research title about behavior of students

• Review article
• Open access
• Published: 30 January 2021

Understanding students’ behavior in online social networks: a systematic literature review

• Maslin Binti Masrom 1 ,
• Abdelsalam H. Busalim   ORCID: orcid.org/0000-0001-5826-8593 2 ,
• Hassan Abuhassna 3 &
• Nik Hasnaa Nik Mahmood 1  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  6 ( 2021 ) Cite this article

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The use of online social networks (OSNs) has increasingly attracted attention from scholars’ in different disciplines. Recently, student behaviors in online social networks have been extensively examined. However, limited efforts have been made to evaluate and systematically review the current research status to provide insights into previous study findings. Accordingly, this study conducted a systematic literature review on student behavior and OSNs to explicate to what extent students behave on these platforms. This study reviewed 104 studies to discuss the research focus and examine trends along with the important theories and research methods utilized. Moreover, the Stimulus-Organism-Response (SOR) model was utilized to classify the factors that influence student behavior. This study’s results demonstrate that the number of studies that address student behaviors on OSNs have recently increased. Moreover, the identified studies focused on five research streams, including academic purpose, cyber victimization, addiction, personality issues, and knowledge sharing behaviors. Most of these studies focused on the use and effect of OSNs on student academic performance. Most importantly, the proposed study framework provides a theoretical basis for further research in this context.

Introduction

The rapid development of Web 2.0 technologies has caused increased usage of online social networking (OSN) sites among individuals. OSNs such as Facebook are used almost every day by millions of users (Brailovskaia et al. 2020 ). OSNs allow individuals to present themselves via virtual communities, interact with their social networks, and maintain connections with others (Brailovskaia et al. 2020 ). Therefore, the use of OSNs has continually attracted young adults, especially students (Kokkinos and Saripanidis 2017 ; Paul et al. 2012 ). Given the popularity of OSNs and the increased number of students of different ages, many education institutions (e.g., universities) have used them to market their educational programs and to communicate with students (Paul et al. 2012 ). The popularity and ubiquity of OSNs have radically changed education systems and motivated students to engage in the educational process (Lambić 2016 ). The children of the twenty-first century are technology-oriented, and thus their learning style differs from previous generations (Moghavvemi et al. 2017a , b ). Students in this era have alternatives to how and where they spend time to learn. OSNs enable students to share knowledge and seek help from other students. Lim and Richardson ( 2016 ) emphasized that one important advantage of OSNs as an educational tool is to increase connections between classmates, which increases information sharing. Furthermore, the use of OSNs has also opened new communication channels between students and teachers. Previous studies have shown that students strengthened connections with their teachers and instructors using OSNs (e.g., Facebook, and Twitter). Therefore, the characteristics and features of OSNs have caused many students to use them as an educational tool, due to the various facilities provided by OSN platforms, which makes learning more fun to experience (Moghavvemi et al. 2017a ). This has caused many educational institutions to consider Facebook as a medium and as a learning tool for students to acquire knowledge (Ainin et al. 2015 ).

OSNs including Facebook, YouTube, and Twitter have been the most utilized platforms for education purposes (Akçayır and Akçayır 2016 ). For instance, the number of daily active users on Facebook reached 1.73 billion in the first quarter of 2020, with an increase of 11% compared to the previous year (Facebook 2020 ). As of the second quarter of 2020, Facebook has over 2.7 billion active monthly users (Clement 2020 ). Lim and Richardson ( 2016 ) empirically showed that students have positive perceptions toward using OSNs as an educational tool. A review of the literature shows that many studies have investigated student behaviors on these sites, which indicates the significance of the current review in providing an in-depth understanding of student behavior on OSNs. To date, various studies have investigated why students use OSNs and explored different student behaviors on these sites. Although there is an increasing amount of literature on this emerging topic, little research has been devoted to consolidating the current knowledge on OSN student behaviors. Moreover, to utilize the power of OSNs in an education context, it is important to study and understand student behaviors in this setting. However, current research that investigates student behaviors in OSNs is rather fragmented. Thus, it is difficult to derive in-depth and meaningful implications from these studies. Therefore, a systematic review of previous studies is needed to synthesize previous findings, identify gaps that need more research, and provide opportunities for further research. To this end, the purpose of this study is to explore the current literature in order to understand student behaviors in online social networks. Accordingly, a systematic review was conducted in order to collect, analyze, and synthesize current studies on student behaviors in OSNs.

This study drew on the Stimulus-Organism-Response (SOR) model to classify factors and develop a framework for better understanding of student behaviors in the context of OSNs. The S-O-R model suggests that various aspects of the environment (S), incite individual cognitive and affective reactions (O), which in turn derives their behavioral responses (R) (Mehrabian and Russell 1974 ). In order to achieve effective results in a clear and understandable manner, five research questions were proposed as shown below.

What was the research regional context covered in previous studies?

What were the focus and trends of previous studies?

What were the research methods used in previous studies?

What were the major theories adopted in previous studies?

What important factors were studied to understand student usage behaviors in OSNs?

This paper is organized as follows. The second section discusses the concept of online social networks and their definition. The third section describes the review method used to extract, analyze, and synthesize studies on student behaviors. The fourth section provides the result of analyzing the 104 identified primary studies and summarizes their findings based on the research questions. The fifth section provides a discussion on the results based on each research question. The sixth section highlights the limitations associated with this study, and the final section provides a conclusion of the study.

• Online social networks

Since online social networks such as Facebook were introduced last decade, they have attracted millions of users and have become integrated into our daily routines. OSNs provide users with virtual spaces where they can find other people with similar interests to communicate with and share their social activities (Lambić et al. 2016 ). The concept of OSNs is a combination of technology, information, and human interfaces that enable users to create an online community and build a social network of friends (Borrero et al. 2014 ). Kum Tang and Koh ( 2017 ) defined OSNs as “web-based virtual communities where users interact with real-life friends and meet other people with shared interests” . A more detailed and well-cited definition of OSN was introduced by Boyd and Ellison ( 2008 ) who defined OSNs as “web-based services that allow individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system” . Due to its popularity, many researches have examined the effect of OSNs on different disciplines such as business (Kujur and Singh 2017 ), healthcare (Chung 2014 ; Lin et al. 2016 ; Mano 2014 ), psychology (Pantic 2014 ), and education (Hamid et al.  2016 , 2015 ; Roblyer et al. 2010 ).

The heavy use of OSNs by students has led many studies to examine both positive and negative effects of these sites on students, including the time spent on OSNs usage (Chang and Heo 2014 ; Wohn and Larose 2014 ), engagement in academic activities (Ha et al. 2018 ; Sheeran and Cummings 2018 ), as well as the effect of OSN on students’ academic performance. Lim and Richardson ( 2016 ) stated that the main reasons for students to use OSNs as an educational tool is to increase their interactions and establish connections with classmates. Tower et al. ( 2014 ) found that OSN platforms such as Facebook have the potential to improve student self-efficacy in learning and develop their learning skills to a higher level. Therefore, some education institutions have started to develop their own OSN learning platforms (Tally 2010 ). Mazman and Usluel ( 2010 ) highlighted that using OSNs for educational and instructional contexts is an idea worth developing because students spend a lot of time on these platforms. Yet, the educational activities conducted on OSNs are dependent on the nature of the OSNs used by the students (Benson et al. 2015 ). Moreover, for teaching and learning, instructors have begun using OSNs platforms for several other purposes such as increasing knowledge exchanges and effective learning (Romero-Hall 2017 ). On the other hand, previous studies have raised some challenges of using OSNs for educational purposes. For example, students tend to use OSNs as a social tool for entraining rather than an educational tool (Baran 2010 ; Gettman and Cortijo 2015 ). Moreover, the active use of OSNs on daily basis may develop students’ negative behavior such as addiction and distraction. In this context, Kitsantas et al. ( 2016 ) found that college students in the United States reported some concerns such as the OSNs usage can turn into addictive behavior, distraction, privacy threats, the negative impact on their emotional health, and the inability to complete the tasks on time. Another challenge of using OSNs as educational tools is gender differences. Kim and Yoo ( 2016 ) found some differences between male and female students concerning the negative impact of OSNs, for example, female students are more conserved about issues related to security, and the difficulty of task/work completion. Furthermore, innovation is a key aspect in the education process (Serdyukov 2017 ), however, using OSNs as an educational tool, students could lose creativity due to the easy access to everything using these platforms (Mirabolghasemi et al. 2016 ).

Review method

This study employed a Systematic Literature Review (SLR) approach in order to answer the research questions. The SLR approach creates a foundation that advances knowledge and facilitates theory development for a specific topic (Webster and Watson 2002 ). Kitchenham and Charters ( 2007 ) defined SLR as a process of identifying, evaluating, and synthesizing all available research that is related to research questions, area of research, or new phenomenon. This study follows Kitchenhand and Charters’ guidelines (Kitchenham 2004 ), which state that the SLR approach involves three main stages: planning the review, conducting the review, and reporting the review results. There are several motivations for carrying out this systematic review. First, to summarize existing knowledge and evidence on research related to OSNs such as the theories, methods, and factors that influence student behaviors on these platforms. Second, to discover the current research focus and trends in this setting. Third, to propose a framework that classifies the factors that influence student behaviors on OSNs using the S-O-R model. The reasons for using S-O-R model in this study are twofold. First, S-O-R is a crucial theoretical framework to understand individuals’ behavior, and it has been extensively used in previous studies on consumer behavior (Wang and Chang 2013 ; Zhang et al. 2014 ; Zhang and Benyoucef 2016 ), and online users’ behavior (Islam et al. 2018 ; Luqman et al. 2017 ). Second, using the S-O-R model can provide a structured manner to understand the effect of the technological features of OSNs as environmental stimuli on individuals’ behavior (Luqman et al. 2017 ). Therefore, the application of the S-O-R model can provide a guide in the OSNs literature to better understand the potential stimulus and organism factors that drive a student’s behavioral responses in the context of OSNs. The SLR was guided by five research questions (see “ Introduction ” section), which provide an in-depth understanding of the research topic. The rationale and motivation beyond considering these questions are stated in Table 1 .

Stage one: Planning

Before conducting any SLR, it is necessary to clarify the goal and the objectives of the review (Kitchenham and Charters 2007 ). After identifying the review objectives and the research questions, in the planning stage, it is important to design the review protocol that will be used to conduct the review (Kitchenham and Charters 2007 ). Using a clear review protocol will help define criteria for selecting the literature source, database, and search keywords. Review protocol reduce research bias and specifies the research method used to perform a systematic review (Kitchenham and Charters 2007 ). Figure  1 shows the review protocol used for this study.

Review protocol

Stage two: Conducting the review

In this stage relevant literature was collected using a two-stage approach, which was followed by the removal of duplicated articles using Mendeley software. Finally, the researchers applied selection criteria to identify the most relevant articles to the current review. The details of each step of this stage are discussed below:

Literature identification and collection

This study used a two-stage approach (Webster and Watson 2002 ) to identify and collect relevant articles for review. In the first stage, this study conducted a systematic search to identify studies that address student behaviors and the use of online social networks using selected academic databases, including the Web of Science, Wiley Online Library ScienceDirect, Scopus, Emerald, and Springer. The choice of these academic databases is consistent with previous SLR studies (Ahmadi et al. 2018 ; Balaid et al. 2016 ; Busalim and Hussin 2016 ). Derived from the structure of this review and the research questions, these online databases were searched by focusing on title, abstract, and keywords. The search in these databases started in May 2019 using the specific keywords of “students’ behavior”, “online social networking”, “social networking sites”, and “Facebook”. This study performed several searches in each database using Boolean logic operators (i.e., AND and OR) to obtain a large number of published studies related to the review topic.

The results from this stage were 164 studies published between 2010 and 2018. In the second stage, important peer-reviewed journals were checked to ensure that all relevant articles were collected. We used the same keywords to search on information systems and education journals such as Computers in Human Behavior, International Journal of Information Management, Computers and Education, and Education and Information Technologies. These journals among the top peer-reviewed journals that publish topics related to students' behavior, education technologies, and OSNs. The result from both stages was 188 studies related to student behaviors in OSN. Table 2 presents the journals with more than two articles published in these areas.

Study selection

Following the identification of these studies, and after deleting duplicated studies, this study examined title, abstract, or the content of each study using three selection criteria: (1) a focus on student behavior; (2) an examination of the context of online social networks; (3) and a qualification as an empirical study. After applying these criteria, a total of 96 studies remained as primary studies for review. We further conducted a forward manual search on a reference list for the identified primary studies, through which an additional 8 studies were identified. A total of 104 studies were collected. As depicted in Fig.  2 , the frequency of published articles related to student behaviors in online social networks has gradually increased since 2010. In this regard, the highest number of articles were published in 2017. We can see that from 2010 to 2012 the number of published articles was relatively low and significant growth in published articles was seen from 2013 to 2017. This increase reveals that studying the behavior of students on different OSN platforms is increasingly attractive to researchers.

Timeline of publication

For further analysis, this study summarized the key topics covered during the review timeline. Figure  3 visualizes the development of OSNs studies over the years. Studies in the first three years (2010–2012) revolved around the use of OSNs by students and the benefits of using these platforms for educational purposes. The studies conducted between 2013 and 2015 mostly focused on the effect of using OSNs on student academic performance and achievement. In addition, in the same period, several studies examined important psychological issues associated with the use of OSNs such as anxiety, stress, and depression. In the years 2016 to 2018, OSNs studies were expanded to include cyber victimization behavior, OSN addiction behavior such as Facebook addiction, and how OSNs provide a collaborative platform that enables students to share information with their colleagues.

Evolution of OSNs studies over the years

Review results

To analyze the identified studies, this study guided its review using four research questions. Using research questions allows the researcher to synthesize findings from previous studies (Chan et al. 2017 ). The following subsection provides a detailed discussion of each of these research questions.

RQ1: What was the research regional context covered in previous studies?

As shown in Fig.  3 , most primary studies were conducted in the United States (n = 37), followed by Asia (n = 21) and Europe (n = 15). Relatively few studies were conducted in Australia, Africa, and the Middle East (n = 6 each), and only five studies were conducted in more than one country. Most of these empirical studies used university or college students to examine and validate the research models. Furthermore, many of these studies examined student behavior by considering Facebook as an online social network (n = 58) and a few studies examined student behavior on Microblogging platforms like Twitter (n = 7). The rest of the studies used multiple online social networks such as Instagram, YouTube, and Moodle (n = 31).

As shown in Fig.  4 , most of the reviewed studies are conducted in the United States (US). Furthermore, these studies considered Facebook as the main OSN platform. However, the focus on examining the usage behavior of Facebook in Western countries, particularly the US, is one of the challenges of Facebook research, because Facebook is used in many countries with 80% of its users are outside of the US (Peters et al. 2015 ).

Distribution of published studies by region

RQ2: What were the focus and trends of previous studies?

The results indicate that the identified primary studies for student behaviors on online social networks covered a wide spectrum of different research contexts. Further examination shows that there are five research streams in the literature.

The first research stream focused on using OSNs for academic purposes. The educational usage of OSNs relies on their purpose of use. OSNs can improve student engagement in a course and provide them with a sense of connection to their colleagues (Lambić 2016 ). However, the use of OSNs by students can affect their education as students can easily shift from using OSNs for educational to entertainment purposes. Thus, many studies under this stream focus on the effect of OSNs use on student academic performance. For instance, Lambić ( 2016 ) examined the effect of frequent Facebook use on the academic performance of university students. The results showed that students using Facebook as an educational tool to facilitate knowledge sharing and discussion positively impacted academic performance. Consistent with this result, Ainin et al. ( 2015 ) found that data from 1165 university students revealed a positive relationship between Facebook use and student academic performance. On the other hand, Paul et al. ( 2012 ) found that time spent on OSNs negative impacted student academic behavior. Moreover, the results statistically highlight that increased student attention spans resulted in increased time spent on OSNs, which eventually results in a negatively effect on academic performance. The results from Karpinski et al. ( 2013 ) showed that the effect of OSNs usage on student academic performance could differ from one country to another.

In summary, previous studies on the relationship between OSN use and academic performance show mixed results. From the reviewed studies, there were disparate results due to a few reasons. For example, recent studies found that multitasking plays an important role in determining the relationship between OSN usage and student academic performance. Karpinski et al. ( 2013 ) found a negative relationship between using social network sites (SNSs) and Grade Point Average (GPA) that was moderated by multitasking. Moreover, results from Junco ( 2015 ), illustrated that besides multitasking, student class rank is another determinant of the relationship between OSN platforms like Facebook and academic performance. The results revealed that senior students spent significantly less time on Facebook while doing schoolwork than freshman and sophomore students.

The second research stream is related to cyber victimization. Studies in this stream focused on negative interactions on OSNs like Facebook, which is the main platform where cyber victimization occurs (Kokkinos and Saripanidis 2017 ). Moreover, most studies in this stream examined the cyberbullying concept on OSNs. Cyberbullying is defined as “any behavior performed through electronic media by individuals or groups of individuals that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (Tokunaga 2010 , p. 278). For instance, Gahagan et al. ( 2016 ) investigated the experiences of college students with cyberbullying on SNSs, and the results showed that 46% of the tested sample witnessed someone who had been bullied through the use of SNSs. Walker et al. ( 2011 ) conducted an exploratory study among undergraduate students to investigate their cyberbullying experiences. The results of the study highlighted that the majority of respondents knew someone who had been bullied on SNSs (Benson et al. 2015 ).

The third research stream focused on student addiction to OSNs use. Recent research has shown that excessive OSN use can lead to addictive behavior among students (Shettar et al. 2017 ). In this stream, Facebook was the main addictive ONS platform that was investigated (Shettar et al. 2017 ; Hong and Chiu 2016 ; Koc and Gulyagci 2013 ). Facebook addiction is defined as an excessive attachment to Facebook that interferes with daily activities and interpersonal relationships (Elphinston and Noller 2011 ). According to Andreassen et al. ( 2012 ), Facebook addiction has six general characteristics including salience, tolerance, mood modification, withdrawal, conflict, and relapse. As university students frequently have high levels of stress due to various commitments, such as assignment deadlines, exams, and high pressure to perform, they tend to use Facebook for mood modification (Brailovskaia and Margraf 2017 ; Brailovskaia et al. 2018 ). On further analysis, it was noticed that Facebook addiction among students was associated with other factors such as loneliness (Shettar et al. 2017 ), personality traits (i.e., openness agreeableness, conscientiousness, emotional stability, and extraversion) (Błachnio et al. 2017 ; Tang et al. 2016 ), and physical activities (Brailovskaia et al. 2018 ). Studies have examined student addiction behavior on different OSNs platforms. For instance, Ndasauka et al. ( 2016 ), empirically examined excessive Twitter use among college students. Kum Tang and Koh ( 2017 ) investigated the prevalence of different addiction behaviors (i.e., food and shopping addiction) and effective disorders among college students. In addition, a study by Chae and Kim (Chae et al. 2017 ) examined psychosocial differences in ONS addiction between female and male students. The results of the study showed that female students had a higher tendency towards OSNs addiction than male students.

The fourth stream of research highlighted in this review focused on student personality issues such as self-disclosure, stress, depression, loneliness, and self-presentation. For instance, Chen ( 2017 ) investigated the antecedents that predict positive student self-disclosure on SNSs. Tandoc et al. ( 2015 ) used social rank theory and Facebook envy to test the depression scale between college students. Skues et al. ( 2012 ) examined the relationship between three traits in the Big Five Traits model (neuroticism, extraversion, and openness) and student Facebook usage. Chang and Heo ( 2014 ) investigated the factors that explain the disclosure of a student’s personal information on Facebook.

The fifth reviewed research stream focused on student knowledge sharing behavior. For instance, Kim et al. ( 2015 ) identified the personal factors (self-efficacy) and environmental factors (strength of social ties and size of social networks) that affect information sharing behavior amongst university students. Eid and Al-Jabri ( 2016 ) examined the effect of various SNS characteristics (file sharing, chatting and online discussion, content creation, and enjoyment and entertainment) on knowledge sharing and student learning performance. Moghavvemi et al. ( 2017a , b ) examined the relationship between enjoyment, perceived status, outcome expectations, perceived benefits, and knowledge sharing behavior between students on Facebook. Figure  5 provides a mind map that shows an overview of the research focus and trends found in previous studies.

