbaum Associates, Inc.; Mahwah: 2004. [ Google Scholar ]
Deming David J. The growing importance of social skills in the labor market. The Quarterly Journal of Economics. 2015; 132 :1593–640. doi: 10.1093/qje/qjx022. [ CrossRef ] [ Google Scholar ]
Dev Meenu. Factors e Affecting the Academic Achievement: A Study of Elementary School Students of NCR Delhi, India. Journal of Education and Practice. 2016; 7 :70–74. [ Google Scholar ]
Di Fabio Annamaria, Palazzeschi Letizia. An In-Depth Look at Scholastic Success: Fluid Intelligence, Personality Traits or Emotional Intelligence? Personality and Individual Differences. 2009; 46 :581–85. doi: 10.1016/j.paid.2008.12.012. [ CrossRef ] [ Google Scholar ]
Díaz-Morales Juan F., Escribano Cristina. Social jetlag, academic achievement and cognitive performance: Understanding gender/sex differences. Chronobiology International. 2015; 32 :822–31. doi: 10.3109/07420528.2015.1041599. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Dijkstra Anne Bert, Peschar Jules. The International Handbook on the Sociology of Education. Rowman Littlefield; Lanham: 2003. Social capital in education. Theoretical issues and empirical knowledge in attainment research; pp. 58–81. [ Google Scholar ]
Duckworth Angela Lee, Kern Margaret L. A meta-analysis of the convergent validity of self-control measures. Journal of Research in Personality. 2011; 45 :259–68. doi: 10.1016/j.jrp.2011.02.004. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Duckworth Angela, Peterson Christopher, Matthews Michael D., Kelly Dennis R. Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology. 2007; 92 :1087–101. doi: 10.1037/0022-3514.92.6.1087. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Eccles Jacquelynne S., Wigfield Allan. Motivational Beliefs, Values, and Goals. Annual Review of Psychology. 2002; 53 :109–32. doi: 10.1146/annurev.psych.53.100901.135153. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Erhuvwu Okotie Stella, Adeyemi Florence T. Achievement motivation as a predictor of academic achievement of senior secondary school students in mathematics. European Journal of Educational and Development Psychology. 2019; 7 :36–45. [ Google Scholar ]
Fornell Claes, Larcker David F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research. 1981; 18 :39–50. doi: 10.1177/002224378101800104. [ CrossRef ] [ Google Scholar ]
Gardner Howard. Frames of Mind: The Theory of Multiple Intelligences. Basic Books; New York: 1983. [ Google Scholar ]
Garson David G. Partial Least Squares Regression and Structural Equation Models [Kindle Edition] Statistical Associates Publishers; Asheboro: 2016. [ Google Scholar ]
Hair Joseph F., Black William, Babin Barry J., Anderson Rolph E. Multivariate data analysis: A Global Perspective. 7th ed. Prentice Hall; Upper Saddle River: 2010. Multivariate Data Analysis. [ Google Scholar ]
Hair Joe F., Sarstedt Marko, Ringle Christian M., Mena Jeannette A. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science. 2012; 40 :414–33. doi: 10.1007/s11747-011-0261-6. [ CrossRef ] [ Google Scholar ]
Hammouri Hind. Attitudinal and motivational variables related to mathematics achievement in Jordan: Findings from the Third International Mathematics and Science Study (TIMSS) Educational Research. 2004; 46 :241–57. doi: 10.1080/0013188042000277313. [ CrossRef ] [ Google Scholar ]
Harris Belinda. Facing the challenges of education reform in Hong Kong: An experiential approach to teacher development. Pastoral Care in Education. 2001; 19 :21–31. doi: 10.1111/1468-0122.00195. [ CrossRef ] [ Google Scholar ]
Hennig-Thurau Thorsten, Langer Markus F., Hansen Ursula. Modeling and managing student loyalty: An approach based on the concept of relationship quality. Journal of Service Research. 2001; 3 :331–34. doi: 10.1177/109467050134006. [ CrossRef ] [ Google Scholar ]
Henseler Jörg, Ringle Christian M., Sarstedt Marko. Testing measurement invariance of composites using partial least squares. International Marketing Review. 2016; 33 :405–31. doi: 10.1108/IMR-09-2014-0304. [ CrossRef ] [ Google Scholar ]
Heslin Peter A., Klehe Ute-Christine. Self-efficacy. In: Rogelberg Steven G., editor. Encyclopedia of Industrial/Organizational Psychology. Sage; Thousand Oaks: 2006. [ Google Scholar ]
Hill Winfred F. Learning: A Survey of Psychological Interpretations. 7th ed. Allyn and Bacon; Boston: 2002. [ Google Scholar ]
Honicke Toni, Broadbent Jaclyn. The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review. 2016; 17 :63–84. doi: 10.1016/j.edurev.2015.11.002. [ CrossRef ] [ Google Scholar ]
Jasim Basima. J. Evaluation of Students’ Self–management and Academic Achievement in the University of Baghdad. Medico-Legal Update. 2020; 20 :1257–63. doi: 10.37506/v11/i2/2020/ijphrd/195209. [ CrossRef ] [ Google Scholar ]
Jeynes William H. A meta-analysis of the relationship between phonics instruction and minority elementary school student academic achievement. Education and Urban Society. 2008; 40 :151–66. doi: 10.1177/0013124507304128. [ CrossRef ] [ Google Scholar ]
Kadiyono Anissa Lestari, Hafiar Hanny. Ideas for 21st Century Education. Taylor and Francis Group; London: 2017. The role of academic self-management in improving students’ academic achievement; pp. 117–20. [ Google Scholar ]
Kell Harrison J., Lubinski David, Benbow Camilla P. Who rises to the top? Early indicators. Psychological Science. 2013; 24 :648–59. doi: 10.1177/0956797612457784. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Kim Yeo-eun, Yu Shirley L., Koenka Alison C., Lee Hyewon, Heckler Andrew F. Can self-efficacy and task values buffer perceived costs? Exploring introductory- and upper-level physics courses. The Journal of Experimental Education. 2021; 90 :839–861. doi: 10.1080/00220973.2021.1878992. [ CrossRef ] [ Google Scholar ]
King-Sears Margaret E. Self-Management for Students with Disabilities: The importance of Teacher Follow-Up. International Journal of Special Education. 2006; 21 :94–108. [ Google Scholar ]
Köseoğlu Yaman. Self-Efficacy and Academic Achievement—A Case from Turkey. Journal of Education and Practice. 2015; 6 :131–41. [ Google Scholar ]
Kryshko Olena, Fleischer Jens, Grunschel Carola, Leutner Detlev. Self-efficacy for motivational regulation and satisfaction with academic studies in STEM undergraduates: The mediating role of study motivation. Learning and Individual Differences. 2022; 93 :102096. doi: 10.1016/j.lindif.2021.102096. [ CrossRef ] [ Google Scholar ]
Kuhfeld Megan, Soland James, Tarasawa Beth, Johnson Angela, Ruzek Erik, Liu Jing. Projecting the potential impact of COVID-19 school closures on academic achievement. Educational Researcher. 2020; 49 :549–65. doi: 10.3102/0013189X20965918. [ CrossRef ] [ Google Scholar ]
Levasseur Robert E. People skills: Developing soft skills—A change management perspective. Interfaces. 2013; 43 :566–71. doi: 10.1287/inte.2013.0703. [ CrossRef ] [ Google Scholar ]
Maldonado Joana Elisa, de Witte Kristof. The effect of school closures on standardised student test outcomes. British Educational Research Journal. 2022; 48 :49–94. doi: 10.1002/berj.3754. [ CrossRef ] [ Google Scholar ]
Martinez Isabel, Youssef-Morgan Carolyn, Chambel Maria, Marques-Pinto Alexandra. Antecedents of academic performance of university students: Academic engagement and psychological capital resources. Educational Psychology. 2019; 39 :1047–67. doi: 10.1080/01443410.2019.1623382. [ CrossRef ] [ Google Scholar ]
Meeter Martijn. Primary school mathematics during COVID-19: No evidence of learning gaps in adaptive practicing results. Trends in Neuroscience and Education. 2021; 25 :1–8. doi: 10.1016/j.tine.2021.100163. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Meral Mustafa, Colak Esma, Zereyak Ertan. The relationship between self-efficacy and academic performance. Procedia-Social and Behavioral Sciences. 2012; 46 :1143–46. doi: 10.1016/j.sbspro.2012.05.264. [ CrossRef ] [ Google Scholar ]
Miller Raymond, Brickman Stephanie. A model of future-oriented motivation and self-regulation. Educational Psychology Review. 2004; 16 :9–33. doi: 10.1023/B:EDPR.0000012343.96370.39. [ CrossRef ] [ Google Scholar ]
Nasiriyan Asharaf, Azar Himan Khezri, Noruzy Ali, Dalvand Mohammad Reza. A model of self-efficacy, task value, achievement goals, effort, and mathematics achievement. International Journal of Academic Research. 2011; 3 :612–18. [ Google Scholar ]
Novo Ana, Calixto José António. Academic Achievement and/or Educational Attainment—The Role of Teacher Librarians in Students’ Future: Main Findings of Research in Portugal. International Association of School Librarianship; Pádua: 2009. pp. 1–12. [ Google Scholar ]
Öberg Ulrika, Hörnsten Åsa, Isaksson Ulf. The Self-Management Assessment Scale: Development and psychometric testing of a screening instrument for person-centered guidance and self-management support. Nursing Open. 2019; 6 :504–13. doi: 10.1002/nop2.233. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
Oettingen Gabriele. Cross-cultural perspectives on self-efficacy. In: Bandura Albert., editor. Self-Efficacy in Changing Societies. Cambridge University Press; New York: 1997. pp. 149–57. [ Google Scholar ]
Ringle Christian M., Wende Sven, Becker Jan-Michael. SmartPLS 3. Bönningstedt: SmartPLS. 2015. [(accessed on 18 January 2022)]. Available online: http://www.smartpls.com
Rivkin Steven G., Hanushek Eric A., Kain John F. Teachers, schools, and academic achievement. Econometrica. 2005; 73 :417–58. doi: 10.1111/j.1468-0262.2005.00584.x. [ CrossRef ] [ Google Scholar ]
Robbins Steven B., Lauver Kristy, Le Huy, Davis Daniel, Langley Ronelle, Carlstrom Aaron. Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin. 2004; 130 :261. doi: 10.1037/0033-2909.130.2.261. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Sanchez-Ruiz Maria-Jose, El-Khoury Jamil, Saadé George, Salkhanian Miriam. Non-Cognitive Variables and Academic Achievement. In: Khine Myint Swe, Areepattamannil Shaljan., editors. Non-cognitive Skills and Factors in Educational Attainment. Sense Publishers; Rotterdam: 2016. Contemporary Approaches to Research in Learning Innovations. [ Google Scholar ]
Scholz Urte, Doña Benicio Gutiérrez, Sud Shonali, Schwarzer Ralf. Is general self-efficacy a universal construct? Psychometric findings from 25 countries. European Journal of Psychological Assessment. 2002; 18 :242–51. doi: 10.1027//1015-5759.18.3.242. [ CrossRef ] [ Google Scholar ]
Schult Johannes, Nicole Mahler, Benjamin Fauth, Marlit Lindner. Did students learn less during the COVID-19 pandemic? Reading and mathematics competencies before and after the first pandemic wave. School Effectiveness and School Improvement. 2022:1–20. doi: 10.1080/09243453.2022.2061014. [ CrossRef ] [ Google Scholar ]
Schunk Dale, Pajares Frank. The development of academic self-efficacy. In: Wigfield Allan, Eccles Jacquelynne., editors. Development of Achievement Motivation. Academic Press; San Diego: 2002. pp. 15–31. [ Google Scholar ]
Sharma Hemant Lata, Nasa Gunjan. Academic Self-efficacy: A reliable Predictor of Educational Performances. British Journal of Education. 2014; 2 :57–64. [ Google Scholar ]
Sikhwari T. D. A study of the Relationship between Motivation Self-Concept and Academic Achievement of Students at a University of Limpopo Province, South Africa. International Journal of Educational Science. 2014; 6 :19–25. [ Google Scholar ]
Stan Maria Magdalena. Self-Management Skills and Student Achievement: A Pilot Study, Book chapters-LUMEN Proceedings. In: Clipa Otilia., editor. ATEE 2020—Winter Conference. Teacher Education for Promoting Well-Being in School. Suceava. 2020. 1st ed. vol. 16. Editura Lumen; Iasi: 2021. pp. 490–506. chap. 34. [ CrossRef ] [ Google Scholar ]
Sukmak Vatinee, Sirisoonthon Acharee, Meena Prapapron. Validity of the general perceived self-efficacy scale. Journal of the Psychiatric Association of Thailand. 2001; 47 :31–37. [ Google Scholar ]
Trautwein Ulrich, Lüdtke Oliver, Köller Olaf, Baumert Jürgen. Self-Esteem, Academic Self-Concept, and Achievement: How the Learning Environment Moderates the Dynamics of Self-Concept. Journal of Personality and Social Psychology. 2006; 90 :334–49. doi: 10.1037/0022-3514.90.2.334. [ PubMed ] [ CrossRef ] [ Google Scholar ]
Travis Justin, Kaszycki Alyssa, Geden Michael, Bunde James. Some stress is good stress: The challenge-hindrance framework, academic self-efficacy, and academic outcomes. Journal of Educational Psychology. 2020 doi: 10.1037/edu0000478. [ CrossRef ] [ Google Scholar ]
Turner Ashley Patricia. Incorporating Motivation Into Predictive Models of Academic Performance: Development and Validation of the Motivation for Academic Achievement Scale. The University of Toronto; Toronto: 2007. [ Google Scholar ]
Usher Ellen, Pajares Frank. Sources of self-efficacy in mathematics: A validation study. Contemporary Educational Psychology. 2009; 34 :89–101. doi: 10.1016/j.cedpsych.2008.09.002. [ CrossRef ] [ Google Scholar ]
Vermeiren Sofie, Duchatelet Dorothy, Gijbels David. Assessing students’ self-efficacy for negotiating during a role-play simulation of political decision-making. Taking student characteristics and simulation features into account. Studies in Educational Evaluation. 2022; 72 :101124. [ Google Scholar ]
Wood Lesely, Olivier M. A. A self-efficacy approach to holistic student development. South African Journal of Education. 2004; 24 :289–94. [ Google Scholar ]

