academic achievement of students research paper

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

Mohammed hasan ali al-abyadh.

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

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

Hani Abdel Hafeez Abdel Azeem

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

Associated Data

Not applicable.

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

1. Introduction

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

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

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

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

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

2. Literature Review and Hypotheses Development

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The research conceptual framework and hypotheses.

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

3. Materials and Methods

3.1. sampling and data collection.

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

Sociodemographic characteristics of the students.

3.2. Measurements

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

3.3. Data Analysis and Hypotheses Testing

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

3.4. The Measurement Model

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

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

Assessment results of the measurement model.

NB. AA9 and AA10 in italic were dropped.

Heterotrait–Monotrait Ratio (HTMT).

3.5. Multigroup Analysis

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

3.6. Testing the Research Hypotheses and Results

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

Results of the structural model with data from all students.

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

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

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

Results of hypotheses.

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

Results of invariance measurement testing using permutation.

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

4. Discussion

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

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

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

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

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

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

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

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

5. Conclusions

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

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

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

Acknowledgments

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

Funding Statement

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

Author Contributions

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

Institutional Review Board Statement

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

Informed Consent Statement

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

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

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

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ORIGINAL RESEARCH article

Academic achievement, self-concept, personality and emotional intelligence in primary education. analysis by gender and cultural group.

• 1 Department of Developmental and Educational Psychology, University of Granada, Melilla, Spain
• 2 Early Childhood and Primary Education School “Pedro de Estopiñán”, Melilla, Spain

A review of the scientific literature shows that many studies have analyzed the relationship between academic achievement and different psychological constructs, such as self-concept, personality, and emotional intelligence. The present work has two main objectives. First, to analyze the academic achievement, as well as the self-concept, personality and emotional intelligence, according to gender and cultural origin of the participants (European vs. Amazigh). Secondly, to identify what dimensions of self-concept, personality and emotional intelligence predict academic achievement. For this, a final sample consisting of 407 students enrolled in the last 2 years of Primary Education were utilized for the study. By gender, 192 were boys (47.2%) and 215 girls (52.8%), with an average age of 10.74 years old. By cultural group, 142 were of European origin (34.9%) and 265 of Amazigh origin (65.1%). The academic achievements were evaluated from the grades obtained in three school subjects: Natural Sciences, Spanish Language and Literature, and Mathematics, and the instruments used for data collection of the psychological constructs analyzed were the Self-Concept Test-Form 5, the Short-Form Big Five Questionnaire for Children, and the BarOn Emotional Quotient Inventory: Youth Version-Short. Based on the objectives set, first, the grades in the subject of Spanish Language and Literature varied depending on the gender of the students. Likewise, differences were found in self-concept, personality, and emotional intelligence according to gender. Also, the physical self-concept varied according to the cultural group. Regarding the second objective, in the predictive analysis for each of the subjects of the curriculum of Primary Education, the academic self-concept showed a greater predictive value. However, so did other dimensions of self-concept, personality and emotional intelligence. The need to carry out a comprehensive education in schools that addresses the promotion of not only academic but also personal and social competences is discussed. Also, that the study of the variables that affect gender differences must be deepened.

Introduction

A review of the scientific literature has shown that many studies have analyzed the relationship between academic achievement and different psychological constructs such as self-concept ( Susperreguy et al., 2018 ; Wolff et al., 2018 ; Sewasew and Schroeders, 2019 ), personality ( Janošević and Petrović, 2019 ; Perret et al., 2019 ; Smith-Woolley et al., 2019 ), and emotional intelligence ( Corcoran et al., 2018 ; Deighton et al., 2019 ; Piqueras et al., 2019 ). In this work, these psychological constructs are analyzed together with primary school children by gender and cultural group. Gender has been a highly studied variable since there are differences between boys and girls in academic performance as well as in the psychological constructs mentioned above ( Chrisler and McCreary, 2010 ; Voyer and Voyer, 2014 ; Carvalho, 2016 ; Herrera et al., 2017 ; Janošević and Petrović, 2019 ). There are also studies that analyze the possible differences that may exist in the school context between children from different cultures ( Schmitt et al., 2007 ; Strayhorn, 2010 ; Cvencek et al., 2018 ; Min et al., 2018 ). In this sense, there is a disadvantage in the school context for children of minority culture. The present study has been developed in Melilla, a Spanish city located in North Africa, close to Morocco. In their schools, children of European culture and children of Amazigh culture (also known as Berber) have been together from early childhood education. In addition, the predictive value of each of the dimensions that integrate self-concept, personality and emotional intelligence regarding the grades in three subjects of the Primary Education curriculum are analyzed. The psychological constructs analyzed in the present study are described below.

Self-Concept

Many research studies have highlighted that the psychological construction of a positive self-concept by the students, during their academic stage, leads to success in educational environments and social and emotional situations ( Eccles, 2009 ; Harter, 2012 ; Nasir and Lin, 2012 ; Chen et al., 2013 ). Therefore, the positive self-concept acquired in the formative years could help in the development of the strategies and skills needed for confronting life challenges ( Huang, 2011 ). It has also been found that self-concept is positively associated with different factors such as the individual experiencing greater happiness ( Hunagund and Hangal, 2014 ); a greater and better academic performance ( Salami and Ogundokun, 2009 ); greater and more pro-social behaviors ( Schwarzer and Fuchs, 2009 ); and lastly, an overall greater well-being ( Mamata and Sharma, 2013 ).

Among the different models that link self-concept and academic performance, we found the Reciprocal Effects Model (REM), with a theoretical, methodological and empirical review conducted by Marsh and Martin (2011) . This model argues that academic self-concept and performance mutually re-enforce themselves, with one producing advances in the other.

Starting with the evolution perspective, the Developmental Equilibrium Hypothesis has also been highlighted. The objective of this hypothesis is centered on achieving equilibrium between two factors that are directly related: self-concept and academic performance ( Marsh et al., 2016a , b ). Hence, achieving a state of equilibrium has important implications for the development of the individual, but it cannot be ignored that each individual’s development of self-concept is different depending on the personal, emotional, and social characteristics surrounding them ( Eccles, 2009 ; Murayama et al., 2013 ; Paramanik et al., 2014 ).

The studies that relate self-concept with school or academic performance are exhaustive in the first educational stages as well as higher education ( Guay et al., 2010 ; Möller et al., 2011 ; Skaalvik and Skjaalvik, 2013 ). The student’s self-concept, and the academic self-concept within it, has a strong influence on student self-efficacy ( Ferla et al., 2009 ). Additionally, academic self-concept significantly correlates with school adjustment in Primary Education ( Wosu, 2013 ; Mensah, 2014 ) and predicts academic achievement ( Marsh and Martin, 2011 ; Guo et al., 2016 ). Therefore, in this research it is expected to find such predictive value.

The results from cross-cultural studies have shown that a negative self-concept had detrimental effects on the academic performance of the students from the different samples and countries ( Marsh and Hau, 2003 ; Seaton et al., 2010 ; Nagengast and Marsh, 2012 ). Cvencek et al. (2018) , when analyzing primary school students from a minority group and a majority group in North America, found that the academic performance, as well as the academic self-concept of the children from the minority group, were lower as compared to those from majority group. Similar results that show the disadvantage of minority groups in schools are found in other studies ( Strayhorn, 2010 ). According to these results, it would be expected that in the present study children of Amazigh cultural origin obtained lower scores than those of European cultural origin in their academic performance and academic self-concept.

Another variable that has been analyzed along with self-concept and academic performance has been gender ( Chrisler and McCreary, 2010 ; DiPrete and Jennings, 2012 ). Thus, in the meta-analysis study by Voyer and Voyer (2014) , it was shown that a certain advantage in school performance existed in women, with their results showing differences in favor of the women for the Language subject. Differences according to gender were also found in self-concept ( Nagy et al., 2010 ). Huang (2013) , in a meta-analysis study, identified that the women had a greater self-concept in the subject matter or courses related to language, as well as the arts as compared to the men. Therefore, in this study we expect to find that girls obtain higher grades than boys in Spanish Language and Literature as well as academic self-concept.

Personality

In general terms, personality and self-concept predict satisfaction with life ( Parker et al., 2008 ). Also, personality moderates the effects of the frame of reference that are central for the shaping of self-concept ( Jonkmann et al., 2012 ).

Within the models of personality, the Five Factor Model ( McCrae and Costa, 1997 ) has been the most developed ( Herrera et al., 2018 ), and it represents the dominant conceptualization of the structure of personality in current literature. It postulates that the five great factors of personality (emotional instability, extraversion, intellect/imagination, agreeableness, and conscientiousness) are found at the highest level in the hierarchy of personality.

Among the strongest arguments utilized to show that the measurements of personality, based on the Big-Five Factor Structure ( Goldberg, 1990 , 1992 ), correlate with academic performance, we find the evidence that supports the importance of the personality factors to predict behaviors that are socially valued and the recognition of personality as a component of the individual’s will ( Chamorro-Premuzic et al., 2006 ). In this respect, the scientific literature shows studies that relate personality, through the five-factor model, with academic performance. Thus, agreeableness, and intellect/imagination (also known as openness) are related to academic performance ( Poropat, 2009 ; Smith-Woolley et al., 2019 ). Specifically, conscientiousness predicts academic achievement ( O′Connor and Paunonen, 2007 ), which is expected to be found in the present study.

Personality has been analyzed in different cultures ( Allik et al., 2012 ). A good example of a broad study, which included 56 countries, is the one conducted by Schmitt et al. (2007) . Among the main results, it was found that the five-factor structure of personality was robust among the main regions of the world. Also, the inhabitants from South America and East Asia were significantly different in their intellect/imagination characteristics as compared to the rest of the world regions. Thus, while the South American and European countries tended to occupy a higher position in openness, the cultures from East Asia were found in lower positions. This is attributed, among other factors, in that the Asian cultures are more collective, so that the openness dimension could be difficult to clearly identify, as proposed in the starting theoretical model. Based on these results, differences in personality dimensions are expected to be found among children of European and Amazigh cultural origin.

As for gender, differences have also been found. For example, the academic achievement in Primary Education is related to a higher conscientiousness in girls than in boys ( Janošević and Petrović, 2019 ).

Emotional Intelligence

Another factor that should be taken into account, as related to the academic achievements and school adjustment, is the emotional intelligence (EI). The models or theoretical approaches of EI are different ( Cherniss, 2010 ; Herrera et al., 2017 ). On the one hand, models have been identified that are based on the processing of emotional information, which are focused on basic emotional abilities ( Brackett et al., 2011 ). On the other hand, mixed models of EI have also been identified, which involve both intellectual and personality factors. The socio-emotional competence model by Bar-On (2006) forms part of the second group. In it, different dimensions are identified: intrapersonal, interpersonal, stress management, adaptability, and general mood.

Numerous research studies have examined the relationship between EI and academic performance ( Pulido and Herrera, 2017 ). They have also analyzed their relationship with other variables such as adjustment and permanence in the school context ( Hogan et al., 2010 ; Szczygieł and Mikolajczak, 2017 ), coping styles ( MacCann et al., 2011 ), the degree of social competence ( Franco et al., 2017 ), and school motivation ( Usán and Salavera, 2018 ).

Emotional intelligence has also been analyzed in groups with different ethnic or cultural origins ( Dewi et al., 2017 ; Min et al., 2018 ), and according to gender, differences were found in EI as well. Thus, for example, Herrera et al. (2017) obtained results that showed that girls in primary schools in Colombia exceeded the boys in the interpersonal dimension, while the boys stood out in the adaptability dimension. Similarly, Ferrándiz et al. (2012) identified that Spanish girls had higher scores in the interpersonal dimensions and the boys had higher scores in adaptability and general mood. Accordingly, we expect to find differences in emotional intelligence based on the cultural origin and gender of primary school children in this study.

As a function of what has been described until now, the present work has two main objectives. Firstly, to analyze the academic performance, as well as self-concept, personality and emotional intelligence, as a function of gender and cultural origin (European vs. Amazigh) of the participants. It is important to mention that the research study took place in the autonomous city of Melilla, a Spanish city that neighbors Morocco, with unique social, cultural and economic characteristics. In it, people from different cultures co-habit: European, Amazigh (also known as Berber, and who come from the Moroccan Rif), Sephardic and Hindu, although the majority of the population is of European and Amazigh descent and culture. The children with an Amazigh culture origin cohabit live and grow between their maternal culture, which counts with the Tamazight (a dialect that is orally transmitted) as a means of communication, and the European culture, with Spanish being the language employed at school and administrative environments of the city ( Herrera et al., 2011 ).

Secondly, to identify which dimensions of self-concept, personality and emotional intelligence predict academic performance.

In addition, different hypotheses are raised based on the results found in the scientific literature that addresses the research topics described above.

Hypothesis 1 . Academic grades differ depending on the gender and cultural origin of students. Thus, for example, as indicated by Voyer and Voyer (2014) , girls will achieve higher grades than boys in the subject of Spanish Language and Literature. Likewise, children of cultural origin different from the school (i.e., children of Amazigh culture) will obtain lower grades than Spanish children ( Strayhorn, 2010 ).

Hypothesis 2 . The psychology constructs evaluated (self-concept, personality and emotional intelligence) differ according to gender and cultural origin. Among other issues, it is expected to find that girls have a higher academic self-concept than boys ( Chrisler and McCreary, 2010 ), higher scores in the personality dimension of conscientiousness ( Janošević and Petrović, 2019 ) as well as in the interpersonal EI dimension ( Ferrándiz et al., 2012 ; Herrera et al., 2017 ). Likewise, children of European cultural origin are expected to obtain higher scores than those of Amazigh cultural origin in academic self-concept ( Cvencek et al., 2018 ), intellect/imagination ( Schmitt et al., 2007 ) and in the intrapersonal and interpersonal EI dimensions ( Dewi et al., 2017 ; Min et al., 2018 ).

Hypothesis 3 . Academic self-concept ( Marsh and Martin, 2011 ; Guo et al., 2016 ), conscientiousness ( O′Connor and Paunonen, 2007 ) and adaptability ( Hogan et al., 2010 ) predict academic achievement.

Materials and Methods

Participants.

A non-probabilistic sampling was used. Initially, 422 Primary school students were included in the research study. Nevertheless, once the non-valid cases were eliminated, defined as those who did not complete the evaluation instruments, or whose scores did not comply to what was set, the final sample was comprised of 407 students. These students were enrolled in eight of the twelve public early childhood and primary education centers in the autonomous city of Melilla, Spain (see Table 1 ), with a minimum age of 10 and a maximum of 12 years old. The description of the participants according to cultural origin, gender, grade and age is presented in Table 2 .

