academic performance among students essay

Essay on Academic Performance

Students are often asked to write an essay on Academic Performance in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Academic Performance

What is academic performance.

Academic performance is how well a student does in school. It’s measured by grades, test scores, and teacher reports. Good academic performance shows the student is learning and understanding their schoolwork.

Factors Affecting Academic Performance

Many things can affect academic performance. For example, a student’s health, family life, and study habits can all make a difference. If a student is healthy, has a supportive family, and studies regularly, they are likely to perform well acadically.

Importance of Academic Performance

Academic performance is important as it can affect a student’s future. Good grades can lead to opportunities like scholarships and acceptance into good colleges. It also builds skills for future jobs.

Improving Academic Performance

To improve academic performance, students can set study goals, organize their time, and seek help when needed. They should also take care of their health and get enough sleep. With hard work and determination, any student can improve their academic performance.

The Role of Teachers and Parents

Teachers and parents play a key role in a student’s academic performance. They can provide support, encouragement, and resources. They can also help students overcome challenges and build confidence in their abilities.

250 Words Essay on Academic Performance

Academic performance is a measure of how well a student is doing in their studies. It is often shown through grades, scores on tests, or other forms of evaluation. Good academic performance is important as it can open doors to higher education and good jobs in the future.

Many things can affect a student’s academic performance. For example, a student’s health, their home life, and their level of motivation can all play a part. A student who is healthy, has a supportive home environment, and is motivated to learn is likely to do well in school.

Importance of Good Academic Performance

Good academic performance is important for many reasons. It can help a student get into a good college or university. It can also lead to scholarships and other opportunities. Plus, the skills a student learns while working to improve their academic performance can also help them in other areas of life.

There are many ways to improve academic performance. One way is to set clear goals and work towards them. Another way is to seek help when needed, such as getting a tutor or asking a teacher for help. It’s also important to take care of your health and make sure you have a good balance between school work and other activities.

In summary, academic performance is a key part of a student’s education. It’s influenced by many factors and can have a big impact on a student’s future. With hard work and dedication, every student has the potential to improve their academic performance.

500 Words Essay on Academic Performance

Academic performance is how well a student does in school. It is measured by the grades a student gets on tests, homework, and projects. Good academic performance is often seen as a sign of a student’s ability to learn and understand the material taught in school.

Several things can affect a student’s academic performance. These include the student’s own skills and effort, the teaching methods used in school, the student’s home environment, and even their physical health.

Firstly, a student’s own skills and effort play a big role. If a student is good at studying and works hard, they are likely to do well in school. On the other hand, if a student struggles with studying or doesn’t put in much effort, their grades may suffer.

Secondly, the teaching methods used in school can also have an impact. Different students learn in different ways. Some students may do well with lectures, while others might learn better through hands-on activities. If a school’s teaching methods match a student’s learning style, the student is more likely to do well.

Thirdly, a student’s home environment can also affect their academic performance. If a student has a quiet, comfortable place to study at home, they are more likely to do well in school. But if a student’s home is noisy or stressful, it can be hard for them to focus on their studies.

Lastly, a student’s physical health can also play a role. If a student is healthy, they can focus better and learn more. But if a student is often sick, they may miss a lot of school and struggle to keep up with their studies.

There are several ways to improve academic performance. One way is to improve study skills. This might mean learning how to take better notes, how to manage time better, or how to study more effectively.

Another way is to make sure the student is healthy. This could mean making sure the student gets enough sleep, eats healthy food, and gets regular exercise. These things can help the student focus better and learn more.

A third way is to create a good study environment at home. This might mean setting up a quiet, comfortable study area, or setting aside specific times for studying.

Academic performance is important because it can affect a student’s future. Good grades can help a student get into a good college or university, which can lead to a good job in the future. Even if a student doesn’t plan to go to college, good grades can still be helpful. They can show potential employers that the student is hardworking and capable.

In conclusion, academic performance is a measure of a student’s ability to learn and understand the material taught in school. It can be affected by many factors, including the student’s own skills and effort, the teaching methods used in school, the student’s home environment, and their physical health. There are many ways to improve academic performance, and doing well in school can have many benefits for a student’s future.

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SYSTEMATIC REVIEW article

A meta-analysis of the relationship between emotional intelligence and academic performance in secondary education: a multi-stream comparison.

• 1 Department of Basic Psychology, Faculty of Psychology, University of Málaga, Málaga, Spain
• 2 Department of Social Psychology, Faculty of Psychology, University of Jaén, Jaén, Spain
• 3 Department of Social Psychology, Faculty of Psychology, University of Málaga, Málaga, Spain

This study was a quantitative meta-analysis of empirical research on the relationship between emotional intelligence (EI) and academic performance (AP) that included the three main theoretical models of EI. We conducted a computerized literature search in the main electronic databases. Forty-four of an initial 3,210 articles met the inclusion criteria. With 49 effect sizes and a cumulative sample size of 19,861 participants, we found significant heterogeneity indices indicating a variety of results. In general, the results of this study indicated a significant effect of EI on AP ( Z ¯ = 0.26). Average association between EI and AP was higher in studies measured EI as ability ( Z ¯ = 0.31), than studies measured EI as self-report ( Z ¯ = 0.24), and self-report mixed EI ( Z ¯ = 0.26). In the educational field, this meta-analysis provides information on the specific role of EI as a function of used measures. Some practical implications are discussed.

Introduction

In the educational field, academic performance (AP) is the construct that has been studied most. Teaching, learning, and all the cognitive factors related to AP have been widely examined ( Pellitteri and Smith, 2007 ). Recently, one of the most analyzed research lines concerns the influence of personality factors and personal skills on achievement of AP ( Poropat, 2009 ; MacCann et al., 2019 ). In the last 20 years, a large portion of research has been guided by a recent theoretical focus on emotional abilities, specifically emotional intelligence (EI), which has been viewed as a key component of the factors that influence well-being as well as adaptive processes in specific contexts ( Zeidner et al., 2012 ). Several reviews showed the relevance of EI as a personal resource associated with health outcomes ( Martins et al., 2010 ), well-being ( Sánchez-Álvarez et al., 2016 ), and even task performance ( Miao et al., 2017 ). Likewise, literature reviews focused on analyzing the role of EI in AP have been published ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ). These studies showed significant effects of EI in predicting AP after controlling the effects of intelligence and personality traits. In addition, EI has emerged as a strong predictor in secondary education.

Academic Performance

Academic success or performance by students in educational centers is a key goal in the development of all educational programs. AP has been commonly measured through continuous exams or evaluations, with a general consensus about the most important aspects to evaluate, such as skills, and declarative and procedural knowledge ( Ward et al., 1996 ). Although there is no common agreement for the evaluation of AP, measures of cognitive skills or declarative knowledge are the main factors evaluated ( Perera and DiGiacomo, 2013 ), and the most commonly used indicators to measure AP are usually: Grade Performance Academic (GPA), Achievement Test (AT), Grade Average (GA), Academic Achievement (AA), Standard Assessment Test (SAT), and Teacher Ratings Academic (TRA) ( Perera and DiGiacomo, 2013 ).

Recent empirical research in education regarding predictors of AP has focussed on intelligence, IQ, or personal cognitive abilities. This research movement has accumulated an extensive research literature on the measurement of cognitive intelligence ( Ritchie and Tucker-Drob, 2018 ). Moreover, there are other personal skills that differ from traditional cognitive intelligence that could affect academic success ( Furnham et al., 2009 ). Currently, there are several lines of research that analyse individual non-cognitive factors that increase the prediction of AP, which requires broader educational models that integrate personal and contextual factors ( Gutman and Schoon, 2013 ). Other non-cognitive skills include attitude, motivation, personality traits, self-regulation, resilience, and social and emotional skills, which are beyond the academic skills that determine successful performance ( Bowles and Gintis, 2007 ). Likewise, personal factors such as motivation and emotional self-regulation in the classroom are associated with school performance, that is, students who are more motivated and have greater skill to manage emotions to obtain higher academic qualifications ( Pintrich and de Groot, 1990 ). Currently, an increasing number of studies have examined the role of emotional skills such as EI in AP.

Emotional Intelligence

Since the EI concept was first introduced in the scientific literature by Salovey and Mayer (1990) , different EI models have been developed. Based on the measurement methods used, the different theoretical conceptions of EI can be grouped into three main streams: (stream 1) Mayer and Salovey (1997) four branch ability model of EI, which defines ability EI as having four components, including the capacity to perceive, value, and express emotions accurately; the ability to access and generate feelings that facilitate thinking; the ability to understand emotions and emotional awareness; and the ability to regulate emotions and promote emotional and intellectual growth; (stream 2) cognitive emotional abilities three-branch self-perception model of Salovey and Mayer (1990) , self-report EI proposes the existence of a continuous reflexive process associated with one's mood; (stream 3) cognitive emotional competences and other non-cognitive features like personal skills, motivation, and social aspects is conceived how EI mixed model ( Goleman, 1995 ; Mayer and Salovey, 1997 ; Petrides et al., 2004a ; Bar-On, 2006 ).

The ability EI stream (stream 1), also defined as EI-performance, is the conception of EI that seems to have the most similarity to AP, because EI is measured by exercises and problems to assess emotional ability, just as exams are used to measure AP in schools. On the other hand, because ability EI is assessed in a similar way to AP, students with higher levels of EI-performance could better manage stress related to exams, resulting in better AP ( Brackett and Salovey, 2006 ). At the same time, students with inadequate or poor emotional skills will have school maladjustment, interpersonal problems that affect their anxiety ( Rivers et al., 2012 ), and/or a lack of social support from their peers that affects their AP ( Mestre et al., 2006 ). The instruments developed to assess ability EI, the Mayer, Salovey, and Caruso Emotional Intelligence Test (MSCEIT) ( Mayer et al., 2002 ) and the Multifactor Emotional Intelligence Scale (MEIS) ( Mayer et al., 1999 ), have objective criteria for correct and wrong answers.

The self-report EI stream (stream 2), based on self-perception of one's emotional skills, assesses a person's subjective emotional abilities. This means that each individual indicates their level of EI according to their previous experiences and their level of self-esteem, including the mood in which they find themselves when completing the EI self-report scale ( Davies et al., 1998 ). This type of measure is usually related to well-established personality factors such as neuroticism, extraversion, agreeableness, openness, and psychoticism, and this connection can yield false correlations with performance and academic achievement ( Gannon and Ranzijn, 2005 ). Representative self-report EI instruments include the Wong and Law Emotional Intelligence Scale (WLEIS) ( Wong and Law, 2002 ), Trait Meta-Mood Scale (TMMS) ( Salovey and Mayer, 1990 ), Schutte Emotional Intelligence Scale (SEIS) ( Schutte et al., 1998 ; Saklofske and Zeidner, 2006 ), and Swinburne University Emotional Intelligence Test (SUEIT) ( Palmer and Stough, 2001 ).

In the mixed EI stream (stream 3), the integration of different personal and social skills leads to overlapping effects with other factors that may influence AP. When evaluating personality variables, cognitive skills, and social-emotional traits together, one obtains a profile that may be more associated with the different skills that are implemented in an academic context. Therefore, students with better social-emotional traits, with high cognitive abilities ( Shen and Comrey, 1997 ), and adaptive personality trait variables achieve better test scores ( Pulford and Sohal, 2006 ; Poropat, 2009 ). Therefore, students with better adaptation to the school context will obtain better scores in AP than students with profiles less oriented toward academic adaptation. Representative measures of mixed EI include the Emotional Quotient Inventory (EQi) ( Bar-On, 1997 ), Trait Emotional Intelligence Questionnaire (TEIQ) ( Petrides, 2009 ), and Emotion Identification Skills (EIS) ( Ciarrochi et al., 2008 ).

Each of the three main streams has contributed to research linking EI and AP, with heterogenous results, despite being evaluated with instruments developed under the same theoretical conceptions of EI. It is not surprising that EI is conceived from several theoretical approaches. A possible cause of the lack of consensus on the results may be the multitude of instruments to evaluate EI from the different theoretical approaches.

Theoretical Linkages Between Emotional Intelligence and Academic Performance

The EI literature has shown that individuals with a higher capacity to process information typically perform better on cognitive tasks ( Saklofske et al., 2012 ). Interpersonal and intrapersonal skills are of great importance in secondary education, since it is a period that involves many social, contextual, and personal changes and stresses. During adolescence, the peer group is of great relevance to adolescents' emotional development and identity formation ( Duncan et al., 2006 ; Eccles and Roeser, 2009 ), with immediate contexts such as the school environment being one of the most relevant ( Monreal and Guitart, 2012 ). In this sense, the events and early experiences lived in the different contexts, the reactions and responses of adolescents to the different situations of risk and stress throughout their development, as well as the existence of resource vulnerability protection, are relevant and important to understanding individual differences between young people ( Monreal and Guitart, 2012 ). Greater emotional regulation and a better process of adaptability are useful to cope with academic stress and achieve academic success ( Saklofske et al., 2012 ). Interestingly, emotional perceptive people appear to be more strongly impacted by stress than their less perceptive counterparts, expressing higher levels of psychological distress ( Ciarrochi et al., 2002 ). It is hypothesized that low perceptive people might ignore thoughts of daily hassles and therefore might be more likely to be confused about the experienced negative feelings showing less coherence between their levels of perceived stress and psychological maladjustment. Thus, people with high EI are more resilient, adapting more easily to changes, reacting better under stress conditions, and coping with difficulties in the form of challenges ( Schneider et al., 2013 ). Finally, students with a better management of their emotions are happier and have better social relationships ( Eryilmaz, 2011 ). In turn, having better interpersonal management is generally associated with higher social networks, as well as better friendships quality ( Brackett et al., 2005 ). Similarly, having a greater social network in a classroom might stimulate an adequate social environment for better cooperative work, better group learning, greater support from classmates ( Hogan et al., 2010 ), and better relationships with teachers ( Di Fabio and Kenny, 2015 ). Together, both the academic climate involving classmates and professors, as well as a better predisposition of learning-oriented abilities might be associated with a greater AP ( Brackett et al., 2011 ; Johnson, 2016 ). In summary, there are several plausible theoretical mechanisms that might explain the relationship between EI as a set of skills and optimal academic functioning in secondary education.

Current Meta-Analysis

Previous work has excluded studies conducted with instruments developed under other theoretical approaches of EI ( Perera and DiGiacomo, 2013 ), or has contemplated the role of EI in AP in a more global way and by levels ( MacCann et al., 2019 ), making it difficult to compare the results between different instruments. The present study examined the association between EI and AP, considering instruments developed from all the theoretical approaches to EI in studies conducted in secondary school students, as an educational level of greater relevance according to previous literature ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ). Our meta-analysis aimed to examine previous review studies, comparing the results by the main streams and EI instruments used in secondary education including native English and Spanish speakers. The current meta-analysis study was carried out to (1) asses the associations of AP and EI, hypothesizing that there will be a significant correlation between EI; (2) show the associations of different instruments used to assess EI based on three main streams and levels of AP; in line with previous studies, it was hypothesized that EI ability instruments would have a greater association with AP.

Literature Search

We searched relevants studies of EI y AP on electronic database: PsychoINFO, MEDLINE, SCOPUS, PubMed, ISI Web of Science, Google Scholar, and ProQuest Dissertations and Theses. The search term (emotional intelligence) AND (academic performance OR academic achievement OR grades performance OR academic OR education OR school) AND (secondary level). We also reviewed specialized database journals of relevant papers. This review was conducted from June 2017 to January 2020.

Inclusion Criteria

Studies eligible were scanned titles and abstracts, and included in the review all those that referred to the above terms. To be included in the review, papers had to meet the following inclusion criteria for eligibility of studies ( Lipsey and Wilson, 2000 ): (1) empirical study that provides data on the association between EI and AP; (2) minimum sample size at least 20 participants; (3) studies had to have been performed between 1999 and 2020 (January); published article and unpublished doctoral thesis without published and conference paper, (4) studies written in Spanish and English.

Following a Lipsey and Wilson (2000) : (a) country, (b) publication type, (c) design features, (d) measure used to asses EI, (e) AP index, (f) study sample size, (g) size of the association between key variables, (h) level of significance. Finally, extrinsic characteristics coded were results reporting the year and publication source (see Table A1 ).

Statistical Analysis

All data were conducted in R ( Team, 2012 ), using the “stats” and “metaphor” packages ( Viechtbauer, 2010 ). For the meta-analysis the technique by DerSimonian and Laird (1986) was used. The Q -value indicated heterogeneity among studies ( p < 0.10), thus applied random effect models was used in the meta-analysis. Additionally, we quantified the effect of heterogeneity using I 2 ( Higgins and Thompson, 2002 ). The I 2 value indicate proportion of inconsistency due to heterogeneity rather than chance. The effect size index was converted by Fisher r – Z following the procedures recommended by Hedges and Olkin (1985) . The categorical model between-class results were obtained through a goodness-of-fit statistic Q b , and the within-class goodness-of-fit statistic Q w . The statistic Q wj within-category heterogeneity is under the null hypothesis of within-category homogeneity.

Publication Bias

Publication bias was evaluated by rank correlation with Kendall's tau method, in which a significant correlation indicates publication bias, and Egger's regression test asymmetry, in which significant asymmetry indicates publication bias ( Fernández-Castilla et al., 2019 ). The Egger regression test should not differ significantly ( z = −1.189, p = 0.234), and the rank correlation yielded non-significant results ( T = 0.03, p = 0.243). Non-significant results showed symmetry and absence of publication bias. Regression tests and the funnel plot indicated a non-significant asymmetry, so the results showed no evidence of publication bias between EI and AP.