Practical Assessment, Research, and Evaluation

ScholarWorks@UMassAmherst

Home > PARE > Vol. 20 (2015)

Defining and Measuring Academic Success

Travis T. York Charles Gibson Susan Rankin

https://doi.org/10.7275/hz5x-tx03

Despite, and perhaps because of its amorphous nature, the term â€˜academic successâ€™ is one of the most widely used constructs in educational research and assessment within higher education. This paper conducts an analytic literature review to examine the use and operationalization of the term in multiple academic fields. Dominant definitions of the term are conceptually evaluated using Astinâ€™s I-E-O model resulting in the proposition of a revised definition and new conceptual model of academic success. Measurements of academic success found throughout the literature are presented in accordance with the presented model of academic success. These measurements are provided with details in a user-friendly table (Appendix B). Results also indicate that grades and GPA are the most commonly used measure of academic success. Finally, recommendations are given for future research and practice to increase effective assessment of academic success. Accessed 112,251 times on https://pareonline.net from March 15, 2015 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.

Creative Commons License

Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License

Recommended Citation

York, Travis T.; Gibson, Charles; and Rankin, Susan (2019) "Defining and Measuring Academic Success," Practical Assessment, Research, and Evaluation : Vol. 20, Article 5. DOI: https://doi.org/10.7275/hz5x-tx03 Available at: https://scholarworks.umass.edu/pare/vol20/iss1/5

Since November 25, 2019

Journal Home
About This Journal
Submission Guidelines
Editorial Board
Journal Policies
Most Popular Papers
Receive Email Notices or RSS

Advanced Search

ISSN: 1531-7714

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

Sponsored by the University of Massachusetts Amherst Libraries

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

View all journals
My Account Login
Explore content
About the journal
Publish with us
Sign up for alerts
Open access
Published: 09 May 2024

Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors

Yujie Su ORCID: orcid.org/0000-0003-1444-1598 1 ,
Xiaoshu Xu ORCID: orcid.org/0000-0002-0667-4511 1 ,
Yunfeng Zhang 2 ,
Xinyu Xu 1 &
Shanshan Hao 3

Humanities and Social Sciences Communications volume 11 , Article number: 594 ( 2024 ) Cite this article

131 Accesses

Metrics details

Language and linguistics

This study delves into the retrospections of undergraduate students concerning their online learning experiences after the COVID-19 pandemic, using the nine key influencing factors: behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation. 46 Year 1 students from a comprehensive university in China were asked to maintain reflective diaries throughout an academic semester, providing first-person perspectives on the strengths and weaknesses of online learning. Meanwhile, 18 college teachers were interviewed with the same questions as the students. Using thematic analysis, the research identified 9 factors. The research revealed that instruction ranked highest among the 9 factors, followed by engagement, self-regulation, interaction, motivation, and others. Moreover, teachers and students had different attitudes toward instruction. Thirdly, teacher participants were different from student participants given self-efficacy and self-regulation due to their variant roles in online instruction. Lastly, the study reflected students were not independent learners, which explained why instruction ranked highest in their point of view. Findings offer valuable insights for educators, administrators, and policy-makers involved in higher education. Recommendations for future research include incorporating a more diverse sample, exploring relationships between the nine factors, and focusing on equipping students with skills for optimal online learning experiences.

Determinants of behaviour and their efficacy as targets of behavioural change interventions

literature review about academic success

Interviews in the social sciences

Mechanisms linking social media use to adolescent mental health vulnerability

Introduction.

The outbreak of the COVID-19 pandemic has had a profound impact on education worldwide, leading to the widescale adoption of online learning. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), at the peak of the pandemic, 192 countries had implemented nationwide closures, affecting approximately 99% of the world’s student population (UNESCO 2020 a). In response, educational institutions, teachers, and students quickly adapted to online learning platforms, leveraging digital technologies to continue education amidst the crisis (Marinoni et al. 2020 ).

The rapid and unexpected shift to online learning brought about a surge in research aiming to understand its impact, effectiveness, and challenges. Researchers across the globe have been investigating various dimensions of online learning. Some focus on students’ experiences and perspectives (Aristovnik et al. 2021 ), technological aspects (Bao 2020 ), pedagogical strategies (Hodges et al. 2020 ), and the socio-emotional aspect of learning (Ali 2020 ). Tan et al. ( 2021 ) found that motivation and satisfaction were mostly positively perceived by students, and lack of interaction was perceived as an unfavorable online instruction perception. Some center on teachers’ perceptions of the benefits and challenges (Lucas and Vicente, 2023 ; Mulla et al. 2023 ), post-pandemic pedagogisation (Rapanta et al. 2021 ), and post-pandemic further education (Kohnke et al. 2023 ; Torsani et al. 2023 ). It was worth noting that elements like interaction and engagement were central to the development and maintenance of the learning community (Lucas and Vincente 2023 ),

The rise of online learning has also posed unprecedented challenges. Studies have pointed out the digital divide and accessibility issues (Crawford et al. 2020 ), students’ motivation and engagement concerns (Martin and Bolliger 2018 ), and the need for effective online instructional practices (Trust and Whalen 2020 ). The rapid transition to online learning has highlighted the need for robust research to address these challenges and understand the effectiveness of online learning in this new educational paradigm.

Despite the extensive research on online learning during and after the COVID-19 pandemic, there remains a notable gap in understanding the retrospective perspectives of both undergraduates and teachers. Much of the current literature has focused on immediate response strategies to the transition to online learning, often overlooking the detailed insights that reflective retrospection can provide (Marinoni et al. 2020 ; Bao 2020 ). In addition, while many studies have examined isolated aspects of online learning, they have not often employed a comprehensive framework, leaving undergraduates’ voices, in particular, underrepresented in the discourse (Aristovnik et al. 2021 ; Crawford et al. 2020 ). This study, situated in the context of the COVID-19 pandemic’s impetus toward online learning, seeks to fill this crucial gap. By exploring online learning from the perspectives of both instructors and undergraduates, and analyzing nine key factors that include engagement, motivation, and self-efficacy, the research contributes vital insights into the dynamics of online education (Wang and Wang 2021 ). This exploration is especially pertinent as digital learning environments become increasingly prevalent worldwide (UNESCO 2020b ). The findings of our study are pivotal for shaping future educational policies and enhancing online education strategies in this continuously evolving educational landscape (Greenhow et al. 2021 ). Thus, three research questions were raised:

Q1: How do undergraduates and teachers in China retrospectively perceive the effectiveness of online learning after the COVID-19 pandemic?

Q2: Which of the nine outcome influencing factors had the most significant impact on online learning experiences after the pandemic, and why?

Q3: What recommendations can be proposed to enhance the effectiveness of online learning in the future?

The research takes place at a comprehensive university in China, with a sample of 46 Year 1 students and 18 experienced teachers. Their reflections on the effectiveness of online learning were captured through reflective diaries guided by four questions. These questions investigated the students’ online learning states and attitudes, identified issues and insufficiencies in online learning, analyzed the reasons behind these problems, and proposed improvements. By assessing their experiences and perceptions, we seek to explore the significant factors that shaped online learning outcomes after the pandemic and the means to enhance its effectiveness.

This paper first presents a review of the existing literature, focusing on the impact of the pandemic on online learning and discussing the nine significant factors influencing online learning outcomes. Following this, the methodology utilized for this study is detailed, setting the stage for a deeper understanding of the research process. Subsequently, we delve into the results of the thematic analysis conducted based on undergraduate students and teachers’ retrospections. Finally, the paper concludes by offering meaningful implications of the findings for various stakeholders and suggesting directions for future research in this critical area.

Literature review

Online learning application and evaluation in higher education.

Online learning, also known as e-learning or distance learning, refers to education that takes place over the Internet rather than in a traditional classroom setting. It has seen substantial growth over the past decade and has been accelerated due to the COVID-19 pandemic (Trust and Whalen 2020 ). Online learning allows for a flexible learning environment, breaking the temporal and spatial boundaries of traditional classroom settings (Bozkurt and Sharma 2020 ). In response to the COVID-19 pandemic, educational institutions globally have embraced online learning at an unprecedented scale. This has led to an immense surge in research focusing on the effects of the pandemic on online learning (Crawford et al. 2020 ; Marinoni et al. 2020 ).