Reviewed studies research focus and trends

RQ3: What were the research methods used in previous studies?

As presented in Fig.  6 , previous studies used several research methods to examine student behavior on online social networks. Surveys were the method used most frequently in primary studies to understand the different types of determinants that effect student behaviors on online social networks, followed by the experiment method. Studies used the experiment method to examine the effect of online social networks content and features on student behavior, For example, Corbitt-Hall et al. ( 2016 ) had randomly assigned students to interact with simulated Facebook content that reflected various suicide risk levels. Singh ( 2017 ) used data mining techniques to collect student interaction data from different social networking sites such as Facebook and Twitter to classify student academic activities on these platforms. Studies that investigated student intentions, perceptions, and attitudes towards OSNs used survey data. For instance, Doleck et al. ( 2017 ) distributed an online survey to college students who used Facebook and found that perceived usefulness, attitude, and self-expression were influential factors towards the use of online social networks. Moreover, Ndasauka et al. ( 2016 ) used the survey method to assess the excessive use of Twitter among college students.

Research method distribution

RQ4: What were the major theories adopted in previous studies?

The results from the SLR show that previous studies used several theories to understand student behavior in online social networks. Table 3 depicts the theories used in these studies, with Use and Gratification Theory (UGT) being the most popular theory use to understand students' behaviors (Asiedu and Badu 2018 ; Chang and Heo 2014 ; Cheung et al. 2011 ; Hossain and Veenstra 2013 ). Furthermore, the social influence theory and the Big Five Traits model were applied in at least five studies each. The theoretical insights into student behaviors on online social networks provided by these theories are listed below:

Motivation aspect: since the advent of online social networks, many studies have been conducted to understand what motivates students to use online social networks. Theories such as UGT have been widely used to understand this issue. For example, Hossain and Veenstra ( 2013 ) conducted an empirical study to investigate what drives university students in the United States of America to use Social Networking Sites (SNSs) using the theoretical foundation of UGT. The study found that the geographic or physical displacement of students affects the use and gratification of SNSs. Zheng Wang et al. ( 2012a , b ) explained that students are motivated to use social media by their cognitive, emotional, social, and habitual needs as well as that all four categories significantly drive students to use social media.

Social-related aspect: Social theories such as Social Influence Theory, Social Learning Theory, and Social Capital Theory have also been used in several previous studies. Social Influence Theory determines what individual behaviors or opinions are affected by others. Venkatesh, Morris, Davis, and Davis (2003) defined social influence as “the degree to which an individual perceives that important others believe he or she should use a new system” . Cheung et al. ( 2011 ) applied Social Influence Theory to examine the effect of social influence factors (subjective norms, group norms, and social identity) on intentions to use online social networks. The empirical results from 182 students revealed that only Group Norms had a significant effect on student intentions to use OSNs. Other studies attempted to empirically examine the effect of other social theories. For instance, Liu and Brown ( 2014 ) adapted Social Capital Theory to investigate whether college students' self-disclosure on SNSs directly affected their social capital. Park et al. ( 2014a , b ) investigated the effect of using SNSs on university student learning outcomes using social learning theory.

Behavioral aspect: This study have noticed that the Theory of Planned Behavior (TPB), Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Unified Theory of Acceptance, and Use of Technology (UTAUT) were also utilized as a theoretical foundation in a number of primary studies. These theories have been widely applied in the information systems (IS) field to provide insights into information technology adoption among individuals (Zhang and Benyoucef 2016 ). In the context of online social networks, there were empirical studies that adapted these theories to understand student usage behaviors towards online social networks such as Facebook. For example, Doleck et al. ( 2017 ) applied TAM to investigate college student usage intentions towards SNSs. Chang and Chen ( 2014 ) applied TRA and TPB to investigate why college students share their location on Facebook. In addition, a recent study used UTAUT to examine student perceptions towards using Facebook as an e-learning platform (Moghavvemi et al. 2017a , b ).

RQ5: What important factors were studied to understand student usage behaviors in OSNs?

Throughout the SLR, this study has been able to identify the potential factors that influence student behaviors in online social networks. Furthermore, to synthesize these factors and provide a comprehensive overview, this study proposed a framework based on the Stimulus-Organism-Response (S-O-R) model. The S-O-R model was developed in environmental psychology by Mehrabian and Russell ( 1974 ). According to Mehrabian and Russell ( 1974 ), environmental cues act as stimuli that can affect an individual’s internal cognitive and affective states, which subsequently influences their behavioral responses. To do so, this study extracted all the factors examined in 104 identified primary studies and classified them into three key concepts: stimulus, organism, and response. The details on the important factors of each component are presented below.

Online social networks stimulus

Stimulus factors are triggers that encourage or prompt students to use OSNs. Based on the SLR results, there are three stimulus dimensions: social stimulus, personal stimulus, and OSN characteristics. Social stimuli are cues embedded in the OSN that drive students to use these platforms. As shown in Fig.  7 , this study has identified six social stimulus factors including social support, social presence, social communication, social enhancement, social network size, and strength of social ties. Previous studies found that social aspects are a potential driver of student usage of OSNs. For instance, Kim et al. ( 2011 ) explored the motivation behind college student use of OSNs and found that seeking social support is one of the primary usage triggers. Lim and Richardson ( 2016 ) stated that using OSNs as educational tools will increase interactions and establish connections between students, which will enhance their social presence. Consistent with this, Cheung et al. ( 2011 ) found that social presence and social enhancement both have a positive effect on student use of OSNs. Other studies have tested the effect of other social factors such as social communication (Lee 2015 ), social network size, and strength of social ties (Chang and Heo 2014 ; Kim et al. 2015 ). Personal stimuli are student motivational factors associated with a specific state that affects their behavioral response. As depicted in Table 4 , researchers have tested different personal student needs that stimulate OSN usage. For instance, Zheng Wang et al. ( 2012a , b ) examined the emotional, social, and cognitive needs that drive students to use OSNs. Moghavvemi et al. ( 2017a , b ) empirically showed that students with a hedonic motivation were willing to use Facebook as an e-learning tool.

Classification framework for student behaviors in online social networks

OSN website characteristics are stimuli related to the cues implanted in an OSN website. In the reviewed studies, it was found that the most well studied OSN characteristics are usefulness and ease of use. Ease of use refers to student perceptions on the extent to which OSN are easy to use whereas usefulness refers to the degree that students believed that using OSN was helpful in enhancing their task performance (Arteaga Sánchez et al. 2014 ). Although student behaviors in OSNs have been widely studied, few studies have focused on OSN characteristics that stimulate student behaviors. For example, Eid and Al-Jabri ( 2016 ) examined the effect of OSN characteristics such as chatting, discussion, content creation, and file sharing. The results showed that file sharing, chatting, and discussion had a positive impact on student knowledge sharing behavior. In summary, Table 4 shows the stimulus factors identified in previous studies and their classification.

Online social networks organisms

Organism in this study’s framework refers to student internal evaluations towards using OSNs. There are four types of organism factors that have been highlighted in the literature. These types include personality traits, values, social, and cognitive reactions. Student personality traits influence the use of OSNs (Skues et al. 2012 ). As shown in Table 4 , self-esteem and self-disclosure were the most examined personality traits associated with student OSN behaviors. Self-esteem refers to an individual’s emotional evaluation of their own worth (Chen 2017 ). For example, Wang et al. ( 2012a , b ) examined the effect of the Big Five personality traits on student use of specific OSN features. The results found that students with high self-esteem were more likely to comment on other student profiles. Self-disclosure refers to the process by which individuals share their feelings, thoughts, information, and experiences with others (Dindia 1995 ). Previous studies have examined student self-disclosure in OSNs to explore information disclosure behavior (Chang and Heo 2014 ), location disclosure (Chang and Chen 2014 ), self-disclosure, and mental health (Zhang 2017 ). The second type of organism factors is value. It has been noticed that there are several value related factors that affect student internal organisms in OSNs. As shown in Table 4 , entertainment and enjoyment factors were the most common value examined in previous studies. Enjoyment is one of the potential drivers of student OSN use (Nawi et al. 2017 ). Eid and Al-Jabri ( 2016 ) found that YouTube is the most dominant OSN platform used by students for enjoyment and entertainment. Moreover, enjoyment and entertainment directly affected student learning performance.

Social organism refers to the internal social behavior of students that affect their use of OSNs. Students interact with OSN platforms when they experience positive social reactions. Previous studies have examined some social organism factors including relationship with faculty members, engagement, leisure activities, social skills, and chatting and discussion. The fourth type of organism factors is cognitive reactions. Parboteeah et al. ( 2009 ) defined cognitive reaction as “the mental process that occurs in an individual’s mind when he or she interacts with a stimulus” . The positive or negative cognitive reaction of students influences their responses towards OSNs. Table 5 presents the most common organism reactions that effect student use of OSNs.

Online social networks response

In this study’s framework, response refers to student reactions to OSNs stimuli and organisms. As shown in Table 5 , academic related behavior and negative behavior are the most common student responses towards OSNs. Studying the effect of OSN usage on student academic performance has been the most common research topic (Lambić 2016 ; Paul et al. 2012 ; Wohn and Larose 2014 ). On the other hand, other studies have examined the negative behavior of students during their usage of ONS, mostly towards ONS addiction (Hong and Chiu 2016 ; Shettar et al. 2017 ) or cyberbullying (Chen 2017 ; Gahagan et al. 2016 ). Table 6 summarizes student responses associated with OSNs use in previous studies.

Discussion and implications

The last two decades have witnessed a dramatic growth in the number of online social networks used among the youth generation. Examining student behaviors on OSN platforms has increasingly attracted scholars. However, there has been little effort to summarize and synthesize these findings. In this review study, a systematic literature review was conducted to synthesize previous research on student behaviors in OSNs to consolidate the factors that influence student behaviors into a classification framework using the S-O-R model. A total of 104 journal articles were identified through a rigorous and systematic search procedure. The collected studies from the literature show an increasing interest in the area ever since 2010. In line with the research questions, our analysis offers insightful results of the research landscape in terms of research regional context, studies focus trends, methodological trends, factors, and theories leveraged. Using the S-O-R model, we synthesized the reviewed studies highlighting the different stimuli, organism, and response factors. We synthesize and classify these factors into social stimuli, personal stimuli, and OSN characteristics, organism factors; personality traits, value, social, and cognitive reaction, and response; academic related behavior, negative behavior, and other responses.

Research regional perspective

The first research question focused on research regional context. The review revealed that most of the studies were conducted in the US followed by European countries, with the majority focusing on Facebook. The results show that the large majority of the studies were based on a single country. This indicates a sustainable research gap in examining the multi-cultural factors in multiple countries. As OSN is a common phenomenon across many counties, considering the culture and background differences can play an essential role in understanding students’ behavior on these platforms. For example, Ifinedo ( 2016 ) collected data from four countries in America (i.e., USA, Canada, Argentina, and Mexico) to understand students’ pervasive adoption of SNSs. The results from the study revealed that the individualism–collectivism culture factor has a positive impact on students' pervasive adoption behavior of SNSs, and the result reported high level of engagement from students who have more individualistic cultures. In the same manner, Kim et al. ( 2011 ) found some cultural differences in use of the SNSs platforms between Korean and US students. For example, considering the social nature of SNSs, the study found that Korean students rely more on online social relationships to obtain social support, where US students use SNSs to seek entertainment. Furthermore, Karpinski et al. ( 2013 ) empirically found significant differences between US and European students in terms of the moderating effect of multitasking on the relationship between SNS use and academic achievement of students. The confirms that culture issues may vary from one country to another, which consequently effect students’ behavior to use OSNs (Kim et al. 2011 ).

Studies focus and trends

The second research question of this review focused on undersigning the topics and trends that have been discussed in extant studies. The review revealed evidence of five categories of research streams based on the research focus and trend. As shown in Fig.  5 , most of the reviewed studies are in the first stream, which is using OSNs for academic purposes. Moreover, the trend of these studies in this stream focus on examining the effect of using OSNs on students’ academic performance and investigating the use of OSNs for educational purposes. However, a number of other trends are noteworthy. First, as cyber victimization is a relatively new concept, most of the studies provide rigorous effort in exporting the concept, and the reasons beyond its existence among students; however, we have noticed that no effort has been made to investigate the consequences of this negative behavior on students’ academic performance, social life, and communication. Second, we identified only two studies that examined the differences between undergraduate and postgraduate students in terms of cyber victimization. Therefore, there are many avenues for further research to untangle the demographic, education level, and cultural differences in this context. Third, our analysis revealed that Facebook was the most studied ONS platform in terms of addiction behavior, however, over the last ten years, the rapid growth of using image-based ONS such as Instagram and Pinterest has attracted many students (Alhabash and Ma 2017 ). For example, Instagram represents the fastest growing OSNs among young adult users age between 18 and 29 years old (Alhabash and Ma 2017 ). The overwhelming majority of the studies focus on Facebook users, and very few studies have examined excessive Instagram use (Kırcaburun and Griffiths 2018 ; Ponnusamy et al. 2020 ). Although OSNs have many similar features, each platform has unique features and a different structure. These differences in OSNs platforms urge further research to investigate and understand the factors related to excessive and addiction use by students (Kircaburun and Griffiths 2018 ). Therefore, based on the current research gaps, future research agenda including three topics/trend need to be considered. We have developed research questions for each topic as a direction for any further research as shown in Table 7 .

Theories and research methods

The third and fourth research questions focused on understanding the trends in terms of research methods and theories leveraged in existing studies. In relation to the third research question, the review highlighted evidence of the four research methods (i.e., survey, experiment, focus group/interview, and mix method) with a heavy focus on using a quantitative method with the majority of studies conducting survey. This may call for utilizing a variety of other research methods and research design to have more in-depth understanding of students’ behavior on OSN. For example, we noticed that few studies leveraged qualitative methods such as interviews and focus groups (n = 5). In addition, using mix method may derive more results and answer research questions that other methods cannot answer (Tashakkori and Teddlie 2003 ). Experimental methods have been used sparingly (n = 10), this may trigger an opportunity for more experimental research to test different strategies that can be used by education institutions to leverage the potential of OSN platforms in the education process. Moreover, considering that students’ attitude and behavior will change over time, applying longitudinal research method may offer opportunities to explore students’ attitude and behavior patterns over time.

The fourth research question focused on understanding the theoretical underpinnings of the reviewed studies. The analysis revealed two important insights; (1) a substantial number of the reviewed studies do not explicitly use an applied theory, and (2) out of the 34 studies that used theory, nine studies applied UGT to understand the motivation beyond using the OSN. Our findings categorized these theories under three aspects; motivational, social, and behavioral. While each aspect and theory offers useful lenses in this context, there is a lack of leveraging other theories in the extant literature. This motivates researchers to underpin their studies in theories that provide more insights into these three aspects. For example, majority of the studies have applied UGT to understand students’ motivate for using OSNs. However, using other motivational theories could uncover different factors that influence students' motivation for using OSNs. For example, self-determination theory (SDT) focuses on the extent to which individual’s behavior is self-motivated and determined. According to Ryan and Deci ( 2000 ), magnitude and types both shape individuals’ extrinsic motivation. The extrinsic motivation is a spectrum and depends on the level of self-determination (Wang et al. 2019 ). Therefore, the continuum aspect proposed by SDT can provide in-depth understanding of the extrinsic motivation. Wang et al. ( 2016 ) suggested that applying SDT can play a key role in understanding SNSs user satisfaction.

Another theoretical perspective that is worth further exploration relates to the psychological aspect. Our review results highlighted that a considerable number of studies focused on an important issue arising from the daily use of OSNs, such as excessive use/addiction (Koc and Gulyagci 2013 ; Shettar et al. 2017 ), Previous studies have investigated the behavior aspect beyond these issues, however, understanding the psychological aspect of Facebook addiction is worth further investigation. Ryan et al. ( 2014 ) reviewed Facebook addiction related studies and found that Facebook addiction is also linked to psychological factors such as depression and anxiety.

Factors that influence students behavior: S-O-R Framework

The fifth research question focused on determining the factors studies in the extant literature. The review analysis showed that stimuli factors included social, personal, and OSNs website stimuli. However, different types of stimuli have received less attention than other stimuli. Most studies leveraged the social and students’ personal stimuli. Furthermore, few studies conceptualized the OSNs websites characterises in terms of students beliefs about the effect of OSNs features and functions (e.g., perceived ease of use, user friendly) on students stimuli; it would be significant to develop a typology of the OSNs websites stimuli and systematically examine their effect on students’ attitude and behavior. We recommend applying different theories (as mentioned in Theories and research methods section) as an initial step to further identify stimuli factors. The results also highlight that cognitive reaction plays an essential role in the organism dimension. When students encounter stimuli, their internal evaluation is dominated by emotions. Therefore, the cognitive process takes place between students’ usage behavior and their responses (e.g., effort expectancy). In this review, we reported few studies that examined the effect of the cognitive reaction of students.

Response factors encompass students’ reaction to OSNs platforms stimuli and organism. Our review revealed an unsurprisingly dominant focus on the academic related behavior such as academic performance. While it is important to examine the effect of various stimuli and organism factors on academic related behavior and OSNs negative behavior, the psychological aspect beyond OSNs negative behavior is equallty important.

Limitations

Similar to other systematic review studies, this study has some limitations. The findings of our review are constrained by only empirical studies (journal articles) that meet the inclusion criteria. For instance, we only used the articles that explicitly examined students’ behavior in OSNs. Moreover, other different types of studies such as conference proceedings are not included in our primary studies. Further research efforts can gain additional knowledge and understanding from practitioner articles, books and, white papers. Our findings offer a comprehensive conceptual framework to understand students’ behavior in OSNs; future studies are recommended to perform a quantitative meta-analysis to this framework and test the relative effect of different stimuli factors.

Conclusions

The use of OSNs has become a daily habit among young adults and adolescents these days (Brailovskaia et al. 2020 ). In this review, we used a rigorous systematic review process and identified 104 studies related to students’ behavior in OSNs. We systematically reviewed these studies and provide an overview of the current state of this topic by uncovering the research context, research focus, theories, and research method. More importantly, we proposed a classification framework based on S-O-R model to consolidate the factors that influence students in online social networks. These factors were classified under different dimensions in each category of the S-O-R model; stimuli (Social Stimulus, Personal Stimulus, and OSN Characteristics), organism (Personality traits, value, social, Cognitive reaction), and students’ responses (academic-related behavior, negative behavior, and other responses). This framework provides the researchers with a classification of the factors that have been used in previous studies which can motivate further research on the factors that need more empirical examination (e.g., OSN characteristics).

Availability of data and materials

Not applicable.

Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2018). The application of internet of things in healthcare: A systematic literature review and classification. Universal Access in the Information Society . https://doi.org/10.1007/s10209-018-0618-4 .

Article   Google Scholar  

Ainin, S., Naqshbandi, M. M., Moghavvemi, S., & Jaafar, N. I. (2015). Facebook usage, socialization and academic performance. Computers and Education, 83, 64–73. https://doi.org/10.1016/j.compedu.2014.12.018 .

Akbari, E., Naderi, A., Simons, R.-J., & Pilot, A. (2016). Student engagement and foreign language learning through online social networks. Asian-Pacific Journal of Second and Foreign Language Education, 1 (1), 4. https://doi.org/10.1186/s40862-016-0006-7 .

Akcaoglu, M., & Bowman, N. D. (2016). Using instructor-led Facebook groups to enhance students’ perceptions of course content. Computers in Human Behavior, 65, 582–590. https://doi.org/10.1016/j.chb.2016.05.029 .

Akçayır, G., & Akçayır, M. (2016). Research trends in social network sites’ educational use: A review of publications in all SSCI journals to 2015. Review of Education, 4 (3), 293–319.

Alhabash, S., & Ma, M. (2017). A tale of four platforms: Motivations and uses of Facebook, Twitter, Instagram, and Snapchat among college students. Social Media + Society, 3 (1), 205630511769154. https://doi.org/10.1177/2056305117691544 .

Amador, P., & Amador, J. (2014). Academic advising via Facebook: Examining student help seeking. Internet and Higher Education, 21, 9–16. https://doi.org/10.1016/j.iheduc.2013.10.003 .

Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook addiction scale. Psychological Reports, 110 (2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517 .