Table 1. Distribution of participants according to the center of early childhood and primary education.

Table 2. Distribution of participants according to cultural origin, gender, grade, and age.

The children of European cultural origin are mainly of Spanish nationality and Catholic religion. They were born in the autonomous city of Melilla or elsewhere in the Iberian Peninsula. Their parents were born in Melilla or have changed their residence to this city for professional reasons (mainly to work in public administration or in the army). Children of Amazigh cultural origin were born in the autonomous city of Melilla, so their nationality is Spanish, or they reside in that city. Many of them are Muslims and have family in Morocco so, given the short distance away, they usually travel at weekends or holidays to Moroccan cities close to Melilla. Rearing practices of children in families of each cultural group developed, among other things, based on cultural values and identities that define them. Thus, for example, the raising of children of Amazigh cultural origin is similar to that of children in the Rif region of Morocco. However, these same children socialize not only with children of their own cultural group but also with children of European cultural origin in a Spanish city, that is, the autonomous city of Melilla. The same can be indicated for children of European cultural origin.

Instruments

Academic achievement.

The final grades of the students of the school subjects Natural Sciences, Spanish Language and Literature, and Mathematics were obtained through a registry, provided by the student’s teachers. These were classified as insufficient (0–4.9 points), sufficient (5–5.9 points), good (6–6.9 points), notable (7–8.9 points) and outstanding (9–10 points).

A Self-Concept Test-Form 5 (AF-5, García and Musitu, 2001 ) was utilized. It is composed of 30 items that evaluate the self-concept of an individual in academic (e.g., “I do my homework well”), social (e.g., “I make friends easily”), emotional (e.g., “I am afraid of some things”), family (e.g., “I feel that my parents love me”) and physical (e.g., “I take good care of my physical health”) contexts. This form has to be answered according to an attributive scale ranging from 1 to 99, according to how the item adjusts to what the individual evaluated thinks of it. For example, if a phrase indicates “music helps human well-being” and the student strongly agrees, he/she would answer with a high number, such as 94. But if the student disagreed, he/she would choose a low number, for example 9. Esnaola et al. (2011) , when analyzing the psychometric properties of this test in the Spanish population from 12 to 84 years old, indicated that its total reliability was α = 0.74. The index of internal consistency, Cronbach’s alpha , calculated for the present work, had a value of α = 0.795. Also, its factorial or construct validity was corroborated in other research works ( Elosua and Muñiz, 2010 ; Malo et al., 2011 ).

For the evaluation of personality, the Short-Form Big Five Questionnaire for Children (S-BFQ-C, Beatton and Frijters, 2012 ) was utilized. It is based on the model of personality structured by five factors (Big-Five Factor Structure), formulated by Goldberg (1990 , 1992) . These factors are denominated as emotional instability (e.g., “I am often sad”), extraversion (e.g., “I make friends easily”), intellect/imagination (e.g., “When the teacher explains something, I understand immediately”), agreeableness (e.g., “I share my things with other people”) and conscientiousness (e.g., “During class I concentrate on the things I do”), creating the Big Five Questionnaire-Children (BFQ-C). This questionnaire, is directed at children aged between 9 to 15 years old, and was designed and validated by Barbaranelli et al. (2003) . In its initial version, its psychometric properties were analyzed with Italian children, although there are studies that have analyzed them in other populations such as for example the German ( Muris et al., 2005 ), Spanish ( Carrasco et al., 2005 ) or Argentinian ( Cupani and Ruarte, 2008 ) populations. Nevertheless, one of the problems of this instrument is its length, given that is composed by 65 items, 13 for each scale. This is the reason why Beatton and Frijters (2012) , in a broader study that sought to measure the effects of personality and satisfaction with life on the happiness of Australian youth aged from 9 to 14 years old, reduced the BFQ-C to a shorter version. This shorter version, named S-BFQ-C, is composed by 30 items, so that each of the scales is composed by 6 items. In this version, the questions have to be answered using a Likert -type scale with 5 response options (1 = Almost never; 5 = Almost always). The reliability, measured with Cronbach’s Alpha , was found to be between 0.60 and 0.80 for each of the five scales. For the present study, the total reliability found was α = 0.783.

The BarOn Emotional Quotient Inventory: Youth Version-Short (EQ-i: YV-S, Bar-On and Parker, 2000 ) was used. It is directed at children aged from 7 to 18 years old, and is composed of 30 items which have to be answered with a Likert scale with four possible responses (1 = Very seldom or Not true of me, 4 = Very often or True of me). Six items shape each of the following scales: intrapersonal (e.g., “It is easy to tell people how I feel”), interpersonal (e.g., “I care what happens to other people”), adaptability (e.g., “I can come up with good answers to hard questions”), stress management (e.g., “I can stay calm when I am upset”), and positive impression (e.g., “I like everyone I meet”). This last scale is useful for eliminating the cases of high social desirability. The sum of the first four scales provides the total EQ.

The reliability or internal consistency of the EQ-i YV-S scale oscillates between 0.65 and 0.87 ( Bar-On and Parker, 2000 ). For this study, the total reliability was α = 0.745. Its internal structure was confirmed in Spanish ( Esnaola et al., 2016 ), Hungarian ( Kun et al., 2012 ), Mexican ( Esnaola et al., 2018b ), English ( Davis and Wigelsworth, 2018 ) and Chinese ( Esnaola et al., 2018a ) populations.

Information Collection

In the first place, the participation of the management teams of the 12 early childhood and primary school education centers in Melilla was solicited. Of these, eight centers answered affirmatively. Afterward, within each center, the professor-tutor from each class or classes interested were contacted. A group meeting was conducted with the parents from each group-class, where information was provided about the objectives of the research study. The authorization of the children’s parents for the exclusive use of the results obtained, for educational and scientific purposes, was requested.

Once this process was finished, a document was provided to the teachers-tutors of each participating class which explained how to access the web program utilized for the management of the student’s grades in order to download this information in pdf format. Once this information was downloaded, they were asked to write down, in a double-entry table provided for each student, the final grades obtained in the subjects of Natural Sciences, Spanish Language and Literature, and Mathematics, using the scoring system of insufficient, sufficient, good, notable or outstanding. Teachers provided students’ grades to researchers at the end of the academic year.

The AF-5, the S-BFQ-C and the EQ-i: YV-S questionnaires were administered in the first school term to the students in fifth and sixth grade of Primary Education, collectively according to group-class. The maximum time provided for this was 55 min. Previously, the students were told that there were no right or wrong answers, and that they should answer with total sincerity, given that the test was anonymous. Also, that they should not write their name; and that what they were about to answer did not have any relation with the school grades; and lastly, that they should read the questions, and if they had any doubts (for example, not understanding a term), they should raise their hand so that the question could be resolved.

In order to be able to relate the results of the evaluation of the different psychological constructs and the academic grades, the teacher of each class assigned a number to each student. This number was recorded both in the grades provided by him/her and on the first page of each of the questionnaires administered.

Statistical Analysis of the Data

Before proceeding with the statistical analysis, from the 422 students who participated, it was determined if there were students who had not completed the three evaluation tests, and also if they obtained high scores in the positive impression scale of the EQ-i: YV-S. This resulted in the elimination of 15 individuals, resulting in a final sample of 407 students.

The statistical program IBM SPSS Statistics 23 was used to carry out the statistical analysis. Descriptive statistics were utilized to describe the data (frequencies, percentages, mean and standard deviation). In other words, to answer the first research objective and the first two hypotheses, two Analysis of variance (ANOVA) were performed in which the Academic achievement was used as the dependent variable in one case, and self-concept, personality and EI as dependent variables in the other. In both cases, the independent variables were gender (boy or girl) and cultural group (European vs. Amazigh). The effect size was calculated with the partial eta-squared as the post hoc test, through the use of the Bonferroni test.

To address the second objective and the third hypothesis, three multiple linear regression analysis (with the enter method) were conducted, in which each subject was introduced as the dependent variable, with the predictive variables being the different dimensions which comprised the self-concept, personality and EI constructs. To justify the method used, the non-autocorrelation of the data was determined, using the Durbin Watson test, and the non-existence of multicollinearity, through the Variance Inflation Factor.

Academic Achievement by Gender and Cultural Group

All the subjects had a maximum of five points, and were scored as: 1 = Insufficient, 2 = Sufficient, 3 = Good, 4 = Notable, 5 = Outstanding. The mean grade in Natural Sciences was 3.26 ( SD = 1.33), for Spanish Language and Literature it was 3.33 ( SD = 1.24) and in Mathematics, it was 3.19 ( SD = 1.25).

Academic achievement as a function of the student’s gender and cultural group is presented in Table 3 . The analysis of variance performed as a function of gender and cultural group showed that there were differences according to gender for the subject Spanish Language and Literature, F = 5.812, p = 0.016, Eta2p = 0.014, so that the girls obtained higher grades than the boys, t = 0.313, p = 0.016. No differences were found neither in Nature Sciences, F = 0.763, p = 0.383, Eta2p = 0.002, nor Mathematics, F = 1.692, p = 0.194, Eta2p = 0.004. On their part, no differences were found as a function of the cultural group, F Natural Sciences = 0.376, p = 0.540, Eta2p = 0.001; F Language and Literature = 0.565, p = 0.453, Eta2p = 0.001; F Mathematics = 0.576, p = 0.448, Eta2p = 0.001.

Table 3. Academic achievement by gender and cultural group.

Self-Concept, Personality and EI by Gender and Cultural Group

The analysis of variance results (see Supplementary Table S1 ) showed that there were significant differences as a function of gender for self-concept, more specifically in academic self-concept, with the girls achieving higher grades in post hoc comparisons using the Bonferroni test, t = 0.667, p = 0.007, and self-esteem, t = 1.139, p < 0.001, where the boys stood out. Likewise, differences were found in personality in favor of the girls within the conscientiousness, t = 1.136, p = 0.018, and agreeableness dimensions, t = 1.641, p = 0.001. Also, with respect to the EI, the girls had a higher score in the interpersonal scale, t = 1.016, p = 0.007, while the boys had a higher score in the stress management, t = 1.513, p < 0.001, and adaptability, t = 1.110, p = 0.008. Lastly, with respect to the analysis according to cultural group, there were only significant differences in the physical self-concept, with higher scores reached by the children of Amazigh cultural origin, t = 0.420, p = 0.036.

Predictive Value of the Different Dimensions Evaluated With Respect to Academic Achievement

In first place, a linear regression analysis was conducted, where the dependent variable was the subject Natural Sciences and the predictive variables were the five dimensions of the self-concept, the five dimensions from personality and the four dimensions from EI (see Table 4 ). The model was significant with values F = 11.003, p < 0.001. Likewise, the coefficient of determination was R 2 = 0.311 (adjusted R 2 = 0.282). Durbin–Watson’s d test showed that there was no auto-correlation in the data ( d = 1.583). Values of the Durbin Watson test between 1.5 and 2.5 indicate that the data are not correlated ( Durbin and Watson, 1951 ). Also, the Variance Inflation Factor (VIF) obtained values lower than 5, so multicollinearity was not present ( Berry and Feldman, 1985 ; Belsley, 1991 ).

Table 4. Regression analysis of the different dimensions analyzed with respect to the natural sciences subject.

In the order from greater to lesser predictive value, the dimensions were: academic self-concept, physical self-concept, intrapersonal, intellect/imagination, and family self-concept. The physical self-concept, as well as intrapersonal intelligence, negatively predicted the grades in Natural Sciences.

In second place, as related to the subject Spanish Language and Literature (see Table 5 ), the model was significant with values of F = 10.442, p < 0.001 and with a coefficient of determination of R 2 = 0.299, adjusted R 2 = 0.271. The data was not correlated ( d = 1.672) and no multicollinearity was found.

Table 5. Regression analysis of the different dimensions analyzed with respect to the Spanish language and literature subject.

Once again, the academic self-concept dimension had the greatest predictive value, followed by the physical self-concept, intrapersonal intelligence, and intellect/imagination dimensions. The negative predictions remained the same.

In third and last place, for the subject of Mathematics (see Table 6 ), the model had a statistical significance, as shown by F = 10.790, p < 0.001. The coefficient of determination obtained was R 2 = 0.306, adjusted R 2 = 0.278. The data was not correlated ( d = 1.600) and multicollinearity was not present.

Table 6. Regression analysis of the different dimensions analyzed with respect to the mathematics subject.

The predictive dimensions were academic self-concept, physical self-concept (in a negative manner), adaptability, intellect/imagination, and conscientiousness.

Based on the hypotheses set, first, the grades of the Spanish Language and Literature school subject varied depending on the gender of the students, which coincided with the results from other studies, which highlighted the girls’ higher grades ( Huang, 2013 ; Voyer and Voyer, 2014 ). In this regard, it could be argued that academic and social expectations are different depending on gender ( Voyer and Voyer, 2014 ). Likewise, the influence of socialization on the formation of gender behaviors must be taken into account in accordance with the cultural norms of masculinity and femininity ( Gibb et al., 2008 ). Gender differences in academic achievement remain between different countries, regardless of their political, economic or social equality ( Stoet and Geary, 2015 ). However, it is noteworthy that in adulthood women occupy fewer representations of political, economic and academic leadership than men.

Contrary to expectations ( Strayhorn, 2010 ; Whaley and Noël, 2012 ), children of Amazigh origin did not obtain lower grades than those of European origin. These results may be due to the fact that in the city of Melilla children of both cultures are educated from early childhood education in schools where the language used is Spanish. Thus, the academic performance at the end of Primary Education does not differ depending on the cultural origin of the students. However, it is necessary to show that early childhood teachers dedicate great efforts so that children of Amazigh cultural origin develop the linguistic skills necessary for the correct learning and use of the Spanish language ( Herrera et al., 2011 ). Therefore, hypothesis 1 is partially confirmed. That is, the results found indicate that academic achievement varies according to gender but not the cultural origin of the students.

Likewise, differences were found according to gender in self-concept, specifically in the academic self-concept and self-esteem; for personality, within the factors of conscientiousness and agreeableness; in addition to emotional intelligence, particularly in the interpersonal, stress management and adaptability scales. As for the differences found for self-concept according to gender ( Nagy et al., 2010 ), the results found for academic self-concept showed differences in favor of the girls ( Malo et al., 2011 ). Nevertheless, other factors should be taken into account, such as the academic responsibilities associated to school success and failure, given that, for example, the boys in Compulsory Secondary Education attribute their academic success to their skills, while the girls attribute them to their effort ( Inglés et al., 2012 ). As for emotional self-concept or self-esteem, the boys exceeded the girls ( Xie et al., 2019 ). Cross-cultural studies show that differences in self-esteem according to gender are maintained in different countries, although their magnitude differ according to the cultural differences found in the socioeconomic, sociodemographic, gender equality and cultural value indicators ( Bleidorn et al., 2016 ). In this respect, the emotion literacy programs, based on the development of emotional intelligence, could be a useful tool for the development of self-esteem ( Cheung et al., 2014 ).