Selected Studies

The sample consisted of 3,210 studies, 678 were duplicate studies. Eventually, 1,973 did not correspond to association between EI and AP. They associated lack of personal distress and absence of mental disorder to higher levels of well-being. The full text of the remaining 559 articles were reviewed, obtaining 44 items that were selected and evaluated more deeply (see Figure 1 ).

Figure 1 . PRISMA flowchart for the identification, screening, and inclusion of publications in the meta-analyses.

Study Characteristics

The articles included in the meta-analysis showed a closed association between target variables. The overall sample consisted of 19,714 participants, and the mean age was of 15.82 years. Several studies included some scales for assessing EI, obtaining 49 effect sizes. The studies included were conducted in 16 countries, with the largest number conducted in the US (14 studies).

Association of EI and AP

The main results of this study indicated that the association between EI and AP had a significant low to moderate cumulative effect ( Z ¯ = 0.26; CI from 0.14 to 0.38). A DerSimonian test and Laird's random effect showed statistical evidence of heterogeneity ( Q = 1,206.16, p < 0,001), indicating a greater variance of effect sizes between studies than anticipated by chance. In addition, the I 2 estimated of 96% suggests a high proportion of variation between samples.

Main EI Streams

The categorical model test that examined the subgroup model results intra-group showed statistical evidence of heterogeneity ( Q b = 0.39, p = 0.540). The Q w statistics revealed that the model was misspecified ( Q w = 1,205.77, p < 0.001). Therefore, significant differences were found between the effect sizes, indicating heterogeneity within each category (see Table 1 ). The ability stream showed lower levels of heterogeneity ( Q wj = 24.16, p < 0.012), with smaller variation between scores ( I 2 = 54%) obtained between the different studies that used ability stream instruments. When examining the effect size results by grouping the EI instruments by main streams, we found larger effect sizes for those studies that used instruments based on the ability EI stream ( Z ¯ = 0.31). At the same time, the degree of inconsistency between studies that used instruments based on the ability EI stream was lower ( I 2 = 54%) than in the other groups of studies (self-report EI stream I 2 = 99%; mixed EI stream I 2 = 92%).

Table 1 . Olkin and Pratt weighted average ( Z ¯ ), effect size number (K), homogeneity test ( Q wj ), and the degree of inconsistency ( I 2 ) between EI main stream.

Type of EI Measure

As shown in Table 2 , the different instruments used to assess EI had differing levels of association with AP. Moreover, there was much variability in the scores obtained in studies using the same EI instrument. Only the MSCEIT ( Q wj = 3.05, p = 0.880), SUEIT ( Q wj = 0.63, p = 0.426), and Situational Test of Emotion Management for Youths (STEM-Y) ( Q wj = 0.51, p = 0.476) measures did not show significant levels of heterogeneity between the effect sizes of the different studies. On the other hand, the largest effect sizes were observed in studies that used the Behavior Emotional Quotient Inventory (EQBI) ( Z ¯ = 0.94, K = 1), followed by the studies carried out with the MEIS ( Z ¯ = 0.50, K = 1), EIS ( Z ¯ = 0.40, K = 5), and MSCEIT ( Z ¯ = 0.35, K = 8) instruments. At the same time, the lowest degree of inconsistency between studies that used the same instruments was found for the SUEIT ( I 2 = 58%, K = 2), followed by the MSCEIT ( I 2 = 78%, K = 8), and Emotional Quotient Inventory (EQ-i) ( I 2 = 82%, K = 15), with the EQ-i being the most widely used instrument.

Table 2 . Olkin and Pratt weighted average ( Z ¯ ), effect size number (K), homogeneity test ( Q wj ), and the degree of inconsistency ( I 2 ) between EI measure.

Type of AP Measure

Subgroup analysis was conducted to examine the variability in the scores obtained in studies using the same AP instrument (see Table 3 ). The highest degree of variability in the scores between studies using the same instruments was found for the GPA ( Q wj = 246.68, p < 0.001), AA ( Q wj = 16.35, p = 0.003), and GCSE ( Q wj = 35.07, p < 0.001). Furthermore, the largest effect sizes were observed in studies using the WAEC ( Z ¯ = 0.74, K = 1), followed by the studies using the VSLECRA ( Z ¯ = 0.38, K = 1), and GPA ( Z ¯ = 0.28, K = 30) instruments. Simultaneously, the lowest degree of inconsistency between studies using the same instruments was found for the TRA ( I 2 = 47%, K = 2), followed by the AA ( I 2 = 76%, K = 5) and GPA ( I 2 = 88%, K = 30), with the GPA being the most widely used instrument.

Table 3 . Olkin and Pratt weighted average ( Z ¯ ), effect size number (K), homogeneity test ( Q wj ), and the degree of inconsistency ( I 2 ) between AP measure.

The current study was designed to examine the relationship between EI and AP through meta-analyses comparing diverse main EI streams and instruments used in secondary education. Filling the gaps in previous meta-analytic research, our study provides new data, and expands past findings. After a literature review, 44 studies with 49 independent effect sizes based on 19,714 secondary school students were included in cumulative quantitative research on the link between EI and AP. Publication bias analysis showed that these findings are robust and reliable.

Regarding hypothesis 1, we found a moderate significant cumulative effect between EI and AP, including measures of the three main EI streams, and diverse indicators of AP. These findings support previous research ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ) suggesting that EI levels are moderately associated with academic success, which suggests that knowledge of one's own and others' feelings, as well as the ability to solve adaptive problems, provides an essential basis for academic learning ( Zeidner and Matthews, 2016 ). Additionally, these results show that EI is a personal resource with an important influence in the academic field, as a process of adaptation to the environment ( Zeidner et al., 2012 ). EI has a dual role; on the one hand, it has intrapersonal affective influences on aspects related to AP, such as motivation and self-regulation. On the other hand, interpersonal skills increase social networks in the academic environment, improving teamwork, which is so important in secondary education level. Teaching staff, through workshops can develop emotional skills to help improve mental health and interpersonal aspects, which is supported by previous literature. Current programs aim to reduce aggressive behavior and substance use; future programs should also target school performance. To deepen these interactions between emotional skills and relevant factors in AP, it would be interesting for future meta-analytical studies to focus on revealing and quantifying each of these links, especially those that are relevant at the secondary level, as it is a period full of changes, is very sensitive to risks, and involves searching for immediate well-being.

With respect to hypothesis 2, we found differences in the levels of association of EI and AP as a function of the EI measures category. The results showed non-significant differences, with ability EI measures ( Mayer and Salovey, 1997 ) showing a greater association with AP, followed by self-report EI ( Salovey and Mayer, 1990 ), and finally the mixed EI stream ( Bar-On, 2006 ). This higher index of association between EI measured with ability instruments and AP may be due to similarities with the tests used to obtain AP, as both of them use performance-based tests. In this sense it is possible this collinearity effect occurred because students who have good abilities to respond to performance tests will obtain high scores in both EI tests and tests that evaluate AP ( Ogundokun and Adeyemo, 2010 ). At the same time, and contrary to other meta-analytical studies on EI ( Martins et al., 2010 ; Sánchez-Álvarez et al., 2016 ), the most commonly used instruments in academic contexts are instruments developed from the mixed EI approach. Future studies should analyse in detail these effects of overlap and collinearity with personality and other aspects to obtain non-biased findings. Previous review studies ( Perera and DiGiacomo, 2013 ; MacCann et al., 2019 ) did not assess the impact of different measures of EI on the association with AP, so these findings provide relevant information for future studies. The results showed great heterogeneity within each instrument category, presenting large differences between different studies that used the same instrument to measure EI ( Sánchez-Álvarez et al., 2016 ). This variability could be caused by moderating variables such as sex, IQ, and personality traits, that moderate the EI–AP association when the same instruments are used ( Petrides et al., 2004b ; Furnham et al., 2005 ). Furthermore, they may be due to variations in adaptations to different languages or variations due to cultural differences ( Fernández-Berrocal et al., 2005 ; Ang and van Dyne, 2015 ). These results go beyond differences between the various instruments to evaluate EI, since they show differences despite using the same instrument. Although it is logical for each theoretical approach to develop and use its own instruments to analyse emotional skills, the results of this type of meta-analysis show the difficulties encountered when comparing the results of studies investigating this area of interest. This is certainly one of the sources of heterogeneity, and the consequent controversy about the results. To clarify this issue, it would be necessary for future studies to select instruments to evaluate emotional skills that have a robust trajectory and well-confirmed psychometric replicative properties in cross-cultural studies. Few studies have been conducted with Spanish-speaking samples. Therefore, more research is needed in Spanish and Latin American population.

The findings of this review should be considered with caution because there were several limitations. The current study was done without controlling for IQ, personality, and other variables that could influence the results. Other studies have been published in languages other than English and Spanish. On the other hand, EI integrates several dimensions, and this study did not take into account the individual associations that each of the dimensions of EI have with AP. It is possible that the associative effect of some dimensions of EI are greater than others, which implies that unifying all the dimensions of EI and analyzing the overall effect they have with AP could produce bias. Future studies should analyze each of the dimensions and their relationship with AP individually, and then compare them to analyze the differences.

These findings have several implications for research and application contexts. The school setting is one of the most important contexts for learning emotional skills and competencies ( Zeidner and Matthews, 2016 ). EI training improves other associated issues, as well as improving performance. Developing emotional skills in early stages of adolescence ( Herrera et al., 2020 ), will allow them to become consolidated personal resources to face risks and promote motivation oriented toward academic success and well-being. For this reason, this review study provides relevant information for the development of programs focused on increasing emotional skills in students, as well as providing tools for teachers and counselors, providing an empirical basis for the development of theoretical educational models oriented to AP. These findings cover the ages at which socio-emotional skills are most important, as well as relevant information for educators and teaching staff on the use of appropriate tools to assess EI in secondary education. We recommend that practitioners be cautious in choosing EI measurement instruments because of differences in their use. In the field of research, this meta-analysis provides information on which future studies should be conducted, helping to clarify the different EI concepts and evaluation measures. Future studies would need to replicate these findings with a larger sample and more of the different EI measures, including variables that may influence AP.

In conclusion, the results of this study found great heterogeneity in the outcomes assessed, so the findings should be considered with caution. The results of this meta-analysis show a moderate association between EI and AP. Future research should explore how other variables influence this relationship, improving our understanding of EI and how it influences our lives. This meta-analytic study presents a quantitative review of the association between EI and AP globally and categorically, shedding light on the gaps in previous studies on the topic on adolescents. This study also shows the inadequacies in the review of studies in this field and provides guidelines to be followed in future empirical studies on AP. These discoveries are of great relevance in the explanatory models intended to predict academic success in secondary education.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

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

This work was supported by Instituto de Estudios Giennenses. Diputación Provincial de Jaén. Convocatoria 2018 (Ref. 2018.160.3340.45100).

Conflict of Interest

The authors declare 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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Table A1 . Studies included in the meta-analysis of the relationships between EI and AP.

Keywords: emotional intelligence, academic performance, secondary education, meta-analysis, instruments

Citation: Sánchez-Álvarez N, Berrios Martos MP and Extremera N (2020) A Meta-Analysis of the Relationship Between Emotional Intelligence and Academic Performance in Secondary Education: A Multi-Stream Comparison. Front. Psychol. 11:1517. doi: 10.3389/fpsyg.2020.01517

Received: 14 April 2020; Accepted: 08 June 2020; Published: 21 July 2020.

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Copyright © 2020 Sánchez-Álvarez, Berrios Martos and Extremera. 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: Nicolás Sánchez-Álvarez, nsa@uma.es

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Intelligence Among Students: Impact on the Academic Performance Essay

Introduction.

The motivation system for pupils in schools is a crucial aspect of the functioning of the education system. The emphasis on social or intellectual components predisposes to discussion and debate among sociologists and child psychologists. An article from the New York Times newspaper is an example of such a discourse. The aspects it discusses are directly related to the humanities field, such as communication theories. Scholars such as Wechsler, Sternberg, and Gardner, when examined in detail, present their views on the issue. Thus, intelligence has additional values represented by social and operational skills, especially when it comes to evaluating students; they are described in the New York Times article and in the theories of the above-mentioned scholars.

Smarts vs. Personality in School: Nature vs. Nurture Analysis

The article “Smarts vs. Personality in School” reveals what additional factors influence each student’s academic opportunities and achievements. Based on the information provided in the article, these include grit, extraversion, social openness, and friendliness (New York Times, 2015). Moreover, the author notes that the evaluators, in this case, are teachers and not peers. The critical thesis can be considered as the phrase, “Maybe intelligence is less important than grit” (New York Times, 2015, par. 7). This logical sequence touches on aspects such as nature opposing nurture.

In real-world circumstances, practical talents serve three purposes: adapting to current surroundings, modifying existing settings to create new surroundings, and choosing new environments. Adaptation is learning the rules of a new environment and figuring out how to prosper in it (Sternberg & Williams, 2010). As an example, when a student first entered college, they have likely attempted to determine campus life’s explicit and tacit standards (Sternberg & Williams, 2010). One must additionally learn how to effectively use these skills in the new setting, for instance, molding surroundings by choosing the courses and activities that would take most of a student’s free time. Youth may have even attempted to influence the conduct of others through social interactions (Sternberg & Williams, 2010). Lastly, if one were unable to adjust themselves or their surroundings to the desired needs, they may have considered choosing a different setting. Social skills are vital in any environment, including the academic one, which becomes evident in this situation.

Nature, which is affected by genetic heritage and any biological variables, is comparable to prewiring. Nurture is often understood as the impact of external variables on a person after conception, such as exposure, life events, and learning. The reason to agree with the author’s statement is that school is a prototype of real social life, where innate charisma and the ability to build social connections are vital pathways to success in life. Thus, nature in the matter of intelligence is an essential factor in learning, but the ability to build social contacts in the scholar community, as part of people’s collective work, appears to be crucial.

Intelligence: Sociology Scholars’ Theories

Theories’ overview.

Proposing a multidimensional perspective of intelligence, Wechsler’s theory of intelligence is comparable to ideas presented by several of his contemporaries as well as other cognitive psychologists. He argued that previous evaluations of general intelligence were too limited since they did not account for all non-cognitive aspects, such as education, experiences, feelings, and the surroundings (Bryce, 2018). The scholar stated that these previous tests were not realistic since many of them were only applicable for specific early literacy abilities and did not account for other ability levels (Bryce, 2018). Wechsler recognized these constraints early in his academic work (Bryce, 2018). When he originally joined the military as a psychiatrist, he worked with a variety of intelligence tests, including those created by Pearson, Spearman, Yerkes, and Thorndike (Bryce, 2018, par. 9). He discovered that the employed exams not only had significant limits, but additionally had prejudices, including not being tailored for persons who could not read or write or who were from a different nation.

According to Howard Gardner’s hypothesis of multiple intelligences, humans are not born with all the knowledge they will ever possess. This hypothesis challenged the conventional belief that there is just one sort of intelligence, additionally referred to as “g” for general intelligence, which focuses solely on cognitive ability (Cherry, 2021). He identified eight different bits of intelligence to widen this concept of intelligence: verbal, rational, bodily-kinesthetic, musical, naturalist, interpersonal, spatial, and intrapersonal (Cherry, 2021, par. 3). According to the theorist, the linguistic and logical-mathematical modalities are the most prized in school and society (Cherry, 2021, par. 3). Gardner additionally proposes that there may be more “candidate” intelligence, including spiritual intelligence, existential intelligence, and moral intelligence.

According to the hypothesis offered by dr. Robert J. Sternberg, the human mind has three forms of intelligence. These are operational (the ability to adapt to diverse situations), creative (the capacity to generate fresh concepts), and analytic, the ability to solve problems and evaluate information (Vinney, 2020, par. 1). The experience-based theory is the context-specific sub-theory, which is directly connected to the suitable type of intelligence or the skills to properly function in one’s surroundings (Vinney, 2020). The latter relates to inventive intelligence, the capacity to deal with new situations or concerns, and the multidimensional sub-theory, which, as a result, systematically describes analytical intelligence.

Theories’ Comparison and Response

Based on the information provided, an analysis of the theories described is required. At the core of their categorizations and assertions, both scholars concur in their views, presenting a system by which the intellectual level of a person can be assessed. It follows that the academic community in psychology and sociology supports the fact that there are additional values which affect the human mind. Specifically, the main comparison point is evident in the categorization and proposal of the multidimensional intelligence being present as a foundational basis of the examined concept. This conclusion leads one to propose that the described model is particularly relevant to school students and their academic perspectives for the reasons stated in this and the previous sections.

However, a response to the theories presented in this paper will not only be to agree with the theses presented but additionally to supplement them in part. In particular, the main reason why a person’s social and communicative abilities should be considered a measure of intelligence lies in the factors used to analyze the situation. Being comfortable in a group of other individuals with their own opinions, personalities, and desirable and undesirable topics of conversation and leading over it requires the ability to interpret and understand the thinking of others quickly. While human empathy plays an essential role in this process, so do the cognitive abilities to analyze, categorize and persuade. Thus, neglecting such skills in the academic sphere can seriously negatively impact the development of disciplines such as diplomacy, political science, sociology, and psychology.

Finally, several analyses of the New York Times article and psychological theories were presented to support the thesis that additional values have a strong influence on determining levels of intelligence and success in academic performance. The initial analysis demonstrated the underlying principle of contrasting natural and acquired human skills. Furthermore, the academic theories of influential psychologists were presented to support such a position. Their subsequent analysis determined and reinforced the assertion made. Drawing public attention to such reasoning is a critical factor that can lead to a positive reform of the education system that balances and accommodates the skills of both introverts and extroverts.