Arteaga Sánchez, R., Cortijo, V., & Javed, U. (2014). Students’ perceptions of Facebook for academic purposes. Computers and Education, 70, 138–149. https://doi.org/10.1016/j.compedu.2013.08.012 .

Asiedu, N. K., & Badu, E. E. (2018). Motivating issues affecting students’ use of social media sites in Ghanaian tertiary institutions. Library Hi Tech, 36 (1), 167–179. https://doi.org/10.1108/LHT-10-2016-0108 .

Asterhan, C. S. C., & Bouton, E. (2017). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16–34. https://doi.org/10.1016/j.compedu.2017.03.007 .

Balaid, A., Abd Rozan, M. Z., Hikmi, S. N., & Memon, J. (2016). Knowledge maps: A systematic literature review and directions for future research. International Journal of Information Management, 36 (3), 451–475.

Baran, B. (2010). Facebook as a formal instructional environment. British Journal of Educational Technology, 41 (6), 146–149. https://doi.org/10.1111/j.1467-8535.2010.01115.x .

Benson, V., & Filippaios, F. (2015). Collaborative competencies in professional social networking: Are students short changed by curriculum in business education? Computers in Human Behavior, 51, 1331–1339. https://doi.org/10.1016/j.chb.2014.11.031 .

Benson, V., Saridakis, G., & Tennakoon, H. (2015). Purpose of social networking use and victimisation: Are there any differences between university students and those not in HE? Computers in Human Behavior, 51, 867–872. https://doi.org/10.1016/j.chb.2014.11.034 .

Błachnio, A., Przepiorka, A., Senol-Durak, E., Durak, M., & Sherstyuk, L. (2017). The role of personality traits in Facebook and Internet addictions: A study on Polish, Turkish, and Ukrainian samples. Computers in Human Behavior, 68, 269–275. https://doi.org/10.1016/j.chb.2016.11.037 .

Borrero, D. J., Yousafzai, Y. S., Javed, U., & Page, L. K. (2014). Perceived value of social networking sites (SNS) in students’ expressive participation in social movements. Journal of Research in Interactive Marketing, 8 (1), 56–78. https://doi.org/10.1108/JRIM-03-2013-0015 .

Borrero, J. D., Yousafzai, S. Y., Javed, U., & Page, K. L. (2014). Expressive participation in Internet social movements: Testing the moderating effect of technology readiness and sex on student SNS use. Computers in Human Behavior, 30, 39–49. https://doi.org/10.1016/j.chb.2013.07.032 .

Boyd, D. M., & Ellison, N. B. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13 (1), 210–230. https://doi.org/10.1111/j.1083-6101.2007.00393.x .

Brailovskaia, J., & Margraf, J. (2017). Facebook Addiction Disorder (FAD) among German students—A longitudinal approach. PLoS ONE, 12 (12), 1–15. https://doi.org/10.1371/journal.pone.0189719 .

Brailovskaia, J., Ströse, F., Schillack, H., & Margraf, J. (2020). Less Facebook use—More well-being and a healthier lifestyle? An experimental intervention study. Computers in Human Behavior, 108 (March), 106332. https://doi.org/10.1016/j.chb.2020.106332 .

Brailovskaia, J., Teismann, T., & Teismann, T. (2018). Physical activity mediates the association between daily stress and Facebook Addiction Disorder. Computers in Human Behavior, 86, 199–204. https://doi.org/10.1016/j.chb.2018.04.045 .

Busalim, A. H., & Hussin, A. R. C. (2016). Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management, 36 (6), 1075–1088. https://doi.org/10.1016/j.ijinfomgt.2016.06.005 .

Cain, J., Scott, D. R., Tiemeier, A. M., Akers, P., & Metzger, A. H. (2013). Social media use by pharmacy faculty: Student friending, e-professionalism, and professional use. Currents in Pharmacy Teaching and Learning, 5 (1), 2–8. https://doi.org/10.1016/j.cptl.2012.09.002 .

Chae, D., Kim, H., & Kim, Y. A. (2017). Sex differences in the factors influencing Korean College Students’ addictive tendency toward social networking sites. International Journal of Mental Health and Addiction . https://doi.org/10.1007/s11469-017-9778-3 .

Chan, T. K. H., Cheung, C. M. K., & Lee, Z. W. Y. (2017). The state of online impulse-buying research: A literature analysis. Information & Management, 54 (2), 204–217. https://doi.org/10.1016/j.im.2016.06.001 .

Chang, C. W., & Chen, G. M. (2014). College students’ disclosure of location-related information on Facebook. Computers in Human Behavior, 35, 33–38. https://doi.org/10.1016/j.chb.2014.02.028 .

Chang, C. W., & Heo, J. (2014). Visiting theories that predict college students’ self-disclosure on Facebook. Computers in Human Behavior, 30, 79–86. https://doi.org/10.1016/j.chb.2013.07.059 .

Chen, B., & Marcus, J. (2012). Students’ self-presentation on Facebook: An examination of personality and self-construal factors. Computers in Human Behavior, 28 (6), 2091–2099. https://doi.org/10.1016/j.chb.2012.06.013 .

Chen, H. (2017). Antecedents of positive self-disclosure online: An empirical study of US college students Facebook usage. Psychology Research and Behavior Management, 10, 147–153. https://doi.org/10.2147/PRBM.S136049 .

Cheung, C. M. K., Chiu, P. Y., & Lee, M. K. O. (2011). Online social networks: Why do students use facebook? Computers in Human Behavior, 27 (4), 1337–1343.

Chung, J. E. (2014). Social networking in online support groups for health: How online social networking benefits patients. Journal of Health Communication, 19 (6), 639–659. https://doi.org/10.1080/10810730.2012.757396 .

Čičević, S., Samčović, A., & Nešić, M. (2016). Exploring college students’ generational differences in Facebook usage. Computers in Human Behavior, 56, 83–92. https://doi.org/10.1016/j.chb.2015.11.034 .

Clement, J. (2020). Facebook: Number of monthly active users worldwide 2008–2020 Published by J. Clement, Aug 10, 2020 How many users does Facebook have? With over 2.7 billion monthly active users as of the second quarter of 2020, Facebook is the biggest social network world . Retrieved from https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/

Corbitt-Hall, D. J., Gauthier, J. M., Davis, M. T., & Witte, T. K. (2016). College Students’ responses to suicidal content on social networking sites: An examination using a simulated facebook newsfeed. Suicide and Life-Threatening Behavior, 46 (5), 609–624. https://doi.org/10.1111/sltb.12241 .

Deng, L., & Tavares, N. J. (2013). From Moodle to Facebook: Exploring students’ motivation and experiences in online communities. Computers & Education, 68, 167–176. https://doi.org/10.1016/j.compedu.2013.04.028 .

Dindia, K. (1995). Self-disclosure: A sense of balance. Contemporary psychology: A journal of reviews . Vol. 40. New York: Sage Publications. https://doi.org/10.1037/003319 .

Book   Google Scholar  

Doleck, T., Bazelais, P., & Lemay, D. J. (2017). Examining the antecedents of social networking sites use among CEGEP students. Education and Information Technologies, 22 (5), 2103–2123. https://doi.org/10.1007/s10639-016-9535-4 .

Eid, M. I. M., & Al-Jabri, I. M. (2016). Social networking, knowledge sharing, and student learning: The case of university students. Computers and Education, 99, 14–27. https://doi.org/10.1016/j.compedu.2016.04.007 .

Elphinston, R. A., & Noller, P. (2011). Time to Face It! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology, Behavior, and Social Networking, 14 (11), 631–635. https://doi.org/10.1089/cyber.2010.0318 .

Enskat, A., Hunt, S. K., & Hooker, J. F. (2017). A generational examination of instructional Facebook use and the effects on perceived instructor immediacy, credibility and student affective learning. Technology, Pedagogy and Education, 26 (5), 545–557. https://doi.org/10.1080/1475939X.2017.1354065 .

Facebook. (2020). Facebook Reports First Quarter 2020 Results . Retrieved from http://investor.fb.com/releasedetail.cfm?ReleaseID=842071 .

Fasae, J. K., & Adegbilero-Iwari, I. (2016). Use of social media by science students in public universities in Southwest Nigeria. The Electronic Library, 34 (2), 213–222. https://doi.org/10.1108/EL-11-2014-0205 .

Gahagan, K., Vaterlaus, J. M., & Frost, L. R. (2016). College student cyberbullying on social networking sites: Conceptualization, prevalence, and perceived bystander responsibility. Computers in Human Behavior, 55, 1097–1105. https://doi.org/10.1016/j.chb.2015.11.019 .

George, D. R., Dellasega, C., Whitehead, M. M., & Bordon, A. (2013). Facebook-based stress management resources for first-year medical students: A multi-method evaluation. Computers in Human Behavior, 29 (3), 559–562. https://doi.org/10.1016/j.chb.2012.12.008 .

Gettman, H. J., & Cortijo, V. (2015). “Leave Me and My Facebook Alone!” understanding college students’ relationship with Facebook and its use for academic purposes. International Journal for the Scholarship of Teaching and Learning Article, 9 (1), 1. https://doi.org/10.20429/ijsotl.2015.090108 .

Ha, L., Joa, C. Y., Gabay, I., & Kim, K. (2018). Does college students’ social media use affect school e-mail avoidance and campus involvement? Internet Research, 28 (1), 213–231. https://doi.org/10.1108/IntR-11-2016-0346 .

Hamid, S., Bukhari, S., Ravana, S. D., Norman, A. A., & Ijab, M. T. (2016). Role of social media in information-seeking behaviour of international students: A systematic literature review. Aslib Journal of Information Management, 68 (5), 643–666. https://doi.org/10.1108/AJIM-03-2016-0031 .

Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students’ perceptions of the benefits of online social networking use for teaching and learning. Internet and Higher Education, 26, 1–9. https://doi.org/10.1016/j.iheduc.2015.02.004 .

Hong, F. Y., & Chiu, S. L. (2016). Factors influencing facebook usage and facebook addictive tendency in university students: The role of online psychological privacy and facebook usage motivation. Stress and Health, 32 (2), 117–127. https://doi.org/10.1002/smi.2585 .

Hossain, M. D., & Veenstra, A. S. (2013). Online maintenance of life domains: Uses of social network sites during graduate education among the US and international students. Computers in Human Behavior, 29 (6), 2697–2702. https://doi.org/10.1016/j.chb.2013.07.007 .

Ifinedo, P. (2016). Applying uses and gratifications theory and social influence processes to understand students’ pervasive adoption of social networking sites: Perspectives from the Americas. International Journal of Information Management, 36 (2), 192–206. https://doi.org/10.1016/j.ijinfomgt.2015.11.007 .

Islam, T., Sheikh, Z., Hameed, Z., Khan, I. U., & Azam, R. I. (2018). Social comparison, materialism, and compulsive buying based on stimulus-response- model: A comparative study among adolescents and young adults. Young Consumers, 19 (1), 19–37. https://doi.org/10.1108/MRR-09-2015-0216 .

Josefsson, P., Hrastinski, S., Pargman, D., & Pargman, T. C. (2016). The student, the private and the professional role: Students’ social media use. Education and Information Technologies, 21 (6), 1583–1594. https://doi.org/10.1007/s10639-015-9403-7 .

Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers and Education, 58 (1), 162–171. https://doi.org/10.1016/j.compedu.2011.08.004 .

Junco, R. (2015). Student class standing, Facebook use, and academic performance. Journal of Applied Developmental Psychology, 36, 18–29. https://doi.org/10.1016/j.appdev.2014.11.001 .

Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Computers in Human Behavior, 29 (3), 1182–1192. https://doi.org/10.1016/j.chb.2012.10.011 .

Kim, J., Lee, C., & Elias, T. (2015). Factors affecting information sharing in social networking sites amongst university students. Online Information Review, 39 (3), 290–309. https://doi.org/10.1108/OIR-01-2015-0022 .

Kim, S., & Yoo, S. J. (2016). Age and gender differences in social networking: effects on south Korean students in higher education. In Social networking and education (pp. 69–82). Switzerland: Springer. https://doi.org/10.1007/978-3-319-17716-8_5 .

Kim, Y., Sohn, D., & Choi, S. M. (2011). Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Computers in Human Behavior, 27 (1), 365–372. https://doi.org/10.1016/j.chb.2010.08.015 .

Kircaburun, K., & Griffiths, M. D. (2018). Instagram addiction and the Big Five of personality: The mediating role of self-liking. Journal of Behavioral Addictions, 7 (1), 158–170. https://doi.org/10.1556/2006.7.2018.15 .

Kırcaburun, K., & Griffiths, M. D. (2018). Problematic Instagram use: The role of perceived feeling of presence and escapism. International Journal of Mental Health and Addiction . https://doi.org/10.1007/s11469-018-9895-7 .

Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele University, UK and National ICT Australia, 33, 28.

Google Scholar  

Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and University of Durham, 2, 1051.

Kitsantas, A., Dabbagh, N., Chirinos, D. S., & Fake, H. (2016). College students’ perceptions of positive and negative effects of social networking. In Social networking and education (pp. 225–238). Switzerland: Springer, Cham. https://doi.org/10.1007/978-3-319-17716-8_14 .

Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish College students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking, 16 (4), 279–284. https://doi.org/10.1089/cyber.2012.0249 .

Kokkinos, C. M., & Saripanidis, I. (2017). A lifestyle exposure perspective of victimization through Facebook among university students. Do individual differences matter? Computers in Human Behavior, 74, 235–245. https://doi.org/10.1016/j.chb.2017.04.036 .

Krasilnikov, A., & Smirnova, A. (2017). Online social adaptation of first-year students and their academic performance. Computers and Education, 113, 327–338. https://doi.org/10.1016/j.compedu.2017.05.012 .

Kujur, F., & Singh, S. (2017). Engaging customers through online participation in social networking sites. Asia Pacific Management Review, 22 (1), 16–24. https://doi.org/10.1016/j.apmrv.2016.10.006 .

Kumar Bhatt, R., & Kumar, A. (2014). Student opinion on the use of social networking tools by libraries. The Electronic Library, 32 (5), 594–602. https://doi.org/10.1108/EL-09-2012-0110 .

Kuo, T., & Tang, H. L. (2014). Relationships among personality traits, Facebook usages, and leisure activities—A case of Taiwanese college students. Computers in Human Behavior, 31 (1), 13–19. https://doi.org/10.1016/j.chb.2013.10.019 .

Lambić, D. (2016). Correlation between Facebook use for educational purposes and academic performance of students. Computers in Human Behavior, 61, 313–320. https://doi.org/10.1016/j.chb.2016.03.052 .

Lee, S. (2015). Analyzing negative SNS behaviors of elementary and middle school students in Korea. Computers in Human Behavior, 43, 15–27. https://doi.org/10.1016/j.chb.2014.10.014 .

Lim, J., & Richardson, J. C. (2016). Exploring the effects of students’ social networking experience on social presence and perceptions of using SNSs for educational purposes. The Internet and Higher Education, 29, 31–39. https://doi.org/10.1016/j.iheduc.2015.12.001 .

Lin, W.-Y., Zhang, X., Song, H., & Omori, K. (2016). Health information seeking in the Web 2.0 age: Trust in social media, uncertainty reduction, and self-disclosure. Computers in Human Behavior, 56, 289–294. https://doi.org/10.1016/j.chb.2015.11.055 .

Liu, C. C., Chen, Y. C., & Diana Tai, S. J. (2017). A social network analysis on elementary student engagement in the networked creation community. Computers and Education, 115 (300), 114–125. https://doi.org/10.1016/j.compedu.2017.08.002 .

Liu, D., & Brown, B. B. (2014). Self-disclosure on social networking sites, positive feedback, and social capital among Chinese college students. Computers in Human Behavior, 38, 213–219. https://doi.org/10.1016/j.chb.2014.06.003 .

Luqman, A., Cao, X., Ali, A., Masood, A., & Yu, L. (2017). Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Computers in Human Behavior, 70, 544–555. https://doi.org/10.1016/j.chb.2017.01.020 .

Mano, R. S. (2014). Social media and online health services: A health empowerment perspective to online health information. Computers in Human Behavior, 39, 404–412. https://doi.org/10.1016/j.chb.2014.07.032 .

Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers and Education, 55 (2), 444–453. https://doi.org/10.1016/j.compedu.2010.02.008 .

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology . Cambridge: The MIT Press.

Meier, A., Reinecke, L., & Meltzer, C. E. (2016). Facebocrastination? Predictors of using Facebook for procrastination and its effects on students’ well-being. Computers in Human Behavior, 64, 65–76. https://doi.org/10.1016/j.chb.2016.06.011 .

Mirabolghasemi, M., Iahad, N. A., & Rahim, N. Z. A. (2016). Students’ perception towards the potential and barriers of social network sites in higher education. In Social networking and education (pp. 41–49). Switzerland: Springer. https://doi.org/10.1007/978-3-319-17716-8_3 .

Moghavvemi, S., Paramanathan, T., Rahin, N. M., & Sharabati, M. (2017). Student’s perceptions towards using e-learning via Facebook. Behaviour and Information Technology, 36 (10), 1081–1100. https://doi.org/10.1080/0144929X.2017.1347201 .

Moghavvemi, S., Sharabati, M., Paramanathan, T., & Rahin, N. M. (2017). The impact of perceived enjoyment, perceived reciprocal benefits and knowledge power on students’ knowledge sharing through Facebook. International Journal of Management Education, 15 (1), 1–12. https://doi.org/10.1016/j.ijme.2016.11.002 .

Mostafa, R. B. (2015). Engaging students via social media: Is it worth the effort? Journal of Marketing Education, 37 (3), 144–159. https://doi.org/10.1177/0273475315585825 .

Nawi, N. B. C., Al Mamun, A., Nasir, N. A. B. M., Shokery, N. B., Raston, N. B. A., & Fazal, S. A. (2017). Acceptance and usage of social media as a platform among student entrepreneurs. Journal of Small Business and Enterprise Development, 24 (2), 375–393. https://doi.org/10.1108/JSBED-09-2016-0136 .

Ndasauka, Y., Hou, J., Wang, Y., Yang, L., Yang, Z., Ye, Z., et al. (2016). Excessive use of Twitter among college students in the UK: Validation of the Microblog Excessive Use Scale and relationship to social interaction and loneliness. Computers in Human Behavior, 55, 963–971. https://doi.org/10.1016/j.chb.2015.10.020 .

Nwagwu, W. E. (2017). Social networking, identity and sexual behaviour of undergraduate students in Nigerian universities. The Electronic Library, 35 (3), 534–558. https://doi.org/10.1108/EL-01-2015-0014 .

Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17 (10), 652–657. https://doi.org/10.1089/cyber.2014.0070 .

Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The influence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research, 20 (1), 60–78. https://doi.org/10.1287/isre.1070.0157 .

Park, N., Song, H., & Lee, K. M. (2014). Social networking sites and other media use, acculturation stress, and psychological well-being among East Asian college students in the United States. Computers in Human Behavior, 36, 138–146. https://doi.org/10.1016/j.chb.2014.03.037 .

Park, S. Y., Cha, S.-B., Lim, K., & Jung, S.-H. (2014). The relationship between university student learning outcomes and participation in social network services, social acceptance and attitude towards school life. British Journal of Educational Technology, 45 (1), 97–111. https://doi.org/10.1111/bjet.12013 .

Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Computers in Human Behavior, 28 (6), 2117–2127. https://doi.org/10.1016/j.chb.2012.06.016 .

Peters, A. N., Winschiers-Theophilus, H., & Mennecke, B. E. (2015). Cultural influences on Facebook practices: A comparative study of college students in Namibia and the United States. Computers in Human Behavior, 49, 259–271. https://doi.org/10.1016/j.chb.2015.02.065 .

Ponnusamy, S., Iranmanesh, M., Foroughi, B., & Hyun, S. S. (2020). Drivers and outcomes of Instagram addiction: Psychological well-being as moderator. Computers in Human Behavior, 107, 106294. https://doi.org/10.1016/j.chb.2020.106294 .

Rap, S., & Blonder, R. (2017). Thou shall not try to speak in the Facebook language: Students’ perspectives regarding using Facebook for chemistry learning. Computers and Education, 114, 69–78. https://doi.org/10.1016/j.compedu.2017.06.014 .