Researchers were divided in their attitudes toward the effects of online learning, including positive, neutral, and negative. Research by Bahasoan et al. ( 2020 ), Bernard et al. ( 2004 ), Hernández-Lara and Serradell-López ( 2018 ), and Paechter and Maier ( 2010 ) indicated the effectiveness of online learning, including improved outcomes and engagement in online formats, providing flexibility and enhancing digital skills for instance. Research, including studies by Dolan Hancock and Wareing ( 2015 ) and Means et al. ( 2010 ), indicates that under equivalent conditions and with similar levels of support, there is frequently no substantial difference in learning outcomes between traditional face-to-face courses and completely online courses.

However, online learning was not without its challenges. Research showing less favorable results for specific student groups can be referenced in Dennen ( 2014 ), etc. The common problems faced by students included underdeveloped independent learning ability, lack of motivation, difficulties in self-regulation, student engagement and technical issues (Aristovnik et al. 2021 ; Martin and Bolliger 2018 ; Song et al. 2004 ; Zheng et al. 2022 ).

Moreover, factors like instructional strategies, course design, etc. were also linked to learning outcomes and successful online learning (Ali 2020 ; Hongsuchon et al. 2022 ). Careaga-Butter et al. ( 2020 ) critically analyze online education in pandemic and post-pandemic contexts, focusing on digital tools and resources for teaching in synchronous and asynchronous learning modalities. They discuss the swift adaptation to online learning during the pandemic, highlighting the importance of technological infrastructure, pedagogical strategies, and the challenges of digital divides. The article emphasizes the need for effective online learning environments and explores trends in post-pandemic education, providing insights into future educational strategies and practices.

Determinants of online learning outcomes

Online learning outcomes in this paper refer to the measurable educational results achieved through online learning methods, including knowledge acquisition, skill development, changes in attitudes or behaviors, and performance improvements (Chang 2016 ; Panigrahi et al. 2018 ). The literature review identified key factors influencing online learning outcomes, emphasizing their significant role in academic discourse. These factors, highlighted in scholarly literature, include student engagement, instructional design, technology infrastructure, student-teacher interaction, and student self-regulation.

Student Engagement: The level of a student’s engagement significantly impacts their learning outcomes. The more actively a student is engaged with the course content and activities, the better their performance tends to be. This underscores the importance of designing engaging course content and providing opportunities for active learning in an online environment (Martin and Bolliger 2018 ).

Instructional Design: How an online course is designed can greatly affect student outcomes. Key elements such as clarity of learning objectives, organization of course materials, and the use of diverse instructional strategies significantly impact student learning (Bozkurt and Sharma 2020 ).

Technology Infrastructure: The reliability and ease of use of the learning management system (LMS) also play a significant role in online learning outcomes. When students experience technical difficulties, it can lead to frustration, reduced engagement, and lower performance (Johnson et al. 2020 ).

Student-Teacher Interaction: Interaction between students and teachers in an online learning environment is a key determinant of successful outcomes. Regular, substantive feedback from instructors can promote student learning and motivation (Boling et al. 2012 ).

Student Self-Regulation: The autonomous nature of online learning requires students to be proficient in self-regulated learning, which involves setting learning goals, self-monitoring, and self-evaluation. Students who exhibit strong self-regulation skills are more likely to succeed in online learning (Broadbent 2017 ).

While many studies have investigated individual factors affecting online learning, there is a paucity of research offering a holistic view of these factors and their interrelationships, leading to a fragmented understanding of the influences on online learning outcomes. Given the multitude of experiences and variables encompassed by online learning, a comprehensive framework like is instrumental in ensuring a thorough investigation and interpretation of the breadth of students’ experiences.

Students’ perceptions of online learning

Understanding students’ perceptions of online learning is essential for enhancing its effectiveness and student satisfaction. Studies show students appreciate online learning for its flexibility and convenience, offering personalized learning paths and resource access (Händel et al. 2020 ; Johnson et al. 2020 ). Yet, challenges persist, notably in maintaining motivation and handling technical issues (Aristovnik et al. 2021 ; Händel et al. 2020 ). Aguilera-Hermida ( 2020 ) reported mixed feelings among students during the COVID-19 pandemic, including feelings of isolation and difficulty adjusting to online environments. Boling et al. ( 2012 ) emphasized students’ preferences for interactive and communicative online learning environments. Additionally, research indicates that students seek more engaging content and innovative teaching approaches, suggesting a gap between current online offerings and student expectations (Chakraborty and Muyia Nafukho 2014 ). Students also emphasize the importance of community and peer support in online settings, underlining the need for collaborative and social learning opportunities (Lai et al. 2019 ). These findings imply that while online learning offers significant benefits, addressing its shortcomings is critical for maximizing its potential.

The pandemic prompted a reconsideration of instructional modalities, with many students favoring face-to-face instruction due to the immediacy and focus issues (Aristovnik et al. 2021 ; Trust and Whalen 2020 ). Despite valuable insights, research gaps remain, particularly in long-term undergraduate reflections and the application of nine factors of comprehensive frameworks, indicating a need for more holistic research in online learning effectiveness.

Teachers’ perceptions of online learning

The pandemic has brought attention to how teachers manage instruction in virtual learning environments. Teachers and students are divided in terms of their attitudes toward online learning. Some teachers and students looked to the convenience and flexibility of online learning (Chuenyindee et al. 2022 ; Al-Emran and Shaalan 2021 ). They conceived that online learning provided opportunities to improve educational equality as well (Tenório et al. 2016 ). Even when COVID-19 was over, the dependence on online learning was likely here to stay, for some approaches of online learning were well-received by students and teachers (Al-Rahmi et al. 2019 ; Hongsuchon et al. 2022 ).

Teachers had shown great confidence in delivering instruction in an online environment in a satisfying manner. They also agreed that the difficulty of teaching was closely associated with course structures (Gavranović and Prodanović 2021 ).

Not all were optimistic about the effects of online learning. They sought out the challenges facing teachers and students during online learning.

A mixed-method study of K-12 teachers’ feelings, experiences, and perspectives that the major challenges faced by teachers during the COVID-19 pandemic were lack of student participation and engagement, technological support for online learning, lack of face-to-face interactions with students, no work-life balance and learning new technology.

The challenges to teachers’ online instruction included instruction technology (Maatuk et al. 2022 ; Rasheed et al. 2020 ), course design (Khojasteh et al. 2023 ), and teachers’ confidence (Gavranović and Prodanović 2021 ).

Self-regulation challenges and challenges in using technology were the key challenges to students, while the use of technology for teaching was the challenge facing teachers (Rasheed et al. 2020 ).

The quality of course design was another important factor in online learning. A research revealed the competency of the instructors and their expertise in content development contributed a lot to students’ satisfaction with the quality of e-contents.

Theoretical framework

The theoretical foundation of the research is deeply rooted in multifaceted framework for online learning, which provides a comprehensive and interwoven model encompassing nine critical factors that collectively shape the educational experience in online settings. This framework is instrumental in guiding our analysis and enhances the comparability and interpretability of our results within the context of existing literature.

Central to Yu’s framework is the concept of behavioral intention, which acts as a precursor to student engagement in online learning environments. This engagement, inherently linked to the students’ intentions and motivations, is significantly influenced by the quality of instruction they receive. Instruction, therefore, emerges as a pivotal element in this model, directly impacting not only student engagement but also fostering a sense of self-efficacy among learners. Such self-efficacy is crucial as it influences both the performance of students and their overall satisfaction with the learning process.

The framework posits that engagement, a derivative of both strong behavioral intention and effective instruction, plays a vital role in enhancing student performance. This engagement is tightly interlaced with self-regulation, an indispensable skill in the autonomous and often self-directed context of online learning. Interaction, encompassing various forms such as student-teacher and peer-to-peer communications, further enriches the learning experience. It significantly contributes to the development of motivation and self-efficacy, both of which are essential for sustaining engagement and fostering self-regulated learning.

Motivation, especially when intrinsically driven, acts as a catalyst, perpetuating engagement and self-regulation, which ultimately leads to increased satisfaction with the learning experience. In this framework, self-efficacy, nurtured through effective instruction and meaningful interactions, has a positive impact on students’ performance and satisfaction, thereby creating a reinforcing cycle of learning and achievement.

Performance in this model is viewed as a tangible measure of the synergistic interplay of engagement, instructional quality, and self-efficacy, while satisfaction reflects the culmination of the learning experience, shaped by the quality of instruction, the extent and nature of interactions, and the flexibility of the learning environment. This satisfaction, in turn, influences students’ future motivation and their continued engagement with online learning.

Yu’s model thus presents a dynamic ecosystem where changes in one factor can have ripple effects across the entire spectrum of online learning. It emphasizes the need for a holistic approach in the realm of online education, considering the complex interplay of these diverse yet interconnected elements to enhance both the effectiveness and the overall experience of online learning.

The current study employed a qualitative design to explore teachers’ and undergraduates’ retrospections on the effectiveness of online learning during the first semester of the 2022–2023 school year, which is in the post-pandemic period. Data were collected using reflective diaries, and thematic analysis was applied to understand the experiences based on the nine factors.

Sample and sampling

The study involved 18 teachers and 46 first-year students from a comprehensive university in China, selected through convenience sampling to ensure diverse representation across academic disciplines. To ensure a diverse range of experiences in online learning, the participant selection process involved an initial email inquiry about their prior engagement with online education. The first author of this study received ethics approval from the department research committee, and participants were informed of the study’s objectives two weeks before via email. Only those participants who provided written informed consent were included in the study and were free to withdraw at any time. Pseudonyms were used to protect participants’ identities during the data-coding process. For direct citations, acronyms of students’ names were used, while “T+number” was used for citations from teacher participants.

The 46 students are all first-year undergraduates, 9 females and 37 males majoring in English and non-English (see Table 1 ).

The 18 teachers are all experienced instructors with at least 5 years of teaching experience, 13 females and 5 male, majoring in English and Non-English (see Table 2 ).

Data collection

Students’ data were collected through reflective diaries in class during the first semester of the 2022–2023 school year. Each participant was asked to maintain a diary over the course of one academic semester, in which they responded to four questions.

The four questions include:

What was your state and attitude toward online learning?

What were the problems and shortcomings of online learning?

What do you think are the reasons for these problems?

What measures do you think should be taken to improve online learning?

This approach provided a first-person perspective on the participants’ online teaching or learning experiences, capturing the depth and complexity of their retrospections.

Teachers were interviewed separately by responding to the four questions the same as the students. Each interview was conducted in the office or the school canteen during the semester and lasted about 20 to 30 min.

Data analysis

We utilized thematic analysis to interpret the reflective diaries, guided initially by nine factors. This method involved extensive engagement with the data, from initial coding to the final report. While Yu’s factors provided a foundational structure, we remained attentive to new themes, ensuring a comprehensive analysis. Our approach was methodical: familiarizing ourselves with the data, identifying initial codes, systematically searching and reviewing themes, and then defining and naming them. To validate our findings, we incorporated peer debriefing, and member checking, and maintained an audit trail. This analysis method was chosen for its effectiveness in extracting in-depth insights from undergraduates’ retrospections on their online learning experiences post-pandemic, aligning with our research objectives.

According to the nine factors, the interviews of 18 teachers and 46 Year 1 undergraduates were catalogued and listed in Table 3 .

Behavioral intention towards online learning post-pandemic

Since the widespread of the COVID-19 pandemic, both teachers and students have experienced online learning. However, their online teaching or learning was forced rather than planned (Baber 2021 ; Bao 2020 ). Students more easily accepted online learning when they perceived the severity of COVID-19.