As for the differences in the personality dimensions conscientiousness and agreeableness in favor of the girls, the results were in agreement with previous studies ( Rahafar et al., 2017 ; Janošević and Petrović, 2019 ). Within the differences in EI according to gender, the girls scored higher in the interpersonal scale, while the boys did so in stress management and adaptability ( Ferrándiz et al., 2012 ; Herrera et al., 2017 ). In this way, the girls showed competencies and skills that were higher than the boys in empathy, social responsibility, and interpersonal relationships. On the contrary, the boys stood out in stress tolerance and impulse control (stress management), as well as in reality-testing, flexibility, and problem-solving (adaptability). These differences, as a function of gender, could be due to cultural factors and family rearing practices differentiated as a function of gender ( Joseph and Newman, 2010 ).

Also, the physical self-concept varied according to the cultural origin, where children from the Amazigh culture obtained higher scores than children of European culture origin. This may be due to the influence of cultural values (their own, meaning Amazigh, as well as the context in which they live in, given that the children are socialized in a European context), with respect to body image and physical self-concept ( Marsh et al., 2007 ).

Based on the results found, the second hypothesis is partially confirmed. The three psychological constructs evaluated differ according to gender in the expected direction but only in the self-concept are differences found according to the cultural origin. Although it was expected to find differences in favor of children of European cultural origin in academic self-concept ( Cvencek et al., 2018 ), they have been found in physical self-concept in favor of children of Amazigh cultural origin. As previously indicated, children of European and Amazigh culture develop in the same school contexts from the early educational stages. Thus, educational policies developed in schools may have contributed to eliminating the possible socio-cultural disadvantages of children of Amazigh cultural origin. This implies, therefore, that there are no differences depending on the cultural group in the academic self-concept.

In the predictive analysis developed for each of the school subjects of the curriculum of Primary Education, with the aim of answering the second objective and the third hypothesis of the study, the academic self-concept showed a greater predictive value ( Marsh and Martin, 2011 ; Jansen et al., 2015 ; Guo et al., 2016 ; Lösch et al., 2017 ; Susperreguy et al., 2018 ). This result confirms the third hypothesis. That is, the relevance of academic self-concept in school performance. However, so did other dimensions of self-concept. More specifically, the physical self-concept negatively predicted the academic results in the three subjects evaluated ( Lohbeck et al., 2016 ). Children who participated in the study are in the process of transition from childhood to adolescence. Biological changes in their bodies due to this stage of evolutionary development as well as greater attention to appearance and physical abilities may interfere at the end of Primary Education in their academic performance. Furthermore, the family self-concept predicted the grades of the Natural Sciences school subject. This last result points to the influence of the family on self-concept as well as academic results ( Corrás et al., 2017 ; Mortimer et al., 2017 ; Häfner et al., 2018 ).

Personality also predicted the academic results in the three school subjects from the Primary Education curriculum analyzed ( O′Connor and Paunonen, 2007 ; Spengler et al., 2016 ; Bergold and Steinmayr, 2018 ), i.e., the intellect/imagination dimension for the three subjects and conscientiousness for Mathematics. In the first case, it may be because intellect/imagination or openness is a personality dimension that reflects cognitive exploration ( DeYoung, 2015 ). It refers to the ability and tendency to find, understand and use complex patterns of both sensory and abstract information. Therefore, those children who score higher in intellect/imagination will get better academic results than those with lower scores. In the second case, conscientiousness relates to responsibility, persistence, trustworthiness, and being purposeful ( Conrad and Patry, 2012 ). Children with high conscientiousness can develop a variety of effective learning strategies, which may be associated with higher academic performance in Mathematics.

Likewise, EI predicted academic achievement in every case ( Salami and Ogundokun, 2009 ; Hogan et al., 2010 ; Brackett et al., 2011 ; MacCann et al., 2011 ). More specifically, the intrapersonal scale predicted it for the subjects of Natural Sciences and Spanish Language and Literature. Intrapersonal intelligence involves the knowledge and labeling of one’s own feelings. This ability may contribute to achieving better grades in both subjects of the curriculum. For example, in the subject of Spanish Language and Literature it can facilitate the communicative skills related to the reading of different kinds of texts, their reflection and their understanding. On the other hand, in the subject of Nature Sciences it can contribute to interpret reality in order to address the solution to the different problems that arise, as well as to explain and predict natural phenomena and to face the need to develop critical attitudes before the consequences that result from scientific advances. In the case of the Mathematics subject, the adaptability scale predicted the academic achievement. Adaptability implies abilities such as being able to adjust one’s emotions and behaviors to changing situations or conditions, which is closely related to mathematical thinking.

In general, scientific literature shows that academic achievement is related to self-concept ( Susperreguy et al., 2018 ; Wolff et al., 2018 ; Sewasew and Schroeders, 2019 ), personality ( Perret et al., 2019 ; Smith-Woolley et al., 2019 ), and EI ( Corcoran et al., 2018 ; Deighton et al., 2019 ; Piqueras et al., 2019 ). Also, that within these construct, academic self-concept ( Ferla et al., 2009 ; Guay et al., 2010 ; Chen et al., 2013 ; Marsh et al., 2014 ), intellect/imagination ( Poropat, 2009 ; Smith-Woolley et al., 2019 ), and adaptability ( MacCann et al., 2011 ; Szczygieł and Mikolajczak, 2017 ) correlate significantly with academic achievement. In this research the predictive value of the dimensions of self-concept, personality and EI regarding the academic grades obtained in three subjects of the Primary Education curriculum has been established. One of its strengths is that it analyzes the predictive value of these psychological constructs together, not separately as in other studies.

In addition, the study has been developed in a multicultural context where children of European and Amazigh cultural origin coexist. Children of Amazigh cultural origin usually have access to early childhood education centers with a lower knowledge of the Spanish language than children of European cultural origin ( Herrera et al., 2011 ). Although studies carried out with groups of cultural minorities show differences in their school performance ( Strayhorn, 2010 ; Whaley and Noël, 2012 ), in the present study they are not at the end of Primary Education. This fact may be due to the linguistic policy developed in Melilla educational centers, which means that the mother language of children of Amazigh origin does not represent a disadvantage for academic achievement.

Further, gender differences found in the study seem to be more relevant than cultural differences. In fact, they are only in the physical self-concept in the latter case. Personality can mediate in adapting to school demands, so that girls are more conscientiousness than boys and follow norms in a more adaptive way ( Carvalho, 2016 ). Moreover, since girls excel in their academic self-concept, their self-efficacy may also be superior to that of boys, which contributes to a better school adjustment ( Ferla et al., 2009 ). Girls also have greater interpersonal intelligence, indicating better empathy, social responsibility and interpersonal relationships ( Ferrándiz et al., 2012 ). Such non-cognitive abilities can stimulate the development of positive interpersonal relationships in the classroom with both the teachers and their peers. These individual differences may be due to family and social influences where, for example, girls are expected to be more emotionally expressive than boys ( Meshkat and Nejati, 2017 ). In this same direction it could explain why children have greater self-esteem and stress management that girls.

Practical Implications for Education

In light of the results obtained in the present research study, the need to carry out a comprehensive education in schools that addresses the promotion of not only academic but also personal, social and emotional competences, are underlined ( Cherniss, 2010 ; Hunagund and Hangal, 2014 ; Herrera et al., 2017 ; Szczygieł and Mikolajczak, 2017 ; Corcoran et al., 2018 ; Cvencek et al., 2018 ). For this, the application of the principles derived from Positive Psychology in the education field would be an adequate strategy ( Suldo et al., 2015 ; Chodkiewicz and Boyle, 2017 ; Domitrovich et al., 2017 ; Shoshani and Slone, 2017 ). Thus, intellectual, procedural and emotional aspects have to be worked on in learning, the latter being clear drivers of learning. The pleasant emotions experienced by children in educational settings will allow greater happiness and emotional well-being in them ( Gil and Martínez, 2016 ). For it, teachers must be trained in good teaching practices that allow the interest of students to learn as well as guide them in the emotional domain ( Castillo et al., 2013 ; Oberle et al., 2016 ; Conners-Burrow et al., 2017 ).

Likewise, schools must respond to the gender and cultural differences of students ( Chrisler and McCreary, 2010 ; DiPrete and Jennings, 2012 ), particularly the first based on the results of this study. Thus, for example, the development of greater self-esteem in girls ( Bleidorn et al., 2016 ; Xie et al., 2019 ) should be encouraged. As indicated by Cheung et al. (2014) , emotional literacy programs that are based on emotional intelligence are an appropriate strategy for promoting self-esteem. Similarly, gender differences must be taken into account in response to other factors such as cultural group, family beliefs and parenting practices ( Chrisler and McCreary, 2010 ; Joseph and Newman, 2010 ; Nagy et al., 2010 ; Allik et al., 2012 ; Marsh et al., 2015 ).

Study Limitations and Proposal for Future Research

The present study has been developed taking into account only the last two school years of the education stage of Primary Education, just before the transition to Compulsory Secondary Education. Given that the scientific literature shows evolutionary changes in the development of the constructs analyzed ( Huang, 2011 ; Murayama et al., 2013 ; Marsh et al., 2015 ; Bleidorn et al., 2016 ), longitudinal studies could be conducted in future research studies from Primary Education to Compulsory Secondary Education in order to determine the magnitude and direction of these changes.

On the other hand, all the instruments for data collection used to evaluate the psychological constructs analyzed in the present study are based on self-report measures. Different types of measuring instruments (self-report measures and performance measures) should be combined in future studies ( Petrides et al., 2010 ; Mayer et al., 2012 ).

Gender differences in academic achievement as well as the psychological constructs analyzed have been revealed. However, it has to deepen the analysis of personal variables, family, social and cultural factors that contribute to that, even though women get better scores on their school performance across the different educational stages, at adulthood that reach fewer representations than men in leadership positions ( Stoet and Geary, 2015 ).

Finally, given the cultural diversity in schools it is necessary to develop studies that analyze academic achievement as well as its relationship with different psychological variables in students of different cultural groups. Cross-cultural studies comparing different countries are necessary ( Marsh and Hau, 2003 ; Nagengast and Marsh, 2012 ; Bleidorn et al., 2016 ; Min et al., 2018 ) but teachers have to know how to deal with coexistence and cultural diversity within the classrooms.

Data Availability Statement

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

Ethics Statement

The studies involving human participants were reviewed and approved by the Research Commission, Faculty of Educational Sciences and Sports, University of Granada, Melilla, Spain. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

LH, MA-L, and LM shared conception, design, and the final version of the work, were jointly accountable for the content of the work, ensured that all aspects related to accuracy or integrity of the study were investigated and resolved in an appropriate way, and shared the internal consistency of the manuscript. MA-L and LM contributions were mainly in the theoretical part and in revising it critically. LH contribution was mainly in methodological question and data analysis.

This research was co-financed by the Research Group Development, Education, Diversity, and Culture: Interdisciplinary Analysis (HUM-742).

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.03075/full#supplementary-material

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Keywords : academic achievement, self-concept, personality, emotional intelligence, gender, cultural group

Citation: Herrera L, Al-Lal M and Mohamed L (2020) Academic Achievement, Self-Concept, Personality and Emotional Intelligence in Primary Education. Analysis by Gender and Cultural Group. Front. Psychol. 10:3075. doi: 10.3389/fpsyg.2019.03075

Received: 12 September 2019; Accepted: 28 December 2019; Published: 22 January 2020.

Reviewed by:

Copyright © 2020 Herrera, Al-Lal and Mohamed. 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: Lucía Herrera, [email protected]

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

Anxiety and Stress in the Academic Performance of University Students: A Systematic Review (ANSIEDAD Y EL ESTRÉS EN EL RENDIMIENTO ACADÉMICO DE ESTUDIANTES UNIVERSITARIOS: UNA REVISIÓN SISTEMÁTICA)

Fajardo Bedoya, F., Tumi Figueroa, A., Gonzáles Masías, A. M., & Salazar Valdez, G. (2024). Anxiety and stress in the academic performance of university students: A systematic review. Journal of Educational Psychology

Posted: 23 Aug 2024

Fiorella Fajardo Bedoya

Independent

Alfredo Tumi Figueroa

Arnold mauro gonzáles masías, gerson salazar valdez.

Date Written: July 20, 2024

This systematic review examines the impact of stress and anxiety on the academic performance of university students, based on the analysis of 19 scientific articles. The studies, ranging from Jordan to Argentina, demonstrate that exam-related anxiety and academic burden are critical factors negatively affecting student performance. Additionally, the importance of psychological resilience, modulated by variables such as physical activity and educational environment, is highlighted. Interventions like gamification and study skills training have proven effective in reducing anxiety. With these findings, it is emphasized that educational institutions should adopt preventive and supportive strategies to foster an environment conducive to improving both the psychological well-being and academic success of students.

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Open Access

Peer-reviewed

Research Article

Optimized ensemble deep learning for predictive analysis of student achievement

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Student Affairs Department, Institute of Science and Technology, Luoyang, Henan Province, China, Department of Education, Keimyung University, Daegu, Korea

• Kaitong Wang

• Published: August 26, 2024
• https://doi.org/10.1371/journal.pone.0309141
• Reader Comments

Education is essential for individuals to lead fulfilling lives and attain greatness by enhancing their value. It improves self-assurance and enables individuals to navigate the complexities of modern society effectively. Despite the obstacles it faces, education continues to develop. The objective of numerous pedagogical approaches is to enhance academic performance. The development of technology, especially artificial intelligence, has caused a significant change in learning. This has made instructional materials available anytime and wherever easily accessible. Higher education institutions are adding technology to conventional teaching strategies to improve learning. This work presents an innovative approach to student performance prediction in educational settings. The strategy combines the DistilBERT with LSTM (DBTM) hybrid approach with the Spotted Hyena Optimizer (SHO) to change parameters. Regarding accuracy, log loss, and execution time, the model significantly improved over earlier models. The challenges presented by the increasing volume of data in graduate and postgraduate programs are effectively addressed by the proposed method. It produces exceptional performance metrics, including a 15-25% decrease in processing time through optimization, 98.7% accuracy, and 0.03% log loss. This work additionally demonstrates the effectiveness of DBTM-SHO in administering extensive datasets and makes an important improvement to educational data mining. It provides a robust foundation for organizations facing the challenges of evaluating student achievement in the era of vast data.