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Academic performance and behavioral patterns

• Valentin Kassarnig   ORCID: orcid.org/0000-0001-9863-0390 1 ,
• Enys Mones 2 ,
• Andreas Bjerre-Nielsen 3 , 4 ,
• Piotr Sapiezynski 2 , 5 ,
• David Dreyer Lassen 3 , 4 &
• Sune Lehmann 2 , 4 , 6  

EPJ Data Science volume  7 , Article number:  10 ( 2018 ) Cite this article

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Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.

1 Introduction

Since research on academic achievement began to emerge as a field in the 1960s, it has guided educational policies on admissions and dropout prevention [ 1 ]. Although much of the literature has focused on higher education, the knowledge obtained on behavioral phenomena observed in colleges and universities can potentially guide research on student behavior in primary and secondary schools. A number of behavioral patterns have been linked to academic performance, such as time allocation [ 2 ], active social ties [ 3 ], sleep duration and sleep quality [ 4 ], or participation in sport activity [ 5 ]. Most of the existing studies, however, suffer from biases and limitations often associated with surveys and self-reports [ 6 , 7 ], particularly when measuring social networks [ 8 – 11 ].

Here we investigate the performance of 538 students within a novel dataset collected as part of the Copenhagen Network Study (CNS), with data collection ongoing for more than two years [ 12 ]. Due to the scale of the CNS, and the inclusion of directly observed data from smartphones in place of self-reports, we are able to mitigate some of the limitations encountered in existing ‘traditional’ studies. The strength of the CNS data is the high-resolution multi-channel measures for social interactions, including person-to-person proximity (using Bluetooth scans), calls and text messages, activity on online social networks (Facebook), and mobility traces.

The aim of our study was to better understand the impact of individual and network factors on our ability to distinguish between groups of students based on their performance. That is, we wanted to identify the ways in which low performers are significantly different from high performers and vice versa. We divide this goal into three specific objectives:

Identify individual and network factors that correlate with students’ performances.

Analyze the importance of different sets of features for supervised learning models to classify students as low, moderate, or high performers.

Investigate significant differences among performance groups for the most important individual and network features.

2 Related work

2.1 individual behavior.

Through a variety of methods, a large number of studies have investigated the factors that determine academic performance. Vandamme et al. [ 13 ] analyzed a broad range of individual characteristics concerning personal history, behavior, and perception. Similarly, the StudentLife study [ 14 ] used smartphones to collect data on student activity, social behavior, personality, and mental health. Both research groups observed correlations between performance and all feature categories, building a case that factors influencing academic performance are not limited to a single aspect of an individual’s life. Nghe et al. [ 15 ] reframed the problem as a prediction task: using data to predict performance in a population of undergraduate and postgraduate students at two different institutions. Using a wide range of features, they predicted GPA after third year with high accuracy. One of the features included GPA after the second year; in this work we show that even without the knowledge of past achievements it is possible to explain the students’ performance levels to a large extent. Furthermore, prior research has emphasized the positive influence of attending classes [ 16 – 19 ]. The study by Crede et al. [ 19 ] concludes that attendance is the most accurate known predictor of academic performance; see [ 20 ] for a more detailed analysis of the impact of class attendance on academic performance based on the CNS data.

Cao et al. [ 21 ] analyzed behavioral data from the digital records of nearly 19,000 students’ smart cards, such as entering and leaving the library, having a meal in the cafeteria, or taking a shower in the dormitory. They conclude that the students’ orderness (regularity of daily activities) is a strong predictor of academic performance. Our approach shares some similarities with [ 21 ], but the key difference is that we have investigated not only individual behavior but also the students’ social environment.

2.2 Individual traits

A large body of research at the intersection of psychology and education investigated the relationship between personality and performance, as pioneered by [ 22 ]. Many personality traits were found to be linked to academic success: Among the dimensions of the well-studied Big-Five Inventory [ 23 ] Conscientiousness (positive) and Neuroticism (negative) displayed the strongest correlation with academic performance [ 24 – 52 ]. The other three dimensions showed only very weak or no correlation. Furthermore, the characteristics Self Esteem [ 53 ], Satisfaction with Life [ 54 , 55 ], and Positive Affect Schedule [ 56 ] were also found to be positively correlated, while Stress [ 57 , 58 ], Depression [ 59 – 61 ], and Locus of Control [ 54 , 55 ] showed a negative effect on academic achievements.

2.3 Online social media

Only a few prior studies have investigated the impact of social media activity on academic performance, despite the growing availability of such data and undisputed presence of these media in our daily lives. The majority of existing studies found a decrease in academic performance with increasing time spent on social media [ 62 – 69 ]. However, not all studies confirm this result. In some studies, time spent on social media was found to be unrelated to academic performance [ 70 , 71 ] or even a had positive effect on performance [ 72 , 73 ].

2.4 Social interactions

There is a growing interest in the relationship between social interactions (especially online social interactions) and academic performance [ 3 , 74 – 92 ]. In the relevant literature there exist two dominant approaches. The first approach focuses on the relation between own performance and that of peers [ 74 – 81 ], based on a hypothesis of similarity in peer achievement. The similarity between pairs of individuals connected via social ties are attributed to various aspects: selection into friendships by similarity (i.e., homophily); influence by social peers (also know as peer effect); and correlated shocks (e.g., being exposed to the same teacher). As noted by [ 74 , 93 ] the issue of separating these effects is inherently difficult. The second approach emphasizes the positive influence of having a central position in the social network between students [ 85 – 90 ]. The majority of results in the existing research which measure social networks are, however, based on self-reports and therefore subject to various biases [ 8 – 11 ] that are in many ways mitigated by using smartphones to measure the social network [ 94 ]. However, it should be noted that surveys and observational studies often measure very different aspects of reality. For instance, in the case of assessing tie strengths, observational studies may be more accurate in quantifying duration and frequency variables of a relationship, while surveys can provide qualitative insights into depth and intimacy [ 95 , 96 ].

3 Materials and methods

3.1 data collection and preprocessing.

Results presented in this paper are based on the data collected in the Copenhagen Network Study (CNS) [ 12 ]. In the CNS, dedicated smartphones where handed out to students at the Technical University of Denmark (DTU) and used as their primary phones for two years. During this period various data types were recorded: Bluetooth scans, call and text message meta data, Facebook activity logs, and mobility traces. Additionally, participating students answered a survey on personality at the beginning of the study. Due to the possibility to exit the experiment at any given point, the number of participants varied over time. We investigate the data from 538 undergraduate students for whom we have complete data.

The raw data records are cleaned and transformed to meaningful information before the analysis. Bluetooth scans are used to estimate person-to-person interactions corresponding to a physical distance of up to 10 m (30 ft) between participants. While physical proximity is not a perfect proxy for person-to-person interactions, there is evidence that the proximity interactions are predictive of friendship in online social networks and communication using phone calls and text messages [ 97 – 99 ].

Facebook data was obtained via the Facebook Graph API, and contains both static friendship connections as well as various interactions on the social network. All types of interactions are treated equally. Private messages, however, are unavailable since they cannot be obtained from Facebook using the official Graph API.

The location data on the smartphones has varying accuracy depending on the providing sensor. The accuracy of the collected position can vary between a few meters for GPS locations, to hundreds of meters for cell tower location. We group the location data into 15-minute bins and use the median location of all data points with an accuracy below 80 m. In order to compute attendance we combined the smartphone locations with the person-to-person proximity obtained from Bluetooth scans. A detailed description of the method can be found in a companion paper [ 20 ].

We considered social interactions of five different channels: proximity, Facebook (friendships + interactions), calls, and text messages. For each channel we created a network to model the social relations. Note that these models are based only on the interactions among participants of the CNS. Interactions with any people outside the study were not considered. Importantly, for the proximity networks we excluded all meetings that took place during class time in order to eliminate effects caused by class co-attendance. Section B in Additional file 1 discusses further details of the creation of these network models. In the remainder of this paper, the direct neighbors in those networks are refereed to as ‘peers’.

The students’ course grades were provided by DTU administration. Only courses using the Danish 7-point grading scale were considered. This scale consists of the grades 12, 10, 7, 4, 02, 00, and −3 with 12 being the best grade and 00 and −3 indicating that the student failed. The positive weighted mean grades (term or cumulative) were converted to the standard GPA scale ranging from 4.0 (best) to 0.0 (worst). Every negative mean grade was set to 0.0. Only students attending at least three courses were considered. Figure  1 illustrates the distribution of the 538 cumulative GPAs. It shows a left-skewed distribution with a mean GPA of 2.5. More information about the student population can be found in Section A of Additional file 1 .

Distribution of cumulative GPAs. Distribution of 538 cumulative GPAs. The histogram shows a left-skewed distribution with a mean GPA of 2.5

In order to increase the stability of the results we applied bootstrap resampling. Analyses were performed on 100 bootstrap samples, where each has the same size as the original sample. We report as results the mean of the bootstrap analyses with approximated standard errors described by the Standard Error of the Mean .

3.2 Feature sets

To account for the different explanatory power of the individual and network aspects, we constructed four feature sets, each representing a certain aspect of life and corresponding to a specific level of information: personality , individual , network and combined .

3.2.1 Personality features

The personality features contain 16 individual personality traits obtained from questionnaires that the study participants had to fill in before receiving a phone.

3.2.2 Individual features

The individual feature set combines the 16 personality traits with behavioral and personal variables. Behavioral variables include average class attendance and the Facebook activity level (log of average number of posts per week). In terms of personal information, we added the students’ gender and their study year to the feature set. Information about the sociological background of the students was not available to us.

3.2.3 Network features

For the network features we consider metrics from five different networks, each based on a different channel (texts, calls, proximity, Facebook interactions, and Facebook friendships). Despite the large number of possible features to extract from networks, we considered only the metrics that follow the main approaches found in the literature, such as the mean GPA of peers, centrality, and the fraction of low and high performing peers. However, further aspects, such as deviation, skewness, or entropy of peers’ GPAs, would undoubtedly be interesting for future investigations.

The structure of the interaction networks provide further insight into how students’ position in their social environment is correlated with performance. Therefore, we evaluated different centrality measures. Footnote 1 Overall, the degree centrality displayed the strongest correlation and was therefore used as feature in our analyses.

3.2.4 Combined features

The combined feature set contains all 20 individual features and all 20 network features yielding a total of 40 features. See Table  1 for a complete list of features in each category. More details including descriptive statistics can be found in Section E of Additional file 1 .

3.3 Approach

We use machine learning techniques to evaluate the importance of different factors on the academic performance of students. Specifically, we create supervised learning models and evaluate their performance on classifying students as low, moderate, or high performers. This framework allows us to compare our results to related work, in particular, the works by Vandamme et al. [ 13 ] and Nghe et al. [ 15 ]. Furthermore, this approach makes it easier to detect significant differences between the individual performance groups. In contrast to classical statistical modeling with test of significance, machine learning uses a hypothesis-free approach that allows us to model complex interactions driven by the data [ 100 ]. We evaluate the model performance based on the mean classification accuracy of 100 independent 10-fold cross-validations.

A key point to emphasize here is that while classifying students’ performance levels based on current behavior might be useful in a practical context (for example to identify students in need of extra support), it is not our primary reason for using machine learning in the current study. Rather, we use machine learning as a tool for ranking and comparing features. That is, the more predictive a given feature is, the more important it is for describing performance. By training our models on features arising from many categories, previously only studied independently, we can begin to understand their relative importance, as well as their interplay in terms of academic performance.

The following results are reported in three stages. First, we perform an ANOVA F-test on all features to identify the most important features for dividing students into performance groups. Then we utilize supervised learning models to investigate the importance and interplay of the different feature categories. Based on the results of the first two stages, we then conduct an in-depth analysis of the most expressive impact factors of each category. Our primary focus is on the social behavioral features which have only been considered to a limited extent in previous studies.

4.1 Analysis of variance

Figure  2 shows the feature importance for features achieving significance of \(p < 0.001\) obtained from an ANOVA F-test. Footnote 2 Although all feature categories are correlated with academic performance, the result indicates that features which describe the social networks of students have the highest explanatory power. In general, network properties dominate the results with more than half of the significant features corresponding to this category. A potential explanation for the high impact of social relations is that the network connections may act as a proxy for previous performance, since the network features include information on the grades of others. The fraction of low performing peers as well as the mean GPA of peers contacted over text messages and calls display the highest explanatory power. Footnote 3 Class attendance proves to be the most important individual feature and moreover, overall the most important one if we had no information on anyone’s grades. Centrality in the proximity network is also found to be a significant descriptor with moderate importance. Among personality traits, only self-esteem and conscientiousness have significant explanatory power.

Feature importance ranking. Results from ANOVA F-test for 3-class classification. Features which did not achieve sufficient significance ( \(p \geq0.001\) ) are omitted

4.2 Supervised learning

In order to better understand the importance and interplay of different factors on the academic performance we utilized supervised learning techniques. We created models based on the different feature sets to classify the students as low, moderate, and high performers according to their GPAs. Each of those three groups contains the same number of students, corresponding to a baseline accuracy of 33.33%.

We use Linear Discriminant Analysis (LDA) to find an optimal model that separates the three performance classes. Figure  3 illustrates the mean results of 100 independent 10-fold cross-validations. The results show that the LDA model solely based on personality features exceeds the baseline performance by about 9 pps. Adding the four additional individual features (behavior + background info) improves the model’s performance by further 5.2 pps. Using network features instead of individual features results in a performance of about 19 pps above baseline. Combining individual and network features yields a superior model with about 57.9% accuracy; roughly 25 pps above baseline. Figure  4 shows its achieved in-class precision and recall values along with the corresponding \(F_{1}\) values. As the results indicate, once the GPA class is provided, the model has high predictive power among the low and high performers (compared to that of the moderate performers) with \(F_{1}\) values of 0.649 and 0.626, respectively.

Model performances on the different feature sets. Bars show the classification accuracy of the different LDA models

Precision-recall curve. Dots represent the model performance in the low (red), moderate (green) and high (blue) performer classes. Dashed lines mark the profile of constant \(F_{1}\) corresponding to the measured values for the specific class

4.3 Feature analysis

4.3.1 individual behavior.

Among the considered individual effects, class attendance was found to have the highest impact on academic performance. A correlation coefficient of \(r_{S} = 0.294\) for cumulative GPAs was determined ( \(p < 0.001\) ). An in-depth analysis of the observed class attendance patterns along with a detailed description of the method to measure attendance in the CNS dataset is discussed in [ 20 ].

The Facebook activity level measures the average number of published posts. Since the activity levels change significantly over time we consider each semester separately and use the corresponding term GPAs as measure for academic performance. This gives us up to four data points per student (one for each semester of the data collection period) for this analysis. In Fig.  5 students are divided into three groups of equal size according to their activity levels. As Fig.  5 (a) shows, the distribution of posts among students is heavy-tailed and is described by the vast majority of the students having less than 3 posts in a typical week. The distribution of term GPA values in the different tertiles reveals that, on average, students with lower activity perform better (see Fig.  5 (b)). To statistically evaluate the variation in the distribution over the different tertiles, we performed a Kruskal–Wallis H-test. This test rejected the global null hypothesis with \(p<0.001\) that the medians of the groups are all equal. A follow-up Dunn multiple comparison test with Bonferroni correction revealed pair-wise differences among the tertiles: all pairs are significantly different from each other ( \(p<0.001\) ). Thus, groups with different levels of Facebook activity have significantly different academic performances.

Facebook usage and performance in the tertiles. ( a ) Division of students into three groups of equal size according to their active Facebook updates. Each box represents a single tertile, width corresponds to the span of Facebook activity in the specific group and the x -position shows the mean term GPA. ( b ) Grade distribution inside each Facebook activity class

4.3.2 Social interactions

Based on the results presented in Fig.  2 and Fig.  3 we conclude that a student’s performance can be accurately inferred from the achievements of their peers. This effect was consistently observed across different communication and interaction channels, as shown in Fig.  6 . There, each channel is represented by a separate line illustrating the mean correlation of the members of each performance group and their respective peers. We can observe that regardless of the channel considered, each curve shows a strong increasing trend. This is further quantified in Table  2 which displays the corresponding correlation coefficients on the individual level. The most pronounced effect is observed for calls and text messages, which are considered to be proxies for strong social ties because this type of connection requires effort to initiate and maintain [ 101 ].

Similarity in academic performance for social ties. Curves show the mean GPAs of every performance group and their peers from different communication channels

Interestingly, these channels are not dominant in the case of centrality measures. Here, proximity interactions displayed the strongest correlation among all channels. However, we found weak to moderate positive correlations in all social networks, in agreement with the existing literature [ 85 – 90 ].

We further assessed the validity of pairwise similarity in the network by focusing exclusively on social ties based on text messages. Figure  7 shows a scatter plot of the correlation between the own GPA and mean GPA of the texting peers for every student in the dataset. Once again, we observe a clear linear trend; the trend is especially strong in the region where the majority of the students is located (GPAs in the range between 2 and 3). In Fig.  8 we divided the population into tertiles based on the GPA and calculated the fraction of text messages exchanged with members of the different groups. Beyond the correlation, we can see that the students’ communication in each group is dominated by members of the same group. This observation further underlines the importance of the social environment for academic success.