When entering the post-pandemic era, traditional teaching was resumed. Students often compared online learning with traditional learning by mentioning learning interests, eye contact, face-to-face learning and learning atmosphere.

“I don’t think online learning is a good form of learning because it is hard to focus on learning.” (DSY) “In unimportant courses, I would let the computer log to the platform and at the same time do other entertains such as watching movies, listening to the music, having snacks or do the cleaning.” (XYN) “Online learning makes it impossible to have eye contact between teachers and students and unable to create a face-to-face instructional environment, which greatly influences students’ initiative and engagement in classes.” (WRX)

They noted that positive attitudes toward online learning usually generated higher behavioral intention to use online learning than those with negative attitudes, as found in the research of Zhu et al. ( 2023 ). So they put more blame on distractions in the learning environment.

“Online learning relies on computers or cell phones which easily brings many distractions. … I can’t focus on studying, shifting constantly from study and games.” (YX) “When we talk about learning online, we are hit by an idea that we can take a rest in class. It’s because everyone believes that during online classes, the teacher is unable to see or know what we are doing.” (YM) “…I am easily disturbed by external factors, and I am not very active in class.” (WZB)

Teachers reported a majority of students reluctantly turning on their cameras during online instruction and concluded the possible reason for such behavior.

“One of the reasons why some students are unwilling to turn on the camera is that they are worried about their looks and clothing at home, or that they don’t want to become the focus.” (T4)

They also noticed students’ absent-mindedness and lazy attitude during online instruction.

“As for some students who are not self-regulated, they would not take online learning as seriously as offline learning. Whenever they are logged onto the online platform, they would be unable to stay focused and keep their attention.” (T1)

Challenges and opportunities in online instruction post-pandemic

Online teaching brought new challenges and opportunities for students during and after the pandemic. The distractions at home seemed to be significantly underestimated by teachers in an online learning environment (Radmer and Goodchild 2021 ). It might be the reason why students greatly expected and heavily relied on teachers’ supervision and management.

“The biggest problem of online learning is that online courses are as imperative as traditional classes, but not managed face to face the same as the traditional ones.” (PC) “It is unable to provide some necessary supervision.” (GJX) “It is incapable of giving timely attention to every student.” (GYC) “Teachers can’t understand students’ conditions in time in most cases so teachers can’t adjust their teaching plan.” (MZY) “Some courses are unable to reach the teaching objectives due to lack of experimental conduction and practical operation.” (YZH) “Insufficient teacher-student interaction and the use of cell phones make both groups unable to engage in classes. What’s more, though online learning doesn’t put a high requirement for places, its instructional environment may be crucial due to the possible distractions.” (YCY)

Teachers also viewed online instruction as an addition to face-to-face instruction.

“Online learning cannot run as smoothly as face-to-face instruction, but it can provide an in-time supplement to the practical teaching and students’ self-learning.” (T13, T17) “Online instruction is an essential way to ensure the normal function of school work during the special periods like the pandemic” (T1, T15)

Factors influencing student engagement in online learning

Learning engagement was found to contribute to gains in the study (Paul and Diana 2006 ). It was also referred to as a state closely intertwined with the three dimensions of learning, i.e., vigor, dedication, and absorption (Schaufeli et al. 2002 ). Previous studies have found that some key factors like learning interaction, self-regulation, and social presence could influence learning engagement and learning outcomes (Lowenthal and Dunlap 2020 ; Ng 2018 ). Due to the absence of face-to-face interaction like eye contact, facial expressions and body language, both groups of interviewees agreed that the students felt it hard to keep their attention and thus remain active in online classes.

“Students are unable to engage in study due to a lack of practical learning environment of online learning.” (ZMH, T12) “Online platforms may not provide the same level of engagement and interaction as in-person classrooms, making it harder for students to ask questions or engage in discussions.” (HCK) “The Internet is cold, lack of emotional clues and practical connections, which makes it unable to reproduce face-to-face offline learning so that teachers and students are unlikely to know each other’s true feelings or thoughts. In addition, different from the real-time learning supervision in offline learning, online learning leaves students more learning autonomy.” (XGH) “Lack of teachers’ supervision and practical learning environment, students are easily distracted.” (LMA, T9)

Just as Zhu et al. ( 2023 ) pointed out, we had been too optimistic about students’ engagement in online learning, because online learning relied more on students’ autonomy and efforts to complete online learning.

Challenges in teacher-student interaction in online learning

Online learning has a notable feature, i.e., a spatial and temporal separation among teachers and students. Thus, online teacher-student interactions, fundamentals of relationship formation, have more challenges for both teachers and students. The prior studies found that online interaction affected social presence and indirectly affected learning engagement through social presence (Miao and Ma 2022 ). In the present investigation, both teachers and students noted the striking disadvantage of online interaction.

“Online learning has many problems such as indirect teacher-student communication, inactive informative communication, late response of students and their inability to reflect their problems. For example, teachers cannot evaluate correctly whether the students have mastered or not.” (YYN) “Teachers and students are separated by screens. The students cannot make prompt responses to the teachers’ questions via loudspeakers or headphones. It is not convenient for students to participate in questioning and answering. …for most of the time, the students interact with teachers via typing.” (ZJY) “While learning online, students prefer texting the questions to answering them via the loudspeaker.”(T7)

Online learning interaction was also found closely related to online learning engagement, performance, and self-efficacy.

“Teachers and students are unable to have timely and effective communication, which reduces the learning atmosphere. Students are often distracted. While doing homework, the students are unable to give feedback to teachers.” (YR) “Students are liable to be distracted by many other side matters so that they can keep their attention to online learning.” (T15)

In the online learning environment, teachers need to make efforts to build rapport and personalizing interactions with students to help them perform better and achieve greater academic success (Harper 2018 ; Ong and Quek 2023 ) Meanwhile, teachers should also motivate students’ learning by designing the lessons, giving lectures and managing the processes of student interactions (Garrison 2003 ; Ong and Quek 2023 ).

Determinants of self-efficacy in online learning

Online learning self-efficacy refers to students’ perception of their abilities to fulfill specific tasks required in online learning (Calaguas and Consunji 2022 ; Zimmerman and Kulikowich 2016 ). Online learning self-efficacy was found to be influenced by various factors including task, learner, course, and technology level, among which task level was found to be most closely related (Xu et al. 2022 ). The responses from the 46 student participants reveal a shared concern, albeit without mentioning specific tasks; they highlight critical aspects influencing online learning: learner attributes, course structure, and technological infrastructure.

One unifying theme from the student feedback is the challenge of self-regulation and environmental distractions impacting learning efficacy. For instance, participant WSX notes the necessity for students to enhance time management skills due to deficiencies in self-regulation, which is crucial for successful online learning. Participant WY expands on this by pointing out the distractions outside traditional classroom settings, coupled with limited teacher-student interaction, which hampers idea exchange and independent thought, thereby undermining educational outcomes. These insights suggest a need for strategies that bolster students’ self-discipline and interactive opportunities in virtual learning environments.

On the technological front, participants WT and YCY address different but related issues. Participant WT emphasizes the importance of up-to-date course content and learning facilities, indicating that outdated materials and tools can significantly diminish the effectiveness of online education. Participant YCY adds to this by highlighting problems with online learning applications, such as subpar functionalities that can introduce additional barriers to learning.

Teacher participants, on the other hand, shed light on objective factors predominantly related to course content and technology. Participant T5’s response underscores the heavy dependency on technological advancement in online education and points out the current inability of platforms or apps to adequately monitor student engagement and progress. Participant T9 voices concerns about course content not being updated or aligned with contemporary trends and student interests, suggesting a disconnect between educational offerings and learner needs. Meanwhile, participant T8 identifies unstable network services as a significant hindrance to online teaching, highlighting infrastructure as a critical component of online education’s success.

Teachers also believed the insufficient mastery of facilities and unfamiliarity with online instruction posed difficulty.

“Most teachers and students are not familiar with online instruction. For example, some teachers are unable to manage online courses so they cannot design the courses well. Some students lack self-regulation, which leads to their distraction or avoidance in class.” (T9)

Influences on student performance in online learning

Students’ performance during online lessons is closely associated with their satisfaction and self-efficacy. Most of the student participants reflected on their distractions, confusion, and needs, which indicates their dissatisfaction with online learning.

“During online instruction, it is convenient for the students to make use of cell phones, but instead, cell phones bring lots of distraction.” (YSC) “Due to the limits of online learning, teachers are facing the computer screen and unable to know timely students’ needs and confusion. Meanwhile, it’s inconvenient for teachers to make clear explanations of the sample questions or problems.” (HZW)

They thought their low learning efficiency in performance was caused by external factors like the learning environment.

“The most obvious disadvantage of online learning goes to low efficiency. Students find it hard to keep attention to study outside the practical classroom or in a relaxing environment.” (WY) “Teachers are not strict enough with students, which leads to ineffective learning.” (WRX)

Teacher participants conceived students’ performance as closely related to valid online supervision and students’ self-regulation.

“Online instruction is unable to create a learning environment, which helps teachers know students’ instant reaction. Only when students well regulate themselves and stay focused during online learning can they achieve successful interactions and make good accomplishments in the class.” (T11) “Some students need teachers’ supervision and high self-regulation, or they were easily distracted.” (T16)

Student satisfaction and teaching effectiveness in online learning

Online learning satisfaction was found to be significantly and positively associated with online learning self-efficacy (Al-Nasa’h et al. 2021 ; Lashley et al. 2022 ). Around 46% of student participants were unsatisfied with teachers’ ways of teaching.

“Comparatively, bloggers are more interesting than teachers’ boring and dull voices in online learning.” (DSY) “Teachers’ voice sounds dull and boring through the internet, which may cause listeners to feel sleepy, and the teaching content is not interesting enough to the students.” (MFE)

It reflected partly that some teachers were not adapted to online teaching possibly due to a lack in experience of online teaching or learning (Zhu et al. 2022 ).

“Some teachers are not well-prepared for online learning. They are particularly unready for emergent technological problems when delivering the teaching.” (T1) “One of the critical reasons lies in the fact that teachers and students are not well trained before online learning. In addition, the online platform is not unified by the college administration, which has led to chaos and difficulty of online instruction.” (T17)

Teachers recognized their inadequate preparation and mastery of online learning as one of the reasons for dissatisfaction, but student participants exaggerated the role of teachers in online learning and ignored their responsibility in planning and managing their learning behavior, as in the research of (Xu et al. 2022 ).

The role of self-regulation in online learning success

In the context of online learning, self-regulation stands out as a crucial factor, necessitating heightened levels of student self-discipline and autonomy. This aspect, as Zhu et al. ( 2023 ) suggest, grants students significant control over their learning processes, making it a vital component for successful online education.

“Online learning requires learners to be of high discipline and self-regulation. Without good self-regulation, they are less likely to be effective in online learning.” (YZJ) “Most students lack self-control, unable to control the time of using electronic products. Some even use other electronic products during online learning, which greatly reduces their efficiency in learning.” (GPY) “Students are not well developed in self-control and easily distracted. Thus they are unable to engage fully in their study, which makes them unable to keep up with others” (XYN)

Both groups of participants had a clear idea of the positive role of self-regulation in successful learning, but they also admitted that students need to strengthen their self-regulation skills and it seemed they associated with the learning environment, learning efficiency and teachers’ supervision.