Citation: Wang K (2024) Optimized ensemble deep learning for predictive analysis of student achievement. PLoS ONE 19(8): e0309141. https://doi.org/10.1371/journal.pone.0309141

Editor: Hikmat Ullah Khan, University of Sargodha, PAKISTAN

Received: May 28, 2024; Accepted: August 7, 2024; Published: August 26, 2024

Copyright: © 2024 Kaitong Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from https://analyse.kmi.open.ac.uk/open_dataset .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Educational Data Mining is the predominant goal and aspiration of using data collected by educational institutions to acquire valuable insights and foster tactical decision-making to target major problematic issues. EDUM methods illustrated by [ 1 ] are multi-faceted. These include forecast approaches, model discovery, and association mining efforts. The above approaches allow for the continuous progression of data mining implementations within this industry.

The basic objective of the vast EDUM domain is to use neural network analysis to find the relationships that influence learning results. Evaluating how well students do in class is a must for those in charge of higher education [ 2 ]. We must first identify the variables that impact test scores to use this prediction approach to its full potential. Only then can we intervene at the right time to boost performance by enhancing the aspects linked to success.

Several approaches are used to predict student success in predictive models, e.g., fuzzy logical inference framework (FLIF), Bayes networks (BN), Support Vector Regression (SVR), random forest, and Decision Trees [ 3 ]. The preconditioning of FS might be a key preprocessing step in assessing the importance of the attribute number on prediction reliability. SF aims to eliminate redundant, unrelated features and reduce data irrelevance as much as possible to represent the concepts correctly. As a result, the prediction accuracy and processing time are increased [ 4 ].

There are many uses of machine learning, including image processing, text recognition, robotics, and text categorization. One subset of these uses is Deep Neural Networks (DNN) [ 5 ]. The adaptability of DNNs in these diverse tools shows that they can accurately predict and differentiate results. They also solve many scheduling problems in wireless technology scenarios and meet their energy requirements, which is evidence of this [ 6 ].

The integration of computational learning and the concepts of EDUM could fundamentally shape and legalize the achievements in the learning approach that has proven out-of-reach for traditional education. Integration could obtain impressive outcomes that contradict traditional settings’ characteristics. However, it can only be fully understood and achieved with it. The integration will promote the interaction of educators, school administrators, and legislators with the characteristic understanding of students’ complex performance and the development of feasible measures to determine academic and learning progression within the transformed learning environment. Integration with these strategies will help the researcher devise a means to enhance the evaluation of performance accuracies while enabling the establishment of the education environment with the ability to withstand and survive all susceptibilities in the learning achievements of modern students.

Integration of Artificial Intelligence (AI)

In the recent transformation of several industries, including education, machine learning (ML) and AI have been playing an important part. We worked on improving the models, and our research aims at a new Hybrid model using AI and ML for better predictive analysis of Student success. The ultimate objective of our research is to predict with high confidence how well students perform. Spotted Hyena Optimizer(SHO) and DistilBERT with LSTM (DBTM) are sophisticated AI/ML models for predictive analytics. These advanced AI technologies enable us to see more subtle patterns in student data than we could before with conventional methods. This gets us closer to our objectives of understanding student success and developing better prediction models.

Schools often face the challenge of managing large amounts of student data. ML and ML systems excel at efficiently processing and analyzing massive datasets. Our approach merges, cleanses, and preprocesses data from several sources using AI-driven algorithms to guarantee high-quality inputs for precise predictions. The validity of our work is dependent on this ability, which addresses the challenge of managing massive amounts of data in academic settings.

Some critical points of our method are feature engineering and selection. ML techniques such as Random Forest and Elastic Net are used for feature extraction and selection. These techniques help extract the most meaningful features from your dataset and indirectly improve a model’s prediction power. This perfectly aligns with our overarching objective of optimizing mean accuracy, which is to emphasize the most valuable characteristics; this will maximize prediction power. For our model, we leverage AI’s contextual and sequential pattern understanding abilities using a mixture of DistilBERT and LSTM. This dual method is best aligned with the purpose of our study and will hopefully allow more accurate predictions of student performance. Incorporating AI into our prediction model has one big advantage: It can identify complex patterns and correlations in the data.

Our study also mostly depends on adaptive optimization, which is based on the use of SHO; SHO works to ensure the model operates well in many datasets and under numerous circumstances by optimizing the parameters. Therefore, the incorporated capacity of SHO to fine-tune the model parameters and the dependability and resilience of the model for real-time application in education also help the practicality of our stated aim to increase projected accuracy. Our work aims to provide practical insights into student performance using AI and ML approaches. Educators can also use this information to make significant modifications in their lessons that will cater effectively to each student. The result is a boost in academic performance, typically resulting in reduced student participation at risk. By applying artificial intelligence and machine learning, our expected abilities may be enhanced while still attaining our overall aim of leveraging technology to improve education significantly.

Contributions

This work significantly advances the subject of Educational Data Mining and predictive evaluation of student performance in various respects:

• Novel Hybrid Model: we propose a unique hybrid model of DistilBERT with LSTM (DBTM) combined with the Spotted Hyena Optimizer SHO for educational performance prediction tasks. This approach takes advantage of sequential pattern recognition and contextual learning, making prediction more accurate.
• Improved Performance Metrics: The DBTM-SHO performs significantly better accuracy, execution time, and log loss when compared with the earlier models. We experiment with this model and demonstrate 98.7% accuracy, a reduction in execution time by 15-25% while being computationally efficient, displaying a log loss of 0.03%.
• Data handling: We provide a complete data preprocessing pipeline for student performance, merging data from different sources and processing the dataset to fill discrepancies, also applying advanced feature engineering. This guarantees a dataset of high quality for analysis and modeling.
• Scalability: The proposed DBTM model becomes scalable and can adapt to different inputs required as it incorporates the Spotted Hyena optimizer, which increases scalability; hence, it can be used in real-time applications. Due to this capability of dynamic parameter tuning, the model continues to be robust when facing different conditions and sets.
• Practical Relevance for Education: Our research defines an AI-powered framework to monitor and enhance academic performance, which can be effectively embedded into the education ecosystem due to a strong foundation. The system would help educators intervene with at-risk students sooner and provide the necessary support to help them learn better.

Related work

Many studies have been done in the enormous corpus of academic literature to tackle the challenging problem of assessing students’ academic achievement [ 7 ]. Using DT, NN models, and SVM, a study by the authors of [ 8 ] looked at students’ online activities to forecast their academic achievement. The findings highlight a striking association between internet usage patterns and academic achievement. The frequency of internet usage is positively associated with academic performance, while the amount of traffic generated by the internet is adversely associated. Another study [ 9 ] uses data collected from submission forms to create a prediction algorithm for categorization based on a neural network. Three separate components of academic achievement prediction are the focus of this research. Data mining methods for developing and testing prediction models are the subject of the second dimension. The first dimension looks at what goes into determining how far along the academic progression scale a student is. If students’ Grade Point Averages (GPAs) throughout multiple years are taken into account, it is possible to predict their Cumulative Grade Point Averages (CGPAs) [ 10 ]. Researchers searching for the best methodology employ many classification strategies such as Tree Ensemble, Random Forest, Naïve Bayes, Stochastic Neural Network, Decision Tree, and Logistic Regression.

Factors including parental occupation, educational attainment, and student demographics are also considered in the study [ 11 ]. To achieve an impressive accuracy rate of 71.3%, it employs classification methods including Rules-based categorization, Naïve Bayes, and Decision Trees. There is a proliferation of frameworks as researchers develop individualized models using different features and classification schemes. By considering variables like prior course grades, significance, graduation date, campus, and country, the J48 decision tree algorithm [ 12 ] is used to predict students’ ultimate GPA. Meanwhile, [ 13 , 14 ] evaluates five classifiers—ID3, J48, Naïve Bayes, Neural Network, and Bayes Network—using factors like activity, attendance, midterm examinations, and other data. The tool expands by utilizing logistic regression and support vector machines to identify pupils with potential for success based only on their prior academic performance. In addition, [ 15 ] constructs two parallel models that integrate demographic data and survey results, utilizing naïve Bayes and Bayesian networks. The naïve Bayes technique is found to be the most precise. However, difficulties remain when the number of features and factors contributing to the problem rises. This requires the use of tools that can effectively analyze data in the setting of a rising student population. Concurrently, the author of [ 16 ] focuses on using regression analysis techniques to forecast student success in online learning. The author assesses modern regression algorithms to examine their applicability for accurate educational forecasting and analysis. By acquiring important insights, teachers may lower student failures and improve decision-making processes using machine-learning approaches. The author details five ML methods validated via experiments: logistic regression, neural networks, Bayesian networks, and support vector machines (SVMs). These approaches have enhanced prediction accuracy and provided valuable data for improving teaching practices.

The research conducted by [ 17 ] considers cognitive and social factors to forecast student success. By combining social network research with more conventional metrics of academic achievement, they seek to understand how students’ social connections affect their grades. Researchers gained insight into how intellectual and social elements impact students’ growth by finding strong correlations between students’ cooperative activities, social network frameworks, and overall academic achievement. In addition, a similar study [ 18 ] involves analyzing a large amount of textual information, such as student assignments and forum posts, using NLP technology, which allows quickly identifying students at risk. The results of the emotional activity of students have shown that this information can be considered a predictor of early dropouts from training programs. Researchers developed an Early Warning System that will control students’ risk status and, using NLP, will decide if the student has difficulties. From the above, it can be understood that the informal activities of participants are a vital factor that signals emerging problems and dissatisfaction.

The accuracy of data analysis in predicting students’ performance in online learning environments is the primary focus of research on online education. The study aims to develop learner performance prediction models using machine learning methods like clustering and forecasting [ 19 ]. We will accomplish this by examining website data, including task completion times, learner engagement, and responses to instructional materials. This study advances the body of predictive analytics research, especially to address the unique characteristics of online learning settings.

These studies, taken together, show the variety of statistical analysis techniques used to evaluate student achievement as summarized in Table 1 . To raise the standard of education, researchers are always expanding the parameters of predictive modeling. They are leveraging natural language processing, emphasizing social components, and considering the challenges of distance learning.

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https://doi.org/10.1371/journal.pone.0309141.t001

Proposed system model

The proposed method’s initial phase entails consolidating student performance data from multiple CSV files and datasets into a unified data frame for analysis. Data discrepancies, including inconsistencies and omissions, are resolved during preprocessing. As part of the preprocessing phase, the correlation matrix is generated to identify correlations between attributes. Feature engineering is an essential part of the process, which includes not only feature extraction but also feature selection. The algorithm implemented for the feature extraction is Elastic Net, whereas the algorithm used to assess the significance of the previously selected features is Random Forest. Results provided by the selected data are also enhanced through exploratory data analysis. The proposed classification ensemble, DBTM, effectively captures sequential patterns and trains DistillBERT using LSTM. The feature importance analysis also reflects the structural changes of the data by utilizing the central limit theorem. Spotted Hyena Optimizer has also been implemented to boost the classification model’s ability to adapt by enhancing DBMT parameters. The framework is evaluated using various performance criteria to determine the classification outputs. It is used to guarantee that the model is suitable for real-time performance in the system. Fig 1 presents the suggested framework.

https://doi.org/10.1371/journal.pone.0309141.g001

Dataset description

The data collection comprises many CSV files, each of which systematically records distinct features of student information, assessment, and participation in the educational setting [ 20 ].

The dataset contains 200,000 samples from the Open University’s learning analytics. Data, assessments, and student activity in the classroom are all represented in the many CSV files that comprise this dataset. Module codes, presentation details, assessment types, student characteristics, VLE activity on their resources, and performance metrics are all included in each file. Before the dataset was analyzed, efforts were made to handle missing values and maintain data quality. The specifics listed here are essential to this paper’s research, findings, and lived experience to grasp the scope and nature of the dataset. The subsequent subsections comprehensively describe each CSV file, as Fig 2 illustrates. Furthermore, Table 2 shows the details of the dataset CSV files.

https://doi.org/10.1371/journal.pone.0309141.g002

https://doi.org/10.1371/journal.pone.0309141.t002

With this data, we can examine students’ course progress, performance indicators, and engagement with the module presentations. Each CSV file may easily be linked to the others using common IDs, allowing for a comprehensive study of the educational environment.

Data preprocessing

We used data cleaning methods to fix discrepancies and ensure everything was represented consistently.

Feature engineering and EDA

https://doi.org/10.1371/journal.pone.0309141.g003

Exploratory Data Analysis (EDA) entails employing statistical analysis and visualizations to uncover the inherent patterns in the dataset, facilitating the detection of any anomalies and providing a comprehensive understanding of its structure. The meticulous compilation of the data enabled a stable and refined dataset for further examination, establishing the groundwork for additional analysis.

Classification with hybrid DBTM

The proposed DBTM (DistillBERT with LSTM) model achieves effective student performance prediction by integrating sequential pattern recognition with transformer-based contextual learning. The methods will apply DistillBERT (a smaller version of BERT) to capture a complete range of context data, such as academic transcripts, demographic factors, and student activity logs. These embeddings, also called HDB, are the weights representing bonds between whatever is in your input data.

The model features an LSTM layer to account for time and temporal patterns prevalent in student interactions. Then the output from the use of pair-wise DistillBERT (HDB) is also used as an input sequence ( W LSTM ) to LSTM, which generates further sequential embeddings known H LSTM . Time information on student activity engineered by sequential embeddings. The complete representation is then formed by aggregating the contextual embeddings ( S DB ) and sequence embedding dumps from each of these DS (the output for all sentences from LSTM, i.e., E LSTM . The final input for the formal classification job is the hybrid representation ( H Combined ). Model Architecture : There are three primary parts to the architecture:

The model improves its accuracy in predicting student achievement using an integrated approach, using local sequential information and global context. Fig 4 shows the DBMS framework.

https://doi.org/10.1371/journal.pone.0309141.g004

Tuning with SHO

This tuning approach acquires the Spotted Hyena Optimizer (SHO) [ 26 ] to enhance parameters ( ϕ ) & of DistillBERT with LSTM (DBTM) model, therefore enhancing its prediction on higher education student success forecasting as well. Based on some parameter settings specified by the objective function F ( ϕ ), called pack of hyenas and named pack P (0), this is introduced into the optimization process. This function includes key performance metrics tracked at different epochs in the model training, i.e., recall, precision, and F1-S score.