Correlation between performance of strong peers. For each student, we show their cumulative GPA versus the mean GPA of their peers obtained by their text messages. Color denotes density of points in arbitrary units

Own academic performance and peers’ academic performance. Each histogram displays how students distribute their text messages exchanged with others over the various performance groups. Groups are defined by tertiles based on their cumulative GPA

5 Discussion

For the participants of the CNS, we found that the peers’ academic performance has a strong explanatory power for academic performance of individuals. We observed this effect across different channels of social interactions with calls and text messages showing the strongest correlations, further emphasizing the phenomena. As mentioned in the literature review, this effect could be caused by either peer effects (adaption) or homophily (selection). It should be noted that GPA information is used here as target and, in aggregated form, also as network feature. This allows us to analyze and understand the relationships among peers; but should be taken into account when framing the problem as prediction task.

We found network centrality to have a positive correlation with academic performance, in agreement with the literature [ 85 – 90 ]. However, among all types of interaction networks, only proximity networks exhibited a strong effect. A possible limitation in measuring centrality is that the mere physical proximity of two individuals does not necessarily involve direct communication. Nevertheless, it is reasonable to expect an increased level of information exchange in a group of individuals if they are in close proximity, which was the case in our dataset. Footnote 4

Consistent with findings in existing literature, we found that class attendance showed the strongest correlation with academic performance when we consider only individual effects [ 16 , 18 , 19 , 102 – 106 ]. We also found that Facebook activity has a negative relation to academic performance—also in agreement with the majority of the studies that investigated Facebook and social media usage [ 62 – 69 ]. We note, however, that our the data is limited to Facebook activities such as posting a status update or uploading a picture etc, and that we have no information regarding ‘passive’ Facebook usage, such as scrolling and reading. Also, our data does not include direct messages which may constitute a relevant fraction of communications performed via the social network site.

The analysis of the different personality traits revealed that two characteristics, namely conscientiousness and self-esteem, have considerable explanatory power for academic success. These two traits reached a correlation coefficient between 0.2 and 0.3 corresponding to the upper limit achievable for any correlation with a personality trait, according to Mischel [ 107 ]. The impact of other investigated characteristics could not be confirmed with proper significance. These results agree with existing literature [ 24 – 53 ].

In the supervised learning experiment we achieved a classification accuracy of around 25 percentage points above baseline, a result similar to that of Vandamme et al. [ 13 ] While the classification accuracy is similar, comparing our results with theirs is difficult because of the very different feature sets and experimental setups. Vandamme et al. [ 13 ] use nearly ten times as many features to build a model as we did. In addition, the accuracy of Vandamme et al. [ 13 ] is driven by using prior achievement (grades), which is known to be a strong predictor of performance (e.g. due to persistence of skill and motivation). We note here that a potential reason for the similarity in performance to Vandamme et al. [ 13 ] could be that the network features used in our study include the grades of others in the network. Thus, if the network homophily with respect to academic performance is sufficiently strong, the average performance of others could serve as a proxy for each individual’s academic achievements.

Networks originating from different channels were treated separately because each network provides different information. For future studies it could be interesting to combine them and create multiplex network models which capture interactions across multiple channels and provide more information about the actual tie strength.

In summary, our findings—together with the results in the literature—emphasize that there is a considerable dependence of academic performance on personality and social environment. This experiment is by no means an attempt to be exhaustive of the possibilities for impact factors. Rather, we hope that this demonstration will stir interest to further study the impact of the social environment on academic success, as well as the interplay of individual and network factors.

5.1 Limitations

Although we utilized wider and more detailed data than most other studies, our approach also has important limitations which need to be taken into account. First, we only observed students from a single, technical, Danish university. For this reason, the findings may not be generalizable to students at other institutions, of other academic disciplines or with other demographics. Furthermore, only a subset of all the students at DTU participated in our study—for first year students the rate was around 40%. Although we observed a high degree of variation with respect to behavioral and network measures as well as academic performance, our sample may not be representative of the whole student population. Our measures of ego-networks and model estimates reflect only the smaller (and not closed) community of students in the CNS within the larger population of students.

Although direct measures overcome a lot of the limitations of surveys and self-reports, they continue to be affected by standard concerns over observational data, including selection bias, information bias, and confounding [ 108 ]. In particular, confounding plays a big role in our study as there are many factors that we were unable to capture but provenly affect the academic performance directly or interplay with other observed factors. For instance, many socio-economic variables have been identified as good predictors for academic achievements [ 109 – 112 ] but unfortunately such data was not available to us. There was also some tendency of selection into the study as the average student in the study tends to achieve higher grades than non-participants [ 113 ]. Furthermore, investigations on the CNS data have revealed, that findings differ slightly for men and women [ 114 ].

Social network observations were limited to phone calls/texts, meetings, and Facebook activities. Although these are arguably some of the most important means of communication, some students may communicate via other smartphone apps. Our method of inferring attendance is also subject to some noise (as thoroughly discussed in [ 20 ]). Furthermore, it does not imply in-class participation nor attention to the taught material.

Although we have identified many factors that correlate with academic performance, we make no claims regarding causality. The question of establishing causality from purely observational data is far from trivial. Thus, while being beyond the scope of this work we consider this question as promising and interesting for future research.

Details on the evaluation can be found in Section C of Additional file 1 .

Note that F-test should not be interpreted literally here, as the assumption of identical independent draws of errors is likely to violated due to correlation of errors in the network. Rather, we use it only as a guide to select features.

The reliability of this observation has been validated by a permutation test—see Section D of Additional file 1 .

The CNS uses (thresholded) Bluetooth visibility as an indicator of person-to-person proximity.

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Kassarnig, V., Mones, E., Bjerre-Nielsen, A. et al. Academic performance and behavioral patterns. EPJ Data Sci. 7 , 10 (2018). https://doi.org/10.1140/epjds/s13688-018-0138-8

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The Importance of Students’ Motivation for Their Academic Achievement – Replicating and Extending Previous Findings

Ricarda steinmayr.

1 Department of Psychology, TU Dortmund University, Dortmund, Germany

Anne F. Weidinger

Malte schwinger.

2 Department of Psychology, Philipps-Universität Marburg, Marburg, Germany

Birgit Spinath

3 Department of Psychology, Heidelberg University, Heidelberg, Germany

Associated Data

The datasets generated for this study are available on request to the corresponding author.

Achievement motivation is not a single construct but rather subsumes a variety of different constructs like ability self-concepts, task values, goals, and achievement motives. The few existing studies that investigated diverse motivational constructs as predictors of school students’ academic achievement above and beyond students’ cognitive abilities and prior achievement showed that most motivational constructs predicted academic achievement beyond intelligence and that students’ ability self-concepts and task values are more powerful in predicting their achievement than goals and achievement motives. The aim of the present study was to investigate whether the reported previous findings can be replicated when ability self-concepts, task values, goals, and achievement motives are all assessed at the same level of specificity as the achievement criteria (e.g., hope for success in math and math grades). The sample comprised 345 11th and 12th grade students ( M = 17.48 years old, SD = 1.06) from the highest academic track (Gymnasium) in Germany. Students self-reported their ability self-concepts, task values, goal orientations, and achievement motives in math, German, and school in general. Additionally, we assessed their intelligence and their current and prior Grade point average and grades in math and German. Relative weight analyses revealed that domain-specific ability self-concept, motives, task values and learning goals but not performance goals explained a significant amount of variance in grades above all other predictors of which ability self-concept was the strongest predictor. Results are discussed with respect to their implications for investigating motivational constructs with different theoretical foundation.

Introduction

Achievement motivation energizes and directs behavior toward achievement and therefore is known to be an important determinant of academic success (e.g., Robbins et al., 2004 ; Hattie, 2009 ; Plante et al., 2013 ; Wigfield et al., 2016 ). Achievement motivation is not a single construct but rather subsumes a variety of different constructs like motivational beliefs, task values, goals, and achievement motives (see Murphy and Alexander, 2000 ; Wigfield and Cambria, 2010 ; Wigfield et al., 2016 ). Nevertheless, there is still a limited number of studies, that investigated (1) diverse motivational constructs in relation to students’ academic achievement in one sample and (2) additionally considered students’ cognitive abilities and their prior achievement ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). Because students’ cognitive abilities and their prior achievement are among the best single predictors of academic success (e.g., Kuncel et al., 2004 ; Hailikari et al., 2007 ), it is necessary to include them in the analyses when evaluating the importance of motivational factors for students’ achievement. Steinmayr and Spinath (2009) did so and revealed that students’ domain-specific ability self-concepts followed by domain-specific task values were the best predictors of students’ math and German grades compared to students’ goals and achievement motives. However, a flaw of their study is that they did not assess all motivational constructs at the same level of specificity as the achievement criteria. For example, achievement motives were measured on a domain-general level (e.g., “Difficult problems appeal to me”), whereas students’ achievement as well as motivational beliefs and task values were assessed domain-specifically (e.g., math grades, math self-concept, math task values). The importance of students’ achievement motives for math and German grades might have been underestimated because the specificity levels of predictor and criterion variables did not match (e.g., Ajzen and Fishbein, 1977 ; Baranik et al., 2010 ). The aim of the present study was to investigate whether the seminal findings by Steinmayr and Spinath (2009) will hold when motivational beliefs, task values, goals, and achievement motives are all assessed at the same level of specificity as the achievement criteria. This is an important question with respect to motivation theory and future research in this field. Moreover, based on the findings it might be possible to better judge which kind of motivation should especially be fostered in school to improve achievement. This is important information for interventions aiming at enhancing students’ motivation in school.

Theoretical Relations Between Achievement Motivation and Academic Achievement

We take a social-cognitive approach to motivation (see also Pintrich et al., 1993 ; Elliot and Church, 1997 ; Wigfield and Cambria, 2010 ). This approach emphasizes the important role of students’ beliefs and their interpretations of actual events, as well as the role of the achievement context for motivational dynamics (see Weiner, 1992 ; Pintrich et al., 1993 ; Wigfield and Cambria, 2010 ). Social cognitive models of achievement motivation (e.g., expectancy-value theory by Eccles and Wigfield, 2002 ; hierarchical model of achievement motivation by Elliot and Church, 1997 ) comprise a variety of motivation constructs that can be organized in two broad categories (see Pintrich et al., 1993 , p. 176): students’ “beliefs about their capability to perform a task,” also called expectancy components (e.g., ability self-concepts, self-efficacy), and their “motivational beliefs about their reasons for choosing to do a task,” also called value components (e.g., task values, goals). The literature on motivation constructs from these categories is extensive (see Wigfield and Cambria, 2010 ). In this article, we focus on selected constructs, namely students’ ability self-concepts (from the category “expectancy components of motivation”), and their task values and goal orientations (from the category “value components of motivation”).

According to the social cognitive perspective, students’ motivation is relatively situation or context specific (see Pintrich et al., 1993 ). To gain a comprehensive picture of the relation between students’ motivation and their academic achievement, we additionally take into account a traditional personality model of motivation, the theory of the achievement motive ( McClelland et al., 1953 ), according to which students’ motivation is conceptualized as a relatively stable trait. Thus, we consider the achievement motives hope for success and fear of failure besides students’ ability self-concepts, their task values, and goal orientations in this article. In the following, we describe the motivation constructs in more detail.

Students’ ability self-concepts are defined as cognitive representations of their ability level ( Marsh, 1990 ; Wigfield et al., 2016 ). Ability self-concepts have been shown to be domain-specific from the early school years on (e.g., Wigfield et al., 1997 ). Consequently, they are frequently assessed with regard to a certain domain (e.g., with regard to school in general vs. with regard to math).

In the present article, task values are defined in the sense of the expectancy-value model by Eccles et al. (1983) and Eccles and Wigfield (2002) . According to the expectancy-value model there are three task values that should be positively associated with achievement, namely intrinsic values, utility value, and personal importance ( Eccles and Wigfield, 1995 ). Because task values are domain-specific from the early school years on (e.g., Eccles et al., 1993 ; Eccles and Wigfield, 1995 ), they are also assessed with reference to specific subjects (e.g., “How much do you like math?”) or on a more general level with regard to school in general (e.g., “How much do you like going to school?”).

Students’ goal orientations are broader cognitive orientations that students have toward their learning and they reflect the reasons for doing a task (see Dweck and Leggett, 1988 ). Therefore, they fall in the broad category of “value components of motivation.” Initially, researchers distinguished between learning and performance goals when describing goal orientations ( Nicholls, 1984 ; Dweck and Leggett, 1988 ). Learning goals (“task involvement” or “mastery goals”) describe people’s willingness to improve their skills, learn new things, and develop their competence, whereas performance goals (“ego involvement”) focus on demonstrating one’s higher competence and hiding one’s incompetence relative to others (e.g., Elliot and McGregor, 2001 ). Performance goals were later further subdivided into performance-approach (striving to demonstrate competence) and performance-avoidance goals (striving to avoid looking incompetent, e.g., Elliot and Church, 1997 ; Middleton and Midgley, 1997 ). Some researchers have included work avoidance as another component of achievement goals (e.g., Nicholls, 1984 ; Harackiewicz et al., 1997 ). Work avoidance refers to the goal of investing as little effort as possible ( Kumar and Jagacinski, 2011 ). Goal orientations can be assessed in reference to specific subjects (e.g., math) or on a more general level (e.g., in reference to school in general).

McClelland et al. (1953) distinguish the achievement motives hope for success (i.e., positive emotions and the belief that one can succeed) and fear of failure (i.e., negative emotions and the fear that the achievement situation is out of one’s depth). According to McClelland’s definition, need for achievement is measured by describing affective experiences or associations such as fear or joy in achievement situations. Achievement motives are conceptualized as being relatively stable over time. Consequently, need for achievement is theorized to be domain-general and, thus, usually assessed without referring to a certain domain or situation (e.g., Steinmayr and Spinath, 2009 ). However, Sparfeldt and Rost (2011) demonstrated that operationalizing achievement motives subject-specifically is psychometrically useful and results in better criterion validities compared with a domain-general operationalization.

Empirical Evidence on the Relative Importance of Achievement Motivation Constructs for Academic Achievement

A myriad of single studies (e.g., Linnenbrink-Garcia et al., 2018 ; Muenks et al., 2018 ; Steinmayr et al., 2018 ) and several meta-analyses (e.g., Robbins et al., 2004 ; Möller et al., 2009 ; Hulleman et al., 2010 ; Huang, 2011 ) support the hypothesis of social cognitive motivation models that students’ motivational beliefs are significantly related to their academic achievement. However, to judge the relative importance of motivation constructs for academic achievement, studies need (1) to investigate diverse motivational constructs in one sample and (2) to consider students’ cognitive abilities and their prior achievement, too, because the latter are among the best single predictors of academic success (e.g., Kuncel et al., 2004 ; Hailikari et al., 2007 ). For effective educational policy and school reform, it is crucial to obtain robust empirical evidence for whether various motivational constructs can explain variance in school performance over and above intelligence and prior achievement. Without including the latter constructs, we might overestimate the importance of motivation for achievement. Providing evidence that students’ achievement motivation is incrementally valid in predicting their academic achievement beyond their intelligence or prior achievement would emphasize the necessity of designing appropriate interventions for improving students’ school-related motivation.

There are several studies that included expectancy and value components of motivation as predictors of students’ academic achievement (grades or test scores) and additionally considered students’ prior achievement ( Marsh et al., 2005 ; Steinmayr et al., 2018 , Study 1) or their intelligence ( Spinath et al., 2006 ; Lotz et al., 2018 ; Schneider et al., 2018 ; Steinmayr et al., 2018 , Study 2, Weber et al., 2013 ). However, only few studies considered intelligence and prior achievement together with more than two motivational constructs as predictors of school students’ achievement ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). Kriegbaum et al. (2015) examined two expectancy components (i.e., ability self-concept and self-efficacy) and eight value components (i.e., interest, enjoyment, usefulness, learning goals, performance-approach, performance-avoidance goals, and work avoidance) in the domain of math. Steinmayr and Spinath (2009) investigated the role of an expectancy component (i.e., ability self-concept), five value components (i.e., task values, learning goals, performance-approach, performance-avoidance goals, and work avoidance), and students’ achievement motives (i.e., hope for success, fear of failure, and need for achievement) for students’ grades in math and German and their GPA. Both studies used relative weights analyses to compare the predictive power of all variables simultaneously while taking into account multicollinearity of the predictors ( Johnson and LeBreton, 2004 ; Tonidandel and LeBreton, 2011 ). Findings showed that – after controlling for differences in students‘ intelligence and their prior achievement – expectancy components (ability self-concept, self-efficacy) were the best motivational predictors of achievement followed by task values (i.e., intrinsic/enjoyment, attainment, and utility), need for achievement and learning goals ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). However, Steinmayr and Spinath (2009) who investigated the relations in three different domains did not assess all motivational constructs on the same level of specificity as the achievement criteria. More precisely, students’ achievement as well as motivational beliefs and task values were assessed domain-specifically (e.g., math grades, math self-concept, math task values), whereas students’ goals were only measured for school in general (e.g., “In school it is important for me to learn as much as possible”) and students’ achievement motives were only measured on a domain-general level (e.g., “Difficult problems appeal to me”). Thus, the importance of goals and achievement motives for math and German grades might have been underestimated because the specificity levels of predictor and criterion variables did not match (e.g., Ajzen and Fishbein, 1977 ; Baranik et al., 2010 ). Assessing students’ goals and their achievement motives with reference to a specific subject might result in higher associations with domain-specific achievement criteria (see Sparfeldt and Rost, 2011 ).