“If they are self-motivated, online learning can be conducted more easily and more efficiently. However, a majority are not strong in regulating themselves. Teachers’ direct supervision in offline learning can do better in motivating them to study hard…lack of interaction makes students less active and motivated.” (LY) “Students have a low level of self-discipline. Without teachers’ supervision, they find it hard to listen attentively or even quit listening. Moreover, in class, the students seldom think actively and independently.” (T13)

The analysis of participant responses, categorized into three distinct attitude groups – positive, neutral, and negative – reveals a multifaceted view of the disadvantages of online learning, as shown in Tables 4 and 5 . This classification provides a clearer understanding of how attitudes towards online learning influence perceptions of self-regulation and other related factors.

In Table 4 , the division among students is most pronounced in terms of interaction and self-efficacy. Those with neutral attitudes highlighted interaction as a primary concern, suggesting that it is less effective in an online setting. Participants with positive attitudes noted a lack of student motivation, while those with negative views emphasized the need for better self-efficacy. Across all attitudes, instruction, engagement, self-regulation, and behavior intention were consistently identified as areas needing improvement.

Table 5 sheds light on teachers’ perspectives, revealing a consensus on the significance and challenges of instruction, motivation, and self-efficacy in online learning. Teachers’ opinions vary most significantly on self-efficacy and engagement. Those with negative attitudes point to self-efficacy and instructional quality as critical areas needing attention, while neutral attitudes focus on the role of motivation.

Discussions

Using a qualitative and quantitative analysis of the questionnaire data showed that among the 18 college teachers and 46 year 1 undergraduate students of various majors taking part in the interview, about 38.9% of teachers and about 30.4% of students supported online learning. Only two teachers were neutral about online learning, and 50% of teachers did not support virtual learning. The percentages of students who expressed positive and neutral views on online learning were the same, i.e., 34.8%. This indicates that online learning could serve as a complementary approach to traditional education, yet it is not without challenges, particularly in terms of student engagement, self-regulation, and behavioral intention, which were often attributed to distractions inherent in online environments.

In analyzing nine factors, it was evident that both teachers and students did not perceive these factors uniformly. Instruction was a significant element for both groups, as validated by findings in Tables 3 and 5 . The absence of face-to-face interactions in online learning shifted the focus to online instruction quality. Teachers cited technological challenges as a central concern, while students criticized the lack of engaging content and teaching methods. This aligns with Miao and Ma ( 2022 ), who argued that direct online interaction does not necessarily influence learner engagement, thus underscoring the need for integrated approaches encompassing interactions, self-regulation, and social presence.

Furthermore, the role of technology acceptance in shaping self-efficacy was highlighted by Xu et al. ( 2022 ), suggesting that students with higher self-efficacy tend to challenge themselves more. Chen and Hsu ( 2022 ) noted the positive influence of using emojis in online lessons, emphasizing the importance of innovative pedagogical approaches in online settings.

The study revealed distinct priorities between teachers and students in online learning: teachers emphasized effective instruction delivery, while students valued learning outcomes, self-regulation, and engagement. This divergence highlights the unique challenges each group faces. Findings by Dennen et al. ( 2007 ) corroborate this, showing instructors focusing on content and guidance, while students prioritize interpersonal communication and individualized attention. Additionally, Lee et al. ( 2011 ) found that reduced transactional distance and increased student engagement led to enhanced perceptions of learning outcomes, aligning with students’ priorities in online courses. Understanding these differing perspectives is crucial for developing comprehensive online learning strategies that address the needs of both educators and learners.

Integrating these findings with broader contextual elements such as technological infrastructure, pedagogical strategies, socio-economic backgrounds, and environmental factors (Balanskat and Bingimlas 2006 ) further enriches our understanding. The interplay between these external factors and Yu’s nine key aspects forms a complex educational ecosystem. For example, government interventions and training programs have been shown to increase teachers’ enthusiasm for ICT and its routine use in education (Balanskat and Bingimlas 2006 ). Additionally, socioeconomic factors significantly impact students’ experiences with online learning, as the digital divide in connectivity and access to computers at home influences the ICT experience, an important factor for school achievement (OECD 2015 ; Punie et al. 2006 ).

In sum, the study advocates for a holistic approach to understanding and enhancing online education, recognizing the complex interplay between internal factors and external elements that shape the educational ecosystem in the digital age.

Conclusion and future research

This study offered a comprehensive exploration into the retrospective perceptions of college teachers and undergraduate students regarding their experiences with online learning following the COVID-19 pandemic. It was guided by a framework encompassing nine key factors that influence online learning outcomes. To delve into these perspectives, the research focused on three pivotal questions. These questions aimed to uncover how both undergraduates and teachers in China view the effectiveness of online learning post-pandemic, identify which of the nine influencing factors had the most significant impact, and propose recommendations for enhancing the future effectiveness of online learning.

In addressing the first research question concerning the retrospective perceptions of online learning’s effectiveness among undergraduates and teachers in China post-COVID-19 pandemic, the thematic analysis has delineated clear divergences in attitude between the two demographics. Participants were primarily divided into three categories based on their stance toward online learning: positive, neutral, and negative. The results highlighted a pronounced variance in attitude distribution between teachers and students, with a higher percentage of teachers expressing clear-cut opinions, either favorably or unfavorably, towards the effectiveness of online learning.

Conversely, students displayed a pronounced inclination towards neutrality, revealing a more cautious or mixed stance on the effectiveness of online learning. This prevalent neutrality within the student body could be attributed to a range of underlying reasons. It might signify students’ uncertainties or varied experiences with online platforms, differences in engagement levels, gaps in digital literacy, or fluctuating quality of online materials and teaching methods. Moreover, this neutral attitude may arise from the psychological and social repercussions of the pandemic, which have potentially altered students’ approaches to and perceptions of learning in an online context.

In the exploration of the nine influential factors in online learning, it was discovered that both teachers and students overwhelmingly identified instruction as the most critical aspect. This was closely followed by engagement, interaction, motivation, and other factors, while performance and satisfaction were perceived as less influential by both groups. However, the attitudes of teachers and students towards these factors revealed notable differences, particularly about instruction. Teachers often attributed challenges in online instruction to technological issues, whereas students perceived the quality of instruction as a major influence on their learning effectiveness. This dichotomy highlights the distinct perspectives arising from their different roles within the educational process.

A further divergence was observed in views on self-efficacy and self-regulation. Teachers, with a focus on delivering content, emphasized the importance of self-efficacy, while students, grappling with the demands of online learning, prioritized self-regulation. This reflects their respective positions in the online learning environment, with teachers concerned about the efficacy of their instructional strategies and students about managing their learning process. Interestingly, the study also illuminated that students did not always perceive themselves as independent learners, which contributed to the high priority they placed on instruction quality. This insight underlines a significant area for development in online learning strategies, emphasizing the need for fostering greater learner autonomy.

Notably, both teachers and students concurred that stimulating interest was a key factor in enhancing online learning. They proposed innovative approaches such as emulating popular online personalities, enhancing interactive elements, and contextualizing content to make it more relatable to students’ lives. Additionally, practical suggestions like issuing preview tasks and conducting in-class quizzes were highlighted as methods to boost student engagement and learning efficiency. The consensus on the importance of supervisory roles underscores the necessity for a balanced approach that integrates guidance and independence in the online learning environment.

The outcomes of our study highlight the multifaceted nature of online learning, accentuated by the varied perspectives and distinct needs of teachers and students. This complexity underscores the necessity of recognizing and addressing these nuances when designing and implementing online learning strategies. Furthermore, our findings offer a comprehensive overview of both the strengths and weaknesses of online learning during an unprecedented time, offering valuable insights for educators, administrators, and policy-makers involved in higher education. Moreover, it emphasized the intricate interplay of multiple factors—behavioral intention, instruction, engagement, interaction, motivation, self-efficacy, performance, satisfaction, and self-regulation—in shaping online learning outcomes. presents some limitations, notably its reliance on a single research method and a limited sample size.

However, the exclusive use of reflective diaries and interviews restricts the range of data collection methods, which might have been enriched by incorporating additional quantitative or mixed-method approaches. Furthermore, the sample, consisting only of students and teachers from one university, may not adequately represent the diverse experiences and perceptions of online learning across different educational contexts. These limitations suggest the need for a cautious interpretation of the findings and indicate areas for future research expansion. Future research could extend this study by incorporating a larger, more diverse sample to gain a broader understanding of undergraduate students’ retrospections across different contexts and cultures. Furthermore, research could also explore how to better equip students with the skills and strategies necessary to optimize their online learning experiences, especially in terms of the self-regulated learning and motivation aspects.

Data availability

The data supporting this study is available from https://doi.org/10.6084/m9.figshare.25583664.v1 . The data consists of reflective diaries from 46 Year 1 students from a comprehensive university in China and 18 college teachers. We utilized thematic analysis to interpret the reflective diaries, guided initially by nine factors. The results highlight the critical need for tailored online learning strategies and provide insights into its advantages and challenges for stakeholders in higher education.

Aguilera-Hermida AP (2020) College students’ use and acceptance of emergency online learning due to COVID-19. Int. J. Educ. Res. Open 1:100011. https://doi.org/10.1016/j.ijedro.2020.100011

Article Google Scholar

Al-Emran, M, & Shaalan, K (2021, October 27). E-podium technology: A medium of managing knowledge at Al Buraimi University College via M-learning. In Proceedings of the 2nd BCS International IT Conference, Abu Dhabi, United Arab Emirates 2014. Retrieved October 17, 2023, from https://dblp.uni-trier.de/rec/conf/bcsit/EmranS14.html

Ali W (2020) Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. High. Educ. Stud. 10(3):16–25. https://doi.org/10.5539/hes.v10n3p16

Al-Nasa’h M, Al-Tarawneh L, Awwad FMA, Ahmad I (2021) Estimating Students’ Online Learning Satisfaction during COVID-19: A Discriminant Analysis. Heliyon 7(12):1–7. https://doi.org/10.1016/j.helyon.2021.e08544

Al-Rahmi WM, Yahaya N, Aldraiweesh AA, Alamri MM, Aljarboa NA, Alturki U (2019) Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE Access 7:26797–26809. https://doi.org/10.1109/ACCESS.2019.2899368

Aristovnik A, Keržič D, Ravšelj D, Tomaževič N, Umek L (2021) Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability 12(20):8438. https://doi.org/10.3390/su12208438

Article CAS Google Scholar

Baber H (2021) Modelling the Acceptance of E-learning during the Pandemic Of COVID-19-A Study of South Korea. Int. J. Manag. Educ. 19(2):1–15. https://doi.org/10.1016/j.ijme.2021.100503

Article MathSciNet Google Scholar

Bahasoan AN, Ayuandiani W, Mukhram M, Rahmat A (2020) Effectiveness of online learning in pandemic COVID-19. Int. J. Sci., Technol. Manag. 1(2):100–106

Google Scholar

Balanskat A, Bingimlas KA (2006) Barriers to the Successful Integration of ICT in Teaching and Learning Environments: A Review of the Literature. Eurasia J. Math. Sci. Technol. Educ. 5(3):235–245. https://doi.org/10.12973/ejmste/75275

Bao W (2020) COVID-19 and Online Teaching in Higher Education: A Case Study of Peking University. Hum. Behav. Emerg. Technol. 2(2):113–115. https://doi.org/10.1002/hbe2.191