Raymond, J., & Wang, H. (2015). Social network sites and international students ’ cross-cultural adaptation. Computers in Human Behavior, 49, 400–411. https://doi.org/10.1016/j.chb.2015.03.041 .

Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. Internet and Higher Education, 13 (3), 134–140. https://doi.org/10.1016/j.iheduc.2010.03.002 .

Romero-Hall, E. (2017). Posting, sharing, networking, and connecting: Use of social media content by graduate students. TechTrends, 61 (6), 580–588. https://doi.org/10.1007/s11528-017-0173-5 .

Rui, J. R., & Wang, H. (2015). Social network sites and international students’ cross-cultural adaptation. Computers in Human Behavior, 49, 400–411. https://doi.org/10.1016/j.chb.2015.03.041 .

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. Social Development, and Well-Being, 55 (1), 68–78.

Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions, 3 (3), 133–148. https://doi.org/10.1556/JBA.3.2014.016 .

Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching & Learning, 10 (1), 4–33. https://doi.org/10.1108/jrit-10-2016-0007 .

Sheeran, N., & Cummings, D. J. (2018). An examination of the relationship between Facebook groups attached to university courses and student engagement. Higher Education, 76, 937–955.

Shettar, M., Karkal, R., Kakunje, A., Mendonsa, R. D., & Chandran, V. V. M. (2017). Facebook addiction and loneliness in the post-graduate students of a university in southern India. International Journal of Social Psychiatry, 63 (4), 325–329. https://doi.org/10.1177/0020764017705895 .

Shim, M., Lee-Won, R. J., & Park, S. H. (2016). The self on the Net: The joint effect of self-construal and public self-consciousness on positive self-presentation in online social networking among South Korean college students. Computers in Human Behavior, 63, 530–539. https://doi.org/10.1016/j.chb.2016.05.054 .

Sin, S. C. J., & Kim, K. S. (2013). International students’ everyday life information seeking: The informational value of social networking sites. Library and Information Science Research, 35 (2), 107–116. https://doi.org/10.1016/j.lisr.2012.11.006 .

Singh, A. (2017). Mining of social media data of University students. Education and Information Technologies, 22 (4), 1515–1526. https://doi.org/10.1007/s10639-016-9501-1 .

Skues, J. L., Williams, B., & Wise, L. (2012). The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students. Computers in Human Behavior, 28 (6), 2414–2419. https://doi.org/10.1016/j.chb.2012.07.012 .

Smith, R., Morgan, J., & Monks, C. (2017). Students’ perceptions of the effect of social media ostracism on wellbeing. Computers in Human Behavior, 68, 276–285. https://doi.org/10.1016/j.chb.2016.11.041 .

Special, W. P., & Li-Barber, K. T. (2012). Self-disclosure and student satisfaction with Facebook. Computers in Human Behavior, 28 (2), 624–630. https://doi.org/10.1016/j.chb.2011.11.008 .

Tally, S. (2010). Mixable blends Facebook with academics to improve student success . Purdue: Purdue University News.

Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 .

Tang, C. S. K., & Koh, Y. Y. W. (2017). Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. Asian Journal of Psychiatry, 25, 175–178. https://doi.org/10.1016/j.ajp.2016.10.027 .

Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33 (1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003 .

Tashakkori, A., & Teddlie, C. (2003). Issues and dilemmas in teaching research methods courses in social and behavioural sciences: US perspective. International Journal of Social Research Methodology: Theory and Practice, 6 (1), 61–77. https://doi.org/10.1080/13645570305055 .

Teo, T., Doleck, T., & Bazelais, P. (2017). The role of attachment in Facebook usage: a study of Canadian college students. Interactive Learning Environments, 4820 (April), 1–17. https://doi.org/10.1080/10494820.2017.1315602 .

Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior . https://doi.org/10.1016/j.chb.2009.11.014 .

Tower, M., Latimer, S., & Hewitt, J. (2014). Social networking as a learning tool: Nursing students’ perception of efficacy. Nurse Education Today, 34 (6), 1012–1017. https://doi.org/10.1016/j.nedt.2013.11.006 .

Van Hoof, J. J., Bekkers, J., & Van Vuuren, M. (2014). Son, you’re smoking on Facebook! College students’ disclosures on social networking sites as indicators of real-life risk behaviors. Computers in Human Behavior, 34, 249–257. https://doi.org/10.1016/j.chb.2014.02.008 .

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540 .

Walker, C. M., Sockman, B. R., & Koehn, S. (2011). An exploratory study of cyberbullying with undergraduate students. Tech Trends, 55 (2), 31–38. https://doi.org/10.1007/s11528-011-0481-0 .

Wang, J. C., & Chang, C. H. (2013). How online social ties and product-related risks influence purchase intentions: A Facebook experiment. Electronic Commerce Research and Applications, 12 (5), 337–346.

Wang, J. L., Jackson, L. A., Gaskin, J., & Wang, H. Z. (2014). The effects of Social Networking Site (SNS) use on college students’ friendship and well-being. Computers in Human Behavior, 37, 229–236. https://doi.org/10.1016/j.chb.2014.04.051 .

Wang, J. L., Jackson, L. A., Zhang, D. J., & Su, Z. Q. (2012a). The relationships among the Big Five Personality factors, self-esteem, narcissism, and sensation-seeking to Chinese University students’ uses of social networking sites (SNSs). Computers in Human Behavior, 28 (6), 2313–2319. https://doi.org/10.1016/j.chb.2012.07.001 .

Wang, X., Li, Y., & Wang, X. (2016). Users’ satisfaction with social network sites: A self-determination perspective. Journal of Computer Information Systems , 4417 (February). https://doi.org/10.1080/08874417.2015.11645800

Wang, X., Lin, X., & Spencer, M. K. (2019). Exploring the effects of extrinsic motivation on consumer behaviors in social commerce: Revealing consumers’ perceptions of social commerce benefits. International Journal of Information Management, 45 (March 2018), 163–175. https://doi.org/10.1016/j.ijinfomgt.2018.11.010 .

Wang, Z., Tchernev, J. M., & Solloway, T. (2012b). A dynamic longitudinal examination of social media use, needs, and gratifications among college students. Computers in Human Behavior, 28 (5), 1829–1839. https://doi.org/10.1016/j.chb.2012.05.001 .

Webster, J., & Watson, R. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26 (2), 13–23.

Wickramanayake, L., & Muhammad Jika, S. (2018). Social media use by undergraduate students of education in Nigeria: A survey. The Electronic Library, 36 (1), 21–37. https://doi.org/10.1108/EL-01-2017-0023 .

Wodzicki, K., Schwämmlein, E., & Moskaliuk, J. (2012). “Actually, I Wanted to Learn”: Study-related knowledge exchange on social networking sites. Internet and Higher Education, 15 (1), 9–14. https://doi.org/10.1016/j.iheduc.2011.05.008 .

Wohn, D. Y., & Larose, R. (2014). Effects of loneliness and differential usage of Facebook on college adjustment of first-year students. Computers and Education, 76, 158–167. https://doi.org/10.1016/j.compedu.2014.03.018 .

Yazdanparast, A., Joseph, M., & Qureshi, A. (2015). An investigation of Facebook boredom phenomenon among college students. Young Consumers, 16 (4), 468–480. https://doi.org/10.1108/YC-02-2015-00506 .

Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences. Information Management, 51, 1017–1030.

Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision Support Systems, 86, 95–108.

Zhang, R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Computers in Human Behavior, 75, 527–537. https://doi.org/10.1016/j.chb.2017.05.043 .

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This paper is supported by Fundamental Research Grant Scheme (FRGS) (Vote No. R.K130000.7840.4F245), and UTM Razak School of Engineering and Advanced Technology research grant or DPUTMRAZAK (Vote No. R.K13000.7740.4J313).

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Masrom, M.B., Busalim, A.H., Abuhassna, H. et al. Understanding students’ behavior in online social networks: a systematic literature review. Int J Educ Technol High Educ 18 , 6 (2021). https://doi.org/10.1186/s41239-021-00240-7

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• Students’ behavior
• Social media
• Systematic review
• Stimulus-organism-response model

• Our Mission

11 Research-Based Classroom Management Strategies

Discover kernels—simple, quick, and reliable ways to deal with behavior challenges.

Do unresolved behavior issues keep you awake at night thinking about what strategies might enhance responsible decision making and increase academic learning time? It’s natural to feel personally and professionally challenged—as I have, too many times to count.

The good news is that there are some research-based strategies called kernels that you can add to your classroom management toolkit.

What Are Kernels?

In a 2008 paper published in Clinical Child and Family Psychology , Dennis Embry and Anthony Biglan describe kernels as “fundamental units of behavioral influence”—bite-size strategies that are validated by mountains of empirical evidence and teacher experience. (Barry Parsonson’s “ Evidence-Based Classroom Behavior Management Strategies ” offers another deep dive into the research.)

Embry and Biglan describe how a kernel might help the parent whose child is struggling to get out the door on time for school: “Alone, such a complaint does not merit implementing parenting skills training. However, a simple behavior change strategy, such as the ‘Beat the Timer’ game ( Adams and Drabman 1995 ), in which the child receives a reward for completing a behavior before the timer goes off, could solve the problem, and prevent parent-child conflict.”

Particularly at the beginning of the year, before you’ve had a chance to develop deeper relationships with your students, kernels can offer useful approaches to classroom management. Administrators and coaches recommend kernels because implementing them with fidelity is intuitive and observable. They require neither special training nor expensive consultants.

11 Classroom Management Kernels

While veteran teachers may read the annotated list of kernels as common knowledge, their ubiquity is an advantage. You’ll often find them embedded in more complex constellations of evidence-based behavioral programs because of their effectiveness in cuing self-awareness, self-regulation, and pro-academic dispositions.

1. Nonverbal Cues: A teacher can use subtle body movements (like proximity) or more explicit hand signals to cue self-regulation. One popular cue involves moving to the front of the room and making eye contact with the high schooler who is acting out, then pausing until you have the individual’s attention. Younger students are less familiar with social cues and might require a verbal signal to accompany the nonverbal cues. Example: “What should you be doing right now?”

2. Nonverbal Transition Cues: Kids can become so immersed in an activity that they might not notice your attempts to shift them into the next learning event. Ringing a bell or turning lights on and off are unmistakable signals that shift attention to the teacher or a new task. Asking a class to collectively decide what signal to use can be a community builder.

3. Timeouts: Hundreds of studies support the timeout strategy , which is now considered an indispensable component of many evidence-based behavior management systems. Unlike the dunce cap punishment, which intentionally shames and stigmatizes students, a timeout is now used in progressive classrooms to provide an emotional breather in a less socially charged area of the room. It’s also a way for students to decompress, reflect on and enhance their self-awareness, and then return to their seats with improved self-regulation.

4. Over-Correction: Younger students may find classroom routines foreign or overwhelming. Take the time to model the appropriate procedure and then rehearse it three times or more until each step of the routine becomes second nature. After these rehearsals, my second graders took pride in executing the required actions quickly and perfectly for the rest of the year.

5. Notes of Praise: A private note left on a student’s desk praising improved classroom effort is a powerful reinforcement, especially when the note is heartfelt . Studies also show that sending positive letters home improves kids’ self-management and decision making.

6. Private Reminders: When partnered with discreet praise, private reminders to students about how to act responsibly increase on-task behaviors. Researchers recommend using short and unemotional reminders.

7. Greetings: It might seem like an insignificant gesture, but greeting students by name and making a positive statement enhances their self-regulation and increases class participation. Example: “Hey, Marcus. How is my brilliant student today?”

8. On-the-Spot Corrections: During a lesson, don’t leave behavioral missteps unaddressed . Immediately, briefly, and without drama, cue students about responsible conduct. Example: “What should you be doing right now? Right. Let’s see that happen.”

9. Mindfulness Practice: Citing numerous studies , Emily Campbell writes that teaching a student to meditate or practice nasal breathing (inhale through the nose, exhale through the mouth) enhances emotional regulation. This animated gif helps students (and teachers) learn the technique.

10. Notice and Comment: The Peacebuilders website shares several “ Minute Recipes for Building Peace ,” such as recognizing changes in student behavior and showing interest. Example: “I really like how you’re acting today. Did something happen to make you feel better about your group?” Noticing and commenting sends an unmistakable and powerful message: I care.

11. When-Then: Another intervention published by Peacebuilders, “ When-Then ” helps students make responsible decisions—but also leaves the choice in the students’ hands: “When you start talking to me with a lowered voice, then we’ll problem-solve this situation.”

An overwhelming number of studies recommend that classroom instructors systematically teach self-regulation, relationship management, and responsible decision making at the beginning of the school year, so implement these kernels soon.

CONCEPTUAL ANALYSIS article

The effect of social media on the development of students’ affective variables.

• 1 Science and Technology Department, Nanjing University of Posts and Telecommunications, Nanjing, China
• 2 School of Marxism, Hohai University, Nanjing, Jiangsu, China
• 3 Government Enterprise Customer Center, China Mobile Group Jiangsu Co., Ltd., Nanjing, China

The use of social media is incomparably on the rise among students, influenced by the globalized forms of communication and the post-pandemic rush to use multiple social media platforms for education in different fields of study. Though social media has created tremendous chances for sharing ideas and emotions, the kind of social support it provides might fail to meet students’ emotional needs, or the alleged positive effects might be short-lasting. In recent years, several studies have been conducted to explore the potential effects of social media on students’ affective traits, such as stress, anxiety, depression, and so on. The present paper reviews the findings of the exemplary published works of research to shed light on the positive and negative potential effects of the massive use of social media on students’ emotional well-being. This review can be insightful for teachers who tend to take the potential psychological effects of social media for granted. They may want to know more about the actual effects of the over-reliance on and the excessive (and actually obsessive) use of social media on students’ developing certain images of self and certain emotions which are not necessarily positive. There will be implications for pre- and in-service teacher training and professional development programs and all those involved in student affairs.

Introduction

Social media has turned into an essential element of individuals’ lives including students in today’s world of communication. Its use is growing significantly more than ever before especially in the post-pandemic era, marked by a great revolution happening to the educational systems. Recent investigations of using social media show that approximately 3 billion individuals worldwide are now communicating via social media ( Iwamoto and Chun, 2020 ). This growing population of social media users is spending more and more time on social network groupings, as facts and figures show that individuals spend 2 h a day, on average, on a variety of social media applications, exchanging pictures and messages, updating status, tweeting, favoring, and commenting on many updated socially shared information ( Abbott, 2017 ).

Researchers have begun to investigate the psychological effects of using social media on students’ lives. Chukwuere and Chukwuere (2017) maintained that social media platforms can be considered the most important source of changing individuals’ mood, because when someone is passively using a social media platform seemingly with no special purpose, s/he can finally feel that his/her mood has changed as a function of the nature of content overviewed. Therefore, positive and negative moods can easily be transferred among the population using social media networks ( Chukwuere and Chukwuere, 2017 ). This may become increasingly important as students are seen to be using social media platforms more than before and social networking is becoming an integral aspect of their lives. As described by Iwamoto and Chun (2020) , when students are affected by social media posts, especially due to the increasing reliance on social media use in life, they may be encouraged to begin comparing themselves to others or develop great unrealistic expectations of themselves or others, which can have several affective consequences.

Considering the increasing influence of social media on education, the present paper aims to focus on the affective variables such as depression, stress, and anxiety, and how social media can possibly increase or decrease these emotions in student life. The exemplary works of research on this topic in recent years will be reviewed here, hoping to shed light on the positive and negative effects of these ever-growing influential platforms on the psychology of students.

Significance of the study

Though social media, as the name suggests, is expected to keep people connected, probably this social connection is only superficial, and not adequately deep and meaningful to help individuals feel emotionally attached to others. The psychological effects of social media on student life need to be studied in more depth to see whether social media really acts as a social support for students and whether students can use social media to cope with negative emotions and develop positive feelings or not. In other words, knowledge of the potential effects of the growing use of social media on students’ emotional well-being can bridge the gap between the alleged promises of social media and what it actually has to offer to students in terms of self-concept, self-respect, social role, and coping strategies (for stress, anxiety, etc.).

Exemplary general literature on psychological effects of social media

Before getting down to the effects of social media on students’ emotional well-being, some exemplary works of research in recent years on the topic among general populations are reviewed. For one, Aalbers et al. (2018) reported that individuals who spent more time passively working with social media suffered from more intense levels of hopelessness, loneliness, depression, and perceived inferiority. For another, Tang et al. (2013) observed that the procedures of sharing information, commenting, showing likes and dislikes, posting messages, and doing other common activities on social media are correlated with higher stress. Similarly, Ley et al. (2014) described that people who spend 2 h, on average, on social media applications will face many tragic news, posts, and stories which can raise the total intensity of their stress. This stress-provoking effect of social media has been also pinpointed by Weng and Menczer (2015) , who contended that social media becomes a main source of stress because people often share all kinds of posts, comments, and stories ranging from politics and economics, to personal and social affairs. According to Iwamoto and Chun (2020) , anxiety and depression are the negative emotions that an individual may develop when some source of stress is present. In other words, when social media sources become stress-inducing, there are high chances that anxiety and depression also develop.

Charoensukmongkol (2018) reckoned that the mental health and well-being of the global population can be at a great risk through the uncontrolled massive use of social media. These researchers also showed that social media sources can exert negative affective impacts on teenagers, as they can induce more envy and social comparison. According to Fleck and Johnson-Migalski (2015) , though social media, at first, plays the role of a stress-coping strategy, when individuals continue to see stressful conditions (probably experienced and shared by others in media), they begin to develop stress through the passage of time. Chukwuere and Chukwuere (2017) maintained that social media platforms continue to be the major source of changing mood among general populations. For example, someone might be passively using a social media sphere, and s/he may finally find him/herself with a changed mood depending on the nature of the content faced. Then, this good or bad mood is easily shared with others in a flash through the social media. Finally, as Alahmar (2016) described, social media exposes people especially the young generation to new exciting activities and events that may attract them and keep them engaged in different media contexts for hours just passing their time. It usually leads to reduced productivity, reduced academic achievement, and addiction to constant media use ( Alahmar, 2016 ).

The number of studies on the potential psychological effects of social media on people in general is higher than those selectively addressed here. For further insights into this issue, some other suggested works of research include Chang (2012) , Sriwilai and Charoensukmongkol (2016) , and Zareen et al. (2016) . Now, we move to the studies that more specifically explored the effects of social media on students’ affective states.

Review of the affective influences of social media on students

Vygotsky’s mediational theory (see Fernyhough, 2008 ) can be regarded as a main theoretical background for the support of social media on learners’ affective states. Based on this theory, social media can play the role of a mediational means between learners and the real environment. Learners’ understanding of this environment can be mediated by the image shaped via social media. This image can be either close to or different from the reality. In the case of the former, learners can develop their self-image and self-esteem. In the case of the latter, learners might develop unrealistic expectations of themselves by comparing themselves to others. As it will be reviewed below among the affective variables increased or decreased in students under the influence of the massive use of social media are anxiety, stress, depression, distress, rumination, and self-esteem. These effects have been explored more among school students in the age range of 13–18 than university students (above 18), but some studies were investigated among college students as well. Exemplary works of research on these affective variables are reviewed here.

In a cross-sectional study, O’Dea and Campbell (2011) explored the impact of online interactions of social networks on the psychological distress of adolescent students. These researchers found a negative correlation between the time spent on social networking and mental distress. Dumitrache et al. (2012) explored the relations between depression and the identity associated with the use of the popular social media, the Facebook. This study showed significant associations between depression and the number of identity-related information pieces shared on this social network. Neira and Barber (2014) explored the relationship between students’ social media use and depressed mood at teenage. No significant correlation was found between these two variables. In the same year, Tsitsika et al. (2014) explored the associations between excessive use of social media and internalizing emotions. These researchers found a positive correlation between more than 2-h a day use of social media and anxiety and depression.

Hanprathet et al. (2015) reported a statistically significant positive correlation between addiction to Facebook and depression among about a thousand high school students in wealthy populations of Thailand and warned against this psychological threat. Sampasa-Kanyinga and Lewis (2015) examined the relationship between social media use and psychological distress. These researchers found that the use of social media for more than 2 h a day was correlated with a higher intensity of psychological distress. Banjanin et al. (2015) tested the relationship between too much use of social networking and depression, yet found no statistically significant correlation between these two variables. Frison and Eggermont (2016) examined the relationships between different forms of Facebook use, perceived social support of social media, and male and female students’ depressed mood. These researchers found a positive association between the passive use of the Facebook and depression and also between the active use of the social media and depression. Furthermore, the perceived social support of the social media was found to mediate this association. Besides, gender was found as the other factor to mediate this relationship.