Taken together, although previous work underlines the important roles of expectancy and value components of motivation for school students’ academic achievement, hitherto, we know little about the relative importance of expectancy components, task values, goals, and achievement motives in different domains when all of them are assessed at the same level of specificity as the achievement criteria (e.g., achievement motives in math → math grades; ability self-concept for school → GPA).

The Present Research

The goal of the present study was to examine the relative importance of several of the most important achievement motivation constructs in predicting school students’ achievement. We substantially extend previous work in this field by considering (1) diverse motivational constructs, (2) students’ intelligence and their prior achievement as achievement predictors in one sample, and (3) by assessing all predictors on the same level of specificity as the achievement criteria. Moreover, we investigated the relations in three different domains: school in general, math, and German. Because there is no study that assessed students’ goal orientations and achievement motives besides their ability self-concept and task values on the same level of specificity as the achievement criteria, we could not derive any specific hypotheses on the relative importance of these constructs, but instead investigated the following research question (RQ):

RQ. What is the relative importance of students’ domain-specific ability self-concepts, task values, goal orientations, and achievement motives for their grades in the respective domain when including all of them, students’ intelligence and prior achievement simultaneously in the analytic models?

Materials and Methods

Participants and procedure.

A sample of 345 students was recruited from two German schools attending the highest academic track (Gymnasium). Only 11th graders participated at one school, whereas 11th and 12th graders participated at the other. Students of the different grades and schools did not differ significantly on any of the assessed measures. Students represented the typical population of this type of school in Germany; that is, the majority was Caucasian and came from medium to high socioeconomic status homes. At the time of testing, students were on average 17.48 years old ( SD = 1.06). As is typical for this kind of school, the sample comprised more girls ( n = 200) than boys ( n = 145). We verify that the study is in accordance with established ethical guidelines. Approval by an ethics committee was not required as per the institution’s guidelines and applicable regulations in the federal state where the study was conducted. Participation was voluntarily and no deception took place. Before testing, we received written informed consent forms from the students and from the parents of the students who were under the age of 18 on the day of the testing. If students did not want to participate, they could spend the testing time in their teacher’s room with an extra assignment. All students agreed to participate. Testing took place during regular classes in schools in 2013. Tests were administered by trained research assistants and lasted about 2.5 h. Students filled in the achievement motivation questionnaires first, and the intelligence test was administered afterward. Before the intelligence test, there was a short break.

Ability Self-Concept

Students’ ability self-concepts were assessed with four items per domain ( Schöne et al., 2002 ). Students indicated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree) how good they thought they were at different activities in school in general, math, and German (“I am good at school in general/math/German,” “It is easy to for me to learn in school in general/math/German,” “In school in general/math/German, I know a lot,” and “Most assignments in school/math/German are easy for me”). Internal consistency (Cronbach’s α) of the ability self-concept scale was high in school in general, in math, and in German (0.82 ≤ α ≤ 0.95; see Table 1 ).

Means ( M ), Standard Deviations ( SD ), and Reliabilities (α) for all measures.

Task Values

Students’ task values were assessed with an established German scale (SESSW; Subjective scholastic value scale; Steinmayr and Spinath, 2010 ). The measure is an adaptation of items used by Eccles and Wigfield (1995) in different studies. It assesses intrinsic values, utility, and personal importance with three items each. Students indicated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree) how much they valued school in general, math, and German (Intrinsic values: “I like school/math/German,” “I enjoy doing things in school/math/German,” and “I find school in general/math/German interesting”; Utility: “How useful is what you learn in school/math/German in general?,” “School/math/German will be useful in my future,” “The things I learn in school/math/German will be of use in my future life”; Personal importance: “Being good at school/math/German is important to me,” “To be good at school/math/German means a lot to me,” “Attainment in school/math/German is important to me”). Internal consistency of the values scale was high in all domains (0.90 ≤ α ≤ 0.93; see Table 1 ).

Goal Orientations

Students’ goal orientations were assessed with an established German self-report measure (SELLMO; Scales for measuring learning and achievement motivation; Spinath et al., 2002 ). In accordance with Sparfeldt et al. (2007) , we assessed goal orientations with regard to different domains: school in general, math, and German. In each domain, we used the SELLMO to assess students’ learning goals, performance-avoidance goals, and work avoidance with eight items each and their performance-approach goals with seven items. Students’ answered the items on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree). All items except for the work avoidance items are printed in Spinath and Steinmayr (2012) , p. 1148). A sample item to assess work avoidance is: “In school/math/German, it is important to me to do as little work as possible.” Internal consistency of the learning goals scale was high in all domains (0.83 ≤ α ≤ 0.88). The same was true for performance-approach goals (0.85 ≤ α ≤ 0.88), performance-avoidance goals (α = 0.89), and work avoidance (0.91 ≤ α ≤ 0.92; see Table 1 ).

Achievement Motives

Achievement motives were assessed with the Achievement Motives Scale (AMS; Gjesme and Nygard, 1970 ; Göttert and Kuhl, 1980 ). In the present study, we used a short form measuring “hope for success” and “fear of failure” with the seven items per subscale that showed the highest factor loadings. Both subscales were assessed in three domains: school in general, math, and German. Students’ answered all items on a 4-point scale ranging from 1 (does not apply at all) to 4 (fully applies). An example hope for success item is “In school/math/German, difficult problems appeal to me,” and an example fear of failure item is “In school/math/German, matters that are slightly difficult disconcert me.” Internal consistencies of hope for success and fear of failure scales were high in all domains (hope for success: 0.88 ≤ α ≤ 0.92; fear of failure: 0.90 ≤ α ≤ 0.91; see Table 1 ).

Intelligence

Intelligence was measured with the basic module of the Intelligence Structure Test 2000 R, a well-established German multifactor intelligence measure (I-S-T 2000 R; Amthauer et al., 2001 ). The basic module of the test offers assessments of domain-specific intelligence for verbal, numeric, and figural abilities as well as an overall intelligence score (a composite of the three facets). The overall intelligence score is thought to measure reasoning as a higher order factor of intelligence and can be interpreted as a measure of general intelligence, g . Its construct validity has been demonstrated in several studies ( Amthauer et al., 2001 ; Steinmayr and Amelang, 2006 ). In the present study, we used the scores that were closest to the domains we investigated: overall intelligence, numerical intelligence, and verbal intelligence (see also Steinmayr and Spinath, 2009 ). Raw values could range from 0 to 60 for verbal and numerical intelligence, and from 0 to 180 for overall intelligence. Internal consistencies of all intelligence scales were high (0.71 ≤ α ≤ 0.90; see Table 1 ).

Academic Achievement

For all students, the school delivered the report cards that the students received 3 months before testing (t0) and 4 months after testing (t2), at the end of the term in which testing took place. We assessed students’ grades in German and math as well as their overall grade point average (GPA) as criteria for school performance. GPA was computed as the mean of all available grades, not including grades in the nonacademic domains Sports and Music/Art as they did not correlate with the other grades. Grades ranged from 1 to 6, and were recoded so that higher numbers represented better performance.

Statistical Analyses

We conducted relative weight analyses to predict students’ academic achievement separately in math, German, and school in general. The relative weight analysis is a statistical procedure that enables to determine the relative importance of each predictor in a multiple regression analysis (“relative weight”) and to take adequately into account the multicollinearity of the different motivational constructs (for details, see Johnson and LeBreton, 2004 ; Tonidandel and LeBreton, 2011 ). Basically, it uses a variable transformation approach to create a new set of predictors that are orthogonal to one another (i.e., uncorrelated). Then, the criterion is regressed on these new orthogonal predictors, and the resulting standardized regression coefficients can be used because they no longer suffer from the deleterious effects of multicollinearity. These standardized regression weights are then transformed back into the metric of the original predictors. The rescaled relative weight of a predictor can easily be transformed into the percentage of variance that is uniquely explained by this predictor when dividing the relative weight of the specific predictor by the total variance explained by all predictors in the regression model ( R 2 ). We performed the relative weight analyses in three steps. In Model 1, we included the different achievement motivation variables assessed in the respective domain in the analyses. In Model 2, we entered intelligence into the analyses in addition to the achievement motivation variables. In Model 3, we included prior school performance indicated by grades measured before testing in addition to all of the motivation variables and intelligence. For all three steps, we tested for whether all relative weight factors differed significantly from each other (see Johnson, 2004 ) to determine which motivational construct was most important in predicting academic achievement (RQ).

Descriptive Statistics and Intercorrelations

Table 1 shows means, standard deviations, and reliabilities. Tables 2 –4 show the correlations between all scales in school in general, in math, and in German. Of particular relevance here, are the correlations between the motivational constructs and students’ school grades. In all three domains (i.e., school in general/math/German), out of all motivational predictor variables, students’ ability self-concepts showed the strongest associations with subsequent grades ( r = 0.53/0.61/0.46; see Tables 2 –4 ). Except for students’ performance-avoidance goals (−0.04 ≤ r ≤ 0.07, p > 0.05), the other motivational constructs were also significantly related to school grades. Most of the respective correlations were evenly dispersed around a moderate effect size of | r | = 0.30.

Intercorrelations between all variables in school in general.

Intercorrelations between all variables in German.

Intercorrelations between all variables in math.

Relative Weight Analyses

Table 5 presents the results of the relative weight analyses. In Model 1 (only motivational variables) and Model 2 (motivation and intelligence), respectively, the overall explained variance was highest for math grades ( R 2 = 0.42 and R 2 = 0.42, respectively) followed by GPA ( R 2 = 0.30 and R 2 = 0.34, respectively) and grades in German ( R 2 = 0.26 and R 2 = 0.28, respectively). When prior school grades were additionally considered (Model 3) the largest amount of variance was explained in students’ GPA ( R 2 = 0.73), followed by grades in German ( R 2 = 0.59) and math ( R 2 = 0.57). In the following, we will describe the results of Model 3 for each domain in more detail.

Relative weights and percentages of explained criterion variance (%) for all motivational constructs (Model 1) plus intelligence (Model 2) plus prior school achievement (Model 3).

Beginning with the prediction of students’ GPA: In Model 3, students’ prior GPA explained more variance in subsequent GPA than all other predictor variables (68%). Students’ ability self-concept explained significantly less variance than prior GPA but still more than all other predictors that we considered (14%). The relative weights of students’ intelligence (5%), task values (2%), hope for success (4%), and fear of failure (3%) did not differ significantly from each other but were still significantly different from zero ( p < 0.05). The relative weights of students’ goal orientations were not significant in Model 3.

Turning to math grades: The findings of the relative weight analyses for the prediction of math grades differed slightly from the prediction of GPA. In Model 3, the relative weights of numerical intelligence (2%) and performance-approach goals (2%) in math were no longer different from zero ( p > 0.05); in Model 2 they were. Prior math grades explained the largest share of the unique variance in subsequent math grades (45%), followed by math self-concept (19%). The relative weights of students’ math task values (9%), learning goals (5%), work avoidance (7%), and hope for success (6%) did not differ significantly from each other. Students’ fear of failure in math explained the smallest amount of unique variance in their math grades (4%) but the relative weight of students’ fear of failure did not differ significantly from that of students’ hope for success, work avoidance, and learning goals. The relative weights of students’ performance-avoidance goals were not significant in Model 3.

Turning to German grades: In Model 3, students’ prior grade in German was the strongest predictor (64%), followed by German self-concept (10%). Students’ fear of failure in German (6%), their verbal intelligence (4%), task values (4%), learning goals (4%), and hope for success (4%) explained less variance in German grades and did not differ significantly from each other but were significantly different from zero ( p < 0.05). The relative weights of students’ performance goals and work avoidance were not significant in Model 3.

In the present studies, we aimed to investigate the relative importance of several achievement motivation constructs in predicting students’ academic achievement. We sought to overcome the limitations of previous research in this field by (1) considering several theoretically and empirically distinct motivational constructs, (2) students’ intelligence, and their prior achievement, and (3) by assessing all predictors at the same level of specificity as the achievement criteria. We applied sophisticated statistical procedures to investigate the relations in three different domains, namely school in general, math, and German.

Relative Importance of Achievement Motivation Constructs for Academic Achievement

Out of the motivational predictor variables, students’ ability self-concepts explained the largest amount of variance in their academic achievement across all sets of analyses and across all investigated domains. Even when intelligence and prior grades were controlled for, students’ ability self-concepts accounted for at least 10% of the variance in the criterion. The relative superiority of ability self-perceptions is in line with the available literature on this topic (e.g., Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ; Steinmayr et al., 2018 ) and with numerous studies that have investigated the relations between students’ self-concept and their achievement (e.g., Möller et al., 2009 ; Huang, 2011 ). Ability self-concepts showed even higher relative weights than the corresponding intelligence scores. Whereas some previous studies have suggested that self-concepts and intelligence are at least equally important when predicting students’ grades (e.g., Steinmayr and Spinath, 2009 ; Weber et al., 2013 ; Schneider et al., 2018 ), our findings indicate that it might be even more important to believe in own school-related abilities than to possess outstanding cognitive capacities to achieve good grades (see also Lotz et al., 2018 ). Such a conclusion was supported by the fact that we examined the relative importance of all predictor variables across three domains and at the same levels of specificity, thus maximizing criterion-related validity (see Baranik et al., 2010 ). This procedure represents a particular strength of our study and sets it apart from previous studies in the field (e.g., Steinmayr and Spinath, 2009 ). Alternatively, our findings could be attributed to the sample we investigated at least to some degree. The students examined in the present study were selected for the academic track in Germany, and this makes them rather homogeneous in their cognitive abilities. It is therefore plausible to assume that the restricted variance in intelligence scores decreased the respective criterion validities.

When all variables were assessed at the same level of specificity, the achievement motives hope for success and fear of failure were the second and third best motivational predictors of academic achievement and more important than in the study by Steinmayr and Spinath (2009) . This result underlines the original conceptualization of achievement motives as broad personal tendencies that energize approach or avoidance behavior across different contexts and situations ( Elliot, 2006 ). However, the explanatory power of achievement motives was higher in the more specific domains of math and German, thereby also supporting the suggestion made by Sparfeldt and Rost (2011) to conceptualize achievement motives more domain-specifically. Conceptually, achievement motives and ability self-concepts are closely related. Individuals who believe in their ability to succeed often show greater hope for success than fear of failure and vice versa ( Brunstein and Heckhausen, 2008 ). It is thus not surprising that the two constructs showed similar stability in their relative effects on academic achievement across the three investigated domains. Concerning the specific mechanisms through which students’ achievement motives and ability self-concepts affect their achievement, it seems that they elicit positive or negative valences in students, and these valences in turn serve as simple but meaningful triggers of (un)successful school-related behavior. The large and consistent effects for students’ ability self-concept and their hope for success in our study support recommendations from positive psychology that individuals think positively about the future and regularly provide affirmation to themselves by reminding themselves of their positive attributes ( Seligman and Csikszentmihalyi, 2000 ). Future studies could investigate mediation processes. Theoretically, it would make sense that achievement motives defined as broad personal tendencies affect academic achievement via expectancy beliefs like ability self-concepts (e.g., expectancy-value theory by Eccles and Wigfield, 2002 ; see also, Atkinson, 1957 ).

Although task values and learning goals did not contribute much toward explaining the variance in GPA, these two constructs became even more important for explaining variance in math and German grades. As Elliot (2006) pointed out in his hierarchical model of approach-avoidance motivation, achievement motives serve as basic motivational principles that energize behavior. However, they do not guide the precise direction of the energized behavior. Instead, goals and task values are commonly recruited to strategically guide this basic motivation toward concrete aims that address the underlying desire or concern. Our results are consistent with Elliot’s (2006) suggestions. Whereas basic achievement motives are equally important at abstract and specific achievement levels, task values and learning goals release their full explanatory power with increasing context-specificity as they affect students’ concrete actions in a given school subject. At this level of abstraction, task values and learning goals compete with more extrinsic forms of motivation, such as performance goals. Contrary to several studies in achievement-goal research, we did not demonstrate the importance of either performance-approach or performance-avoidance goals for academic achievement.

Whereas students’ ability self-concept showed a high relative importance above and beyond intelligence, with few exceptions, each of the remaining motivation constructs explained less than 5% of the variance in students’ academic achievement in the full model including intelligence measures. One might argue that the high relative importance of students’ ability self-concept is not surprising because students’ ability self-concepts more strongly depend on prior grades than the other motivation constructs. Prior grades represent performance feedback and enable achievement comparisons that are seen as the main determinants of students’ ability self-concepts (see Skaalvik and Skaalvik, 2002 ). However, we included students’ prior grades in the analyses and students’ ability self-concepts still were the most powerful predictors of academic achievement out of the achievement motivation constructs that were considered. It is thus reasonable to conclude that the high relative importance of students’ subjective beliefs about their abilities is not only due to the overlap of this believes with prior achievement.

Limitations and Suggestions for Further Research

Our study confirms and extends the extant work on the power of students’ ability self-concept net of other important motivation variables even when important methodological aspects are considered. Strength of the study is the simultaneous investigation of different achievement motivation constructs in different academic domains. Nevertheless, we restricted the range of motivation constructs to ability self-concepts, task values, goal orientations, and achievement motives. It might be interesting to replicate the findings with other motivation constructs such as academic self-efficacy ( Pajares, 2003 ), individual interest ( Renninger and Hidi, 2011 ), or autonomous versus controlled forms of motivation ( Ryan and Deci, 2000 ). However, these constructs are conceptually and/or empirically very closely related to the motivation constructs we considered (e.g., Eccles and Wigfield, 1995 ; Marsh et al., 2018 ). Thus, it might well be the case that we would find very similar results for self-efficacy instead of ability self-concept as one example.