Changes in learning rates, dropout rates, and other critical factors are utilized to develop the model’s structure. One essential property of SHO is its ability to respond to changes in data. The DBTM model is more adaptable to changes in student performance data due to the ability of its changed parameters, making it scalable. This iterative optimization loop refines parameter settings, leading to the optimum set ( ϕ ) that maximizes the fitness function. Algorithm 1: Tuning DBTM using SHO.

Algorithm 1 Tuning using SHO

Input: Clean Data from Feature Engineering Step

Objective Function: F ( ψ ) representing the fitness of DBTM model parameters.

Initial Pack of Hyenas: H (0) with diverse parameter configurations.

Maximum Number of Iterations: T .

Output: Optimal set of parameters: * ψ *.

1: Algorithm Steps:

2: Initialization: Release a pack of hyenas, each embodying a distinct parameter configuration: H (0) ← ReleaseHyenaPack()

3: Set the iteration counter k = 1.

4: Optimization Loop: While k ≤ T do:

5:  a. Hunting Phase: Evaluate the fitness of each hyena’s parameter configuration:

6:   Evaluate Fitness: F ( ψ ( k ) )

7:  b. Update Hyena Pack: Update the hyena pack based on the fitness values:

8:   Update Hyena Pack: H ( k +1) ← UpdateHyenaPack ( H ( k ) , F )

9:  c. Exploration: Facilitate the exploration of novel parameter configurations through hyena collaboration and communication.

10:  d. Update Parameters: Update the DBTM model parameters based on hyena collaboration:

11:   Update Parameters: ψ ( k +1) ← UpdateParameters ( ψ ( k ) , H ( k +1) )

12: Increment Iteration Counter: k ← k + 1

13: Result: ψ ← arg max ψ F ( ψ )

Adding an attribute configuration that materially increases the prediction power of the DBTM model. These rationalizations have been confirmed by thorough investigation and analysis using various data sets across different studies, among which study11 has further supported the updated DBTM model in real-life settings. Meanwhile, the analytical and prescriptive simulation framework associated with predicting academic success in kids is improved practically due to its simple optimization parameter process, which Spotted Hyena Optimizer wanted. Fig 5 describes the tuning process.

https://doi.org/10.1371/journal.pone.0309141.g005

Performance assessment

The efficacy of the proposed hybrid technique is evaluated using several classification measures, such as the confusion matrix, log loss, statistical analysis, and execution time. Using these assessment indicators, we analyze the effectiveness of categorization algorithms. The techniques are illustrated in Fig 6 .

https://doi.org/10.1371/journal.pone.0309141.g006

Simulation and results

This study aims to improve the precision of predicting student performance by utilizing a machine with an Intel Core i7 11th Gen CPU operating at 2.4 GHz and coupled with a 4GB RTC graphics card. Python is the main language to run simulations in the Anaconda Spyder IDE.

The first stage is importing the dataset into the framework and preparing it for preprocessing. The dataset contains missing values, which are resolved using two independent approaches. Any row with more than 50% missing values is excluded. Alternatively, when the number of missing values in a row is below 50.

Exploratory Data Analysis (EDA) is conducted on the dataset to acquire insights and understand the data. Fig 7 displays the histogram representing the variability of assessment results obtained from the OULAD dataset. Score ranges are shown on the x-axis, and the frequency of occurrences within each range is shown on the y-axis. A kernel density estimate (KDE) curve is superimposed over the distribution to offer a normalized representation of the data distribution. The histogram clearly shows that most students’ results are concentrated within specified intervals, as the conspicuous peaks show. The KDE curve facilitates the identification of any underlying trends or patterns in the assessment scores. This graphical representation facilitates the rapid assessment of outliers and core tendencies. The distribution’s tails can indicate the presence of exceptionally high or low scores.

https://doi.org/10.1371/journal.pone.0309141.g007

The relationship between student involvement and academic results is well shown in Fig 8 . A count plot of distinct VLE activity categories is shown on the left side of the figure, illustrating the varied degrees of student participation. A unique color palette makes it simpler to differentiate between different activities, making it easier to identify engagement patterns and how frequently each type is utilized. Conversely, the main focus of the figure on the right is the average number of interactions (after clicks) according to students’ final findings. Students with better grades, particularly those who get distinctions, use the virtual learning environment (VLE) considerably more than their peers who do worse. Taken as a whole, these numbers show how vital participation is for students to do well in school and illuminate the connections between various VLE activities and grades.

https://doi.org/10.1371/journal.pone.0309141.g008

Fig 9 explains how students’ participation in the VLE and their assessment results relate. As a measure student’s academic achievement, the x-axis displays assessment results. In contrast, the total quantity of clicks pupils performed in the VLE is displayed on the y-axis, measuring their online activity.

https://doi.org/10.1371/journal.pone.0309141.g009

Patterns and trends within the data can be observed in Fig 9 . A positive correlation between assessment scores and Virtual Learning Environment (VLE) clicks is suggested if the graph’s points demonstrate an increasing trend from left to right, learners actively participating in the virtual classroom typically achieve higher test scores. Conversely, a lack of clear trends or discrepancies among points indicates a weak or nonexistent relationship between VLE engagement and academic performance.

Fig 10 shows the dataset’s component correlation matrix, which provides useful information about the correlations between different assessment measures. On the heatmap, different colors represent different degrees of association. Lighter shades show weak correlations, dark red shows strong positive correlations, and dark blue shows major negative correlations. The numerical annotations within each heatmap cell represent specific correlation coefficients ranging from -1 to 1. By interpreting these coefficients, readers can discern trends and associations between pairs of variables. Negative coefficients suggest an inverse relationship, while positive coefficients indicate variables that tend to move in tandem. The heatmap facilitates the identification of clusters of factors with strong correlations, guiding further research efforts.

https://doi.org/10.1371/journal.pone.0309141.g010

In Table 3 , we present a comprehensive performance evaluation of our proposed method alongside existing approaches documented in the literature. The results are extensively examined, compared to those obtained by other researchers, using numerous methodologies. A DBTM-SHO performs well in all metrics, making it visible from a performance evaluation perspective. These numbers indicate how effective our architecture is and what its scale can be in the industry.

https://doi.org/10.1371/journal.pone.0309141.t003

A comparison of the categorization approach proposed in this research with others is shown, which can be seen in Table 4 . Understanding how valid and valuable the results of this thorough investigation are. Table findings display positive benefits across all research variables, which validates our strategy as being hardy. This detailed statistical analysis validates the performance obtained by our method and, more importantly, sheds light on the architecture underlying categorization. The positive correlations among the variables help confirm that our classification algorithms are authentic and valuable, maybe even practical under real-world use.

https://doi.org/10.1371/journal.pone.0309141.t004

Fig 11 depicts a sensitivity study that investigates the impact of various factors on the model’s efficacy. The effect of each parameter on the model’s overall performance may be seen clearly in the bar chart. A red trend line shows the overall trend in the parameters’ effects, and each bar’s height shows the trend’s size. If the parameters are above the benchmark line (the dotted green line at 0.1), then the effect is above average; if they are below, then the influence is below average. There is a numerical description of the influence of each parameter next to each bar, which represents that parameter. This sensitivity analysis may assist researchers and practitioners in making informed decisions during model optimization by illuminating elements that significantly affect model accuracy.

https://doi.org/10.1371/journal.pone.0309141.g011

Fig 12 shows the proposed DBTM-SHO technology and other existing methods as a function of data size and execution time. By plotting execution times in seconds against data sizes on the x-axis and 10,000 to 200,000 on the y-axis, we can see the relationship between the two variables. Markers on the graph indicate the amount of time required to execute for different data amounts, and each line on the graph indicates a different approach. As represented by the figure, the execution time could vary significantly depending on the approach and the dataset size. Database Transform SHO’s execution time remains relatively consistent as the volume of data increases. The processing efficacy of SVM and NB is more notable when the datasets are larger. The distinct patterns of execution time that NN, DT, LR, and ResNet display in response to data size demonstrate that various approaches have varied processing demands and scalability characteristics.

https://doi.org/10.1371/journal.pone.0309141.g012

The results of this study demonstrate that educational institutions can benefit from utilizing enhanced ML algorithms to predict how well their students will do in the future. Significant enhancements in accuracy, decrease in log loss, and improvement in execution time have been accomplished by building a comprehensive framework that unifies data preparation, feature engineering, and classification utilizing the hybrid DBTM-SHO model. Demonstrating its capacity to efficiently manage expanding volumes of data in graduate and postgraduate programs, the model achieves noteworthy performance metrics—98.7% accuracy, 0.03% log loss, and 15%–25% optimized execution time—or more. The findings underscore the model’s potential as a valuable instrument for examining educational data. The consequences of these discoveries are significant for all those involved in education. The methodology that has been suggested establishes a robust framework for the timely identification of children who might be at risk, thereby facilitating tailored support strategies and interventions. Through the implementation of predictive analytics, educators and administrators have the potential to improve student outcomes, maximize the efficiency of current resources, and elevate the overall efficacy of the educational system. In addition, the amalgamation of optimization algorithms and cutting-edge machine learning methodologies streamlines the decision-making process based on data. It facilitates individualized learning experiences, thereby augmenting student achievement.

Limitations and future research directions

The study’s findings are encouraging; however, some constraints must be addressed to make the model more applicable and generalizable. For example, the model may not apply to other educational settings due to its exclusivity to the OULAD dataset; these settings and student demographics may vary greatly. Future research should assess this approach utilizing many datasets from various institutions to ensure its durability and flexibility. Second, the objective of this work is to predict student performance with structured data that contains demographic information and module-related algorithms, as well as public software for interaction within the virtual classroom. The model can be improved further by adding more unstructured data sources such as forum entries, textual notes, etc. Applying NLP techniques to text data would provide a fuller picture of students’ learning behaviors and performance predictors. In addition, the computational overhead and resource requirements of the proposed hybrid model (SHO-DBTM) must be considered. Future work should focus on optimizing model performance without impairing prediction. Model reduction, feature selection, and distributed computing architectures effectively address these challenges; consequently, the final model is more suited for educational online applications.

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• Published: 23 August 2024

Different motivation, different achievements: the relationship of motivation and dedication to academic pursuits with final grades among Jewish and Arab undergraduates studying together

• Efrat Gill 1   na1 ,
• Oz Guterman 1   na1 &
• Ari Neuman   ORCID: orcid.org/0000-0002-1416-5522 2   na1  

Humanities and Social Sciences Communications volume  11 , Article number:  1079 ( 2024 ) Cite this article

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Much research has indicated disparities between majority and minority groups in academic achievements. In Israel, differences have been recorded between the ethnic majority of students of Jewish origin and the ethnic minority of students of Arab origin. One possible reason for these findings might be differences in motivation, influenced by the respective cultures of the Jewish ethnic majority and Arab ethnic minority. The present research examined the relationship between differences in academic achievements of 73 students of Jewish origin and 74 students of Arab origin studying together and patterns of motivation and dedication to academic pursuits. The findings indicate considerable differences between the two populations in final grades and in motivational patterns and dedication to academic pursuits. In addition, in each of the research populations, different motivations were associated with a higher level of grades.

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Academic achievement among majority and minority groups.

In many societies, academic education is considered key to social mobility and is especially important for minority groups (Alsulami, 2018 ; Baskakova et al., 2017 ; Bautista et al., 2023 ; Dominguez-Whitehead, 2017 ). In a review of studies that examined the effect of academic education on the social status of minority groups, Arar and Mustafa ( 2011 ) found that academic education strengthened minority groups by enabling better integration into the labor market, thereby enabling the minority groups better economic, social, and political status. Given the importance of this factor, it is alarming to see data on the gaps between the majority and minority groups in academic achievement. This gap does not exist in all minority groups and there are even cases in which minority groups have higher academic achievements. However, in cases where there is a gap, it is an obstacle to the progress of minority groups (Pérez-Martín and Villardón-Gallego, 2023 ).

Gaps in education and academic success between majority and minority groups at all levels of education have been widely documented in the literature. According to Martin et al. ( 2017 ), this is one of the most difficult and frustrating problems for policymakers. For example, the data on the gaps between students of African American and of white origin in the United States over the last three decades indicated significant differences in grades in favor of the latter (Bowen and Bok, 1998 ; Charles et al., 2009 ; Hung et al., 2020 ). At the college level, students of Latin American and African American origin in the United States had lower academic achievements (average grade and graduation scores) and took a longer time to graduate compared with students of white or Asian origin (Bowen et al., 2009 ; Kugelmass and Ready, 2011 ).

The gap between academic achievements of majority and minority groups in Israel

In Israel, too, there are gaps between the ethnic majority and minority groups, such as, for example, students of Jewish and Arab origin. Arar and Mustafa ( 2011 ) contended that the characteristics of students of Arab origin in Israel’s higher education system were similar to those of other minority groups in the world.

Lufi and Parish-Plass ( 2010 ) argued that the differences between Jewish-origin and Arab-origin students in academic achievements were reflected in a number of academic and educational indicators. Feniger et al. ( 2013 ) found that after completing high school, about 69% of Jewish-origin students eligible for a matriculation certificate continued on to higher education, compared with only 51% of Arab-origin students. Moreover, 74% of the Jewish-origin students completed their studies at the end of the qualifying period, compared with 62% of the Arab-origin students. In other words, about half (51%) of the Jewish-origin students entitled to a matriculation certificate completed a bachelor’s degree, compared with about one-third (32%) of the Arab-origin students. In addition to their relatively low rates of enrollment in academic institutions, the dropout rate among Arab-origin students has been shown to be higher, approximately 16.6% within two years of entering, compared with 12% among Jewish-origin students (Mustafa, 2007 ). Examination of the data on gaps in education over several years revealed an upward trend in the level of education among the Arab population in the last decades, however, the gap between Jews and Arabs remained (Guterman and Gill, 2023 ; Central Bureau of Statistics, 2020 ). These differences were also found to be associated with cultural disparities. The Arab society in Israel is characterized by a higher level of collectivism compared to Jewish society, and these differences were found to be linked to the academic achievements of students, among other factors. (Guterman et al., 2024a ). The Arab society in Israel is generally more traditional, placing a greater emphasis on belonging to the extended family. In this regard, family identity is, on average, more significant for Arab-origin students, and there is a tendency in Arab society in Israel towards more collectivist motivations (Guterman et al., 2024b ).