Article PubMed PubMed Central Google Scholar

Bernard RM et al. (2004) How Does Distance Education Compare with Classroom Instruction? A Meta-Analysis of the Empirical Literature. Rev. Educ. Res. 74(3):379–439

Boling EC, Hough M, Krinsky H, Saleem H, Stevens M (2012) Cutting the distance in distance education: Perspectives on what promotes positive, online learning experiences. Internet High. Educ. 15:118–126. https://doi.org/10.1016/j.iheduc.2011.11.006

Bozkurt A, Sharma RC (2020) Emergency remote teaching in a time of global crisis due to Coronavirus pandemic. Asian J. Distance Educ. 15(1):i–vi

Broadbent J (2017) Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High. Educ. 33:24–32. https://doi.org/10.1016/j.iheduc.2017.01.004

Calaguas NP, Consunji PMP (2022) A Structural Equation Model Predicting Adults’ Online Learning Self-efficacy. Educ. Inf. Technol. 27:6233–6249. https://doi.org/10.1007/s10639-021-10871-y

Careaga-Butter M, Quintana MGB, Fuentes-Henríquez C (2020) Critical and Prospective Analysis of Online Education in Pandemic and Post-pandemic Contexts: Digital Tools and Resources to Support Teaching in synchronous and Asynchronous Learning Modalities. Aloma: evista de. psicologia, ciències de. l’educació i de. l’esport Blanquerna 38(2):23–32

Chakraborty M, Muyia Nafukho F (2014) Strengthening Student Engagement: What do Students Want in Online Courses? Eur. J. Train. Dev. 38(9):782–802

Chang V (2016) Review and Discussion: E-learning for Academia and Industry. Int. J. Inf. Manag. 36(3):476–485. https://doi.org/10.1016/j.ijinfomgt.2015.12.007

Chen YJ, Hsu LW (2022) Enhancing EFL Learners’ Self-efficacy Beliefs of Learning English with emoji Feedbacks in Call: Why and How. Behav. Sci. 12(7):227. https://doi.org/10.3390/bs12070227

Chuenyindee T, Montenegro LD, Ong AKS, Prasetyo YT, Nadlifatin R, Ayuwati ID, Sittiwatethanasiri T, Robas KPE (2022) The Perceived Usability of the Learning Management System during the COVID-19 Pandemic: Integrating System Usability Scale, Technology Acceptance Model, and Task-technology Fit. Work 73(1):41–58. https://doi.org/10.3233/WOR-220015

Article PubMed Google Scholar

Crawford J, Butler-Henderson K, Rudolph J, Malkawi B, Glowatz M, Burton R, Lam S (2020) COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. J. Appl. Teach. Learn. 3(1):120. https://doi.org/10.37074/jalt.2020.3.1.7

Dennen VP (2014) Becoming a blogger: Trajectories, norms, and activities in a community of practice. Computers Hum. Behav. 36:350–358. https://doi.org/10.1016/j.chb.2014.03.028

Dennen VP, Darabi AA, Smith LJ (2007) Instructor–learner Interaction in Online Courses: The Relative Perceived Importance of Particular Instructor Actions on Performance and Satisfaction. Distance Educ. 28(1):65–79

Dolan E, Hancock E, Wareing A (2015) An evaluation of online learning to teach practical competencies in undergraduate health science students. Internet High. Educ. 24:21–25

Garrison DR (2003) Cognitive Presence for Effective Asynchronous Online Learning: The Role of Reflective. Inq., Self-Direction Metacognition. Elem. Qual. Online Educ.: Pract. Direction 4(10):47–58

Gavranović V, Prodanović M (2021) ESP Teachers’ Perspectives on the Online Teaching Environment Imposed in the Covid-19 Era-A Case Study. N. Educ. Rev. 2:188–197. https://doi.org/10.15804/TNER.2021.64.2.15

Greenhow C, Lewin C, Staudt Willet KB (2021) The Educational Response to Covid-19 across Two Countries: a Critical Examination of Initial Digital Pedagogy Adoption. Technol., Pedagog. Educ. 30(1):7–25

Händel M, Stephan M, Gläser-Zikuda M, Kopp B, Bedenlier S, Ziegler A (2020) Digital readiness and its effects on higher education students’ socio-emotional perceptions in the context of the COVID-19 pandemic. J. Res. Technol. Educ. 53(2):1–13

Harper B (2018) Technology and Teacher-student Interactions: A Review of Empirical Research. J. Res. Technol. Educ. 50(3):214–225

Article ADS Google Scholar

Hernández-Lara AB, Serradell-López E (2018) Student interactions in online discussion forums: their perception on learning with business simulation games. Behav. Inf. Technol. 37(4):419–429

Hodges C, Moore S, Lockee B, Trust T, Bond A (2020) The difference between emergency remote teaching and online learning. Educause Rev. 27:1–12

Hongsuchon T, Emary IMME, Hariguna T, Qhal EMA (2022) Assessing the Impact of Online-learning Effectiveness and Benefits in Knowledge Management, the Antecedent of Online-learning Strategies and Motivations: An Empirical Study. Sustainability 14(5):1–16. https://doi.org/10.3390/su14052570 . (2020)

Johnson N, Veletsianos G, Seaman J (2020) US faculty and administrators’ experiences and approaches in the early weeks of the COVID-19 pandemic. Online Learn. 24(2):6–21

Khojasteh L, Karimian Z, Farahmandi AY, Nasiri E, Salehi N (2023) E-content Development of English Language Courses during COVID-19: A Comprehensive Analysis of Students’ Satisfaction. J. Computer Educ. 10(1):107–133. https://doi.org/10.1007/s40692-022-00224-0

Kohnke, L, & Foung, D (2023). Exploring Microlearning for Teacher Professional Development: Voices from Hong Kong. In Tafazoli, D, M Picard (Eds.). Handbook of CALL Teacher Education Professional Development (pp. 279-292). Singapore: Springer Nature Singapore Pte Ltd

Lai CH, Lin HW, Lin RM, Tho PD (2019) Effect of Peer Interaction among Online Learning Community on Learning Engagement and Achievement. Int. J. Distance Educ. Technol. (IJDET) 17(1):66–77

Lashley PM, Sobers NP, Campbell MH, Emmanuel MK, Greaves N, Gittens-St Hilaire M, Murphy MM, Majumder MAA (2022) Student Satisfaction and Self-Efficacy in a Novel Online Clinical Clerkship Curriculum Delivered during the COVID-19 Pandemic. Adv. Med. Educ. Pract. 13:1029–1038. https://doi.org/10.2147/AMEP.S374133

Lee SJ, Srinivasan S, Trail T, Lewis D, Lopez S (2011) Examining the Relationship among Student Perception of Support, Course Satisfaction, and Learning Outcomes in Online Learning. Internet High. Educ. 14(3):158–163

Lowenthal PR, Dunlap JC (2020) Social Presence and Online Discussions: A Mixed Method Investigation. Distance Educ. 41:490–514. https://doi.org/10.1080/01587919.2020.1821603

Lucas M, Vicente PN (2023) A Double-edged Sword: Teachers’ Perceptions of the Benefits and Challenges of Online Learning and Learning in Higher Education. Educ. Inf. Technol. 23:5083–5103. https://doi.org/10.1007/s10639-022-11363-3

Maatuk AM, Elberkawi EK, Aljawarneh S, Rashaideh H, Alharbi H (2022) The COVID-19 Pandemic and E-learning: Challenges and Opportunities from the Perspective of Students and Instructors. J. Comput. High. Educ. 34:21–38. https://doi.org/10.1007/s12528-021-09274-2

Marinoni G, Van’t Land H, Jensen T (2020) The Impact of Covid-19 on Higher Education around the World. IAU Glob. Surv. Rep. 23:1–17

Martin F, Bolliger DU (2018) Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learn. 22(1):205–222

Means B, et al. (2010). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington, D. C.: U.S. Department of Education

Miao J, Ma L (2022) Students’ Online Interaction, Self-regulation, and Learning Engagement in Higher Education: The Importance of Social Presence to Online Learning. Front. Psychol. 13:1–9. https://doi.org/10.3389/fpsyg.2022.815220

Mulla T, Munir S, Mohan V (2023) An Exploratory Study to Understand Faculty Members’ Perceptions and Challenges in Online Teaching. Int. Rev. Educ. 69:73–99. https://doi.org/10.1007/s11159-023-100

Ng EW (2018) Integrating Self-regulation Principles with Flipped Classroom Pedagogy for First Year University Students. Computer Educ. 126:65–74. https://doi.org/10.1007/s11409-011-9082-8

Ong SGT, Quek GCL (2023) Enhancing teacher–student interactions and student online engagement in an online learning environment. Learn. Environ. Res. 26:681–707. https://doi.org/10.1007/s10984-022-09447-5

Organisation for Economic Co‑operation and Development. (2015). The g20 skills strategy for developing and using skills for the 21st century. Retrieved from https://www.oecd.org/g20/topics/employment-andsocial-policy/The-G20-Skills-Strategy-for-Developing-and-Using-Skills-for-the-21st-Century.pdf

Paechter M, Maier B (2010) Online or face-to-face? Students’ experiences and preferences in e-learning. internet High. Educ. 13(4):292–297

Panigrahi R, Srivastava PR, Sharma D (2018) Online Learning: Adoption, Continuance, and Learning Outcome—A Review of Literature. Int. J. Inf. Manag. 43:1–14

Paul C, Diana M (2006) Teaching Methods and Time on Task in Junior Classrooms. Educ. Res. 30:90–97

Punie, Y, Zinnbauer, D, & Cabrera, M (2006). A review of the impact of ICT on learning. European Commission, Brussels, 6(5), 635-650

Radmer F, Goodchild S (2021) Online Mathematics Teaching and Learning during the COVID-19 Pandemic: The Perspective of Lecturers and Students. Nord. J. STEM Educ. 5(1):1–5. https://doi.org/10.5324/njsteme.v5i1.3914

Rapanta C, Botturi L, Goodyear P, Guadia L, Koole M (2021) Balancing Technology, Pedagogy and the New Normal: Post-pandemic Challenges for Higher Education. Postdigital Sci. Educ. 3:715–742. https://doi.org/10.1007/s42438-021-00249-1

Rasheed, RA, Kamsin, A, & Abdullah, NA (2020) Challenges in the Online Component of Blended Learning: A Systematic Review. Computers & Education, 144. https://doi.org/10.1016/j.compedu.2019.103701

Schaufeli W, Salanova M, Gonzalez-Roma V (2002) The Measurement of Engagement and Burnout: A Two Sample Confirmatory Factor Analytic Approach. J. Happiness Stud. 3:71–92

Song L, Singleton ES, Hill JR, Koh MH (2004) Improving online learning: Student perceptions of useful and challenging characteristics. Internet High. Educ. 7(1):59–70. https://doi.org/10.1016/j.iheduc.2003.11.003

Tan HT, Chan PP, Said NM (2021) Higher Education Students’ Online Instruction Perceptions: A Quality Virtual Learning Environment. Sustainability 13:10840. https://doi.org/10.3390/su131910840

Tenório T, Bittencourt II, Isotani S, Silva AP (2016) Does peer assessment in online learning environments work? A systematic review of the literature. Computers Hum. Behav. 64:94–107. https://doi.org/10.1016/j.chb.2016.06.020