A second limitation is that we only focused on linear relations between motivation and achievement using a variable-centered approach. Studies that considered different motivation constructs and used person-centered approaches revealed that motivation factors interact with each other and that there are different profiles of motivation that are differently related to students’ achievement (e.g., Conley, 2012 ; Schwinger et al., 2016 ). An important avenue for future studies on students’ motivation is to further investigate these interactions in different academic domains.

Another limitation that might suggest a potential avenue for future research is the fact that we used only grades as an indicator of academic achievement. Although, grades are of high practical relevance for the students, they do not necessarily indicate how much students have learned, how much they know and how creative they are in the respective domain (e.g., Walton and Spencer, 2009 ). Moreover, there is empirical evidence that the prediction of academic achievement differs according to the particular criterion that is chosen (e.g., Lotz et al., 2018 ). Using standardized test performance instead of grades might lead to different results.

Our study is also limited to 11th and 12th graders attending the highest academic track in Germany. More balanced samples are needed to generalize the findings. A recent study ( Ben-Eliyahu, 2019 ) that investigated the relations between different motivational constructs (i.e., goal orientations, expectancies, and task values) and self-regulated learning in university students revealed higher relations for gifted students than for typical students. This finding indicates that relations between different aspects of motivation might differ between academically selected samples and unselected samples.

Finally, despite the advantages of relative weight analyses, this procedure also has some shortcomings. Most important, it is based on manifest variables. Thus, differences in criterion validity might be due in part to differences in measurement error. However, we are not aware of a latent procedure that is comparable to relative weight analyses. It might be one goal for methodological research to overcome this shortcoming.

We conducted the present research to identify how different aspects of students’ motivation uniquely contribute to differences in students’ achievement. Our study demonstrated the relative importance of students’ ability self-concepts, their task values, learning goals, and achievement motives for students’ grades in different academic subjects above and beyond intelligence and prior achievement. Findings thus broaden our knowledge on the role of students’ motivation for academic achievement. Students’ ability self-concept turned out to be the most important motivational predictor of students’ grades above and beyond differences in their intelligence and prior grades, even when all predictors were assessed domain-specifically. Out of two students with similar intelligence scores, same prior achievement, and similar task values, goals and achievement motives in a domain, the student with a higher domain-specific ability self-concept will receive better school grades in the respective domain. Therefore, there is strong evidence that believing in own competencies is advantageous with respect to academic achievement. This finding shows once again that it is a promising approach to implement validated interventions aiming at enhancing students’ domain-specific ability-beliefs in school (see also Muenks et al., 2017 ; Steinmayr et al., 2018 ).

Data Availability

Ethics statement.

In Germany, institutional approval was not required by default at the time the study was conducted. That is, why we cannot provide a formal approval by the institutional ethics committee. We verify that the study is in accordance with established ethical guidelines. Participation was voluntarily and no deception took place. Before testing, we received informed consent forms from the parents of the students who were under the age of 18 on the day of the testing. If students did not want to participate, they could spend the testing time in their teacher’s room with an extra assignment. All students agreed to participate. We included this information also in the manuscript.

Author Contributions

RS conceived and supervised the study, curated the data, performed the formal analysis, investigated the results, developed the methodology, administered the project, and wrote, reviewed, and edited the manuscript. AW wrote, reviewed, and edited the manuscript. MS performed the formal analysis, and wrote, reviewed, and edited the manuscript. BS conceived the study, and wrote, reviewed, and edited the manuscript.

Conflict of Interest Statement

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

Funding. We acknowledge financial support by Deutsche Forschungsgemeinschaft and Technische Universität Dortmund/TU Dortmund University within the funding programme Open Access Publishing.

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• Open access
• Published: 22 April 2024

Investigating learning burnout and academic performance among management students: a longitudinal study in English courses

• Thuy Dung Pham Thi 1 &
• Nam Tien Duong 1  

BMC Psychology volume  12 , Article number:  219 ( 2024 ) Cite this article

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This study aims to move away from the cross-sectional approach related to burnout and conduct a longitudinal study to explore the factors influencing learning burnout among management students. The study primarily adopts a questionnaire survey, with students majoring in business management. Descriptive statistics and structural equation modeling (SEM) are used to analyze the data and validate the hypotheses. The findings are: (1) There is a significant negative relationship between English anxiety and self-efficacy and a significant positive relationship between past English learning performance and self-efficacy. (2) The changes in self-efficacy are negatively related to the changes in burnout, while the changes in workload are positively related to the changes in burnout. Additionally, there is a positive relationship between English anxiety and learning burnout. (3) There is a significant negative relationship between English learning performance and burnout. The direct impact of self-efficacy on English learning performance is not supported, but it has an indirect effect through the mediating role of burnout. The study proposes strategies to improve student outcomes and well-being: (1) making English courses more engaging to boost performance and confidence, reducing learning burnout; (2) encouraging and supporting students to enhance self-efficacy and motivation; (3) assigning tasks seen as useful and interesting to lessen perceived workload and emotional exhaustion; (4) and considering English anxiety in admissions to decrease learning burnout, especially as schools gain more autonomy in their policies.

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Introduction

Burnout has mainly focused on people-helping professionals who engage in long-term “people work” and experience a lack of enthusiasm toward their work, indifference towards people, and negative attitudes toward their job [ 1 ]. Previous studies on burnout have primarily targeted healthcare professionals, service industry employees, and social workers [ 2 , 3 ]. Burnout among management professionals has been increasingly recognized as a significant issue, and studies have linked this phenomenon directly to management students, who represent a key future workforce in the field. Evidence suggests that burnout experienced during their academic years can be a precursor to similar challenges in their professional careers. This observation aligns with research indicating that burnout among students in professional programs, such as those preparing for careers in teaching, can predict future occupational burnout and affect job performance post-graduation. Pines and Kafry [ 4 ] comparing burnout levels between university students and professionals in people-oriented fields have shown that students often face higher burnout levels. This suggests that management students experiencing high burnout during their studies are at a greater risk of becoming professionals with significant burnout, affecting their productivity and job satisfaction. Therefore, identifying the factors that lead to burnout in management students is crucial. By understanding these factors, educators and institutions can better assess students’ academic performance and predict potential dropout risks, much like how assessing professional burnout can reveal employees’ job engagement or intentions to leave their positions.

Previous studies have shown that college students typically experience moderate to high levels of burnout. For example, Liu, Xie [ 5 ] found that over half of college students surveyed experienced academic burnout, with varying degrees of severity. This is further supported by Marôco, Assunção [ 6 ], who defined student burnout as exhaustion due to study demands, a cynical attitude towards schooling, and feelings of academic inefficacy. Further research suggests that burnout can occur when students perceive a lack of meaningful rewards or opportunities in their environment, leading to feelings of exhaustion and disengagement [ 7 ]. According to Lin and Kennette [ 8 ], students experiencing burnout often feel a lack of engagement and find classroom routines monotonous. Similar to employees in people-helping professions, student burnout is characterized by increased absenteeism, diminished motivation for coursework, and a higher likelihood of dropping out of college [ 9 , 10 ]. Therefore, in this study, we define student burnout as a condition arising from academic stress, heavy course loads, or other psychological factors, leading to emotional exhaustion, a sense of depersonalization, and a diminished sense of personal achievement.

For the past years, in Vietnam, management colleges have required their students to simultaneously take both English-instructed management courses and English learning courses [ 11 ]. The integration of English-taught management courses aligns with the global trend of internationalization in higher education, where English is increasingly used as the medium of instruction in non-English speaking countries [ 12 ]. For students majoring in business management, the use of English in business management contexts, such as understanding international case studies, communicating in a global business environment, and reading and writing reports [ 13 , 14 ], can lead to an increasingly common phenomenon of academic burnout during their studies. This phenomenon results in a lack of motivation, inability to concentrate on coursework, tardiness, early departures, and feelings of isolation. However, only a small portion of studies has found that academic burnout is an experience among college students, especially management-majored students [ 15 , 16 ]. Therefore, this study attempts to conduct a longitudinal study to understand the factors that influence academic burnout among management students.

Studies point out that many students are afraid of English classes, lack confidence in themselves, experience anxiety, and doubt their abilities to handle the coursework [ 17 ]. Additionally, the top three sources of stress for students are academic pressure, lack of confidence, and feelings of loneliness [ 18 ]. As management students are potential future professionals in the field, some graduates who have worked hard to complete their management education end up not pursuing careers that are related to English. These graduates may have made such decisions due to feeling overwhelmed by academic pressures during their studies, fear of English, or a lack of confidence in their English. Consequently, they may actively avoid working in English or opt for non-English-related professions after graduation, resulting in a waste of educational resources.

Furthermore, numerous factors contributing to work-related burnout have been explored. These factors include environmental factors such as social/teacher support, digital competence, and workload [ 19 , 20 ]; psychological factors such as grit, mindfulness, and emotion regulation [ 21 , 22 ], as well as individual factors such as gender and personality traits [ 23 , 24 , 25 ]. However, most of these studies have focused on people-helping professionals [ 26 , 27 ], medical students or college students, in general [ 28 , 29 , 30 ], with limited studies [ 31 , 32 , 33 ] longitudinally conducted on English learning burnout among college students, especially management students. Therefore, to cultivate better management talents, prevent the waste of educational resources, and provide valuable insights for teachers’ instruction, it is worth investigating the factors that contribute to academic burnout among management students.

Based on the aforementioned background and motivations, this study has two main objectives: (1) To understand the influence of intrinsic personal variables and external environmental factors on academic burnout, using a longitudinal study to investigate the impact of self-efficacy and academic workload on academic burnout. (2) To explore the effects of academic burnout and self-efficacy on English learning outcomes. In addition, the study will also investigate past English learning performance and the impact of English anxiety on self-efficacy among students in colleges of management in a non-native English-speaking country, Vietnam.

In conclusion, this study contribute to the understanding of English learning burnout, emphasizing the need to address this issue to enhance students’ learning experiences, well-being, and academic outcomes. The study offers valuable insights into the complex interplay of psychological, and educational factors contributing to burnout, highlighting the significance of addressing this issue in the context of English language education.

Literature review

Conservation of resources (cor) theory.

The Conservation of Resources (COR) theory [ 34 ] serves as a foundational framework for understanding the origins of burnout and the responses associated with prolonged work-related stress. COR theory outlines the reasons certain situations are perceived as stressful and how individuals react to these stressful events. At its core, the COR theory posits that people are motivated to acquire and protect valued resources. Stress arises when there’s a threat to these resources, whether it is the potential for loss, actual loss, or even the pressure of significant gains. Specifically in the context of burnout, factors like physical exhaustion or excessive workload can undermine individuals’ confidence in their capacity to engage and sustain motivation, highlighting the COR theory’s emphasis on the impact of resource loss during times of stress.

Furthermore, anxiety can be viewed as both a result of resource threats and a contributing factor that exacerbates the perception of resource loss. It acts as a psychological mechanism that heightens the sensitivity to threats, amplifying the stress response and potentially accelerating the cycle of resource depletion.

Student burnout

Leiter and Maslach [ 1 ] defined burnout as a phenomenon characterized by “emotional exhaustion,” “depersonalization,” and a diminished sense of personal accomplishment. Emotional exhaustion refers to a person’s lack of energy and feeling depleted of emotional resources, resulting in a lack of enthusiasm for work and often accompanied by feelings of frustration and tension. Depersonalization involves treating people as objects rather than individuals, exhibiting emotional detachment, callousness, cynicism, and a sense of estrangement towards clients, colleagues, or the organization. Diminished personal accomplishment refers to a person’s negative self-evaluation, feeling of inadequacy in their work, and a decrease in self-esteem.

Therefore, for students, this study adopts the definition of academic burnout as follows: “A phenomenon where students experience emotional exhaustion, depersonalization, and diminished personal accomplishment due to academic pressures, workload, or other personal psychological factors during their learning process.” This definition is based on Leiter and Maslach [ 1 ] and previous definitions of academic burnout [ 28 , 35 ].

Indeed, many studies in the past have utilized the COR theory to explain the phenomenon of burnout [ 23 , 34 ]. However, the findings from these studies have not been consistent. According to the COR, stress occurs when individuals perceive a threat to their valuable resources, experience a loss of resources, or fail to gain expected returns from investing their resources. For example, in the context of students, when they invest a significant amount of valuable time and energy into their academic coursework but do not achieve the expected outcomes, it can lead to feelings of stress. This aligns with the idea that the stress of burnout can arise when individuals perceive a loss or lack of resources, such as time, effort, or desired outcomes in their academic pursuits.

Within the COR, the workload is recognized as a significant environmental variable contributing to stress [ 36 ]. Workload refers to a situation where an individual faces multiple problems simultaneously within a limited time frame and is unable to resolve them, leading to a state of role overload [ 37 ]. The workload can impact a person’s physical health and job performance [ 38 ]. Past research has also found that academic workload is a primary factor contributing to student stress. Studies have shown that academic workload is consistently ranked as the top stressor among college students [ 39 ]. Villanova and Bownas [ 40 ] with 2,408 college students, the academic workload was identified as the most significant stress factor. Furthermore, factors such as exams, time pressure, and workload accounted for a substantial proportion (41.6%) of the perceived stress variance. Additionally, excessive stress has a negative impact on student learning [ 41 ]. Taken together, the aforementioned studies suggest that academic workload is one of the significant factors contributing to academic burnout among college students.

Social cognitive theory and self-efficacy

Identifying and understanding the variations in individual behavior within different environments can often be challenging. Many traditional motivation theories that focus on cognitive processes and expectancies fall short in providing a detailed, process-oriented analysis of how individual actions influence environmental outcomes. Social Cognitive Theory (SCT) [ 42 ] addresses this gap by clearly defining the factors that determine human behavior. Self-efficacy is a central concept in the SCT that emphasizes how individuals assess their capabilities and make decisions regarding whether to engage in a particular behavior [ 43 ]. Social cognitive theory delves into the intricate interplay between (1) environmental influences like societal expectations and specific situations, (2) cognitive/personal factors encompassing individual traits and demographic information, and (3) behavior. Self-efficacy emerges as a vital mediator within this interaction, shaping behavior outcomes. Successful experiences boost individuals’ confidence in their abilities, enhancing their capability to navigate external circumstances.

Bandura [ 43 ] introduced a self-efficacy model, identifying four crucial determinants: (1) mastery experiences, stemming from past successes and seen as the most impactful on self-efficacy; (2) vicarious learning, where observing others’ successful actions helps adjust one’s self-efficacy; (3) verbal persuasion, using language or encouragement to boost self-efficacy; and (4) physiological and emotional arousal, affecting performance and self-efficacy perceptions.

Moreover, perceived self-efficacy significantly impacts various aspects of an individual’s life, including (1) choice behavior, influencing daily decisions on actions and time allocation; (2) performance and effort, where self-efficacy beliefs determine the dedication and perseverance applied to tasks; and (3) emotional reactions and thought patterns, with self-assessments of abilities shaping emotional responses and cognitive interactions with the environment.

English anxiety

With rapid globalization, studying the psychological aspects of inner experiences among English learners has become increasingly important [ 44 ]. Anxiety is considered an important factor in learning English skills [ 45 ] because when students experience fear or anxiety towards English, it can affect their confidence, emotions, and behaviors related to English learning. Hashemi and Sciences [ 46 ] argue that anxiety is an emotion that is stable and can persist over a long period unless the learning environment is changed, such as increasing exposure to English courses or addressing the causes of anxiety. Changing a person’s beliefs and emotions (anxiety) requires a considerable amount of time [ 46 ]. Therefore, only by increasing the duration of English courses and providing more extended time to address learners’ English anxiety and attitudes, can anxiety be potentially changed. Thus, this study defines English anxiety as “an individual’s emotional fear, resistance, discomfort, or uneasiness towards English learning, accompanied by psychological discomfort or unease, which may potentially influence or hinder future English learning or perception of English”.

Workload and burnout

In the context of the COR, the workload is considered an important environmental variable that contributes to stress [ 47 ]. It is also a significant factor in the demand aspect of the theory. When an individual’s valuable resources are threatened, both their physiological and psychological states can be affected. Workload refers to a situation where an individual faces numerous problems within a limited time frame and is unable to resolve them, resulting in role overload [ 37 ]. Excessive workload has been shown to impact both physical health and work quality [ 48 ]. Previous research has demonstrated associations between workload and outcomes such as burnout, increased cholesterol levels, excessive anxiety, and elevated heart rate [ 49 ]. Experiencing excessive workload not only affects employees’ health but also influences the way tasks are performed and employees’ perceptions of themselves and their work. Specifically, the excessive workload can lead to increased job dissatisfaction, reduced productivity with poor quality outcomes, burnout, anger, and feelings of personal failure [ 50 ].

The same situation can also occur in students. When students feel overwhelmed by the workload of school assignments within the available time, and they are unable to relieve the pressure or resolve academic problems, they can get caught in a downward spiral, exacerbating the situation. This can have an impact on students’ emotions and interpersonal relationships, leading to a loss of interest in schoolwork and a diminished sense of achievement. Leiter [ 51 ] proposed that burnout tends to worsen over time. In other words, individuals who initially experience burnout are likely to experience an increasing rate of burnout as time goes on. Rau, Gao [ 52 ] indicated that excessive academic pressure has a negative impact on college students’ learning. Previous studies pointed out that as students face an increasing course load or homework load, the occurrence of burnout significantly increases [ 53 ].