Differences in motivation and dedication to academic pursuits and cultural differences between Jews and Arabs in Israel

One of the explanations for these gaps between students of Jewish and Arab origin in academic achievements may be motivational differences. Several studies examined the level of motivation of high-school students of Jewish and Arab origin and showed that the desire to pursue academic degrees was higher among students of Arab origin (e.g., Feniger et al., 2021 ; Khattab, 2005 ). However, the effect of different types of motivation on the academic success of the two groups was not examined.

One of the possible explanations for the differences between students of Jewish and Arab origin in Israel in level of motivation and dedication to academic pursuits lies in the cultural differences between these population groups in terms of collectivism and individualism. As explained, previous research has found that students of Arab origin have a higher tendency toward collectivism than students of Jewish origin do (e.g., Amzaleg and Masry-Herzallah, 2022 ).

Research on motivation and dedication to academic pursuits

Several academic models and theories are dedicated to examining the motivation and dedication that individuals exhibit toward their academic endeavors. Each of these theoretical frameworks offers different explanations for the gap in academic achievements between Jewish and Arab populations in Israel, as well as potential disparities between these two groups in motivation and dedication to academic pursuits. Achievement goal theory (AGT; Ames, 1992 ), one of the most widely used theoretical frameworks in the research of motivation to learn (Huang, 2012 ; Urdan and Kaplan, 2020 ), explains students’ purposes for studying. Elliot and Trash ( 2001 ) defined motivation for achievement, and especially motivation for academic achievement as the purpose for which a person engages in goal-driven activities (p. 140). A number of studies have shown the connection between achievement goals in an academic setting and students’ learning behaviors (see, e.g., Meece et al., 2006 ) and examined the characteristics of students who continue to succeed in challenging learning environments.

Earlier studies in the field distinguished between two types of goals: mastery goals, in which the purpose is to develop abilities, and performance goals, in which the purpose is to demonstrate mastery (Ames, 1992 ; Dweck and Leggett, 1988 ). Recent studies have added an approach-avoidance dimension, whereby approach goals arise from the motivation to succeed or the motivation to avoid failure, respectively. Huang ( 2012 ) argued that approach motivation is generally associated with higher academic achievement and avoidance motivation is associated with lower academic achievement.

The underlying factor of motivational orientations is the way in which students assign meaning to school and learning. Nicholls ( 1992 ) argues that mastery goals and performance goals stem from students’ theories about education and learning. These theories are shaped by cultural meanings associated with education in their respective communities and their personal experiences within educational contexts (Maehr and Nicholls, 1980 ). This perspective suggests that in schools within different cultural groups, students may have different theories regarding school and learning.

Significant cultural differences are evident between minority and majority groups. Generally, majority groups adhere to a distinct cultural group that is different from that of minority groups, allowing for the distinction between ‘majority culture’ and ‘minority culture.' Indeed, this viewpoint aligns with the findings that highlight disparities between these groups, in academic achievements. For example, the 2009 Program for International Student Assessment (PISA), identified significant achievement gaps between immigrant and non-immigrant students was identified across all domains in most OECD countries. Immigrant students generally exhibited lower proficiency levels compared to their non-immigrant counterparts: in reading in 23 countries, in mathematics in 24 countries, and in science in 25 out of 28 countries. Additionally, academic difficulties were more prevalent among second-generation immigrants compared to first-generation immigrants (OECD, 2012 ).

Disparities in academic achievements were found in Israel between the majority Jewish population and the Arab minority. The gap in achievements was evident in the results of the 2002 PISA exam. According to the exam results, students of Arab origin achieved lower scores than those of Jewish origin, ranking Israel 31st in mathematics, 30th in reading, and 33rd in science out of 42 countries assessed. Conversely, if only scores of Jewish-origin students were considered, Israel would have ranked 12th. It was found that ~60% of students of Arab origin in Israel struggle with reading comprehension, compared to 30% among students of Jewish origin. In the Arab sector, despite an improvement in academic achievements, the eligibility rates for matriculation certificates remain low; among students of Arab origin, the eligibility rate stood at 34%, compared to 51% among students of Jewish origin. Regarding the quality of the matriculation certificate, a higher percentage of Jewish-origin students met the university entry requirements—87%, compared to 73% among Arab-origin students. Additionally, the dropout rate among youth is significantly higher in the Arab sector (Mi-Ami, 2003 ).

Indeed, previous research on differences between Jewish and Arab students in terms of achievements indicated the important role of learning goals (Guterman et al., 2024a ). The study found that the level of approach-avoidance goals of the Arab students was indeed lower than those of the Jewish students. This finding suggests the possibility that the collectivist perceptions of this population might lead to less willingness to create challenges, thus creating more passive and less active coping.

Another common model of motivation is the expectancy-value theory (EVT), a motivational framework that describes the correlation between an individual’s expectation of success in a task and the perceived value attributed to that task. First introduced by Atkinson in 1957 EVT was further developed by Wigfield and Eccles ( 2000 ). This theory comprises two principal components: expectancy and value. Expectancy is an individual’s belief in their ability to achieve success in a task, addressing the question, “Can I effectively execute this task?” Expectancy beliefs are influenced by past accomplishments or failures, thus shaping one’s perception of their likelihood of success. Value refers to the perceived importance, utility, or enjoyment associated with a task, addressing the question, “Do I consider this task worthwhile?” Value is influenced by an individual’s prior experiences, beliefs, and personal objectives. EVT identifies four distinct types of values: intrinsic value (the enjoyment derived from the task), attainment value (the personal significance of achieving success in the task), utility value (the practical usefulness of the task), and cost (the negative aspects associated with engagement in the task).

EVT offers an interesting look at the differences between groups. However, unlike AGT, differences between Jews and Arabs have not yet been examined from the perspective of this theory. In our opinion, and in accordance with EVT, there are no expected differences between the groups in the degree of desire to succeed (value). In other words, even though it is possible that the desire to succeed stems from different motivations, such as a desire for personal achievement (individualism) or a desire for group achievements (collectivism), there is no reason to assume that the desire for success itself would be different.

In contrast, there may be differences in the belief of individuals in their ability to succeed (expectancy). An individualistic perception directs individuals to focus on their own abilities. Indeed, research has shown a correlation between individualism and self-efficacy (Earley, 1994 ). In this respect, according to EVT, students of Jewish origin, who come from a more individualistic society, can be expected to show more active strategies compared with students of Arab origin, whose society is more collectivist.

The present study

The present study focused on motivation and dedication to academic pursuits among students of Jewish and Arab origin who were studying together in a college in Israel. To this end, a questionnaire validated in previous research in Israel was used to examine academic motivational patterns (Eliassy, 1999 ). The students were enrolled at an institution in which approximately half of the students were Jews and the other half were Arabs. The research examined several hypotheses:

In keeping with previous research, including studies conducted with students in academic institutions in Israel (Guterman and Neuman, 2019 ), a gap will be found between the groups in student grades, where the students of Jewish origin will have higher final grades than their peers of Arab origin.

A positive correlation will be found between the level of motivation to study and the final grade point average.

In light of the character of Arab culture in Israel, which is typically more collectivist than Jewish culture (Lapidot-Lefler and Hosri, 2016 ; Sagy et al., 2001 ), the level of passive engagement in learning (which refers to the performance of assignments given by lecturers) will be higher among students of Arab origin compared with those of Jewish origin. As noted, this hypothesis is based on previous findings that suggested a greater reliance on sources of authority as a basis for personal action in Arabs compared with Jewish society in Israel (Lapidot-Lefler and Hosri, 2016 ; Sagy et al., 2001 ).

Participants

The research was conducted with 147 students, all enrolled at the same college in Israel. The sample was divided between 74 students of Arab origin and 73 students of Jewish origin (according to their self-reports, as explained later). To enable examination of differences between the groups, we matched them in terms of the gender and age of the participants.

At the time of the research, the students were in their second year of bachelor’s degree studies. There were 109 female students (74.15% of the sample) and 38 male students (25.85%). Differences between the samples in the distribution of genders were examined using Chi-square analysis; no significant differences were found between the groups of Arab and Jewish origin in terms of the distribution of men and women; Χ 2 (1) = 0.96, p  > 0.5 (among the Arab students: 55 women and 19 men; among the Jewish students: 54 women and 19 men).

The participants’ ages ranged from 18 to 57. The mean age was 24.78 with a standard deviation of 6.64. To examine the differences between the groups in this respect, an independent sample t -test was conducted. No significant differences were found, t (145) = 0.44, p  > 0.05 (Arabs: M  = 24.54, SD = 7.27; Jews: M  = 25.02, SD = 5.98).

The researchers invited social sciences students to participate in the research during their classes at Western Galilee College. They explained that participation was voluntary and were assured that the data would not affect their grades or be used for any purpose other than the research. After the students gave their consent to participate in the research, meetings were arranged to administer the different questionnaires included in the research. The students signed to indicate their consent to participate in the research as well as permission for the research team to examine their final grades. The grades were collected two and a half years after completion of the questionnaires, according to the approval of the Ethics Committee of the college. Seven students dropped out of studies and were therefore not included in the research.

Instruments

Motivational patterns questionnaire.

Eliassy ( 1999 ) developed a questionnaire of 24 items in which the respondents rank the degree to which the statements fit them on a scale of 1 (“not at all true”) to 4 (“true to a great degree”). The items refer to different aspects of studying that represent patterns of high and low motivation in terms of quality and test the degree to which the student expresses willingness to demonstrate each of them. The Hebrew version regarding motivational patterns includes five subscales: (a) persistence when encountering difficulties during studies (such as difficult questions in homework assignments, study material that is hard to understand, and the like). In the present study, the Cronbach’s alpha for this subscale was 0.71; (b) active involvement in studying, where the items examine the degree to which the student demonstrates interest in what is going on in class during lessons and their active participation in activities during lessons (that is, the degree to which the student engages in actions that reflect involvement, such as expressing an opinion, raising one’s hand, and the like). In the present study, the Cronbach’s alpha for this subscale was 0.84; (c) passive involvement in studying, where the items also examine the degree of interest expressed by the student in what is happening in class during lessons, but in this case referring to situations when they turn their attention to what is happening during the lesson but do not take any specific action. In the present study, the Cronbach’s alpha for this subscale was 0.82; (d) willingness to invest effort in studying (the degree to which the student is willing or chooses to put effort and time into studies, both in class and at home). In the present study, the Cronbach’s alpha for this subscale was 0.81; and (e) seeking challenges in studying (the extent to which the student prefers to engage in complex or simple assignments in studies, in terms of the level of difficulty and the personal challenge they pose to the individual). In the present study, the Cronbach’s alpha for this subscale was 0.77.

Academic achievements

The student’s final grades were collected from the college’s databases. The data were collected in accordance with the consent of the study participants and the approval of the Research Ethics Committee.

Demographic questionnaire

The respondents completed a demographic questionnaire that included questions about gender, study track, year of studies, age, and ethnic origin.

To test the hypotheses, several stages of analysis were conducted. First, the differences between Jewish and Arab students in all variables were analyzed. Second, the relationships between the research variables were examined, and finally, hierarchical regression was conducted to examine interactions between the variables in their contribution to explaining the variance in the final bachelor’s degree grades.

Differences between the students of Arab and Jewish origin in terms of the variables

To examine the differences between the students of Jewish origin and those of Arab origin in motivational patterns, a one-way MANOVA was performed. To examine the differences between the groups in final grades, a t -test was conducted.

The MANOVA regarding differences between the students of Jewish and Arab origin in motivational patterns showed a significant difference between the groups, F (5,141) = 4.01, p  < /−1. Eta 2  = 0.13. The results, means, and standard deviations of the motivational patterns by group are presented in Table 1 .

As the table shows, significant differences were found between the students of Jewish origin and their classmates of Arab origin in motivational patterns of willingness to invest effort in studies and seeking challenges in studies. In both these variables, the scores of the students of Jewish origin were higher, on average, than those of the students of Arab origin.

In addition, an independent sample t -test was conducted to examine whether there were differences between the students of Jewish and Arab origin in their final grades; it showed significant differences between the students in final grades, t (206) = 6.53, p  < 0.001. The average grades of the students of Jewish origin were higher than those of the students of Arab origin (Arabs: M  = 71.98, SD = 9.91; Jews: M  = 81.93, SD = 8.51).

Correlations among the research variables

To examine the correlations among the research variables and the between them and the final grades, Pearson correlations were calculated for each group. The correlations between the two motivational patterns and between these patterns and the final grade among students of Jewish origin and of Arab origin are presented in Tables 2 and 3 .

The tables show that among the students of Arab origin, there was a positive correlation between the final grade and passive involvement in studies. In comparison, among the students of Jewish origin, there was a positive correlation between final grade and active involvement in studies. In other words, among the students of Arab origin, the greater their passive involvement in studies was, the higher their final grades were, and among the students of Jewish origin, the greater their active involvement in studies, the higher their final grades were.

In contrast, in both groups, a negative correlation was found between involvement in studies (whether active or passive) and persistence when encountering difficulties with studies. In other words, in both groups, the less the student’s passive or active involvement in studies, the less their persistence when facing difficulty. In fact, in both groups, there was a positive correlation between the two types of involvement in studies (passive and active), where the greater the involvement of one type was, the greater the involvement of the other, as well; in other words, these variables were not independent. Furthermore, in both groups, a negative correlation was found between willingness to invest effort in studies and persistence when encountering academic difficulties. In other words, the greater the willingness to invest effort in studies, the lower the level of persistence when encountering difficulty. In both groups, a positive correlation was found between persistence when encountering academic difficulties and seeking challenges in studies: the greater the persistence when encountering difficulties, the greater, too, was the student’s search for challenges when studying.

In both groups, there was a positive correlation between passive involvement in studies and willingness to invest effort in studies. However, among the students of Jewish origin, there was also a positive correlation between active involvement and willingness to invest effort in studies, but among those of Arab origin such a correlation was not found. In other words, in both groups, the greater the passive involvement in studies, the greater the willingness to invest effort in studies, but only among those of Jewish origin did we also find that the greater the active involvement in studies, the greater the willingness to invest effort in studies.

In both groups, a negative correlation was found between passive involvement in studies and seeking challenges in studies, that is, the greater the passive involvement in studies, the less the search for challenges. Furthermore, among the students of Jewish origin, a negative correlation was also found between passive involvement in studies and willingness to invest effort in studies, that is, the greater the passive involvement in studies, the lower the willingness to invest effort. However, among the studies of Arab origin, no correlation was found between these two variables.