Torsani, S (2023) Teacher Education in Mobile Assisted Language Learning for Adult Migrants: A Study of Provincial Centers for Adult Education in Italy. In Tafazoli, D, & M Picard (eds.). Handbook of CALL Teacher Education Professional Development (pp. 179-192). Singapore: Springer Nature Singapore Pte Ltd

Trust T, Whalen J (2020) Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 pandemic. J. Technol. Teach. Educ. 28(2):189–199

UNESCO (2020a) COVID-19 Impact on education. UNESCO. Retrieved from https://en.unesco.org/covid19/educationresponse

UNESCO (2020b) Education: From Disruption to Recovery. UNESCO. Retrieved from https://en.unesco.org/covid19/educationresponse

Wang M, & Wang F (2021, August) Comparative Analysis of University Education Effect under the Traditional Teaching and Online Teaching Mode. In The Sixth International Conference on Information Management and Technology (pp. 1-6)

Xu Q, Wu J, Peng HY (2022) Chinese EFL University Students’ Self-efficacy for Online Self-regulated Learning: Dynamic Features and Influencing Factors. Front. Psychol. 13:1–12. https://doi.org/10.3389/fpsyg.2022.912970

Zheng RK, Li F, Jiang L, Li SM (2022) Analysis of the Current Situation and Driving Factors of College Students’ Autonomous Learning in the Network Environment. Front. Humanit. Soc. Sci. 2(7):44–50

Zhu XM, Gong Q, Wang Q, He YJ, Sun ZQ, Liu FF (2023) Analysis of Students’ Online Learning Engagement during the COVID-19 Pandemic: A Case Study of a SPOC-Based Geography Education Undergraduate Course. Sustainability 15(5):4544. https://doi.org/10.3390/su15054544

Zhu Y, Geng G, Disney L, Pan Zihao (2023) Changes in University Students’ Behavioral intention to learn online throughout the COVID-19: Insights for Online Teaching in the Post-pandemic Era. Educ. Inf. Technol. 28:3859–3892. https://doi.org/10.1007/s10639-022-11320-0

Zhu YH, Xu YY, Wang XY, Yan SY, Zhao L (2022) The Selectivity and Suitability of Online Learning Resources as Predictor of the Effects of Self-efficacy on Teacher Satisfaction During the COVID-19 Lockdown. Front. Psychol. 13:1–11. https://doi.org/10.3389/fpsyg.2022.765832

Zimmerman WA, Kulikowich JM (2016) Online Learning Self-efficacy in Students with and Without Online Learning Experience. Am. J. Distance Educ. 30(3):180–190. https://doi.org/10.1080/08923647.2016.1193801

Download references

Acknowledgements

The Corresponding author received the National Social Science Foundation of China for Education General Program (BGA210054) for this work.

Author information

Authors and affiliations.

School of Foreign Studies, Wenzhou University, Wenzhou, China

Yujie Su, Xiaoshu Xu & Xinyu Xu

Faculty of Languages and Translation, Macao Polytechnic University, Macao, China

Yunfeng Zhang

Faculty of Applied Sciences, Macao Polytechnic University, Macao, China

Shanshan Hao

You can also search for this author in PubMed Google Scholar

Contributions

XSX was responsible for conceptualization and, alongside YFZ, for data curation. YJS and XYX conducted the formal analysis. Funding acquisition was managed by YFZ. The investigation was carried out by YJS and YFZ. Methodology development was a collaboration between YJS and XSX. XSX and YJS also managed project administration, with additional resource management by SSH and XYX. YJS handled the software aspect, and supervision was overseen by XSX. SSH and XYX were responsible for validation, and visualization was managed by YJS. The original draft was written by XSX and YJS, while the review and editing were conducted by YFZ and SSH.

Corresponding author

Correspondence to Xiaoshu Xu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

The questionnaire and methodology for this study were approved by the Human Research Ethics Committee of Wenzhou University (Ethics approval number WGY202302).

Informed consent

Informed consent was diligently obtained from all participants involved in the study, ensuring adherence to ethical standards. Detailed consent forms, outlining the study’s scope and participants’ rights, were signed by participants. Documentation of this process is well-maintained and can be provided upon request during peer review or post-publication.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Su, Y., Xu, X., Zhang, Y. et al. Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors. Humanit Soc Sci Commun 11 , 594 (2024). https://doi.org/10.1057/s41599-024-03097-z

Download citation

Received : 24 October 2023

Accepted : 17 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1057/s41599-024-03097-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

Explore articles by subject
Guide to authors
Editorial policies

The Academic Debate on Strategic Agility: A Literature Review

First Online: 11 May 2024

Cite this chapter

Marco Balzano ORCID: orcid.org/0000-0002-2452-631X 18 &
Guido Bortoluzzi ORCID: orcid.org/0000-0003-0422-3036 19

Part of the book series: International Series in Advanced Management Studies ((ISAMS))

The aim of this chapter is to discuss the recent evolution of the academic debate on strategic agility. Adopting a two-step approach, we identify and select highly influential contributions to the strategic agility literature from the 1990s onward. This temporal demarcation has been selected to spotlight the modern advancements and the escalating relevance of strategic agility in today’s volatile and complex business environment.

The analysis revealed that the concept of strategic agility constantly evolved. In the 1990s, scholars focused on the importance of implementing agility to cope with change and ultimately survive. In the 2000s, a number of contributions were published in journals at the borders between theory and practice, exploring how strategic agility can enhance business success. In the 2010s, the proliferation of studies on strategic agility led to significant theoretical advancements on the topic. Strategic agility is conceptualized as dynamic and based on a series of organizational meta-capabilities. In more recent times, characterized by high environmental turbulence, many scholars emphasized the importance of bridging the strategic agility literature with other academic debates (especially the human resource management literature), putting at the center the role of individuals and capabilities for cultivating strategic agility inside organizations. The chapter ends with some authors’ considerations on the future of strategic agility in theory and practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Brueller, N. N., Carmeli, A., & Drori, I. (2014). How do different types of mergers and acquisitions facilitate strategic agility? California Management Review, 56 (3), 39–57.

Article Google Scholar

Cai, Z., Liu, H., Huang, Q., & Liang, L. (2019). Developing organizational agility in product innovation: The roles of IT capability, KM capability, and innovative climate. R&D Management, 49 (4), 421–438.

Clauss, T., Abebe, M. A., Tangpong, C., & Hock, M. (2021). Strategic agility, business model innovation, and firm performance: An empirical investigation. IEEE Transactions on Engineering Management, 68 (3), 767–784.

Cunha, M. P., Gomes, E., Mellahi, K., Miner, A. S., & Rego, A. (2020). Strategic agility through improvisational capabilities: Implications for a paradox-sensitive HRM. Human Resource Management Review, 30 (1), 100695.

D’Aveni, R. A. (1994). Hypercompetition . Free Press.

Google Scholar

D’Aveni, R. A., Dagnino, G. B., & Smith, K. G. (2010). The age of temporary advantage. Strategic Management Journal, 31 (13), 1371–1385.

Dattolo, A., & Corbatto, M. (2019). VisualBib: A novel web app for supporting researchers in the creation, visualization and sharing of bibliographies. Knowledge-Based Systems, 182 , 104860.

Debellis, F., Massis, A. D., Messeni Petruzzelli, A., Frattini, F., & Del Giudice, M. (2020). Strategic agility and international joint ventures: The willingness-ability paradox of family firms. Journal of International Management, 27 (1), 100739.

Doz, Y. (2020). Fostering strategic agility: How individual executives and human resource practices contribute. Human Resource Management Review, 30 (1), 100693.

Doz, Y., & Kosonen, M. (2007). The new deal at the top. Harvard Business Review, 85 (6), 98–104.

Doz, Y., & Kosonen, M. (2008). Fast strategy: How strategic agility will help you stay ahead of the game . Wharton School Publishing.

Doz, Y., & Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43 (2–3), 370–382.

Ferraris, A., Degbey, W. Y., Singh, S. K., Bresciani, S., Castellano, S., Fiano, F., & Couturier, J. (2022). Microfoundations of strategic agility in emerging markets: Empirical evidence of Italian MNEs in India. Journal of World Business, 57 (2), 101272.

Fourné, S. P., Jansen, J. J., & Mom, T. J. (2014). Strategic agility in MNEs: Managing tensions to capture opportunities across emerging and established markets. California Management Review, 56 (3), 13–38.

Gunasekaran, A. (1998). Agile manufacturing: Enablers and an implementation framework. International Journal of Production Research, 36 (5), 1223–1247.

Harsch, K., & Festing, M. (2019). Dynamic talent management capabilities and organizational agility—A qualitative exploration. Human Resource Management, 59 (1), 43–61.

Jesson, J., Matheson, L., & Lacey, F. M. (2011). Doing your literature review: Traditional and systematic techniques . SAGE Publications.

Junni, P., Sarala, R. M., Tarba, S. Y., & Weber, Y. (2015). The role of strategic agility in acquisitions. British Journal of Management, 26 (4), 596616.

Lee, H. (2004). The triple-A supply chain. Harvard Business Review, 82 , 102–113.

Lee, O. D., Sambamurthy, V., Lim, K. H., & Wei, K. K. (2015). How does it ambidexterity impact organizational agility? Information Systems Research, 26 (2), 398–417.

Lewis, M. W., Andriopoulos, C., & Smith, W. K. (2014). Paradoxical leadership to enable strategic agility. California Management Review, 56 (3), 58–77.

Magretta, J. (1998). The power of virtual integration: An interview with Dell Computer’s Michael Dell. Harvard Business Review, 76 , 73–84.

McGrath, R. G. (2013). The end of competitive advantage: How to keep your strategy moving as fast as your business . Harvard Business Review Press .

Milliken, F. J. (1987). Three types of perceived uncertainty about the environment: State, effect, and response uncertainty. Academy of Management Review, 12 (1), 133–143.

Morton, J., Stacey, P., & Mohn, M. (2018). Building and maintaining strategic agility: An agenda and framework for executive IT leaders. California Management Review, 61 (1), 94–113.

Nadler, D. A., & Tushman, M. L. (1990). Beyond the charismatic leader: Leadership and organizational change. California Management Review, 32 (2), 77–97.

Pascale, R. T., Millemann, M. A., & Gioja, L. (1997). Changing the way we change. Harvard Business Review, 75 (6), 126–139.

Piccoli, G., & Ives, B. (2005). IT-dependent strategic initiatives and sustained competitive advantage: A review and synthesis of the literature. MIS Quarterly, 29 (49), 747–776.

Porter, M. E. (1996). What is strategy? Harvard Business Review, 74 (6), 61–78.

Sambamurthy, V., Bharadwaj, A. S., & Grover, V. (2003). Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quarterly, 27 , 237–263.

Shams, R., Vrontis, D., Belyaeva, Z., Ferraris, A., & Czinkota, M. R. (2021). Strategic agility in international business: A conceptual framework for “agile” multinationals. Journal of International Management, 27 (1), 100737.

Singh, J., Sharma, G., Hill, J., & Schnackenberg, A. (2013). Organizational agility: What it is, what it is not, and why it matters. Academy of Management Proceedings, 1 , 11813.

Swamidass, P., & Newell, W. T. (1987). Manufacturing strategy, environmental uncertainty and performance: A path analytic model. Management Science, 33 (4), 509–524.

Tallon, P. P., & Pinsonneault, A. (2011). Competing perspectives on the link between strategic information technology alignment and organizational agility: Insights from a mediation model. MIS Quarterly, 35 , 463–486.