Hypothesis development

The main purpose of this study is to investigate the factors influencing learning burnout among students in a management program based on the environmental factors of the COR and the individual factors of the SCT. Additionally, the study aims to understand the impact of learning burnout on English learning performance. This study adopts a longitudinal study design, which involves twice within one year to observe the learning burnout among the same group of students.

The research model of this study, as shown in Fig.  1 , combines the COR [ 34 ] and the SCT [ 43 ]. Based on the literature reviewed, we find that the factors influencing burnout are complex and cannot be explained solely by environmental stimuli. Previous studies on burnout among students have placed too much emphasis on environmental factors while not focusing on individual factors [ 10 ]. Additionally, burnout research should simultaneously consider the influence of individual and environmental factors [ 54 ], with self-efficacy being an important individual factor. However, can a student’s self-efficacy never change? If this self-efficacy does change, would it have an impact on their burnout over time? Previous self-efficacy research has mostly focused on cross-sectional studies, examining specific time points, and lacked longitudinal studies, which involve continuous time periods. This study aims to explore the impact of changes in self-efficacy on changes in learning burnout from a longitudinal perspective. Therefore, we hypothesize that when a student’s self-efficacy changes over time, their learning burnout will also vary. Several researchers have supported this argument through longitudinal studies, suggesting a positive relationship between self-efficacy at Time 1 and Time 2 [ 55 , 56 ]. In other words, self-efficacy tends to increase or decrease over time. Additionally, those who experienced burnout in the previous phase had even higher levels of burnout in the subsequent phase [ 51 ]. This implies that burnout tends to increase over time. Thus, burnout is likely to worsen or lighten over time, and changes in self-efficacy can influence the trajectory of burnout.

Furthermore, an individual’s self-efficacy can influence their behavior [ 43 ]. When individuals perceive themselves as having higher self-efficacy, they are more likely to engage in social activism and cope with specific situations, making them less prone to psychological withdrawal. Rahmati and Sciences [ 57 ] argued that self-efficacy and burnout have a negative relationship. Therefore, considering that both self-efficacy and burnout can change over time, we can infer that the extent of self-efficacy change will affect the extent of burnout change [ 43 ]. In addition, previous studies have found that English anxiety can impact a person’s learning behavior and performance [ 58 ]. This suggests that in addition to environmental factors, individual differences are also important in influencing burnout [ 50 ]. Therefore, this study specifically examines the impact of students’ self-efficacy and English anxiety on learning burnout. Moreover, previous studies have identified academic workload as the primary source of stress for students [ 39 ]. Hence, in this study, the environmental variable focuses only on the workload. The excessive workload can lead to job dissatisfaction, slightly increased productivity but poor quality performance, feelings of stress, anger, and personal failure [ 50 ]. Research conducted in healthcare settings has also shown that the frequency of interactions with patients and the length of rest breaks can influence burnout. Additionally, the study includes the variable of students’ English learning performance since students typically care about their academic achievements.

The research model (Fig.  1 ) illustrates the relationships among the variables in this study. According to the model: (1) past English learning performance affects self-efficacy; English anxiety also influences self-efficacy; (2) self-efficacy affects learning burnout, and the extent of self-efficacy change influences the extent of learning burnout change; workload affects learning burnout, and the extent of workload change influences the extent of learning burnout change. Furthermore, English anxiety also affects learning burnout; (3) learning burnout influences current English learning performance, and self-efficacy influences current English learning performance. Based on the previous literature review and the research model, this study proposes the hypotheses.

Conceptual model

Self-efficacy refers to an individual’s judgment of their capabilities to effectively accomplish their desired goals and tasks [ 59 ]. Based on the previous literature, self-efficacy is influenced by four main factors: performance accomplishments, vicarious learning, verbal persuasion, and physiological states [ 43 ]. Among these factors, performance accomplishments have been identified as the most important in shaping self-efficacy. Past successful experiences reinforce an individual’s belief in their abilities and increase their confidence. Conversely, repeated failures can lower self-efficacy. In other words, positive mastery experiences or achievements enhance self-efficacy, while negative experiences or failures can diminish them [ 60 ].

Empirical studies related to self-efficacy support the existence of the aforementioned relationship. Individuals with high self-efficacy tend to perform better than those with low self-efficacy [ 56 ]. Additionally, a meta-analysis on self-efficacy also confirmed the positive relationship between past performance and self-efficacy [ 61 ]. Furthermore, previous studies have found a positive correlation between students’ academic achievement and self-efficacy [ 62 ]. In summary, individuals with better academic performance tend to have higher self-efficacy, while those with poorer performance have lower self-efficacy. Therefore, we propose:

H1: Students with better past English learning performance will have higher self-efficacy, while students with poorer past English learning performance will have lower self-efficacy.

In Bandura and Adams [ 59 ], self-efficacy has an impact on outcomes, including one’s effort or performance. Self-efficacy influences an individual’s judgment to determine how much effort or persistence they will invest in completing a task or performing in a certain way. Individuals with higher self-efficacy are more energetic and persistent in their efforts to accomplish a task [ 63 ]. Especially when faced with difficult problems or tasks, individuals with lower self-efficacy may start doubting their abilities and weaken their efforts or even give up altogether, avoiding the challenges. On the other hand, individuals with stronger self-efficacy tend to exert more effort in overcoming or solving the difficulties and challenges they encounter [ 64 ].

Previous empirical research has extensively explored the relationship between self-efficacy and various outcomes, such as learning performance, job performance, and career decision-making. The results consistently indicate that individuals with higher self-efficacy tend to outperform those with lower self-efficacy in different domains. Studies have shown that individuals with higher self-efficacy exhibit better academic achievements [ 56 ], skill acquisition, performance in English learning or work effectiveness [ 65 ], and even career decision-making [ 66 ]. Based on these empirical findings, individuals with higher self-efficacy consistently exhibit better performance in various domains compared to those with lower self-efficacy. Therefore, we propose:

H2: Students with higher self-efficacy will have better English learning performance, while students with lower self-efficacy will have poorer English learning performance.

Bandura and Adams [ 59 ] stated that individuals with high self-efficacy have more confidence in themselves, while those with low self-efficacy have less confidence. An individual’s self-efficacy expectations can influence their behavior, which in turn affects their performance [ 59 ]. An individual’s self-efficacy will influence their emotional and cognitive responses. Individuals with high self-efficacy tend to have more positive emotional responses, while individuals with low self-efficacy generally have more negative emotional responses. When individuals complete challenging tasks on their own, it reinforces their work motivation and satisfaction [ 67 ]. This achievement leads to psychological success and encourages individuals to actively engage in their work. Conversely, it can lead to psychological withdrawal. Psychological failure can cause individuals to emotionally withdraw from the work environment, lower their work standards, and become indifferent [ 60 , 68 ]. Therefore, based on this reasoning, it can be inferred that if students’ self-efficacy increases, their level of burnout should decrease, whereas if their self-efficacy decreases, their level of burnout should increase.

Students’ self-efficacy can indeed change over time [ 56 ]. Self-efficacy is not a fixed trait and can be influenced by various factors such as experiences, achievements, and feedback. If a student’s self-efficacy changes, it is reasonable to expect that it may have an impact on their level of burnout. In the past, self-efficacy research has mostly focused on cross-sectional studies, examining self-efficacy at specific time points, and lacking longitudinal studies that observe changes over time. However, our study aims to investigate the influence of changes in self-efficacy on changes in learning burnout using a longitudinal design, which is commendable. There is supporting evidence from longitudinal studies that self-efficacy can change over time. Researchers have found a positive relationship between self-efficacy at Time 1 and self-efficacy at Time 2 [ 55 ]. Additionally, Leiter [ 51 ] found that employees who had a burnout in the first stage experienced increased burnout in the second stage. These findings suggest that burnout can increase over time. Based on the understanding that both self-efficacy and burnout can change over time, it is reasonable to infer that the magnitude of self-efficacy change will influence the magnitude of burnout change. We can hypothesize that.

H3-1: Students with higher self-efficacy will experience lower levels of learning burnout, while students with lower self-efficacy will experience higher levels of learning burnout.

H3-2: Students who experience a greater increase in self-efficacy will have a larger reduction in the magnitude of learning burnout, whereas students who experience a greater decrease in self-efficacy will have a larger increase in the magnitude of learning burnout.

In addition to the influence of past English learning performance on self-efficacy, students’ self-efficacy may be influenced by other factors. According to Bandura [ 43 ], a person’s physiological or emotional arousal can impact their self-efficacy. Anxiety is a characteristic of physiological or emotional arousal. Kavanagh and Bower [ 69 ] proposed that emotions can influence a person’s self-efficacy, and individuals who experience feelings of depression often undermine their thoughts and abilities, perceiving a negative relationship between emotional arousal and self-efficacy. Agyapong, Obuobi-Donkor [ 70 ] suggested that psychological stress or anxiety hinders a person’s ability to discern the problems they face. Besides, individuals with higher English anxiety have lower self-efficacy, which consequently leads to lower English performance [ 45 ]. Additionally, many empirical studies have found that individuals with higher anxiety have lower confidence in themselves, while individuals with lower anxiety have higher confidence [ 59 ]. Liao, Wang [ 58 ] found that anxiety affects a person’s self-efficacy expectations, with individuals experiencing English anxiety having lower self-efficacy expectations. Therefore, we hypothesize:

H4: Students with lower levels of English anxiety will have higher self-efficacy, while students with higher levels of English anxiety will have lower self-efficacy.

Anxiety refers to the psychological responses of fear, discomfort, apprehension, or nervousness that an individual experiences toward certain events or situations [ 71 ]. These psychological responses often persist over time. Anxiety is considered an emotion that is believed to affect our attention to tasks and the processing of information we have learned [ 71 ]. Therefore, if students initially have psychological or emotional fear or aversion towards English, perceiving English as a threat, they are naturally inclined to reject them psychologically and lack the willingness to learn [ 46 , 72 ]. They may find English unattractive and lack a sense of achievement. Endler and Kocovski [ 71 ] proposed that when individuals face psychological anxiety, they often adopt cognitive strategies of avoidance to ignore or deny the existence of the event. Therefore, if students have a preconceived fear of English, based on the aforementioned theoretical foundation, this English anxiety may affect their learning and possibly lead to the occurrence of learning burnout, resulting in strong feelings of frustration or low achievement in class. Indirectly, when students experience English anxiety, they may adopt a distant attitude towards English-related courses, skip classes, and feel a strong sense of frustration in learning English courses, leading to low performance and a lack of a sense of achievement. Past empirical studies indicated that English anxiety can affect students’ English performance [ 45 ]. Based on these findings, we can infer that.

H5: Students with higher levels of English anxiety will have higher levels of learning burnout, while students with lower levels of English anxiety will have lower levels of learning burnout.

In the structured model of burnout [ 50 ], it is evident that excessive workload is an important factor in job-related burnout. Furthermore, the COR argues that when individuals perceive a threat or loss of valuable resources, it can affect their mental and emotional well-being. The loss of resources and psychological distress are highly correlated [ 34 ]. Specifically, when individuals face an overwhelming number of tasks within the available time, it can lead to role overload, which has significant implications for the health and job quality of employees engaged in interpersonal work [ 37 , 49 ]. Moreover, excessive workload can influence the way tasks are performed and how employees perceive their work. It often results in increased job dissatisfaction, decreased productivity with poor quality performance, mental strain, anger, and feelings of personal failure [ 47 ].

The same situation can also occur in students. When students feel overwhelmed by the excessive academic workload within the available time, unable to relieve the pressure or solve the academic challenges, they can spiral into a state of loss, exacerbating the situation [ 39 ]. This can affect students’ emotions and interpersonal relationships, leading to a loss of interest in schoolwork and a decrease in achievement satisfaction [ 73 ]. Besides, burnout tends to worsen over time, meaning that individuals who initially experience burnout are more likely to experience an increased rate of burnout [ 50 ]. Excessive academic pressure has negative effects on college students’ learning [ 39 ]. Previous studies have highlighted the close negative relationship between workload and burnout among students [ 74 , 75 ]. Based on this, we infer that.

H6-1: The greater the academic workload of students, the more severe their learning burnout will be. Conversely, the lower the academic workload of students, the milder their learning burnout will be.

H6-2: Students who experience a larger increase in academic workload will have a greater increase in learning burnout. On the other hand, students who experience a larger decrease in academic workload will have a greater decrease in learning burnout.

In Shirom [ 76 ], emotional exhaustion was identified as a primary dimension of burnout. The intense emotional strain is predicted to interfere with effective functioning [ 1 ]. This perspective suggests a negative relationship between emotional exhaustion and performance. Therefore, for students, if they continuously experience an increasing burden or exhaustion in their emotions, may feel tired, depleted, irritable, frustrated, and emotionally drained, which can result in poor academic performance [ 77 ]. When students feel overwhelmed by their academic workload and unable to cope, they may exhibit behaviors of detachment, indifference towards classmates or school matters, and a lack of focus on their academic responsibilities, resulting in unsatisfactory academic performance. A reduced sense of accomplishment refers to a person’s perception of failure in terms of their abilities and work achievements [ 50 ]. Since burnout is considered a stress response, when students perceive a diminishing sense of accomplishment in their schoolwork, they may start to doubt their abilities, self-evaluate negatively, experience a sense of helplessness, and have diminished self-esteem, ultimately leading to poor learning outcomes [ 78 , 79 ]. Individuals become less sensitive to others, exhibit negative emotions, and experience dissatisfaction after experiencing stressors, which leads to decreased job performance [ 80 ]. Anxiety and depression can impact job performance, with depression being considered one of the manifestations of burnout [ 70 ]. Paloș, Maricuţoiu [ 77 ] explored the relationship between student environment and academic burnout, finding a negative relationship between academic burnout and academic achievement. Studies found a negative relationship between emotional exhaustion and job performance, with emotional exhaustion being a key dimension of burnout [ 81 , 82 ]. Based on these perspectives, we can infer that.

H7: Students with higher levels of academic burnout will have less satisfactory English learning performance, while students with lower levels of academic burnout will have more satisfactory English learning performance.

Constructs and questionnaire design

In terms of operationalizing the research variables, reliable and valid scales from existing literature were utilized. The measurement of academic burnout adopted the Maslach Burnout Inventory-General Survey (MBI-GS) developed by Leiter and Maslach [ 1 ], as it is suitable for assessing academic burnout in the context of management students. The measurement of self-efficacy was based on Bandura [ 43 ], with modifications made to adapt it to the context of academic workload. The measurement of academic workload was adapted from scales developed by Kahn, Wolfe [ 37 ] that assess work demands. The measurement of English anxiety was based on the English anxiety developed by Tien [ 17 ] and Atef-Vahid, Kashani [ 45 ], which assesses individuals’ feelings of threat, fear, nervousness, unease, and hostile or resistant attitudes toward English during English learning. All the scale items above were measured by a 7-point scale.

In general, academic achievement refers to a student’s overall academic performance in school. However, in this study, the focus is solely on the grades of students in English-related subjects. The definition of learning achievement is based on Brown, Lent [ 83 ], which involves calculating the average grades of students in English subjects at the end of a semester. Specifically, there are two measures of English learning performance in this study. The first measure is the average of the grades in various English-related courses taken during the entire academic year of the first grade, referred to as “past English learning performance.” The second measure is the average of the grades in English-related courses taken during the entire academic year of the second grade, referred to as “prior English learning performance.” However, due to variations in English courses offered by the participating schools, the researchers first collected the course schedules of the schools. After analyzing and organizing the data, we found that the English courses offered by the participating schools followed a standardized curriculum. Therefore, the English learning achievement in this study primarily focused on standardized English courses. These courses involved both theoretical and practical components, and at the end of each semester, students were assigned a single grade for each course.

Participants

This longitudinal study focused on management college students across Vietnam, selecting one to two schools from the northern, central, and southern regions for a representative sample. Due to the necessity for high cooperation from school teachers for administering two questionnaires, convenience sampling was employed, targeting more willing schools. A total of 11 classes from six management colleges were included. The first questionnaire was distributed two weeks prior to the first semester’s midterm exams in 2022 during class, and the second followed within two weeks after the second semester’s midterms, also conducted during class time.

In terms of data analysis, an exploratory factor analysis (EFA) was conducted to examine the discriminant validity of the variables’ scales. Principal Component Analysis (PCA) with varimax rotation was used to achieve orthogonal axes, and a significance level of item loading at 0.5 was adopted to determine the explanatory power of each variable scale [ 84 ]. Then, Cronbach’s alpha was computed to assess the reliability of each variable scale, with a cutoff value of 0.7 as the criterion for satisfactory reliability [ 85 ]. Descriptive statistics were utilized to observe the actual distribution of the sample. Additionally, academic performance was standardized. Since different schools have varying grading standards, with some being more lenient and others more strict, to make the grades more representative, the English grades were transformed into T-scores. The standardization process involved calculating Z-scores and then converting them into T-scores with a mean of 50 and a standard deviation of 10 [ 86 ]. Finally, a structural equation model (SEM) using path analysis was employed to test the hypotheses.

Measurement of the constructs

A total of 615 questionnaires were collected for the first survey. After excluding 22 incomplete and invalid questionnaires, there were 593 valid responses, resulting in a response rate of 96.4%. Among the respondents, there were 154 males and 439 females. For the second survey, a total of 529 questionnaires were collected. After excluding 27 incomplete and invalid questionnaires, there were 502 valid responses for statistical analysis (considering those who completed both surveys), resulting in a response rate of 94.9%. Among the respondents, there were 120 males and 382 females.