Regression analysis of explained variance of final grades among students of Jewish and Arab origin

To examine the contribution of the research variables to the explained variance in final grades, a separate regression analysis was performed for each of the groups. In both analyses, the first stage was to perform a multiple regression that included all the variables mentioned, even though some of them were not found to be associated with the final grade. The purpose of this analysis was to examine whether these variables might be found to contribute due to interaction with other variables. Next, a hierarchical regression was performed, where the variables that had been shown to correlate with the final grade, either as a main effect or an interaction, were entered. These regressions included three steps: (a) demographic characteristics (age and gender); (b) motivational patterns (persistence when encountering difficulties in studies, active involvement in studies, passive involvement in studies, willingness to invest effort in studies, and seeking challenges in studies); and (c) interaction of the motivational patterns with the demographic characteristics, to examine whether the contribution of the motivational patterns was dependent on the demographic characteristics of the student.

In the first two steps, the variables were force-entered; in the third step, which examined the contribution of the interactions to the explained variance, only those interactions that had been found to contribute to the explained variance significantly ( p  < 0.05) were entered. The regression regarding the students of Arab origin indicated that the level of explained variance was 30%; in comparison, the regression regarding students of Jewish origin indicated that the level of explained variance was 13%. The beta coefficients of the explained variance in each of the regressions are presented in Table 4 .

As the table shows, in the regression regarding the students of Arab origin, the results of the first step, which included only the demographic variables (age and gender), showed a significant contribution of 14% to the explained variance in final grades. In the regression regarding the students of Jewish origin, the same regression did not indicate a contribution to the explained variance in final grades. In the regression regarding the students of Arab origin, age was found to correlate positively with final grade: the older the student, the higher the grade. In the second step, when the five variables of the student’s motivational patterns (persistence when encountering difficulties in studies, active involvement in studies, passive involvement in studies, willingness to invest effort in studies, and seeking challenges in studies) were entered, both the regression regarding the students of Arab origin and that regarding students of Jewish origin indicated a significant contribution of 12% to the explained variance.

In the regression regarding students of Jewish origin, active involvement in studies correlated positively with the final grade (the greater the active involvement in studies, the higher the final grade). Furthermore, in the regression regarding Arab students, passive involvement in studies correlated positively with final grade (the greater the passive involvement in studies, the higher the final grade). In addition, among the students of Arab origin, a negative correlation was found between active involvement in studies and final grade (the greater the active involvement, the lower the final grade).

In the third step, when the interaction of willingness to invest effort in studies with age was entered, a significant contribution was found in the group of students of Arab origin. This interaction contributed an additional 4% to the explained variance in final grades among the Arab students. In the regression regarding students of Jewish origin, this interaction did not contribute significantly to explained variance in grades.

To reach a deeper understanding of the interactions, Aiken and West’s ( 1991 ) method was employed. Figure 1 presents a graphic description of the interaction of “willingness to invest effort in studies” with “age” among the students of Arab origin.

Relationship between willingness to invest effort in studies and final grades among older and younger students.

As shown, among the younger students of Arab origin, the willingness to invest effort in studies did not correlate significantly with final grades, β  = 0.20, p  > 0.5. In contrast, among the more mature students of Arab origin, a significant negative correlation was found between willingness to invest effort in studies and final grades, β  = –0.23, p  < 0.5. In other words, among these students, the greater their willingness to invest effort in studies, the lower their final grades were.

Consistent with the research hypotheses and the findings of previous research, a significant difference was found between the groups of students in final undergraduate grades. The students from the Jewish ethnic majority had higher grades than those from the Arab ethnic minority. In addition to this finding, which corroborates that of earlier studies, the results of the present study also indicated differences between the groups in terms of motivational patterns. The students of Jewish origin scored higher than those of Arab origin in seeking challenges and willingness to invest effort in learning.

These findings are consistent with the two models presented in the introduction with respect to seeking challenges. One possible explanation arises from the difference between the groups in achievements. The grades of the students of Arab origin were lower; therefore, it is reasonable that the studies were more difficult for them, and this may have posed a greater threat to them compared with their Jewish counterparts. As presented in the introduction, this finding is consistent with many research findings on the gap between Jews and Arabs in Israel in academic achievements (Ayalon et al., 2019 ; Blass, 2020 ; TIMSS, 2023 ; OECD, 2018 ; Zuzovsky, 2008 ).

These figures are not unique to Israel. Modern society is characterized by substantial migration both between and within countries, leading to the intersection of diverse languages, cultures, and identities. Consequently, numerous nations are comprised of different ethnic minorities that are distinguished by distinctive characteristics. This situation, occasionally intensified by successive waves of migration, gives rise to numerous social advantages alongside complex challenges. Among the latter, the socioeconomic assimilation of minority group members stands out prominently. Discrepancies between majority and minority population groups in educational accomplishments often serve as a notable contributor to the prevailing disparities in these realms (Lauri et al., 2022 ; OECD, 2019 ; U.S. Department of Education, National Center for Education Statistics, 2023 ).

In light of the greater difficulty, they may have been less able to seek challenges in their studies. In further research, it would be interesting to examine the level of threat that students experience and the relationship between this and the degree to which they seek challenges.

With regard to the willingness to invest effort in studies, the results were contrary to the research hypotheses. It was hypothesized that the need to cope with a greater academic challenge would lead to a greater willingness to invest in studies. However, the more complex task of coping for the Arab students may have created an opposite effect, that is, perhaps it led them to give up and created a negative feeling, expressed in less willingness to invest effort in studies. Another possible explanation of this finding might be based on the cultural differences between the groups. As discussed in the Introduction (Lapidot-Lefler and Hosri, 2016 ; Sagy et al., 2001 ), Arab society in Israel is more collectivist and places greater emphasis on authority figures than Jewish society does. Accordingly, it is possible that the students of Arab origin tended to invest more according to the social demands led by the authority figures of the lecturers, and were therefore less inclined to invest beyond the formal requirements. In further research, it would be interesting to examine these explanations by means of qualitative interviews with students from both sectors, which could shed light on the feelings created by facing difficulties and its impact on students’ willingness to invest effort in studies, as well as the way members of the two groups perceive the concept of investing in studies.

Another finding that emerged from the research was the positive correlation among the students of Arab origin between passive engagement in learning and final grades, compared to the positive correlation among the Jewish students between active engagement in learning and final grades. Possible explanations for this finding might also be drawn from the results of previous research that compared these two groups. Specifically, here too, the finding may stem from the different attitudes of the students of Jewish and Arab origin to authority figures. Students from the more traditional Arab society, which places greater emphasis on authority, might be less inclined to be active and take the initiative beyond the specific definitions of the system so that their main effort is expressed in passive learning. In contrast, students from less traditional Jewish society, which stresses authority less, may tend to base their efforts more on personal initiative and less on the demands of the system. It would be interesting for further research to include measures associated with cultural variables, such as conformism or the perception of authority figures, in order to examine these explanations.

Another finding of the present study was the lack of correlation between willingness to invest in studies and final grades among the younger students of Arab origin, and the negative correlation between these two variables among the older students of Arab origin. This might indicate a change in perceptions over time. As presented earlier, among students of Jewish origin, there was a positive correlation between willingness to invest effort in studies and final grades. One possible explanation of these findings is that these students, who were raised in modern Arab society, are less traditional and less influenced by the potential conflict between a collectivist society that places more emphasis on instructions of the system and less on personal effort and the competitive, individualistic society in which they study. This might explain the negative correlation found among the older, but not the younger students of Arab origin. In this case, too, further research that combines measures of the level of collectivism of students might be useful in examining this explanation of the present findings.

The findings of the present research are important, but the study nevertheless had some limitations. First, the types of motivation considered were derived from the literature and research instruments. There is an advantage to using instruments that have been tested in previous research as well as previous research findings, but this method also has an intrinsic shortcoming. For example, the present study did not include an examination of the fit of the division into types of motivation presented in the questionnaires with the taxonomy of types of motivation as perceived by these participants. In the future, it would be interesting to conduct a qualitative examination of how students perceive the types of motivation they feel, followed by a quantitative examination of the research questions of the present study, taking this taxonomy into account.

Second, because of the limited number of respondents, the research examined differences between the groups without considering their subgroups, which could have a strong impact on the findings. For example, the Arab ethnic minority in Israel is composed of several different groups, such as Muslims, Christians, and Druze, which have different characteristics. The Jewish population in Israel can also be divided into groups in different ways. It would be interesting for further research to examine the impact of belonging to these groups on the attitudes and perceptions of the respondents in the two groups. Third, the present research referred to undergraduate studies in a mixed institution, where about half of the respondents belonged to each group. Naturally, the results might be different in other institutions, with different proportions between the groups. It is essential to continue to study this important issue among different population groups, in order to obtain a fuller view of the findings.

Despite these limitations, the present research offers an important contribution. It deepens the understanding of the relationship between the types of motivation and the academic achievements of students. Moreover, the results indicated how this relationship might be influenced by the cultural and personal characteristics of the learners, thus providing a new perspective and enhancing the understanding of this field. Understanding differences between ethnic minority and majority groups also contributes, together with further research in the field, to the ability to help members of ethnic minorities integrate and advance socially, by means of evidence-based practice. From this perspective, research in this field may inform better-focused and well-founded assistance to groups that currently demonstrate lower achievements. Thus, it is hoped that the present research will serve as the basis for further studies that will broaden the understanding of this field.

For instance, targeted interventions aimed at low socioeconomic status (SES) populations can concentrate on defining diverse goals and ensuring alignment with these objectives. Such research illustrates that the challenge lies not only in skills but also in addressing a cultural gap concerning learning objectives. Given that learning goals correlate with academic achievements, efforts to uplift low-SES populations should emphasize not only technical aspects but also the perception of learning goals. Moreover, understanding the correlation between culture and learning goals paves the way for a more profound theoretical comprehension of this domain.

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The role of emotions in academic performance of undergraduate medical students: a narrative review

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This paper is devoted to a narrative review of the literature on emotions and academic performance in medicine. The review aims to examine the role emotions play in the academic performance of undergraduate medical students.

Eight electronic databases were used to search the literature from 2013 to 2023, including Academic Search Ultimate, British Education Index, CINAHL, Education Abstract, ERIC, Medline, APA Psych Articles and APA Psych Info. Using specific keywords and terms in the databases, 3,285,208 articles were found. After applying the predefined exclusion and inclusion criteria to include only medical students and academic performance as an outcome, 45 articles remained, and two reviewers assessed the quality of the retrieved literature; 17 articles were selected for the narrative synthesis.

The findings indicate that depression and anxiety are the most frequently reported variables in the reviewed literature, and they have negative and positive impacts on the academic performance of medical students. The included literature also reported that a high number of medical students experienced test anxiety during their study, which affected their academic performance. Positive emotions lead to positive academic outcomes and vice versa. However, Feelings of shame did not have any effect on the academic performance of medical students.

The review suggests a significant relationship between emotions and academic performance among undergraduate medical students. While the evidence may not establish causation, it underscores the importance of considering emotional factors in understanding student performance. However, reliance on cross-sectional studies and self-reported data may introduce recall bias. Future research should concentrate on developing anxiety reduction strategies and enhancing mental well-being to improve academic performance.

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Introduction

Studying medicine is a multi-dimensional process involving acquiring medical knowledge, clinical skills, and professional attitudes. Previous research has found that emotions play a significant role in this process [ 1 , 2 ]. Different types of emotions are important in an academic context, influencing performance on assessments and evaluations, reception of feedback, exam scores, and overall satisfaction with the learning experience [ 3 ]. In particular, medical students experience a wide range of emotions due to many emotionally challenging situations, such as experiencing a heavy academic workload, being in the highly competitive field of medicine, retaining a large amount of information, keeping track of a busy schedule, taking difficult exams, and dealing with a fear of failure [ 4 , 5 , 6 ].Especially during their clinical years, medical students may experience anxiety when interacting with patients who are suffering, ill, or dying, and they must work with other healthcare professionals. Therefore, it is necessary to understand the impact of emotions on medical students to improve their academic outcomes [ 7 ].

To distinguish the emotions frequently experienced by medical students, it is essential to define them. Depression is defined by enduring emotions of sadness, despair, and a diminished capacity for enjoyment or engagement in almost all activities [ 4 ]. Negative emotions encompass unpleasant feelings such as anger, fear, sadness, and anxiety, and they frequently cause distress [ 8 ]. Anxiety is a general term that refers to a state of heightened nervousness or worry, which can be triggered by various factors. Test anxiety, on the other hand, is a specific type of anxiety that arises in the context of taking exams or assessments. Test anxiety is characterised by physiological arousal, negative self-perception, and a fear of failure, which can significantly impair a student’s ability to perform well academically [ 9 , 10 ]. Shame is a self-conscious emotion that arises from the perception of having failed to meet personal or societal standards. It can lead to feelings of worthlessness and inadequacy, severely impacting a student’s motivation and academic performance [ 11 , 12 ]. In contrast, positive emotions indicate a state of enjoyable involvement with the surroundings, encompassing feelings of happiness, appreciation, satisfaction, and love [ 8 ].

Academic performance generally refers to the outcomes of a student’s learning activities, often measured through grades, scores, and other formal assessments. Academic achievement encompasses a broader range of accomplishments, including mastery of skills, attainment of knowledge, and the application of learning in practical contexts. While academic performance is often quantifiable, academic achievement includes qualitative aspects of a student’s educational journey [ 13 ].

According to the literature, 11–40% of medical students suffer from stress, depression, and anxiety due to the intensity of medical school, and these negative emotions impact their academic achievement [ 14 , 15 ]. Severe anxiety may impair memory function, decrease concentration, lead to a state of hypervigilance, and interfere with judgment and cognitive function, further affecting academic performance [ 16 ]. However, some studies have suggested that experiencing some level of anxiety has a positive effect and serves as motivation that can improve academic performance [ 16 , 17 ].

Despite the importance of medical students’ emotions and their relation to academic performance, few studies have been conducted in this area. Most of these studies have focused on the prevalence of specific emotions without correlating with medical students’ academic performance. Few systematic reviews have addressed the emotional challenges medical students face. However, there is a lack of comprehensive reviews that discuss the role of emotions and academic outcomes. Therefore, this review aims to fill this gap by exploring the relationship between emotions and the academic performance of medical students.

Aim of the study

This review aims to examine the role emotions play in the academic performance of undergraduate medical students.

A systematic literature search examined the role of emotions in medical students’ academic performance. The search adhered to the concepts of a systematic review, following the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 18 ]. Then, narrative synthesise was done to analyse the retrieved literature and synthesise the results. A systematic literature search and narrative review provide complete coverage and flexibility to explore and understand findings. Systematic search assures rigour and reduces bias, while narrative synthesis allows for flexible integration and interpretation. This balance improves review quality and utility.