Vernon et al. (2017) explored change in negative investment in social networking in relation to change in depression and externalizing behavior. These researchers found that increased investment in social media predicted higher depression in adolescent students, which was a function of the effect of higher levels of disrupted sleep. Barry et al. (2017) explored the associations between the use of social media by adolescents and their psychosocial adjustment. Social media activity showed to be positively and moderately associated with depression and anxiety. Another investigation was focused on secondary school students in China conducted by Li et al. (2017) . The findings showed a mediating role of insomnia on the significant correlation between depression and addiction to social media. In the same year, Yan et al. (2017) aimed to explore the time spent on social networks and its correlation with anxiety among middle school students. They found a significant positive correlation between more than 2-h use of social networks and the intensity of anxiety.

Also in China, Wang et al. (2018) showed that addiction to social networking sites was correlated positively with depression, and this correlation was mediated by rumination. These researchers also found that this mediating effect was moderated by self-esteem. It means that the effect of addiction on depression was compounded by low self-esteem through rumination. In another work of research, Drouin et al. (2018) showed that though social media is expected to act as a form of social support for the majority of university students, it can adversely affect students’ mental well-being, especially for those who already have high levels of anxiety and depression. In their research, the social media resources were found to be stress-inducing for half of the participants, all university students. The higher education population was also studied by Iwamoto and Chun (2020) . These researchers investigated the emotional effects of social media in higher education and found that the socially supportive role of social media was overshadowed in the long run in university students’ lives and, instead, fed into their perceived depression, anxiety, and stress.

Keles et al. (2020) provided a systematic review of the effect of social media on young and teenage students’ depression, psychological distress, and anxiety. They found that depression acted as the most frequent affective variable measured. The most salient risk factors of psychological distress, anxiety, and depression based on the systematic review were activities such as repeated checking for messages, personal investment, the time spent on social media, and problematic or addictive use. Similarly, Mathewson (2020) investigated the effect of using social media on college students’ mental health. The participants stated the experience of anxiety, depression, and suicidality (thoughts of suicide or attempts to suicide). The findings showed that the types and frequency of using social media and the students’ perceived mental health were significantly correlated with each other.

The body of research on the effect of social media on students’ affective and emotional states has led to mixed results. The existing literature shows that there are some positive and some negative affective impacts. Yet, it seems that the latter is pre-dominant. Mathewson (2020) attributed these divergent positive and negative effects to the different theoretical frameworks adopted in different studies and also the different contexts (different countries with whole different educational systems). According to Fredrickson’s broaden-and-build theory of positive emotions ( Fredrickson, 2001 ), the mental repertoires of learners can be built and broadened by how they feel. For instance, some external stimuli might provoke negative emotions such as anxiety and depression in learners. Having experienced these negative emotions, students might repeatedly check their messages on social media or get addicted to them. As a result, their cognitive repertoire and mental capacity might become limited and they might lose their concentration during their learning process. On the other hand, it should be noted that by feeling positive, learners might take full advantage of the affordances of the social media and; thus, be able to follow their learning goals strategically. This point should be highlighted that the link between the use of social media and affective states is bi-directional. Therefore, strategic use of social media or its addictive use by students can direct them toward either positive experiences like enjoyment or negative ones such as anxiety and depression. Also, these mixed positive and negative effects are similar to the findings of several other relevant studies on general populations’ psychological and emotional health. A number of studies (with general research populations not necessarily students) showed that social networks have facilitated the way of staying in touch with family and friends living far away as well as an increased social support ( Zhang, 2017 ). Given the positive and negative emotional effects of social media, social media can either scaffold the emotional repertoire of students, which can develop positive emotions in learners, or induce negative provokers in them, based on which learners might feel negative emotions such as anxiety and depression. However, admittedly, social media has also generated a domain that encourages the act of comparing lives, and striving for approval; therefore, it establishes and internalizes unrealistic perceptions ( Virden et al., 2014 ; Radovic et al., 2017 ).

It should be mentioned that the susceptibility of affective variables to social media should be interpreted from a dynamic lens. This means that the ecology of the social media can make changes in the emotional experiences of learners. More specifically, students’ affective variables might self-organize into different states under the influence of social media. As for the positive correlation found in many studies between the use of social media and such negative effects as anxiety, depression, and stress, it can be hypothesized that this correlation is induced by the continuous comparison the individual makes and the perception that others are doing better than him/her influenced by the posts that appear on social media. Using social media can play a major role in university students’ psychological well-being than expected. Though most of these studies were correlational, and correlation is not the same as causation, as the studies show that the number of participants experiencing these negative emotions under the influence of social media is significantly high, more extensive research is highly suggested to explore causal effects ( Mathewson, 2020 ).

As the review of exemplary studies showed, some believed that social media increased comparisons that students made between themselves and others. This finding ratifies the relevance of the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ) and Festinger’s (1954) Social Comparison Theory. Concerning the negative effects of social media on students’ psychology, it can be argued that individuals may fail to understand that the content presented in social media is usually changed to only represent the attractive aspects of people’s lives, showing an unrealistic image of things. We can add that this argument also supports the relevance of the Social Comparison Theory and the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ), because social media sets standards that students think they should compare themselves with. A constant observation of how other students or peers are showing their instances of achievement leads to higher self-evaluation ( Stapel and Koomen, 2000 ). It is conjectured that the ubiquitous role of social media in student life establishes unrealistic expectations and promotes continuous comparison as also pinpointed in the Interpretation Comparison Model ( Stapel and Koomen, 2000 ; Stapel, 2007 ).

Implications of the study

The use of social media is ever increasing among students, both at school and university, which is partly because of the promises of technological advances in communication services and partly because of the increased use of social networks for educational purposes in recent years after the pandemic. This consistent use of social media is not expected to leave students’ psychological, affective and emotional states untouched. Thus, it is necessary to know how the growing usage of social networks is associated with students’ affective health on different aspects. Therefore, we found it useful to summarize the research findings in recent years in this respect. If those somehow in charge of student affairs in educational settings are aware of the potential positive or negative effects of social media usage on students, they can better understand the complexities of students’ needs and are better capable of meeting them.

Psychological counseling programs can be initiated at schools or universities to check upon the latest state of students’ mental and emotional health influenced by the pervasive use of social media. The counselors can be made aware of the potential adverse effects of social networking and can adapt the content of their inquiries accordingly. Knowledge of the potential reasons for student anxiety, depression, and stress can help school or university counselors to find individualized coping strategies when they diagnose any symptom of distress in students influenced by an excessive use of social networking.

Admittedly, it is neither possible to discard the use of social media in today’s academic life, nor to keep students’ use of social networks fully controlled. Certainly, the educational space in today’s world cannot do without the social media, which has turned into an integral part of everybody’s life. Yet, probably students need to be instructed on how to take advantage of the media and to be the least affected negatively by its occasional superficial and unrepresentative content. Compensatory programs might be needed at schools or universities to encourage students to avoid making unrealistic and impartial comparisons of themselves and the flamboyant images of others displayed on social media. Students can be taught to develop self-appreciation and self-care while continuing to use the media to their benefit.

The teachers’ role as well as the curriculum developers’ role are becoming more important than ever, as they can significantly help to moderate the adverse effects of the pervasive social media use on students’ mental and emotional health. The kind of groupings formed for instructional purposes, for example, in social media can be done with greater care by teachers to make sure that the members of the groups are homogeneous and the tasks and activities shared in the groups are quite relevant and realistic. The teachers cannot always be in a full control of students’ use of social media, and the other fact is that students do not always and only use social media for educational purposes. They spend more time on social media for communicating with friends or strangers or possibly they just passively receive the content produced out of any educational scope just for entertainment. This uncontrolled and unrealistic content may give them a false image of life events and can threaten their mental and emotional health. Thus, teachers can try to make students aware of the potential hazards of investing too much of their time on following pages or people that publish false and misleading information about their personal or social identities. As students, logically expected, spend more time with their teachers than counselors, they may be better and more receptive to the advice given by the former than the latter.

Teachers may not be in full control of their students’ use of social media, but they have always played an active role in motivating or demotivating students to take particular measures in their academic lives. If teachers are informed of the recent research findings about the potential effects of massively using social media on students, they may find ways to reduce students’ distraction or confusion in class due to the excessive or over-reliant use of these networks. Educators may more often be mesmerized by the promises of technology-, computer- and mobile-assisted learning. They may tend to encourage the use of social media hoping to benefit students’ social and interpersonal skills, self-confidence, stress-managing and the like. Yet, they may be unaware of the potential adverse effects on students’ emotional well-being and, thus, may find the review of the recent relevant research findings insightful. Also, teachers can mediate between learners and social media to manipulate the time learners spend on social media. Research has mainly indicated that students’ emotional experiences are mainly dependent on teachers’ pedagogical approach. They should refrain learners from excessive use of, or overreliance on, social media. Raising learners’ awareness of this fact that individuals should develop their own path of development for learning, and not build their development based on unrealistic comparison of their competences with those of others, can help them consider positive values for their activities on social media and, thus, experience positive emotions.

At higher education, students’ needs are more life-like. For example, their employment-seeking spirits might lead them to create accounts in many social networks, hoping for a better future. However, membership in many of these networks may end in the mere waste of the time that could otherwise be spent on actual on-campus cooperative projects. Universities can provide more on-campus resources both for research and work experience purposes from which the students can benefit more than the cyberspace that can be tricky on many occasions. Two main theories underlying some negative emotions like boredom and anxiety are over-stimulation and under-stimulation. Thus, what learners feel out of their involvement in social media might be directed toward negative emotions due to the stimulating environment of social media. This stimulating environment makes learners rely too much, and spend too much time, on social media or use them obsessively. As a result, they might feel anxious or depressed. Given the ubiquity of social media, these negative emotions can be replaced with positive emotions if learners become aware of the psychological effects of social media. Regarding the affordances of social media for learners, they can take advantage of the potential affordances of these media such as improving their literacy, broadening their communication skills, or enhancing their distance learning opportunities.

A review of the research findings on the relationship between social media and students’ affective traits revealed both positive and negative findings. Yet, the instances of the latter were more salient and the negative psychological symptoms such as depression, anxiety, and stress have been far from negligible. These findings were discussed in relation to some more relevant theories such as the social comparison theory, which predicted that most of the potential issues with the young generation’s excessive use of social media were induced by the unfair comparisons they made between their own lives and the unrealistic portrayal of others’ on social media. Teachers, education policymakers, curriculum developers, and all those in charge of the student affairs at schools and universities should be made aware of the psychological effects of the pervasive use of social media on students, and the potential threats.

It should be reminded that the alleged socially supportive and communicative promises of the prevalent use of social networking in student life might not be fully realized in practice. Students may lose self-appreciation and gratitude when they compare their current state of life with the snapshots of others’ or peers’. A depressed or stressed-out mood can follow. Students at schools or universities need to learn self-worth to resist the adverse effects of the superficial support they receive from social media. Along this way, they should be assisted by the family and those in charge at schools or universities, most importantly the teachers. As already suggested, counseling programs might help with raising students’ awareness of the potential psychological threats of social media to their health. Considering the ubiquity of social media in everybody’ life including student life worldwide, it seems that more coping and compensatory strategies should be contrived to moderate the adverse psychological effects of the pervasive use of social media on students. Also, the affective influences of social media should not be generalized but they need to be interpreted from an ecological or contextual perspective. This means that learners might have different emotions at different times or different contexts while being involved in social media. More specifically, given the stative approach to learners’ emotions, what learners emotionally experience in their application of social media can be bound to their intra-personal and interpersonal experiences. This means that the same learner at different time points might go through different emotions Also, learners’ emotional states as a result of their engagement in social media cannot be necessarily generalized to all learners in a class.

As the majority of studies on the psychological effects of social media on student life have been conducted on school students than in higher education, it seems it is too soon to make any conclusive remark on this population exclusively. Probably, in future, further studies of the psychological complexities of students at higher education and a better knowledge of their needs can pave the way for making more insightful conclusions about the effects of social media on their affective states.

Suggestions for further research

The majority of studies on the potential effects of social media usage on students’ psychological well-being are either quantitative or qualitative in type, each with many limitations. Presumably, mixed approaches in near future can better provide a comprehensive assessment of these potential associations. Moreover, most studies on this topic have been cross-sectional in type. There is a significant dearth of longitudinal investigation on the effect of social media on developing positive or negative emotions in students. This seems to be essential as different affective factors such as anxiety, stress, self-esteem, and the like have a developmental nature. Traditional research methods with single-shot designs for data collection fail to capture the nuances of changes in these affective variables. It can be expected that more longitudinal studies in future can show how the continuous use of social media can affect the fluctuations of any of these affective variables during the different academic courses students pass at school or university.

As already raised in some works of research reviewed, the different patterns of impacts of social media on student life depend largely on the educational context. Thus, the same research designs with the same academic grade students and even the same age groups can lead to different findings concerning the effects of social media on student psychology in different countries. In other words, the potential positive and negative effects of popular social media like Facebook, Snapchat, Twitter, etc., on students’ affective conditions can differ across different educational settings in different host countries. Thus, significantly more research is needed in different contexts and cultures to compare the results.

There is also a need for further research on the higher education students and how their affective conditions are positively and negatively affected by the prevalent use of social media. University students’ psychological needs might be different from other academic grades and, thus, the patterns of changes that the overall use of social networking can create in their emotions can be also different. Their main reasons for using social media might be different from school students as well, which need to be investigated more thoroughly. The sorts of interventions needed to moderate the potential negative effects of social networking on them can be different too, all requiring a new line of research in education domain.

Finally, there are hopes that considering the ever-increasing popularity of social networking in education, the potential psychological effects of social media on teachers be explored as well. Though teacher psychology has only recently been considered for research, the literature has provided profound insights into teachers developing stress, motivation, self-esteem, and many other emotions. In today’s world driven by global communications in the cyberspace, teachers like everyone else are affecting and being affected by social networking. The comparison theory can hold true for teachers too. Thus, similar threats (of social media) to self-esteem and self-worth can be there for teachers too besides students, which are worth investigating qualitatively and quantitatively.

Probably a new line of research can be initiated to explore the co-development of teacher and learner psychological traits under the influence of social media use in longitudinal studies. These will certainly entail sophisticated research methods to be capable of unraveling the nuances of variation in these traits and their mutual effects, for example, stress, motivation, and self-esteem. If these are incorporated within mixed-approach works of research, more comprehensive and better insightful findings can be expected to emerge. Correlational studies need to be followed by causal studies in educational settings. As many conditions of the educational settings do not allow for having control groups or randomization, probably, experimental studies do not help with this. Innovative research methods, case studies or else, can be used to further explore the causal relations among the different features of social media use and the development of different affective variables in teachers or learners. Examples of such innovative research methods can be process tracing, qualitative comparative analysis, and longitudinal latent factor modeling (for a more comprehensive view, see Hiver and Al-Hoorie, 2019 ).

Author contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This study was sponsored by Wuxi Philosophy and Social Sciences bidding project—“Special Project for Safeguarding the Rights and Interests of Workers in the New Form of Employment” (Grant No. WXSK22-GH-13). This study was sponsored by the Key Project of Party Building and Ideological and Political Education Research of Nanjing University of Posts and Telecommunications—“Research on the Guidance and Countermeasures of Network Public Opinion in Colleges and Universities in the Modern Times” (Grant No. XC 2021002).

Conflict of interest

Author XX was employed by China Mobile Group Jiangsu Co., Ltd.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Aalbers, G., McNally, R. J., Heeren, A., de Wit, S., and Fried, E. I. (2018). Social media and depression symptoms: A network perspective. J. Exp. Psychol. Gen. 148, 1454–1462. doi: 10.1037/xge0000528

PubMed Abstract | CrossRef Full Text | Google Scholar

Abbott, J. (2017). Introduction: Assessing the social and political impact of the internet and new social media in Asia. J. Contemp. Asia 43, 579–590. doi: 10.1080/00472336.2013.785698

CrossRef Full Text | Google Scholar

Alahmar, A. T. (2016). The impact of social media on the academic performance of second year medical students at College of Medicine, University of Babylon, Iraq. J. Med. Allied Sci. 6, 77–83. doi: 10.5455/jmas.236927

Banjanin, N., Banjanin, N., Dimitrijevic, I., and Pantic, I. (2015). Relationship between internet use and depression: Focus on physiological mood oscillations, social networking and online addictive behavior. Comp. Hum. Behav. 43, 308–312. doi: 10.1016/j.chb.2014.11.013

Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., and Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. J. Adolesc. 61, 1–11. doi: 10.1016/j.adolescence.2017.08.005

Chang, Y. (2012). The relationship between maladaptive perfectionism with burnout: Testing mediating effect of emotion-focused coping. Pers. Individ. Differ. 53, 635–639. doi: 10.1016/j.paid.2012.05.002

Charoensukmongkol, P. (2018). The impact of social media on social comparison and envy in teenagers: The moderating role of the parent comparing children and in-group competition among friends. J. Child Fam. Stud. 27, 69–79. doi: 10.1007/s10826-017-0872-8

Chukwuere, J. E., and Chukwuere, P. C. (2017). The impact of social media on social lifestyle: A case study of university female students. Gender Behav. 15, 9966–9981.

Google Scholar

Drouin, M., Reining, L., Flanagan, M., Carpenter, M., and Toscos, T. (2018). College students in distress: Can social media be a source of social support? Coll. Stud. J. 52, 494–504.

Dumitrache, S. D., Mitrofan, L., and Petrov, Z. (2012). Self-image and depressive tendencies among adolescent Facebook users. Rev. Psihol. 58, 285–295.

PubMed Abstract | Google Scholar

Fernyhough, C. (2008). Getting Vygotskian about theory of mind: Mediation, dialogue, and the development of social understanding. Dev. Rev. 28, 225–262. doi: 10.1016/j.dr.2007.03.001

Festinger, L. (1954). A Theory of social comparison processes. Hum. Relat. 7, 117–140. doi: 10.1177/001872675400700202

Fleck, J., and Johnson-Migalski, L. (2015). The impact of social media on personal and professional lives: An Adlerian perspective. J. Individ. Psychol. 71, 135–142. doi: 10.1353/jip.2015.0013

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol. 56, 218–226. doi: 10.1037/0003-066X.56.3.218

Frison, E., and Eggermont, S. (2016). Exploring the relationships between different types of Facebook use, perceived online social support, and adolescents’ depressed mood. Soc. Sci. Compu. Rev. 34, 153–171. doi: 10.1177/0894439314567449

Hanprathet, N., Manwong, M., Khumsri, J., Yingyeun, R., and Phanasathit, M. (2015). Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98, S81–S90.

Hiver, P., and Al-Hoorie, A. H. (2019). Research Methods for Complexity Theory in Applied Linguistics. Bristol: Multilingual Matters. doi: 10.21832/HIVER5747

Iwamoto, D., and Chun, H. (2020). The emotional impact of social media in higher education. Int. J. High. Educ. 9, 239–247. doi: 10.5430/ijhe.v9n2p239

Keles, B., McCrae, N., and Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25, 79–93. doi: 10.1080/02673843.2019.1590851

Ley, B., Ogonowski, C., Hess, J., Reichling, T., Wan, L., and Wulf, V. (2014). Impacts of new technologies on media usage and social behavior in domestic environments. Behav. Inform. Technol. 33, 815–828. doi: 10.1080/0144929X.2013.832383

Li, J.-B., Lau, J. T. F., Mo, P. K. H., Su, X.-F., Tang, J., Qin, Z.-G., et al. (2017). Insomnia partially mediated the association between problematic Internet use and depression among secondary school students in China. J. Behav. Addict. 6, 554–563. doi: 10.1556/2006.6.2017.085

Mathewson, M. (2020). The impact of social media usage on students’ mental health. J. Stud. Affairs 29, 146–160.