First, the detection of common method bias issues was conducted using Harman’s single-factor test for exploratory factor analysis (EFA). The analysis revealed that 7 factors could be extracted, with the first factor explaining 37.807% of the variance and the sum of squared loadings for the rotation being 10.595%. Since this did not reach the 50% threshold for determining the presence of common method bias, the sample data does not have a severe issue with common method bias.

Next, an exploratory factor analysis (EFA) was conducted to examine the measurement model of the questionnaire. The “English burnout” measurement model was tested with EFA, utilizing principal component analysis to extract common factors, followed by the varimax method for orthogonal rotation based on extracted factors with Eigenvalues greater than 1. This process identified three factors: emotional exhaustion, depersonalization, and deminished personal accomplishment. The cumulative explained variance was 62.1%. For “English anxiety,” “workload,” and “self-efficacy,” the explained variances were 29.8%, 45.2%, and 55.5%, respectively.

To assess the reliability of the measurement instrument, an internal consistency (Cronbach α) test is conducted. For the current sample, the reliability analysis using Cronbach’s coefficient alpha was quite acceptable for overall burnout (α = 0.83), emotional exhaustion (α = 0.82), depersonalization (α = 0. 84), and diminished personal accomplishment (α = 0.76), anxiety (α = 0.81), self-efficacy (α = 0.78), workload (α = 0.84), suggesting that the questionnaire has relatively high reliability [ 85 ].

The study assessed the discriminant validity of its measurement model by examining the correlation coefficients and standard errors among different factors, ensuring that they are not equal to 1 within the sampling error range. Table  1 shows that all correlation coefficients between factors were below 0.5, confirming that the constructs are distinct and possess discriminant validity. For instance, the 0.24 correlation coefficient between English anxiety and learning burnout demonstrates their distinctiveness. Additionally, none of the correlation coefficients were 0, indicating substantive relationships among the factors. This further validates the measurement model used in the study [ 85 ].

Descriptive statistics

The variable distribution indicates slight variations in learning burnout and self-efficacy scores between two questionnaires, with average scores around 42.59/42.68 and 22.77/22.74, respectively. While differences are minimal, a slight decrease in self-efficacy is noted in the second questionnaire, hinting at a downtrend in students’ confidence in their abilities. Workload perception has increased, evident from average scores of 17.53 to 18.46. English anxiety, measured once due to its stability, showed an average score of 22.10 and an item mean of 2.21.

In this study, paired-sample t-tests were conducted to compare the differences between the pre-and post-periods for each variable. The results showed that there were no significant differences between the pre-and post-periods for learning burnout, self-efficacy, and English learning performance. However, there was a significant difference in the pre-and post-periods for the workload. The average score for workload in the post-period was higher than in the pre-period, indicating that students perceived an increasing workload. This difference in workload may be influenced by factors such as the duration of the college program and the arrangement of courses, which could explain the significant difference observed.

Hypothesis testing

This study employed Structural Equation Modeling (SEM) to test the hypotheses (Fig.  2 ). “Performance 0” represents past English learning performance, " Performance 1” represents current English learning performance, " Self-efficacy 1” represents self-efficacy from the first questionnaire, " Self-efficacy 2” represents self-efficacy from the second questionnaire, “Burnout 1” represents learning burnout from the first questionnaire, " Burnout 2” represents learning burnout from the second questionnaire, “Workload 1” represents workload from the first questionnaire, “Workload 2” represents workload from the second questionnaire, and “Anxiety” represents English anxiety. There are 26 coefficients estimated in this study. To ensure an adequate sample size for SEM, the recommended guideline is a minimum of five times the number of estimated coefficients (Bentler & Chou, 1988). With 502 valid questionnaires in this study, the sample size meets the basic requirement for estimating the SEM. Therefore, a configuration of the limited information model is adopted. In the structural model, learning burnout is represented by the average sum of scores from the three dimensions: emotional exhaustion, depersonalization, and low personal accomplishment. The constructs of English anxiety, workload, and self-efficacy are also measured by the average sum of scores based on the limited information model configuration.

Furthermore, to meet the model identification requirement, this study sets the estimation parameters between factors and variables to 1. The estimation error variances of each variable are set to 1 minus the Cronbach’s α value of the corresponding construct, multiplied by the variance of that construct. This yields the estimation error variances (E) according to the formula: (1 - α) * σ 2 . In this study, the constructs that require the calculation of estimation error variances (E) in the SEM are “Self-efficacy 1”, " Self-efficacy 2”, “Burnout 1”, “Burnout 2”, “Anxiety”, “Workload 1”, and “Workload 2”. Additionally, for the English learning performance, as there is only one item with an average total score, the estimation error variances (E) for past English learning performance and current English learning performance are both set to 0 in this study.

Table  2 indicates that the relationship between self-efficacy and English learning performance is not significant ( p  > 0.1), while the remaining relationships are significant. The results from Table  2 are depicted in Fig.  2 to illustrate the path relationships in the structural model for the entire sample. In Fig.  2 , the numbers on the paths represent the estimated path coefficients, and the asterisk (*) denotes the significance level of each path relationship. According to the model fit indices, CFI = 0.959 (> 0.9), NFI = 0.950 (> 0.9), NNFI = 0.902 (> 0.9), and AASR = 0.0467 (< 0.1), it can be concluded that the structural model demonstrates a good fit with the data.

Path analysis. * p  < 0.05; ** p  < 0.1; *** p  < 0.001

Note PM = Performance; SE = Self-efficacy; BO = Burnout; ANX = Anxiety; WL = Workload

The complete SEM is supported by the path analysis. Therefore, based on the results of the path analysis and regression analysis mentioned earlier, the findings regarding the validation of each hypothesis are summarized in Table  3 .

Finally, based on the standardized coefficients (Table  4 ), it is possible to determine the direct and indirect impacts of each variable on performance 1. This analysis allows for a comparison between the influence of internal and external variables on performance 1. When considering the cumulative effect of all variables on performance, PM0 emerges as the most significant predictor, followed by BO2, WL1, SE2, WL2, ANX, BO1, SE1. Therefore, it is evident that PM0 stand out as the primary determinants influencing performance 1.

Discussions

• Self-efficacy

Hypothesis 1 (H1): The impact of past English learning performance on self-efficacy. According to Bandura [ 43 ], an individual’s self-efficacy level is influenced by their past performance. Successful experiences strengthen one’s confidence, while unsuccessful experiences weaken it. This study found that students who performed well in English-related subjects in the past have higher self-confidence. On the other hand, students who had poor performance in English -related subjects in the past exhibit lower self-confidence.

Hypothesis 4 (H4): The impact of English anxiety on self-efficacy. According to Bandura [ 43 ], physiological or emotional arousal can influence an individual’s self-efficacy. A person’s bodily or emotional state can affect their judgment of confidence. Anxiety is considered one of the factors in this regard. This study found that students with higher levels of English anxiety tend to have lower confidence in their abilities related to English courses.

This study examines the impact of self-efficacy and workload on learning burnout based on social cognitive theory [ 43 ] and the COR [ 34 ], using a longitudinal research design. The hypotheses are described as follows:

H3-1 and H3-2: The influence of self-efficacy variability on learning burnout variability. Self-efficacy affects an individual’s emotional responses and cognitive patterns, and burnout is one of the emotional responses [ 43 ]. In other words, if individuals perceive that their self-efficacy is insufficient to meet the demands of the environment, it can affect their efforts and attention, leading to stress and impairing their judgment of their abilities, thereby resulting in emotional exhaustion or dehumanization. However, self-efficacy can change over time and can also influence changes in burnout [ 43 , 57 ]. The present study found that fluctuations in self-efficacy negatively impact the extent of learning burnout fluctuations. This result suggests that self-efficacy may be strengthened or weakened through the accumulation of experiences over time, and the degree of this change is negatively related to the extent of learning burnout fluctuations.

H6-1 and H6-2: The influence of workload variability on learning burnout variability. According to the COR, when individuals perceive threats or losses to their valuable resources, it can affect their emotions or psychological well-being [ 34 ]. Therefore, when individuals feel that their workload is increasing, their experience of burnout tends to increase significantly. The present study found that as students’ academic workload increased over time, the extent of their learning burnout also increased noticeably [ 75 ]. Conversely, when students’ workload decreased, the extent of their learning burnout became less pronounced. Thus, fluctuations in workload directly and positively affect the fluctuations in students’ learning burnout.

H5: The influence of English anxiety on learning burnout. English anxiety refers to an emotional response at the psychological level toward English [ 17 ]. This study found that English anxiety has a positive impact on learning burnout. Students with higher levels of English anxiety experience more pronounced learning burnout. On the other hand, students with lower levels of learning burnout experience less pronounced learning burnout. Anxiety and burnout are positively related [ 70 ]; individuals with higher anxiety levels tend to exhibit impatience, indifference, reduced sense of accomplishment and decreased engagement in their tasks.

Academic performance

H2: The influence of self-efficacy on English learning performance. An individual’s level of self-efficacy affects their performance and level of effort [ 43 ]. Individuals with higher self-confidence tend to perform better and are more willing to invest more effort into their work. Conversely, individuals with lower self-confidence tend to perform less effectively and are less willing to invest additional effort. The present study found that students’ performance in English learning is not necessarily better when their self-confidence is higher. Similarly, when students’ self-confidence is lower, their performance in English learning is not necessarily worse. The direct effect of self-efficacy on English learning performance is not significant, but English learning performance is influenced indirectly by the mediating effect of learning burnout. Therefore, the relationship between self-efficacy and English learning performance is mediated by learning burnout, which affects students’ English learning performance. The possible explanation for the result is due to the multifaceted nature of language acquisition and the critical role of psychological well-being in educational outcomes. English learning, being inherently complex, demands more than just high self-confidence; it requires consistent practice, exposure to the language, and effective learning strategies [ 87 ]. High self-efficacy might bolster the initial motivation and effort, but without addressing potential learning burnout, these efforts may not translate into improved performance. This suggests that burnout acts as a crucial mediator, with its potential to undermine the positive effects of self-efficacy by diminishing students’ motivation and capacity to engage with the learning material. Therefore, interventions aimed at enhancing English learning outcomes should not only foster self-confidence among learners but also create supportive learning environments that mitigate the risk of burnout.

H7: The influence of learning burnout on English learning performance. According to the COR, there is a negative relationship between emotional exhaustion, depersonalization, and job performance [ 34 ]. When an individual experiences burnout, their job performance tends to be less satisfactory. The present study found that as students’ learning burnout becomes more prominent, their performance in English learning becomes less satisfactory. There is a significant negative relationship between learning burnout and English learning performance. Therefore, it can be concluded that burnout affects an individual’s job performance.

Implications

Based on these research findings, some recommendations can be made to the education field:

Enhance students’ English learning performance:

To effectively enhance students’ English learning performance, a strategic approach that combines interest-driven course design with supportive educational practices is essential. Initially, the study underscores the importance of designing English courses for freshmen that are simple, engaging, and tailored to capture their interest. Recognizing that students’ past performance in English impacts their self-confidence, which in turn influences learning burnout, prioritizing courses that spark interest from the outset encourages students to invest effort in learning. This approach not only improves English performance but also boosts self-confidence, thereby reducing the likelihood of burnout.

Building on this foundation, incorporating technology and multimedia, such as interactive applications and videos, caters to diverse learning styles and enhances the educational experience. Project-based learning, which addresses real-world challenges, along with customizable learning paths, further motivates students by highlighting the practical applications of English. Peer learning and discussion groups also create a collaborative environment that fosters communication skills and self-confidence. Additionally, establishing robust feedback mechanisms and support systems, including access to tutoring and counseling, ensures continuous improvement and provides necessary encouragement. Cultural and linguistic immersion experiences, such as cultural exchange programs or interactions with native speakers, significantly contribute to linguistic proficiency and cultural awareness.

Enhance students’ self-efficacy:

To bolster students’ self-efficacy and counteract learning burnout, this study proposes a comprehensive strategy rooted in the positive correlation between self-efficacy and learning outcomes. Bandura’s theory highlights the impact of verbal persuasion and social support on self-confidence, suggesting that educators’ regular verbal encouragement and support play a critical role in motivating students. By fostering an affirming environment where students feel valued, their self-confidence is likely to increase, thereby enhancing their self-efficacy.

Therefore, the strategy encompasses several key elements to further support students. Personalized feedback is crucial, focusing on students’ efforts and progress rather than just the outcomes, thereby validating their individual learning journeys and emphasizing growth. Setting achievable goals allows students to experience incremental success, strengthening their belief in their own abilities. The inclusion of role models and the promotion of observational learning act as powerful motivators, illustrating that resilience can lead to success. Skill development workshops on practical skills like time management and stress management provide students with essential tools for academic success, augmenting their self-efficacy. Moreover, creating a supportive community through study groups or mentorship programs helps alleviate feelings of isolation, fostering a sense of belonging and mutual support. Celebrating students’ achievements, no matter the scale, plays a significant role in reinforcing their sense of competence and motivation.

Reduce students’ perception of excessive workload and make English assignments useful and interesting:

To address the challenge of perceived excessive workload and its impact on learning burnout among students, a logical and strategic approach is necessary. Recognizing that heavy workload perceptions contribute to burnout, it’s essential for educators to adjust the way English assignments are structured and perceived. Assignments should be designed to be meaningful, engaging, and clearly connected to real-world applications, thereby enhancing their relevance and interest to students. This entails incorporating practical examples, interactive elements, and projects that allow students to apply what they learn in tangible ways. By making assignments more engaging and relevant, students are more likely to view them as valuable learning opportunities rather than burdensome tasks.

Further, integrating techniques such as gamification can make learning more interactive and enjoyable, thereby reducing the perception of workload. Project-based assignments that encourage deep exploration of topics not only make learning more interesting but also foster a deeper sense of accomplishment and ownership over the learning process. Allowing students some choice in their assignments can also personalize the learning experience, increasing engagement and reducing feelings of overload. Breaking down assignments into smaller, more manageable tasks with clear, achievable objectives can help students better manage their workload and reduce stress. Providing constructive, timely feedback and recognizing achievements can further motivate students and support a growth mindset.

Considering English anxiety as a criterion for selecting students:

Acknowledging the impact of English anxiety on student performance, including self-confidence and susceptibility to learning burnout, there’s a clear need for educational institutions to refine their admission policies. English anxiety can manifest as impatience, indifference, and a diminished motivation for achievement, adversely affecting students’ academic progress. As educational institutions move towards gaining greater autonomy over their admission criteria, shifting from a sole reliance on standardized testing to more comprehensive evaluations offers a strategic opportunity to address these challenges.

Incorporating an assessment of English anxiety into the selection process can lead to several positive outcomes. First, it paves the way for a learning environment that’s less prone to inducing burnout, by selectively admitting students whose anxiety levels are within a manageable range. This proactive measure can substantially improve the learning atmosphere, making it more supportive and reducing stress for all students. Furthermore, identifying students with lower levels of English anxiety allows for a targeted approach to support, including the introduction of programs aimed at building confidence and alleviating anxiety, thus promoting better academic and personal well-being.

Future research

The study outlines future research directions, emphasizing the need to broaden its scope beyond management program students and explore potential learning burnout impacts across various educational systems and college programs. It highlights the necessity of validating the study’s applicability to diverse student groups, given the comparable coursework pressures. Furthermore, the study calls for a critical evaluation of the research tools employed, as many were sourced internationally or from different fields, potentially introducing bias. Future work should prioritize creating culturally relevant and context-specific measurement tools to enhance the accuracy and reliability of research findings.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Pham Thi, T., Duong, N. Investigating learning burnout and academic performance among management students: a longitudinal study in English courses. BMC Psychol 12 , 219 (2024). https://doi.org/10.1186/s40359-024-01725-6

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Investigating the impact of technology-based education on academic motivation, academic perseverance, and academic self-efficacy in english language learning skills

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• Jingxuan Bi 1 ,
• Siros Izadpanah   ORCID: orcid.org/0000-0002-2061-8110 2 ,
• Zohreh Mohammadi 3 &
• Yasaman Mohammad Rezaei 3  

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This study aims to investigate the impact of technology-based education (TBE) on the academic motivation (AM), academic perseverance (AP), and academic self-efficacy (ASE) of high school sophomore males. Technology has an important place in education in the modern digital age since it opens up new avenues for instruction and learning. Research is still being conducted to determine the precise impacts of TBE on different facets of students’ academic performance and attitudes. The study employed a quasi-experimental research design and utilized the cluster sampling method to select participants. Data collection for the study was conducted in the year 2023 and involved the administration of three distinct questionnaires: Harter’s AM questionnaire, Benishek et al.‘s AP questionnaire, and Lent et al.‘s ASE questionnaire. A thorough grasp of how TBE affects male students’ AM, AP, and ASE is one of the research’s predicted objectives. Examining how technology affects these factors might provide insightful information for educational practices and interventions targeted at improving student performance and engagement. The study’s findings revealed that TBE significantly influences both AE and AP while having a distinct impact on ASE. The outcomes of this study may have implications for curriculum designers, instructors, and educational officials by offering evidence-based suggestions for successfully integrating technology into the classroom. Furthermore, the findings might fill a gap unique to male students in the second year of high school by adding to the body of knowledge already available on TBE and its effects on student outcomes.

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The data will be available upon request from the corresponding [email protected].

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Bi, J., Izadpanah, S., Mohammadi, Z. et al. Investigating the impact of technology-based education on academic motivation, academic perseverance, and academic self-efficacy in english language learning skills. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12712-0

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