Teece, D. J., Peteraf, M. A., & Leih, S. (2016). Dynamic capabilities and organizational agility: Risk, uncertainty, and strategy in the innovation economy. California Management Review, 58 (4), 13–35.

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14 (3), 207–222.

vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process. In Proceedings of the 17th European conference on information systems (ECIS 2009), Verona, Italy (pp. 2206–2217).

Warner, K. S., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52 (3), 326–349.

Weber, Y., & Tarba, S. Y. (2014). Strategic agility: A state of the art introduction to the special section on strategic agility. California Management Review, 56 (3), 5–12.

Weill, P., Subramani, M. R., & Broadbent, M. (2002). Building IT infrastructure for strategic agility. MIT Sloan Management Review, 44 , 57–65.

Xing, Y., Liu, Y., Boojihawon, D. K., & Tarba, S. Y. (2020). Entrepreneurial team and strategic agility: A conceptual framework and research agenda. Human Resource Management Review, 30 , 100696.

Zhang, G., Wang, J., Liu, J., & Pan, Y. (2021). Relationship between the metadata and relevance criteria of scientific data. Data Science Journal, 20 (1), 1–21.

Download references

Author information

Authors and affiliations.

Ca’ Foscari University of Venice, Venice, Italy

Marco Balzano

University of Trieste, Trieste, Italy

Guido Bortoluzzi

You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Balzano, M., Bortoluzzi, G. (2024). The Academic Debate on Strategic Agility: A Literature Review. In: Strategic Agility in Dynamic Business Environments. International Series in Advanced Management Studies. Springer, Cham. https://doi.org/10.1007/978-3-031-58657-6_3

Download citation

DOI : https://doi.org/10.1007/978-3-031-58657-6_3

Published : 11 May 2024

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-58656-9

Online ISBN : 978-3-031-58657-6

eBook Packages : Business and Management Business and Management (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

IMAGES

10 Easy Steps: How to Write a Literature Review Example
Sample research paper with literature review
Example of a Literature Review for a Research Paper by
Thesis Literature Review Example by Literaturereviewwritingservice
(PDF) FACTORS INFLUENCING ACADEMIC PERFORMANCE OF THE STUDENTS AT
50 Smart Literature Review Templates (APA) ᐅ TemplateLab

VIDEO

Writing a Review Paper: What,Why, How?
The Literature Review
Integrative Practice: Literature Review
Feedback
Mastering Google Scholar
The fastest way to do your literature review in less than 5 minutes tutorial

COMMENTS

Predicting academic success in higher education: literature review and
Then, section 3 presents the methodology adopted for the literature review. Section 4 reviews data mining techniques used in predicting students' academic success, and compares their predictive accuracy based on various case studies. Section 5 concludes the review, with a recapitulation of the whole process.
(PDF) A Literature Review of Academic Performance, an Insight into
PDF | On Jan 1, 2021, Evans Austin Brew and others published A Literature Review of Academic Performance, an Insight into Factors and their Influences on Academic Outcomes of Students at Senior ...
(PDF) Defining and Measuring Academic Success
success based upon the literature as: …student success is defined as academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge ...
PDF Predicting academic success in higher education: literature review and
In the following, first, section 2 clarifies what is academic success and how it has been defined and measured in various studies with a focus on the factors that can be used for predicting academic success. Then, section 3 presents the methodology adopted for the literature review. Section 4 reviews data mining techniques used in predicting stu-
Motivation-Achievement Cycles in Learning: a Literature Review and
Representation of three types of study designs that can investigate the relationships between motivation and academic achievement. (1) The gray box shows that to establish that motivation causes academic achievement (top) or vice versa (bottom), experimental manipulation is needed, intervening on the predictor at time point 1, which influences the outcome at time point 2 and so on.
(PDF) Defining and Measuring Academic Success
While literature on academic success speaks of the accomplishment of learning objectives as categorically separate, we find the term 'learning objective' is promulgated in assessment literature and to simply mean the stated goals of an educational course or program which includes the acquisition of content knowledge, domain knowledge ...
Practical Assessment, Research, and Evaluation
expansive literature review, What Matters to Student Success, that offered an overly broad definition of academic success. Given this seminal work, we seek to (1) explore the definition of academic success in the literature to both evaluate and critique Kuh et al.'s definition, and (2) examine how academic success has
Academic success from an individual perspective: A proposal for
In a broad review of the literature on academic success, including its conceptualisation and measurement, George Kuh et al. argued that students do not reach higher education as "blank pages"; rather, they differ from each other from the outset, with some arriving more prepared for academic studies than others. At the ...
Mentoring and Undergraduate Academic Success: A Literature Review
This article. provides a critical review of the literature on mentoring, with an emphasis on the links. between mentoring and undergraduate academic success. The first section describes a. variety of ways in which mentoring has been defined within higher education, manage-. ment, and psychology.
Literature review
A literature review is a type of academic writing that provides an overview of existing knowledge in a particular field of research. A good literature review summarises, analyses, evaluates and synthesises the relevant literature within a particular field of research. It illuminates how knowledge has evolved within the field, highlighting what ...
Full article: Academic achievement
Academic achievement was once thought to be the most important outcome of formal educational experiences and while there is little doubt as to the vital role such achievements play in student life and later (Kell, Lubinski, & Benbow, Citation 2013), researchers and policy makers are ever increasingly turning to social and emotional factors, as well as the relationships among them, as ...
Mentoring and Undergraduate Academic Success: A Literature Review
Abstract. Despite a growing body of research about mentoring, definitional, theoretical, and methodological deficiencies reduce the usefulness of existing research. This article provides a critical review of the literature on mentoring, with an emphasis on the links between mentoring and undergraduate academic success.
Academic Achievement: Influences of University Students' Self
Literature Review and Hypotheses Development. ... Moreover, these findings affirmed that self-management is a powerful indicator of academic success, decision-making abilities, and competence in the behavior modification among students. This helps educators and students to modify students' behaviors in a positive manner to establish academic ...
Emotional intelligence and academic performance: A systematic review
The meta-analysis and the literature review suggest that the effect size of EI on academic performance is moderate-high (0.390). Some differences by region were attested. In Asian countries, the correlation is strong in countries with a well-developed education system, such as Indonesia and China, and weaker in countries in which the school ...
Advising Students for Success in Higher Education: An All-Out Effort
A comprehensive literature review, however, shows that these research studies tend to be myopic in scope and quantitative in methodology. In this research, we attempt to depict a holistic view of an all-out effort on advising students for success, by describing practical approaches and collecting relevant narratives at four levels, including ...
Guidance on Conducting a Systematic Literature Review
Literature review is an essential feature of academic research. Fundamentally, knowledge advancement must be built on prior existing work. To push the knowledge frontier, we must know where the frontier is. By reviewing relevant literature, we understand the breadth and depth of the existing body of work and identify gaps to explore.
Predicting Academic Success in Higher Education Literature Review and
tive work gives accuracies of 62% to 89% while predicting success at a course level can. give accuracies more than 89%, which can be seen as a more straightforward task than. predicting success at ...
Defining and Measuring Academic Success
Despite, and perhaps because of its amorphous nature, the term â€˜academic successâ€™ is one of the most widely used constructs in educational research and assessment within higher education. This paper conducts an analytic literature review to examine the use and operationalization of the term in multiple academic fields. Dominant definitions of the term are conceptually evaluated ...
How to Write a Literature Review
Step 5 - Write your literature review. Like any other academic text, your literature review should have an introduction, a main body, and a conclusion. What you include in each depends on the objective of your literature review. Introduction. The introduction should clearly establish the focus and purpose of the literature review.
Well-being, academic success, and the role of relationships
The articles of the current issue all deal with student well-being, academic success, or the role of the relationships with significant others (e.g. peers, parents, teachers) on these outcomes. Student well-being receives much attention nowadays (e.g. Govorova et al., 2020; Pascoe et al., 2020 ). Promoting children and young people's well ...
Mentoring and undergraduate academic success: A literature review
Reviews the literature on the relationship between mentoring and undergraduate success. The variety of ways in which mentoring has been defined within higher education, management, and psychology and issues related to developing a standard operational definition of mentoring are discussed. Four different theoretical perspectives for future research are addressed (involvement in learning ...
A Review of the Literature on Teacher Effectiveness and Student
Researchers agree that teachers are one of the most important school-based resources in determining students' future academic success and lifetime outcomes, yet have simultaneously had difficulties in defining what teacher characteristics make for an effective teacher. This chapter reviews the large body of literature on measures of teacher ...
Structuring a literature review
Structuring a literature review. In general, literature reviews are structured in a similar way to a standard essay, with an introduction, a body and a conclusion. These are key structural elements. Additionally, a stand-alone extended literature review has an abstract. Throughout, headings and subheadings are used to divide up the literature ...
Looking back to move forward: comparison of instructors' and ...
In the online learning environment, teachers need to make efforts to build rapport and personalizing interactions with students to help them perform better and achieve greater academic success ...
The Academic Debate on Strategic Agility: A Literature Review
Rather, we aim at providing the reader with some major theoretical and empirical advancements on strategic agility over years, highlighting how the concept of strategic agility itself evolved in the academic literature. Accordingly, in the next sections, we organize our in-detail review of top management literature on a chronological basis.

Defining and Measuring Academic Success

Related Papers

RELATED PAPERS

RELATED TOPICS

Academic Achievement: Influences of University Students’ Self-Management and Perceived Self-Efficacy

Hani Abdel Hafeez Abdel Azeem

Associated Data

1. Introduction

2. Literature Review and Hypotheses Development

2.1. The Role of Student Self-Management in Increasing Student Self-Efficacy

2.2. The Role of Student Self-Management in Increasing Student Academic Achievement

2.3. The Role of Self-Efficacy in Enhancing Student Academic Achievement

3. Materials and Methods

3.2. Measurements

3.3. Data Analysis and Hypotheses Testing

3.4. The Measurement Model

3.5. Multigroup Analysis

3.6. Testing the Research Hypotheses and Results

4. Discussion

5. Conclusions

Acknowledgments

Funding Statement

Author Contributions

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Defining and Measuring Academic Success

Creative Commons License

Recommended Citation

Looking back to move forward: comparison of instructors’ and undergraduates’ retrospection on the effectiveness of online learning using the nine-outcome influencing factors

Similar content being viewed by others

Determinants of behaviour and their efficacy as targets of behavioural change interventions

Interviews in the social sciences

Mechanisms linking social media use to adolescent mental health vulnerability

Literature review

Determinants of online learning outcomes

Students’ perceptions of online learning

Teachers’ perceptions of online learning

Theoretical framework

Sample and sampling

Data collection

Data analysis

Behavioral intention towards online learning post-pandemic

Challenges and opportunities in online instruction post-pandemic

Factors influencing student engagement in online learning

Challenges in teacher-student interaction in online learning

Determinants of self-efficacy in online learning

Influences on student performance in online learning

Student satisfaction and teaching effectiveness in online learning

The role of self-regulation in online learning success

Discussions

Conclusion and future research

Data availability

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Ethical approval

Informed consent

Additional information

Rights and permissions

About this article

Share this article

Quick links

The Academic Debate on Strategic Agility: A Literature Review

Cite this chapter

Access this chapter

Author information

Rights and permissions

Copyright information

About this chapter

Download citation

Share this chapter

IMAGES

VIDEO

COMMENTS