Neira, B. C. J., and Barber, B. L. (2014). Social networking site use: Linked to adolescents’ social self-concept, self-esteem, and depressed mood. Aus. J. Psychol. 66, 56–64. doi: 10.1111/ajpy.12034

O’Dea, B., and Campbell, A. (2011). Online social networking amongst teens: Friend or foe? Ann. Rev. CyberTher. Telemed. 9, 108–112.

Radovic, A., Gmelin, T., Stein, B. D., and Miller, E. (2017). Depressed adolescents positive and negative use of social media. J. Adolesc. 55, 5–15. doi: 10.1016/j.adolescence.2016.12.002

Sampasa-Kanyinga, H., and Lewis, R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychol. Behav. Soc. Network. 18, 380–385. doi: 10.1089/cyber.2015.0055

Sriwilai, K., and Charoensukmongkol, P. (2016). Face it, don’t Facebook it: Impacts of social media addiction on mindfulness, coping strategies and the consequence on emotional exhaustion. Stress Health 32, 427–434. doi: 10.1002/smi.2637

Stapel, D. A. (2007). “In the mind of the beholder: The interpretation comparison model of accessibility effects,” in Assimilation and Contrast in Social Psychology , eds D. A. Stapel and J. Suls (London: Psychology Press), 143–164.

Stapel, D. A., and Koomen, W. (2000). Distinctiveness of others, mutability of selves: Their impact on self-evaluations. J. Pers. Soc. Psychol. 79, 1068–1087. doi: 10.1037//0022-3514.79.6.1068

Tang, F., Wang, X., and Norman, C. S. (2013). An investigation of the impact of media capabilities and extraversion on social presence and user satisfaction. Behav. Inform. Technol. 32, 1060–1073. doi: 10.1080/0144929X.2013.830335

Tsitsika, A. K., Tzavela, E. C., Janikian, M., Ólafsson, K., Iordache, A., Schoenmakers, T. M., et al. (2014). Online social networking in adolescence: Patterns of use in six European countries and links with psychosocial functioning. J. Adolesc. Health 55, 141–147. doi: 10.1016/j.jadohealth.2013.11.010

Vernon, L., Modecki, K. L., and Barber, B. L. (2017). Tracking effects of problematic social networking on adolescent psychopathology: The mediating role of sleep disruptions. J. Clin. Child Adolesc. Psychol. 46, 269–283. doi: 10.1080/15374416.2016.1188702

Virden, A., Trujillo, A., and Predeger, E. (2014). Young adult females’ perceptions of high-risk social media behaviors: A focus-group approach. J. Commun. Health Nurs. 31, 133–144. doi: 10.1080/07370016.2014.926677

Wang, P., Wang, X., Wu, Y., Xie, X., Wang, X., Zhao, F., et al. (2018). Social networking sites addiction and adolescent depression: A moderated mediation model of rumination and self-esteem. Pers. Individ. Differ. 127, 162–167. doi: 10.1016/j.paid.2018.02.008

Weng, L., and Menczer, F. (2015). Topicality and impact in social media: Diverse messages, focused messengers. PLoS One 10:e0118410. doi: 10.1371/journal.pone.0118410

Yan, H., Zhang, R., Oniffrey, T. M., Chen, G., Wang, Y., Wu, Y., et al. (2017). Associations among screen time and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. Int. J. Environ. Res. Public Health 14:596. doi: 10.3390/ijerph14060596

Zareen, N., Karim, N., and Khan, U. A. (2016). Psycho-emotional impact of social media emojis. ISRA Med. J. 8, 257–262.

Zhang, R. (2017). The stress-buffering effect of self-disclosure on Facebook: An examination of stressful life events, social support, and mental health among college students. Comp. Hum. Behav. 75, 527–537. doi: 10.1016/j.chb.2017.05.043

Keywords : affective variables, education, emotions, social media, post-pandemic, emotional needs

Citation: Chen M and Xiao X (2022) The effect of social media on the development of students’ affective variables. Front. Psychol. 13:1010766. doi: 10.3389/fpsyg.2022.1010766

Received: 03 August 2022; Accepted: 25 August 2022; Published: 15 September 2022.

Reviewed by:

Copyright © 2022 Chen and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miao Chen, [email protected] ; Xin Xiao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Behavior problems and children’s academic achievement: A test of growth-curve models with gender and racial differences

Kristen p. kremer.

a School of Social Work, College for Public Health and Social Justice, Saint Louis University, 3550 Lindell Blvd, St. Louis, MO 63103, United States

Andrea Flower

b Department of Special Education, The University of Texas at Austin, 1912 Speedway, Austin, TX 78712, United States

Michael G. Vaughn

The aim of this study was to examine the longitudinal association between externalizing and internalizing behavior and children’s academic achievement, particularly in terms of whether these variables varied as a function of gender and race. Data pertaining to externalizing and internalizing behavior, academic achievement, gender, and race from three waves of the Child Development Supplement of the Panel Study of Income Dynamics ( N = 2028) were used. Results indicate that behavior problems had a negative relationship with academic performance and some of these associations endured over time. Externalizing behavior impacted reading scores more negatively for females compared to males at baseline, but the impact of externalizing behavior on long-term reading outcomes did not vary by gender. Externalizing behavior impacted reading scores more negatively for Black children than White children at multiple points in time. Differences between males, females, Black, and White children concerning behavior and achievement are explained. Implications, limitations, and ideas for future research are also presented.

1. Introduction

Federal law ( No Child Left Behind, NCLB Act of 2001, 2002 ) mandates academic performance for all children as the top priority for U.S. public schools. NCLB places emphasis on instruction and performance particularly for those with the lowest levels of performance. Although educators work with diligence to achieve at these high levels for all children, often times other factors compromise that progress. For example, some students have extreme academic difficulty that is not easily overcome. Other students have challenging behavior that interferes with teaching and learning. Both of these problems have severe repercussions for the school and life outcomes of these youth ( Battin-Pearson et al., 2000 ; Breslau et al., 2009 ).

To address these challenges and meet the requirements of NCLB, many schools have adopted multi-tiered systems of support for students who have academic difficulties as well as systems for students who have behavioral difficulties ( Doolittle, Horner, Bradley, & Vincent, 2007 ; Spectrum K12, 2009 ). These systems are often referred to as a Response to Intervention (RtI) framework within the academic domain and Positive Behavior Interventions and Support (PBIS) within the behavioral domain ( Sugai & Horner, 2009 ). Although use of such systems is frequent, the systems do not tend to be consciously coupled with one another; yet, research ( Maguin & Loeber, 1996 ; Malecki & Elliot, 2002 ) shows that students with academic problems may also have behavioral problems and that students with behavioral problems may also have academic problems.

The relationship between academic and behavior problems is a long recognized phenomenon ( Alexander, Entwisle, & Horsey, 1997 ; Hinshaw, 1992 ). A significant amount of research (see Lane, Barton-Arwood, Nelson, & Wehby, 2008 ; Nelson, Benner, Lane, & Smith, 2004 ; Reid, Gonzalez, Nordness, Trout, & Epstein, 2004 ) concerning this relationship comes from the study of students with disabilities such as emotional disturbance (ED) and learning disabilities (LD); yet, as Algozzine, Wang, and Violette (2011) indicate, research on this topic regarding these populations does “little to clarify, confirm, or advance the link between achievement and behavior or the causes for it” (p. 5). In fact, the relationship between achievement and behavior also affects other students, not just those with disabilities: For example as boys from low-income families ( Moilanen, Shaw, & Maxwell, 2010 ) or youth with persistent patterns of externalizing behavior ( Vitaro, Brendgen, Larose, & Tremblay, 2005 ).

Hinshaw (1992) suggested four possibilities for the relationship between academic achievement and behavior including: (a) achievement affects behavior, (b) behavior affects achievement, (c) reciprocal relationships exist between academic and behavioral variables, and (d) some third variable mutually affects behavior and achievement. Although researchers have investigated extensively to understand the relationships between these variables, the relationship remains unclear. Literature has examined the potential impacts of academic achievement on behaviors. In their meta-analysis, Maguin and Loeber (1996) found that poor academic performance appears to be related to frequency, persistence, and seriousness of delinquent activity. A more recent study ( Joffe & Black, 2012 ) revealed that among a sample of 352 secondary school students, those with low academic performance had significantly greater social, emotional, and behavioral difficulties. A variety of research has also suggested that intervention components on the academic domain may have an effect on the behavior domain ( Herrenkohl et al., 2001 ; Maguin & Loeber, 1996 ).

The current study, however, focuses on predicting academic achievement from behavior problems, the second possibility suggested by Hinshaw (1992) . DeLisi and Vaughn’s temperament theory can be utilized to understand the process by which behavior predicts academic achievement. According to DeLisi and Vaughn’s theory, temperament involves the “stable, largely inborn tendency with which an individual experiences the environment and regulate his or her responses to the environment” ( Vaughn, DeLisi, & Matto, 2014 , p. 106). Components of temperament include effortful control, the ability to “inhibit a dominant response in favor of performing a subdominant response” ( DeLisi & Vaughn, 2014 , p. 12), and negative emotionality, which includes the display of emotions such as frustration, fear, sadness, and discomfort. Inside the classroom, temperament manifests itself as behavior problems and can greatly impact a variety of academic outcomes, including school readiness, elementary school grades, college admission tests, and high school dropout ( Duckworth & Allred, 2012 ; Gumora & Arsenio, 2002 ).

At school, children have different experiences based on temperament. Research has found, for example, children with low self-control to exhibit poorer work habits than children with higher self-control ( Rimm-Kaufman, Curby, Grimm, Nathanson, & Brock, 2009 ). Additional research has found children with lower effortful control to have greater conflict with teachers, while children with higher effortful control have closer relationships with teachers ( Rudasill & Rimm-Kaufman, 2009 ). As a result of these factors, research by Duckworth and Seligman (2005) found self-discipline to be a better predictor of academic performance than IQ. However, the school setting can also enhance academic outcomes for children with difficult temperaments. In particular, research by Rudasill and Rimm-Kaufman (2009) found emotional support from teachers to moderate the relationship between children’s temperamental attention and school achievement, while Valiente, Lemery-Chalfant, and Castro (2007) observed school liking to mediate the relationship between children’s effortful control and academic competence.

Although the aforementioned research has found temperament and behavior problems to negatively predict academic achievement (e.g., Malecki & Elliot, 2002 ; Myers, Milne, Baker, & Ginsburg, 1987 ; Wentzel, 1993 ), most studies relied on cross-sectional designs and did not take into consideration the nature of behaviors (i.e., externalizing or internalizing behaviors). Few studies investigated the interactions between behaviors and other issues (e.g., race and gender). The present analysis contributes to this topic by investigating the associations of externalizing and internalizing behavior to children’s academic achievement in longitudinal data with further examination of whether the association between behavior problems and academic achievement varies by gender and race.

1.1. Externalizing and internalizing behavior related to academic achievement

Externalizing and internalizing behavioral profiles differ greatly. While externalizing behavior is characterized by defiance, disruptiveness, aggressiveness, impulsivity, antisocial behavior, and over-activity, internalizing behavior is marked by withdrawal, dysphoria, and anxiety ( Achenbach & Edelbrock, 1978 ; Hinshaw, 1992 ). Many researchers have considered the roles of internalizing behavior and externalizing behavior on academic achievement, yet the evidence remains mixed ( Masten et al., 2012 ). Nelson et al. (2004) , studying academic achievement of students with emotional/behavioral disorders, found deficits in reading, math, and written language. Math deficits, in particular, appeared to worsen over time—that is, a greater percentage of adolescents with emotional/behavioral disorders performed below average on math measures. Their analyses found no significant differences between males and females on academic achievement measures. Study findings also observed externalizing behaviors to be related to deficits in all three academic areas, while no association was found for internalizing behaviors. On the other hand, a meta-analysis of 26 studies by Riglin, Petrides, Frederickson, and Rice (2014) found depression, anxiety, and other internalizing behaviors to be associated with increased school failure.

1.2. Race and gender

In addition to the externalizing and internalizing characteristics of behavior, the scholarly conversation about the relationship between academic achievement and behavior problems would be incomplete if other issues were not examined. Race is certainly an issue that should be considered when investigating the relationship between achievement and behavior. Race is a critical factor for several interrelated reasons. First, an achievement gap is widely recognized between Black and White students ( Ladson-Billings, 2006 ; Vanneman, Hamilton, Baldwin Anderson, & Rahman, 2009 ). Second, Black students continue to be excluded from school through suspension and expulsion at much higher rates than White students ( Wallace, Goodkind, Wallace, & Bachman, 2008 ). Differences in disciplinary practices can contribute to the academic performance gap between Black and White students as when students are suspended from school or the classroom they are provided with fewer opportunities for learning ( Gregory, Skiba, & Noguera, 2010 ). Unfortunately, suspension is linked to a number of problems including dropout, which has also been linked to academic performance ( Allensworth & Easton, 2007 ) and Black youth dropout of school at a greater rate than White youth ( Chapman, Laird, Ifill, & Kewal Ramani, 2010 ). Racial differences in suspension rates can be directly linked to differences in problem behaviors. While many researchers suggest racial disparities in suspension rates result from racial discrimination, recent findings by Wright, Morgan, Coyne, Beaver, and Barnes (2014) indicates that racial differences in suspension rates can be explained by prior behavior problems. Finally, Black students are consistently over-identified for special education eligibility particularly in the categories of Emotional Disturbance and Intellectual Disability ( Hosp & Reschly, 2004 ), which are disability categories with academic and behavioral risk profiles. In addition to the aforementioned link between academic achievement and behavior, behavior problems may disproportionately impact academic outcomes for Black students. In particular, Rabiner, Murray, Schmid, and Malone (2004) found challenging behavior in the form of inattention to be rated higher among White students, yet inattention demonstrated a negative association with academic achievement for Black students but not White students.

Research has also shown an achievement gap between males and females. Historically, achievement and academic performance has favored males over females particularly in math and science ( Weaver-Hightower, 2003 ). Although, a number of studies suggest that females have historically outperformed boys in the area of literacy ( Gambell & Hunter, 1999 ; Ready, LoGerfo, Burkam, & Lee, 2005 ) and may begin school with greater literacy skills than boys ( Ready et al., 2005 ). One possible explanation for this performance difference is classroom behavior. For example, using data from the Early Childhood Longitudinal Study-Kindergarten Cohort, Ready et al. (2005) found that approaches to learning and children’s problematic behaviors explained 70% and 15% of the variance in literacy performance, respectively, with girls receiving better ratings in these areas than boys. Additionally, boys have significantly higher rates of grade retention ( Meisels & Liaw, 1993 ; Ready et al., 2005 ), suspensions, and expulsions than girls ( Gregory et al., 2010 ). Males are also overrepresented in disability groups such as emotional/behavioral disorders ( Coutinho & Oswald, 2005 ).

While a number of the issues highlighted here have been widely studied and attended to, only a few of these variables have been studied simultaneously. For example, the effect of externalizing and internalizing behavior on academic achievement has been somewhat minimally examined (e.g., Arnold, 1997 ; Lane et al., 2008 ; Nelson et al., 2004 ; Nelson, Benner, Neill, & Stage, 2006 ; Ready et al., 2005 ), but many of these studies (Lane et al.; Nelson et al., 2004 ; Nelson et al., 2006 ) as stated earlier, concern populations with disabilities defined by academic and behavioral difficulties. Furthermore, studies investigating such relationships using samples of students who do not have disabilities include information about students given by teacher rather than parent rating scales or direct observation. Finally, most studies are cross-sectional rather than longitudinal and those that are longitudinal (e.g., Ready et al., 2005 ) do not consider all of these variables (i.e., academic and type of behavior problems with race and gender).

1.3. Present study aim

The transactional and progressive associations between behavior problems (externalizing and internalizing) and academic achievement can only be detected in longitudinal studies. Nonetheless, there remain surprisingly few longitudinal studies that have been conducted to examine these links ( Bub, McCartney, & Willett, 2007 ; Chen & Li, 1997 ; McGee, Feehan, Williams, & Anderson, 1992 ); and even fewer that appear to use large national datasets ( Bub et al., 2007 ). This study aims to fill this gap by examining the unfolding of the relationship between externalizing and internalizing behaviors and academic achievement from age 3 to 17 in a nationally representative sample of children. In addition, this analysis allows for examination of the heterogeneity in the association between externalizing and internalizing behaviors and academic achievement by gender and race. Focusing on the second mechanism proposed by Hinshaw (1992) , two main hypotheses were tested in this study. First, it is hypothesized that behavior problems will be associated with decreased academic performance contemporaneously and will be negatively associated with the change of academic performance over time. Second, the association between behavior problems and academic performance will differ by gender or race.

2.1. Data and sample selection

The study objectives were examined using data from the three waves of the Child Development Supplement (CDS) of the Panel Study of Income Dynamics (PSID). The PSID is a longitudinal survey that collected demographic information and socioeconomic characteristics from a nationally representative sample of individuals and their families annually between 1968 and 1997 and biennially thereafter. Beginning in 1997, the PSID supplemented its core data with additional information from a group of children 0–12 years old ( N = 3563) in the Child Development Supplement (CDS). The same children were interviewed three times in 1997, 2002, and 2007, respectively, if they were still younger than age 18 at the time of each interview. The recruiting, eligibility, and attrition of the PSID-CDS have been described elsewhere (Institute for Social Research, 2010). The CDS included measures of a broad array of child developmental outcomes, such as physical health and disability, emotional well-being, cognitive and academic achievement, and social relationships with household members and peers. The CDS collected the information on behavior problems for children older than three in the first wave and conducted standardized achievement tests on children in all three waves.

To examine the relationship between behavior problems and the long-term performance of academic achievement, Black and White children who had at least one valid measure on standardized achievement tests among three waves and had valid information on behavior problems measured in the first wave ( N = 2143) were included in the analysis. The sample participants with only one valid measure among three repeated standardized achievement tests were included because they still contribute to the estimation of the development of academic achievement at a specific time point. In addition, the few children with missing values on variables listed in Table 1 were excluded ( n = 115). The excluded children had missing information mainly on the variables of birth weight ( n = 43), mother’s education ( n = 53), mother’s age ( n = 21), and mother’s employment status ( n = 21); the final sample size was 2028. Since only about 5% of children had missing information, we used listwise deletion in main analyses reported below.

Characteristics of the analytic sample: PSID-CDS (1997–2007) a , ( N = 2028 ).

2.2. Outcome variables

The outcome variables were children’s scores on three subtests of the Woodcock-Johnson Revised (WJ-R) Tests of Achievement ( Woodcock & Johnson, 1989 ) across three waves. As a standardized measure of child’s academic skill, the Woodcock-Johnson Revised (WJ-R) Tests of Achievement have been widely used and have demonstrated excellent reliability and validity. The PSID-CDS administered the Letter-Word Identification test (LW subtest) and the Applied Problems test (AP subtest) on children who were three years or older, and the Passage Comprehension test (PC subtest) on those who were six years or older in all three waves. The LW and PC are two subtests on children’s reading ability, and the AP concerns children’s math ability. The three WJ-R subtests are individually administered scales. The LW subtest measures children’s reading skills particularly their ability to name letters and sounds as well as to decode words. The PC subtest measures children’s comprehension and vocabulary skills using multiple-choice questions. The AP subtest measures children’s ability to solve math problems for applied purposes (e.g., determine whether there is enough money available to purchase items shown on the test page given the coins shown.) The WJ-R Test of Achievement standardizes the raw scores of three tests to a 0–200 continuous variable, respectively; all the WJ-R scores presented in the study thus are standardized ones.

2.3. Independent variables

The analysis included four major independent variables: children’s age (3–17), gender (1 = male and 0 = female), race (1 = Black and 0 = White), and behavior problems. Since the PSID was initiated in 1968, the sample included relatively fewer households with racial background different from Black and White, and those participants were not included in the study sample. The PSID-CDS included a 32-item Behavior Problem Index (BPI) developed by Peterson and Zill (1986) to measure the incidence and severity of child behavior problems (externalizing and internalizing). It was based on primary caregiver’s responses for children three years and older as to whether a set of problem behaviors was often (“3”), sometimes (“2”), or never (“1”) true. The index was divided into two subscales, one for externalizing (α = 0.86) or aggressive behavior (16 items), and the other one for internalizing (α = 0.81), withdrawn or sad behavior (13 items). The externalizing subscale included items such as the child “is impulsive and acts without thinking” and “argues too much”. The internalizing subscale had items such as the child “is withdrawn and has trouble getting involved with other children” and “is unhappy, sad or depressed”. Continuous summary scores from these items were used to assess externalizing and internalizing behaviors. Except for children’s age, all independent variables are measured in the first wave of the Panel Study of Income Dynamics (2012) .

2.4. Covariates

Adjustments were made for three groups of covariates measured in 1997, but for presentation purposes only the coefficients for the key explanatory variables (age, gender, race, and behavior problems) were reported. The first group consisted of children’s health and disability characteristics, including preterm birth (1 = gestational age less than 37 weeks and 0 = others), low birth weight (1 = birth weight less than 2500 g and 0 = others), neonatal intensive care at birth (1 = yes and 0 = no), and physical/mental limitations on childhood or school activities (1 = yes and 0 = no). As shown in the literature (e.g., Lane et al., 2008 ; Nelson et al., 2004 ; Reid et al., 2004 ), disability status of children is an important predictor for both academic achievement and behavior problems.

The second group of covariates consisted of mothers’ characteristics, including age, number of schooling years (1–17 years), employment status (1 = yes and 0 = no), and mothers’ parenting practices. Three indicators of parenting practices created by the PSID-CDS were controlled for: parental warmth, emotional support, and cognitive stimulation, because parenting practices are likely to associate with both children’s academic achievement and behavior problems (e.g., Fan & Chen, 2001 ; Jeynes, 2007 ; Shumow, Vandell, & Posner, 1998 ; Stormshak, Bierman, McMahon, & Lengua, 2000 ). Ranging from 1–5, parental warmth was a standardized scale reported by parents and measuring the warmth of the relationship between mothers and children, including the frequency of showing physical affection, appreciation, and so on. Emotional support and cognitive stimulation were based on observed interactions between mothers and children in home environment by the well-trained interviewers employed by the PSID-CDS. Interviewers were extensively trained in techniques and procedures of general interviews and specific data collection, including unique protocols to conducting observations on emotional support and cognitive simulation. The interviewer recorded observed home environment and interactions between the child and the primary caregivers after the in-home interview. Emotional support ranged from 2–14, and was summarized from items observed by interviewers, such as “mother caressed, kissed, or hugged child at least once” and “mother conversed with child at least twice.” Cognitive stimulation ranged from 2–14, and included items such as “how many books child has read” and “mother provided toys or interesting activities.” Having demonstrated their validity and reliability in previous surveys (e.g., the National Longitudinal Survey of Youth; Panel Study of Income Dynamics, 2012 ), all of these three scales have the value of Cronbach’s alpha greater than 0.82 in the data. The correlation among three parenting indicators is lower than 0.32.

Finally, adjustments for household characteristics: household size, number of children in households, food stamp program participation (1 = yes and 0 = no), Aid to Families with Dependent Children (AFDC) or Temporary Assistance for Needy Families (TANF) participation (1 = yes and 0 = no), homeownership (1 = yes and 0 = no), and log-transformed household income. The analyses also controlled for dichotomous indicators of residence states of the PSID-CDS children, which may serve to impart regional socialization differences on study children.

2.5. Data analyses

In order to examine the long-term performance of children’s academic achievement, a growth curve analysis for each test score in the multilevel modeling context ( Rabe-Hesketh & Skronda, 2012 ) was conducted, and the association between behavior problems measured in 1997 and the changes of children’s academic achievement over time is tested. The model specification is listed below:

where Y ti indicates test score of a child i at wave t ; A ti denotes the age of child i at wave t ; G i and R i refer to gender and race of child i ; B i indicates behavior problems (either externalizing or internalizing subscale) measured in the first wave; and X i is a vector of control variables, including all three groups of covariates discussed above. We only included measures of behavior problems in the first wave rather than changes of behavior problems over time because the study focused on the potential mechanism that behavior affects achievement ( Hinshaw, 1992 ). The relationships between changes of behavior problems and academic achievement may instead reflect reciprocal relationships that exist between academic and behavioral variables.

The study centered child’s age to the first year when they were allowed to take specific tests (i.e., age 3 for the LW and AP tests and age 6 for the PC test). A quadratic term of children’s age (A 2 ti ) was used to model the potential nonlinearity of the growth in academic achievement over time. A cubic term of children’s age in a sensitivity test was also added, which generated consistent results with those reported below. As indicated by two random parts (u 0i and u 1i * A ti ), the intercept and the regression coefficient on children’s age were allowed to vary by child in the growth curve model. However, the regression coefficient of the quadratic term of child’s age was not set as a random one. The analysis showed the variance of this coefficient is very small, if it was defined as a random coefficient. To test the first hypothesis, the parameters of interest in this equation are β 5 and β 6 — β 5 indicates the association between children’s behavior problems and their academic achievement at baseline, and β 6 shows the association between behavior problems and the performance of academic achievement over time.

To examine whether the association between behavior problems and academic achievement varies by gender, the two interaction terms were added to Eq. (1) —an interaction between behavior problems and gender and a three-way interaction among behavior problems, gender, and age. The former one shows the difference in the relationship between behavior problems and academic achievement at baseline by gender, and the latter suggests whether the association between behavior problems and the changing rate of academic achievement differs by gender. The same strategy—adding an interaction between behavior problems and race and a three-way interaction among behavior problems, race, and age into Eq. (1) —was used to investigate the potential heterogeneity in the association between behavior problems and academic achievement by race.

In order to decrease complexity, results for control variables are not shown in regression models ( Tables 3 – 5 ). For all statistical analyses, weighted estimates to account for the oversampling of minority children and data attrition and standard errors were computed using Stata 12.1SE ( StataCorp, 2011 ). This approach implements a Taylor series linearization to adjust standard errors of estimates for complex survey sampling design effects including clustered data ( StataCorp, 2011 ). The strategies discussed above added measures of externalizing and internalizing behavior problems separately into analyses to avoid the potential multicollinearity between the two; however, this approach limits the opportunity to explore the interaction between externalizing or internalizing behavior problems on academic achievement. We thus included both measures in the same analyses in robustness tests. Results were generally consistent with those reported below, but with smaller and less significant regression coefficients for externalizing and internalizing problems.

Behavior problems and long-term performance of academic achievement.

a. All three models are adjusted for the following covariates: (1) children’s race, gender, preterm birth, low birth weight, neonatal intensive care, and physical/mental limitation; (2) mother’s age, education, employment status, parental warmth, emotional support, and cognitive stimulation; and (3) household’s size, number of children, food stamp participation, Aid to Families with Dependent Children or Temporary Assistance for Needy Families participation, homeownership, household income, and state fixed effects. b. The analysis on the PC score has a smaller sample size since only children aged 6 or older can take this test

Behavior problems and long-term performance of academic achievement by race.

a. All three models are adjusted for the following covariates: (1) children’s race, gender, preterm birth, low birth weight, neonatal intensive care, and physical/mental limitation; (2) mother’s age, education, employment status, parental warmth, emotional support, and cognitive stimulation; and (3) household’s size, number of children, food stamp participation, Aid to Families with Dependent Children or Temporary Assistance for Needy Families participation, homeownership, household income, and state fixed effects. b. The analysis on the PC score has a smaller sample size since only children aged 6 or older can take this test.

3.1. Descriptive analysis

Table 1 reports the distributions for the outcome and independent variables and demographic characteristics of the analytic sample. The mean standardized scores of the AP, LW, and PC tests were 107.3 ( SD = 16.6), 105.5 ( SD = 18.4), and 105.2 ( SD = 17.7), respectively. The PSID-CDS children, on average, were 8.4 years old in 1997. The age range of these children was from 3–12 in 1997, with a sample size from 150 to 200 for each age. While not reported in Table 1 , the age range of these children was about 8–17 in 2002 when the second wave of the PSID-CDS was conducted. The sample size for each age in the second wave was from 130 to 190. Since the PSID-CDS only collected information for children younger than 18, the age range was approximately 13–17 in 2007 (i.e., the third wave), and the sample size for children aged 17 is reduced to about 100 because some children aged out from the PSID-CDS. Half of the children were male, and nearly 80% of subjects were White. Mean summary scores for the externalizing and internalizing subscales were 23.1 ( SD = 5.7) and 16.0 ( SD = 3.6) respectively. The mean age of their mothers was 36.0 ( SD = 7.7) in 1997, and the mean schooling years for mothers was 13.1 ( SD = 2.7). More than 60% of mothers were employed in 1997. On average, children lived in a household with four members (including two children) and reported a mean income of about $57,000.

Fig. 1 presents the average scores of the three tests by children’s age. The range of the y-axis is nearly two standard deviations around the mean test score. For the AP, LW, and PC tests, the mean scores at different ages were connected using the dashed line, the solid line, and the dotted line, respectively. The figure shows that, overall, the mean score was about 5 points above or below 100. There was a slight upward trend in early childhood, and a slight downward trend was noticed in late childhood.

Average test scores by child’s age.

3.2. Bivariate analysis

Reporting average test scores over three waves, Table 2 confirms that children’s academic achievement and behavior problems vary by gender and race. Female children had higher mean scores on the LW and PC tests of about 2.5 points ( p < 0.001). Conversely, male children had greater mean scores on the AP test (108.9 vs. 105.6, p < 0.001) and the externalizing subscale (23.6 vs. 22.5, p < 0.001). Males and females do not differ significantly with respect to the internalizing subscale.

Means of behavior problems and academic achievement by gender and race (N = 2028).

With respect to race, Black children had test scores nearly 15 points lower than White children on all three tests of academic performance ( p < 0.001 level), and their mean score on the externalizing subscale was 0.7 points higher than that of White children (23.6 vs. 22.9, p < 0.001).

3.3. Associations between behavior problems and academic achievement

Table 3 reports results of an Eq. (1) using the externalizing or internalizing subscale to predict all three test scores while adjusting for previously described control variables. First, except for the PC score, we found statistically significant and positive regression coefficients on the age variable and negative coefficients on the age-squared variable, which suggests that there is a curvilinear relationship between age and children’s achievement test scores. The positive marginal effects of age on test scores decrease when children get older. Second, controlling for behavior problems and all other variables in the model, male children had LW and PC scores about 1.5 points lower than female children and had AP scores three points higher than female children at baseline. The Black-White achievement gap was approximately seven points on two reading tests and nearly 11 points on the AP test at baseline.

Children’s externalizing behavior problems were inversely associated with all three test scores at baseline. Specifically, a one-point increase in the externalizing subscale reduced children’s AP score by 0.35 points at age 3 (95% CI: −0.53, −0.17; p < 0.001), decreased the LW score by 0.22 points at age 3 (95% CI: −0.40, −0.03; p < 0.05), and lowered the PC score by 0.22 points at age 6 (95% CI: −0.39, −0.05; p < 0.05). While the externalizing subscale did not affect the change of the LW and PC scores over time, its interaction with children’s age was significant in the model predicting the AP score ( b = 0.02, 95% CI: 0.00, 0.04; p < 0.05). It indicates the negative associations between externalizing behaviors and the LW and PC scores remain consistent across ages, while the association between externalizing behaviors and the AP score weakens over time. Fig. 2 indicates that a typical child (see the definition of the typical child in Fig. 2 ) with the externalizing subscale at the third quartile point (= 26) has an AP score two points lower than a typical child with the externalizing subscale at the first quartile point (= 19) at baseline, but the score difference reduces over time and disappears at about age 15.

Predicted AP scores by child’s age and externalizing problems. This figure presents the predicted AP scores over children’s age for two typical children in the sample with the externalizing subscale at the third quartile point (= 26) and at the first quartile point (= 19) at baseline. Using the median value of categorical control variables and the mean value of continuous control variables, a typical case is defined as a White male child who had more than 37 weeks of gestational age, had a normal birth weight, did not have neonatal intensive care at birth and did not have physical/mental limitations; whose mother was 36 years old, employed, and had 13 years of schooling; whose mother reported a 4.5 parental warmth score, a 10.6 cognitive simulation score, and a 10.3 emotional support score; whose household had 4.3 members (including 2.4 children) with income equal to $57,000; and whose household did not receive any public assistance.

Children’s internalizing behavior problems were negatively associated with the AP and PC scores but not with the LW score at baseline. A one-point increase in the internalizing subscale reduced children’s AP score by 0.50 points (95% CI: −0.80, −0.20; p < 0.000) and the PC score by 0.35 points (95% CI: −0.64, −0.06; p < 0.01), respectively.

3.4. Associations between behavior problems and academic achievement by gender

Results of the associations between behavior problems and academic achievement by gender are reported in Table 4 . The interaction term between the externalizing subscale and gender is significant in the models using the LW score (b = 0.25, 95% CI: 0.02, 0.48, p < 0.05), suggesting that externalizing behavior programs have greater associations with decreased academic performance for female children at baseline. None of the three-way interaction terms among gender, age, and behavior problems were statistically significant in the models. Based on the results of the second column in Table 4 , Fig. 3 predicts the LW scores over time for four typical cases with different genders and different levels of externalizing. The slopes, indicating the changing rates of reading achievement, are almost the same for the four predicted lines. At baseline, the difference in the LW test score was nearly 3 points for female children on the externalizing subscale at the first quartile point (= 19) and the third quartile point (= 26), but was only 1.2 points for male children.

Predicted LW scores by child’s age, gender, and externalizing problems. Note: This figure presents the predicted LW scores over children’s age for four typical children with different genders and with the externalizing subscale at the third quartile point (= 26) and the first quartile point (= 19) at baseline. Using the median value of categorical control variables and the mean value of continuous control variables, a typical case is defined as a White child who had more than 37 weeks of gestational age, had a normal birth weight, did not have neonatal intensive care at birth and did not have physical/mental limitations; whose mother was 36 years old, employed, and had 13 years of schooling; whose mother reported a 4.5 parental warmth score, a 10.6 cognitive simulation score, and a 10.3 emotional support score; whose household had 4.3 members (including 2.4 children) with income equal to $57,000; and whose household did not receive any public assistance.

Behavior problems and long-term performance of academic achievement by gender.

3.5. Associations between behavior problems and academic achievement by race

Results on the associations between behavior problems and academic achievement by race are presented in Table 5 . The interaction terms between behavior problems and race were not statistically significant in these models; the associations between behavior problems and academic performance do not vary by race at baseline. However, different from the results on gender, the three-way interaction term among behavior problems (either externalizing or internalizing subscale), age, and race were negatively associated with children’s reading test scores (i.e., LW and PC scores). A one-point increase in the externalizing or internalizing subscale at baseline decreases Black children’s reading score about 0.02 points more than White children every year. That is, in addition to their associations with academic performance at baseline, behavior problems are negatively correlated with reading scores over time for Black children. Based on the results shown in the fifth column in Table 5 , Fig. 4 predicts the LW scores over time for two typical cases with the internalizing subscale at the mean level (= 15). The Black-White test gap was 5.1 points at age 3, but reached 11.2 points at age 17.

Predicted LW scores by child’s age, race, and internalizing problems. This figure presents the predicted LW scores over children’s age for two typical children with the mean internalizing subscale (= 15) at baseline. Using the median value of categorical control variables and the mean value of continuous control variables, a typical case is defined as a male child who had more than 37 weeks of gestational age, had a normal birth weight, did not have neonatal intensive care at birth and did not have physical/mental limitations; whose mother was 36 years old, employed, and had 13 years of schooling; whose mother reported a 4.5 parental warmth score, a 10.6 cognitive simulation score, and a 10.3 emotional support score; whose household had 4.3 members (including 2.4 children) with income equal to $57,000; and whose household did not receive any public assistance.

4. Discussion

The purpose of this study was to examine the relationship between externalizing and internalizing behaviors and the long-term trajectory of academic achievement in a nationally representative sample of children. The heterogeneity in the relationship between externalizing and internalizing behaviors and academic achievement by gender and race was examined. It was hypothesized that behavior problems are associated with decreased academic performance at baseline and that negative association continues over time (hypothesis 1). Furthermore, it was hypothesized that the association between behavior problems and academic performance differ by gender and race (hypothesis 2). Findings suggested that both hypotheses were at least partially correct.

At baseline, behavior problems did appear to have a negative relationship with academic performance wherein externalizing behavior impacted all three academic subtests (i.e., LW, AP, and PC) and internalizing behavior impacted PC and AP. Interestingly, the association of externalizing behavior with the AP score faded over time. However, the effect of externalizing behavior remained for LW and PC over time (hypothesis 1). Males also had higher externalizing scores than females and females performed higher than males on reading measures, which is consistent with previous research ( Ready et al., 2005 ). Although, interestingly, even though males scored higher on externalizing problems and, overall, lower on reading measures, externalizing behaviors appeared to have greater negative impacts on female children’s reading achievement in baseline than male children’s achievement (hypothesis 2). However, the long-term trajectories did not appear to be affected.

Our findings with regard to math performance over time appear to contradict previous findings ( Nelson et al., 2004 ). The difference in results between our study and that of Nelson et al. could be due to a variety of reasons. First, Nelson et al. worked with a sample with 155 youth with emotional/behavioral disorders and we based our study on a large national sample. Second, the data that we used were from a longitudinal data set rather than obtained through a cross-sectional design. Finally, Nelson et al. included multiple measures of math achievement from the Woodcock-Johnson III: math calculation, math fluency, and applied problems; whereas, the PSID dataset only included applied problems (AP).

Overall, our results agreed with Hinshaw’s (1992) theory which suggested that achievement and behavior are related. These results suggest that there is an inverse relationship between achievement and behavior and that this relationship has lasting effects over time, particularly for reading scores.

Our findings also corroborated results from other research demonstrating an achievement gap between Black and White students, with Black children performing lower than White children on the academic measures and significantly higher on the externalizing behavior measure (e.g., Rabiner et al., 2004 ). Most significantly, the results of the analyses suggested that behavior problems had a greater effect on Black children’s reading achievement as the children aged than on White children’s reading achievement as they aged (hypothesis 2). Using results in Table 5 to predict LW scores by age, race, and internalizing problems, the differences found between Black and White students at baseline were approximately one-third of a standard deviation on the LW subtest, but 14 years later that gap had widened to nearly three-quarters of a standard deviation. While Black and White children’s scores were just above average on LW (M = 100, SD = 15) at age 3, by age 17 White children’s scores remained near average and Black children’s scores dipped into the low average range. Even using parent-rating scales of children’s behavior, behavioral problems continued to affect students’ achievement. Specifically, behavioral problems appeared to affect females’ and Black students’ reading achievement.

These analyses have benefits over some of the previous research conducted concerning these topics: (1) the data set analyzed here was a longitudinal data set and the findings are based on performance over time rather than on a cross-sectional dataset, and (2) this analysis also simultaneously considered the relationship between externalizing behavior, internalizing behavior, race and gender, which few previous studies considered with a large sample.

4.1. Implications

A few important implications result from this study. First, even though behavior problems are concerns by themselves, the effect of behavior on reading skills is an especially critical finding particularly given research ( Allensworth & Easton, 2007 ), which suggests that course failure in English is a predictor for later school dropout. Our study revealed that the impact of behavior problems remains long-term for LW and PC, two subtests on children’s reading ability. This is an especially critical finding given that coursework becomes more reading intensive as students progress through school and may have critical implications for youth with externalizing behavior as they reach high school, particularly as these students may be most at risk for school dropout given that course failure and behavior problems uniquely contribute to dropout ( Allensworth & Easton, 2007 ). Practitioners need to consider how the interaction between externalizing behavior and reading difficulty affects student performance in classes that require significant reading and comprehension of text. In our study, externalizing behavior did not appear to affect math achievement as greatly as it did reading performance. This is an important implication for school-based screening of students with academic and behavior difficulties: measures of reading achievement and behavior might be useful in determining which students may need greater support. Although we did not find significant effects of behavior on math performance, previous research has found that math is greatly affected ( Nelson et al., 2004 ) particularly across time; thus, it remains an important consideration especially in light of its status as a predictor for school dropout by middle school and high school.

Our findings regarding the association between behavior and achievement suggest that school professionals should consider providing intervention in both domains to the same students; a reasonable way to do this may be to couple Response to Intervention and Positive Behavioral Interventions and Supports in schools. Such multi-tiered systems of support are also needed early, before children have really had the opportunity to begin the cycle of failure. This means that educators must remain vigilant in identifying students at-risk in these areas, perhaps by frequent screening. Given the findings that behavior problems may affect Black children’s reading achievement more profoundly and the conclusions of previous research that indicate overrepresentation issues pertaining to Black students in special education ( Hosp & Reschly, 2004 ), efficient and effective early intervention appears critical for these students.

Furthermore, in line with DeLisi and Vaughn’s temperament theory, changes within the classroom can enhance academic outcomes for children with difficult temperaments. Considering Rudasill et al.’s (2010) findings that teacher’s emotional support moderates the relationship between temperament and academic outcomes, training can be provided to foster teachers’ emotional support. In particular, Strengthening Emotional Support Services is a curriculum designed to equip teachers with behavioral management strategies and minimize classroom disruption, and has been found to increase academic engagement for students with behavioral and emotional disorders ( Sawka, McCurdy, & Mannella, 2002 ). Additionally, given aforementioned researched by Rudasill and Rimm-Kaufman (2009) , increased teacher training on relationship-building with children of all temperaments may also enhance academic outcomes.

4.2. Limitations

While this study offers improvements over research conducted with cross-sectional research designs, limitations are still present. First, this study was based on the PSID dataset, a large national, longitudinal dataset. The analyses presented here rely on the availability and quality of the data contained in PSID. For example, only three subsets of the WJ-R Tests of Achievement are available. The data collected for PSID were initially collected annually and then biannually after 1997. This means that data were not available yearly on outcomes after 1997; however, the growth curve modeling employed for analysis in this study does not require continuous (i.e., annual) data. In addition, the current study only uses behavior problems measured at Wave 1 to predict children’s long-term academic achievement and does not consider the changes of behavior problems in analyses, which may affect children’s academic achievement. Moreover, although various confounding variables were controlled for, there may be other child, parental, or school context characteristics that could possibly threaten these findings; for example, teacher-student relationships, quality of classroom management, and availability of resources at the school may be related to achievement as well. Finally, the reliance on parent-reported behavior may bias findings. While parent report of internalizing behavior has been found to be more highly associated with observed behavior than teacher reports, the opposite has been found for externalizing behavior ( Hinshaw, Han, Erhardt, & Huber, 1992 ). Additionally, research has found parents to report greater problems with externalizing behavior than teachers ( Verhulst & Akkerhuis, 1989 ; Stanger & Lewis, 1993 ). Bias could be reduced by utilizing reports from both parents and teachers, as has been suggested by Verhulst, Koot, and Van der Ende (1994) who found the use of parent and teacher reports together, rather than one or the other, to increase predictive power.

4.3. Future research

The finding that behavior problems appear to more negatively affect Black children’s reading thus widening the achievement gap gives rise to questions about how such a gap might be narrowed or rather how it can be ensured that all children perform at the height of their ability—that is, as a group, closer to the average on such norm-referenced or standardized tests. Future research might investigate whether all children with behavior problems should be provided with additional reading assistance or at the very least close progress monitoring in the area of reading. Moreover, future research might address how Response to Intervention and Positive Behavioral Interventions and Supports might be more closely aligned to be sure that children with behavioral problems are monitored for academic needs and that children with academic needs are monitored for behavioral problems. Finally, future research should continue to investigate the relationship between behavior problems and academic achievement with the realization that these issues might be related bi-directionally—that is, academic or behavior problems could be driving the other. Future researchers might work to distinguish directionality between the achievement and behavior variables using longitudinal data. This research is likely to have serious implications for how practitioners identify and intervene with at risk learners in today’s schools.

Acknowledgements

The authors are grateful for support from the Meadows Center for Preventing Educational Risk, the Institute on Educational Sciences grants (R324A100022 & R324B080008) and from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50 HD052117). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute Of Child Health and Human Development or the National Institutes of Health.

• Achenbach TM, Edelbrock CS. The classification of child psychopathology: A review and analysis of empirical efforts. Psychological Bulletin. 1978; 85 :1275–1301. http://dx.doi.org/10.1037/00332909.85.6.1275 . [ PubMed ] [ Google Scholar ]
• Algozzine B, Wang C, Violette AS. Reexamining the relationship between academic achievement and social behavior. Journal of Positive Behavior Interventions. 2011; 13 :3–16. http://dx.doi.org/10.1177/1098300709359084 . [ Google Scholar ]
• Allensworth EM, Easton JQ. What matters to staying on-track and graduating from Chicago public high schools. Consortium on Chicago School Research; Chicago: 2007. [ Google Scholar ]
• Alexander KL, Entwisle DR, Horsey CS. From first grade forward: Early foundations of high school dropout. Sociology of Education. 1997; 70 :87–107. [ Google Scholar ]
• Arnold DH. Co-occurrence of externalizing behavior problems and emergent academic difficulties in young high-risk boys: A preliminary evaluation of patterns and mechanisms. Journal of Applied Developmental Psychology. 1997; 18 :317–330. http://dx.doi.org/10.1016/S0193-3973(97)80003-2 . [ Google Scholar ]
• Battin-Pearson S, Newcomb MD, Abbott RD, Hill KG, Catalano RF, Hawkins JD. Predictors of early high school dropout: A test of five theories. Journal of Educational Psychology. 2000; 92 :568–583. http://dx.doi.org/10.1037/0022-0663.92.3.568 . [ Google Scholar ]
• Breslau J, Miller E, Breslau N, Bohnert K, Lucia V, Schweitzer J. The impact of early behavior disturbances on academic achievement in high school. Pediatrics. 2009; 123 :1472–1476. http://dx.doi.org/10.1542/peds.2008-1406 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bub KL, McCartney K, Willett J. Behavior problem trajectories and first-grade cognitive ability and achievement skills: A latent growth curve analysis. Journal of Educational Psychology. 2007; 99 :653–670. http://dx.doi.org/10.1037/0022-0663.99.3.653 . [ Google Scholar ]
• Chapman C, Laird J, Ifill N, Kewal Ramani A. Trends in high school dropout and completion rates in the United States: 1972–2008 (NCES 2011-012) National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education; Washington, DC: 2010. [ Google Scholar ]
• Chen X, R. K. H, Li D. Relation between academic achievement and social adjustment: Evidence from Chinese children. Developmental Psychology. 1997; 33 :518–525. http://dx.doi.org/10.1037/0012-1649.33.3.518 . [ PubMed ] [ Google Scholar ]
• Coutinho MJ, Oswald DP. State variation in gender disproportionality in special education: Findings and recommendations. Remedial and Special Education. 2005; 26 :7–15. http://dx.doi.org/10.1177/07419325050260010201 . [ Google Scholar ]
• DeLisi M, Vaughn M. Foundation for a temperament-based theory of antisocial behavior and criminal justice system involvement. Journal of Criminal Justice. 2014; 42 :10–25. [ Google Scholar ]
• Doolittle JH, Horner RH, Bradley S, Vincent C. Importance of student social behavior in the mission, statements, personnel preparation standards, and innovation efforts of state departments of education. The Journal of Special Education. 2007; 40 :239–245. http://dx.doi.org/10.1177/00224669070400040501 . [ Google Scholar ]
• Duckworth AL, Allred KM. Temperament in the classroom. In: Zenter M, Shiner RL, editors. Handbook of temperament. Guilford Press; New York: 2012. pp. 627–644. [ Google Scholar ]
• Duckworth AL, Seligman ME. Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science. 2005; 16 (12):939–944. [ PubMed ] [ Google Scholar ]
• Fan X, Chen M. Parental involvement and students’ academic achievement: A meta-analysis. Educational Psychology Review. 2001; 13 (1):1–22. [ Google Scholar ]
• Gambell TJ, Hunter DM. Rethinking gender differences in literacy. Canadian Journal of Education. 1999; 24 :1–16. [ Google Scholar ]
• Gregory AG, Skiba RJ, Noguera PA. The achievement gap and the discipline gap: Two sides of the same coin. Educational Researcher. 2010; 39 :59–68. http://dx.doi.org/10.3102/0013189X09357621 . [ Google Scholar ]
• Gumora G, Arsenio WF. Emotionality, emotion regulation, and school performance in middle school children. Journal of School Psychology. 2002; 40 (5):395–413. [ Google Scholar ]
• Herrenkohl TI, Guo J, Kosterman R, Hawkins JD, Catalano RF, Smith BH. Early adolescent predictors of youth violence as mediators of childhood risks. The Journal of Early Adolescence. 2001; 21 :447–469. http://dx.doi.org/10.1177/0272431601021004004 . [ Google Scholar ]
• Hinshaw SP. Externalizing behavior problems and academic underachievement in childhood and adolescence: Causal relationships and underlying mechanisms. Psychological Bulletin. 1992; 111 :127–155. [ PubMed ] [ Google Scholar ]
• Hinshaw SP, Han SS, Erhardt D, Huber A. Internalizing and externalizing behavior problems in preschool children: Correspondence among parent and teacher ratings and behavior observations. Journal of Clinical Child Psychology. 1992; 21 (2):143–150. [ Google Scholar ]
• Hosp JL, Reschly DJ. Disproportionate representation of minority students in special education: Academic, demographic, and economic predictors. Exceptional Children. 2004; 70 :185–199. [ Google Scholar ]
• Jeynes WH. The relationship between parental involvement and urban secondary school student academic achievement: A meta-analysis. Urban Education. 2007; 42 (1):82–110. http://dx.doi.org/10.1177/0042085906293818 . [ Google Scholar ]
• Joffe VL, Black E. Social, emotional, and behavioral functioning of secondary students with low academic and language performance: Perspectives from students, teachers, and parents. Language, Speech, and Hearing Services in Schools. 2012; 43 :461–473. http://dx.doi.org/10.1044/0161-1461(2012/11-0088) [ PubMed ] [ Google Scholar ]
• Ladson-Billings G. From the achievement gap to the education debt: Understanding achievement in U.S. schools. Educational Researcher. 2006; 35 (7):3–12. http://dx.doi.org/10.3102/0013189X035007003 . [ Google Scholar ]
• Lane KL, Barton-Arwood SM, Nelson JR, Wehby J. Academic performance of students with emotional and behavioral disorders served in a self-contained setting. Journal of Behavioral Education. 2008; 17 :43–62. [ Google Scholar ]
• Maguin E, Loeber R. School performance and delinquency. Journal of Crime and Justice. 1996; 20 :145–264. [ Google Scholar ]
• Malecki CK, Elliot SN. Children’s social behaviors as predictors of academic achievement: A longitudinal analysis. School Psychology Quarterly. 2002; 17 :1–23. http://dx.doi.org/10.1521/scpq.17.1.1.19902 . [ Google Scholar ]
• Masten AS, Roisman GI, Long JD, Burt KB, Obradović J, Riley JR, Tellegen A. Developmental cascades: Linking academic achievement and externalizing and internalizing symptoms over 20 years. Developmental Psychology. 2012; 41 (5):733–746. http://dx.doi.org/10.1037/0012-1649.41.5.733 . [ PubMed ] [ Google Scholar ]
• McGee R, Feehan M, Williams S, Anderson J. DSM-III disorders from age 11 to age 15 years. Journal of the American Academy of Child and Adolescent Psychiatry. 1992; 31 :50–59. http://dx.doi.org/10.1097/00004583-199201000-00009 . [ PubMed ] [ Google Scholar ]
• Meisels SJ, Liaw F. Failure in grade: Do retained students catch up? The Journal of Educational Research. 1993; 87 :69–77. http://dx.doi.org/10.1080/00220671.1993.9941169 . [ Google Scholar ]
• Moilanen KL, Shaw DS, Maxwell KL. Developmental cascades: Externalizing, internalizing, and academic competence from middle childhood to early adolescence. Development and Psychopathology. 2010; 22 :635–653. http://dx.doi.org/10.1017/50954579410000337 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Myers DE, Milne AM, Baker K, Ginsburg K. Student discipline and high school performance. Sociology of Education. 1987; 60 :18–33. [ Google Scholar ]
• Nelson J, Benner G, Lane K, Smith B. Academic achievement of K-12 students with emotional and behavioral disorders. Exceptional Children. 2004; 71 :59–73. [ Google Scholar ]
• Nelson JR, Benner GJ, Neill S, Stage S. Interrelationships among language skills, externalizing behavior, and academic fluency and their impact on the academic skills of students with ED. Journal of Emotional and Behavioral Disorders. 2006; 14 :209–216. http://dx.doi.org/10.1177/10634266060140040401 . [ Google Scholar ]
• No Child Left Behind (NCLB) Act of 2001 . Pub. L. No. 107–110, §115, Stat. 1425. 2002. [ Google Scholar ]
• Panel Study of Income Dynamics . Produced and distributed by the Institute for Social Research, Survey Research Center. University of Michigan; Ann Arbor, MI: 2012. public use dataset. [ Google Scholar ]
• Peterson JL, Zill N. Marital disruption, parent-child relationships, and behavior problems in children. Journal of Marriage and Family. 1986; 48 :295–307. [ Google Scholar ]
• Rabe-Hesketh S, Skronda A. Multilevel and longitudinal modeling using Stata. 3rd ed. Stata Press; College Station, TX: 2012. [ Google Scholar ]
• Rabiner DL, Murray DW, Schmid L, Malone PS. An exploration of the relationship between ethnicity, attention problems, and academic achievement. School Psychology Review. 2004; 33 :498–509. [ Google Scholar ]
• Ready DD, LoGerfo LF, Burkam DT, Lee VE. Explaining girls’ advantage in kindergarten literacy learning: Do classroom behaviors make a difference. The Elementary School Journal. 2005; 106 :21–38. http://dx.doi.org/10.1086/496905 . [ Google Scholar ]
• Reid R, Gonzalez J, Nordness P, Trout A, Epstein M. A meta-analysis of the academic status of students with emotional/behavioral disturbance. Journal of Special Education. 2004; 38 (3):130–143. http://dx.doi.org/10.1177/00224669040380030101 . [ Google Scholar ]
• Riglin L, Petrides K, Frederickson N, Rice F. The relationship between emotional problems and subsequent school attainment: A meta-analysis. Journal of Adolescence. 2014; 37 (4):335–346. [ PubMed ] [ Google Scholar ]
• Rimm-Kaufman SE, Curby TW, Grimm KJ, Nathanson L, Brock LL. The contribution of children’s self-regulation and classroom quality to children’s adaptive behaviors in the kindergarten classroom. Developmental Psychology. 2009; 45 (4):958–972. [ PubMed ] [ Google Scholar ]
• Rudasill KM, Gallagher KC, White JM. Temperamental attention and activity, classroom emotional support, and academic achievement in third grade. Journal of School Psychology. 2010; 48 (2):113–134. http://dx.doi.org/10.1016/j.jsp.2009.11.002 . [ PubMed ] [ Google Scholar ]
• Rudasill KM, Rimm-Kaufman SE. Teacher–child relationship quality: The roles of child temperament and teacher–child interactions. Early Childhood Research Quarterly. 2009; 24 (2):107–120. [ Google Scholar ]
• Sawka KD, McCurdy BL, Mannella MC. Strengthening emotional support services an empirically based model for training teachers of students with behavior disorders. Journal of Emotional and Behavioral Disorders. 2002; 10 (4):223–232. [ Google Scholar ]
• Shumow L, Vandell DL, Posner JK. Harsh, firm, and permissive parenting in low-income families relations to children’s academic achievement and behavioral adjustment. Journal of Family Issues. 1998; 19 (5):483–507. http://dx.doi.org/10.1177/019251398019005001 . [ Google Scholar ]
• Spectrum K12 . Response to intervention (RTI) adoption survey 2009. Spectrum K12 School Solutions; Towson, MD: 2009. [ Google Scholar ]
• StataCorp . Stata statistical software: Release 12. StataCorp LP; College Station, TX: 2011. [ Google Scholar ]
• Stanger C, Lewis M. Agreement among parents, teachers, and children on internalizing and externalizing behavior problems. Journal of Clinical Child Psychology. 1993; 22 (1):107–116. [ Google Scholar ]
• Stormshak EA, Bierman KL, McMahon RJ, Lengua LJ. Parenting practices and child disruptive behavior problems in early elementary school. Journal of Clinical Child Psychology. 2000; 29 (1):17–29. http://dx.doi.org/10.1207/S15374424jccp2901_3 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sugai G, Horner RH. Responsiveness-to-intervention and school-wide positive behavior supports: Integration of multi-tiered system approaches. Exceptionality. 2009; 17 :223–237. http://dx.doi.org/10.1080/09362830903235375 . [ Google Scholar ]
• Valiente C, Lemery-Chalfant K, Castro KS. Children’s effortful control and academic competence: Mediation through school liking. Merrill-Palmer Quarterly. 2007; 53 (1):1–25. [ Google Scholar ]
• Vanneman A, Hamilton L, Baldwin Anderson J, Rahman T. Achievement gaps: How Black and White students in public schools perform in mathematics and reading on the National Assessment of Educational Progress, (NCES 2009-455) National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education; Washington, DC: 2009. [ Google Scholar ]
• Vaughn MG, DeLisi M, Matto HC. Human behavior: A cell to society approach. John Wiley & Sons; Hoboken, NJ: 2014. [ Google Scholar ]
• Verhulst FC, Akkerhuis GW. Agreement between parents’ and teachers’ ratings of behavioral/emotional problems of children aged 4–12. Journal of Child Psychology and Psychiatry. 1989; 30 (1):123–136. [ PubMed ] [ Google Scholar ]
• Verhulst FC, Koot HM, Van der Ende J. Differential predictive value of parents’ and teachers’ reports of children’s problem behaviors: A longitudinal study. Journal of Abnormal Child Psychology. 1994; 22 (5):531–546. [ PubMed ] [ Google Scholar ]
• Vitaro F, Brendgen M, Larose S, Tremblay RE. Kindergarten disruptive behaviors, protective factors, and educational achievement by early adolescence. Journal of Educational Psychology. 2005; 97 :617–629. http://dx.doi.org/10.1037/0022-0663.97.4.617 . [ Google Scholar ]
• Wallace JM, Goodkind S, Wallace CM, Bachman JG. Racial, ethnic, and gender differences in school discipline among U.S. high school students: 1991–2005. Negro Education Review. 2008; 59 :47–62. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Weaver-Hightower M. The “boy turn” in research on gender and education. Review of Educational Research. 2003; 73 :471–498. http://dx.doi.org/10.3102/00346543073004471 . [ Google Scholar ]
• Wentzel KR. Does being good make the grade? Relations between academic and social competence in early adolescence. Journal of Educational Psychology. 1993; 85 :357–364. [ Google Scholar ]
• Woodcock RW, Johnson MB. Woodcock-Johnson psycho-educational battery-revised. DLM Teaching Resources; Allen, TX: 1989. [ Google Scholar ]
• Wright JP, Morgan MA, Coyne MA, Beaver KM, Barnes JC. Prior problem behavior accounts for the racial gap in school suspensions. Journal of Criminal Justice. 2014; 42 (3):257–266. [ Google Scholar ]

Home > Journals > IJ-SoTL > Vol. 8 (2014) > No. 2

Disentangling The Effects Of Student Attitudes and Behaviors On Academic Performance

Susan Janssen , University of Minnesota Duluth Follow Maureen O'Brien , University of Minnesota - Duluth Follow

The interplay among motivation, ability, attitudes, behaviors, homework, and learning is unclear from previous research. We analyze data collected from 687 students enrolled in seven economics courses. A model explaining homework and exam scores is estimated, and separate analyses of ability and motivation groups are conducted. We find that motivation and ability explain variation in both homework and exam scores. Attitudes and behaviors, such as procrastination and working with others directly, affect homework score, but not exam score. These effects are not the same within all motivation and ability groups. Given that homework is the strongest predictor of exam score, we conclude that graded homework is beneficial to learning, and attitudes and behaviors related to homework may have an indirect benefit for exam performance. Suggestions are made as to how homework and course design might be managed to help students at different ability and motivational levels maximize learning.

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Janssen, Susan and O'Brien, Maureen (2014) "Disentangling The Effects Of Student Attitudes and Behaviors On Academic Performance," International Journal for the Scholarship of Teaching and Learning : Vol. 8: No. 2, Article 7. Available at: https://doi.org/10.20429/ijsotl.2014.080207

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Home » 500+ Quantitative Research Titles and Topics

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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