Eligibility criteria

Inclusion criteria.

The study’s scope was confined to January 2013 to December 2023, focusing exclusively on undergraduate medical students. The research encompassed articles originating within medical schools worldwide, accepting content from all countries. The criteria included only full-text articles in English published in peer-reviewed journals. Primary research was considered, embracing quantitative and mixed-method research. The selected studies had to explicitly reference academic performance, test results, or GPA as key outcomes to address the research question.

Exclusion criteria

The study excluded individuals beyond the undergraduate medical student demographic, such as students in other health fields and junior doctors. There was no imposed age limit for the student participants. The research specifically focused on articles within medical schools, excluding those from alternative settings. It solely considered full-text articles in English-language peer-reviewed journals. Letters or commentary articles were excluded, and the study did not limit itself to a particular type of research. Qualitative studies were excluded from the review because they did not have the quantitative measures required to answer the review’s aim. This review excluded articles on factors impacting academic performance, those analysing nursing students, and gender differences. The reasons and numbers for excluding articles are shown in Table  1 .

Information sources

Eight electronic databases were used to search the literature. These were the following: Academic Search Ultimate, British Education Index, CINAHL, Education Abstract, ERIC, Medline, APA Psych Articles and APA Psych Info. The databases were chosen from several fields based on relevant topics, including education, academic evaluation and assessment, medical education, psychology, mental health, and medical research. Initially, with the help of a subject librarian, the researcher used all the above databases; the databases were searched with specific keywords and terms, and the terms were divided into the following concepts emotions, academic performance and medical students. Google Scholar, EBSCOhost, and the reference list of the retrieved articles were also used to identify other relevant articles.

Search strategy

This review started with a search of the databases. Eight electronic databases were used to search the literature from 2013 to 2023. Specific keywords and terms were used to search the databases, resulting in 3,285,208 articles. After removing duplicates, letters and commentary, this number was reduced to 1,637 articles. Exclusion and inclusion criteria were then applied, resulting in 45 articles. After two assessors assessed the literature, 17 articles were selected for the review. The search terms are as follows:

Keywords: Emotion, anxiety, stress, empathy, test anxiety, exam anxiety, test stress, exam stress, depression, emotional regulation, test scores, academic performance, grades, GPA, academic achievement, academic success, test result, assessment, undergraduate medical students and undergraduate medical education.

Emotions: TI (Emotion* OR Anxiety OR Stress OR empathy) OR emotion* OR (test anxiety or exam anxiety or test stress or exam stress) OR (depression) OR AB ((Emotion* OR Anxiety OR Stress OR empathy) OR emotion* OR (test anxiety or exam anxiety or test stress or exam stress)) (MH “Emotions”) OR (MH “Emotional Regulation”) DE “EMOTIONS”.

Academic performance: TI (test scores or academic performance or grades or GPA) OR (academic achievement or academic performance or academic success) OR (test result* OR assessment*) OR AB (test scores or academic performance or grades or GPA) OR (academic achievement or academic performance or academic success) OR test result* OR assessment*.

Medical Students: TI (undergraduate medical students OR undergraduate medical education) OR AB (undergraduate medical students OR undergraduate medical education), TI “medical students” OR AB “medical students” DE “Medical Students”.

Selection process

This literature review attempts to gather only peer-reviewed journal articles published in English on undergraduate medical students’ negative and positive emotions and academic performance from January 2013 to December 2023. Their emotions, including depression, anxiety, physiological distress, shame, happiness, joy, and all emotions related to academic performance, were examined in quantitative research and mixed methods.

Moreover, to focus the search, the author specified and defined each keyword using advanced search tools, such as subject headings in the case of the Medline database. The author used ‘MeSH 2023’ as the subject heading, then entered the term ‘Emotion’ and chose all the relevant meanings. This method was applied to most of the keywords.

Studies were included based on predefined criteria related to study design, participants, exposure, outcomes, and study types. Two independent reviewers screened each record, and the report was retrieved. In the screening process, reviewers independently assessed each article against the inclusion criteria, and discrepancies were resolved through consensus during regular team meetings. In cases of persistent disagreement, a third reviewer was consulted. Endnote library program was used for the initial screening phase. This tool was used to identify duplicates, facilitated the independent screening of titles and abstracts and helped to retrieve the full-text articles. The reasons for excluding the articles are presented in Table  1 .

Data collection process

Two independent reviewers extracted data from the eligible studies, with any discrepancies resolved through discussion and consensus. If the two primary reviewers could not agree, a third reviewer served as an arbitrator. For each included study, the following information was extracted and recorded in a standardised database: first author name, publication year, study design, sample characteristics, details of the emotions exposed, outcome measures, and results.

Academic performance as an outcome for medical students was defined to include the following: Exam scores (e.g., midterm, final exams), Clinical assessments (e.g., practical exams, clinical rotations), Overall grade point average (GPA) or any other relevant indicators of academic achievement.

Data were sought for all outcomes, including all measures, time points, and analyses within each outcome domain. In cases where studies reported multiple measures or time points, all relevant data were extracted to provide a comprehensive overview of academic performance. If a study reported outcomes beyond the predefined domains, inclusion criteria were established to determine whether these additional outcomes would be included in the review. This involved assessing relevance to the primary research question and alignment with the predefined outcome domains.

Quality assessment

The quality and risk of bias in included studies were assessed using the National Institute of Health’s (NIH) critical appraisal tool. The tool evaluates studies based on the following domains: selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases. Two independent reviewers assessed the risk of bias in each included study. Reviewers worked collaboratively to reach a consensus on assessments. Discrepancies were resolved through discussion and consensus. In cases of persistent disagreement, a third reviewer was consulted.

To determine the validity of eligible articles, all the included articles were critically appraised, and all reviewers assessed bias. The validity and reliability of the results were assessed by using objective measurement. Each article was scored out of 14, with 14 indicating high-quality research and 1 indicating low-quality research. High-quality research, according to the NIH (2013), includes a clear and focused research question, defines the study population, features a high participation rate, mentions inclusion and exclusion criteria, uses clear and specific measurements, reports results in detail, lists the confounding factors and lists the implications for the local community. Therefore, an article was scored 14 if it met all criteria of the critical appraisal tool. Based on scoring, each study was classified into one of three quality categories: good, fair or poor. The poorly rated articles mean their findings were unreliable, and they will not be considered, including two articles [ 16 , 19 ]. Seventeen articles were chosen after critical appraisal using the NIH appraisal tool, as shown in Table  2 .

Effect measures

For each outcome examined in the included studies, various effect measures were utilised to quantify the relationship between emotions and academic performance among undergraduate medical students. The effect measures commonly reported across the studies included prevalence rat, correlation coefficients, and mean differences. The reviewer calculated the effect size for the studies that did not report the effect. The choice of effect measure depended on the nature of the outcome variable and the statistical analysis conducted in each study. These measures were used to assess the strength and direction of the association between emotional factors and academic performance.

The synthesis method

The findings of individual studies were summarised to highlight crucial characteristics. Due to the predicted heterogeneity, the synthesis involved pooling effect estimates and using a narrative method. A narrative synthesis approach was employed in the synthesis of this review to assess and interpret the findings from the included studies qualitatively. The narrative synthesis involved a qualitative examination of the content of each study, focusing on identifying common themes. This synthesis was employed to categorise and interpret data, allowing for a nuanced understanding of the synthesis. Themes related to emotions were identified and extracted for synthesis. Control-value theory [ 20 ] was used as an overarching theory, providing a qualitative synthesis of the evidence and contributing to a deeper understanding of the research question. If the retrieved articles include populations other than medical, such as dental students or non-medical students, the synthesis will distinguish between them and summarise the findings of the medical students only, highlighting any differences or similarities.

The Control-Value Theory, formulated by Pekrun (2006), is a conceptual framework that illustrates the relationship between emotions and academic achievement through two fundamental assessments: control and value. Control pertains to the perceived ability of a learner to exert influence over their learning activities and the results they achieve. Value relates to a student’s significance to these actions and results. The theory suggests that students are prone to experiencing good feelings, such as satisfaction and pride when they possess a strong sense of control and importance towards their academic assignments. On the other hand, individuals are prone to encountering adverse emotions (such as fear and embarrassment) when they perceive a lack of control or worth in these particular occupations. These emotions subsequently impact students’ motivation, learning strategies, and, eventually, their academic achievement. The relevance of control-value theory in reviewing medical student emotions and their influence on academic performance is evident for various reasons. This theory offers a complete framework that facilitates comprehending the intricate connection between emotions and academic achievement. It considers positive and negative emotions, providing a comprehensive viewpoint on how emotions might influence learning and performance. The relevance of control and value notions is particularly significant for medical students due to their frequent exposure to high-stakes tests and difficult courses. Gaining insight into the students’ perception of their power over academic assignments and the importance they attach to their medical education might aid in identifying emotional stimuli and devising remedies. Multiple research has confirmed the theory’s assertions, showing the critical influence of control and value evaluations on students’ emotional experiences and academic achievements [ 21 , 22 ].

Data extraction

For this step, a data extraction sheet was developed using the data extraction template provided by the Cochrane Handbook. To ensure the review is evidence-based and bias-free, the Cochrane Handbook strongly suggests that more than one reviewer review the data. Therefore, the main researcher extracted the data from the included studies, and another reviewer checked the included, excluded and extracted data. Any disagreements were resolved via discussion by a third reviewer. The data extraction Table  2 identified all study features, including the author’s name, the year of publication, the method used the aim of the study, the number and description of participants, data collection tools, and study findings.

Finalisation of references and study characteristics

Prisma sheet and the summary of final studies that have been used for the review.

When the keywords and search terms related to emotions, as mentioned above, in the eight databases listed, 3,285,208 articles were retrieved. After using advanced search and subject headings, the number of articles increased to 3,352,371. Similarly, searching for the second keyword, ‘academic performance,’ using all the advanced search tools yielded 8,119,908 articles. Searching for the third keyword, ‘medical students’, yielded 145,757 articles. All terms were searched in article titles and abstracts. After that, the author combined all search terms by using ‘AND’ and applied the time limit from 2013 to 2023; the search narrowed to 2,570 articles. After duplicates, letters and commentary were excluded, the number was reduced to 1,637 articles. After reading the title and abstract to determine relevance to the topic and applying the exclusion and inclusion criteria mentioned above, 45 articles remained; after the quality of the retrieved literature was assessed by two reviewers, 17 articles were selected for the review. The PRISMA flow diagram summarising the same is presented in Fig.  1 . Additionally, One article by Ansari et al. (2018) was selected for the review; it met most inclusion and exclusion criteria except that the outcome measure is cognitive function and not academic performance. Therefore, it was excluded from the review. Figure  1 shows the Prisma flow diagram (2020) of studies identified from the databases.

Prisma flow diagram (2020)

Study characteristics

Table  2 , summarising the characteristics of the included studies, is presented below.

Findings of the study

Country of the study.

Many of the studies were conducted in developing countries, with the majority being conducted in Europe ( n  = 4), followed by Pakistan ( n  = 2), then Saudi Arabia ( n  = 2), and the United States ( n  = 2). The rest of the studies were conducted in South America ( n  = 1), Morocco ( n  = 1), Brazil ( n  = 1), Australia ( n  = 1), Iran ( n  = 1), South Korea ( n  = 1) and Bosnia and Herzegovina ( n  = 1). No included studies were conducted in the United Kingdom.

Study design

Regarding study design, most of the included articles used a quantitative methodology, including 12 cross-sectional studies. There were two randomised controlled trials, one descriptive correlation study, one cohort study, and only one mixed-method study.

Population and study setting

Regarding population and setting, most of the studies focused on all medical students studying in a medical school setting, from first-year medical students to those in their final year. One study compared medical students with non-medical students; another combined medical students with dental students.

The study aims varied across the included studies. Seven studies examined the prevalence of depression and anxiety among medical students and their relation to academic performance. Four studies examined the relationship between test anxiety and academic performance in medical education. Four studies examined the relationship between medical students’ emotions and academic achievements. One study explored the influence of shame on medical students’ learning.

Study quality

The studies were assessed for quality using tools created by the NIH (2013) and then divided into good, fair, and poor based on these results. Nine studies had a high-quality methodology, seven achieved fair ratings, and only three achieved poor ratings. The studies that were assigned the poor rating were mainly cross-sectional studies, and the areas of weakness were due to the study design, low response rate, inadequate reporting of the methodology and statistics, invalid tools, and unclear research goals.

Outcome measures

Most of the outcome measures were heterogenous and self-administered questionnaires; one study used focus groups and observation ward assessment [ 23 ]. All the studies used the medical students’ academic grades.

Results of the study

The prevalence rate of psychological distress in the retrieved articles.

Depression and anxiety are the most common forms of psychological distress examined concerning academic outcomes among medical students. Studies consistently show concerningly high rates, with prevalence estimates ranging from 7.3 to 66.4% for anxiety and 3.7–69% for depression. These findings indicate psychological distress levels characterised as moderate to high based on common cut-off thresholds have a clear detrimental impact on academic achievement [ 16 , 24 , 25 , 26 ].

The studies collectively examine the impact of psychological factors on academic performance in medical education contexts, using a range of effect sizes to quantify their findings. Aboalshamat et al. (2015) identified a small effect size ( η 2 = 0.018) for depression’s impact on academic performance, suggesting a modest influence. Mihailescu (2016) found a significant negative correlation between levels of depression/anxiety (rho=-0.14, rho=-0.19), academic performance and GPA among medical students. Burr and Beck Dallaghan (2019) reported professional efficacy explaining 31.3% of the variance in academic performance, indicating a significant effect size. However, Del-Ben (2013) et al. did not provide the significant impact of affective changes on academic achievement, suggesting trivial effect sizes for these factors.

In conclusion, anxiety and depression, both indicators of psychological discomfort, are common among medical students. There is a link between distress and poor academic performance results, implying that this relationship merits consideration. Table  3 below shows the specific value of depression and anxiety in retrieved articles.

Test anxiety

In this review, four studies examined the relationship between test anxiety and academic performance in medical education [ 27 , 28 , 29 , 30 ]. The studies found high rates of test anxiety among medical students, ranging from 52% [ 27 ] to as high as 81.1% [ 29 ]. Final-year students tend to experience the highest test anxiety [ 29 ].

Test anxiety has a significant negative correlation with academic performance measures and grade point average (GPA) [ 27 , 28 , 29 ]. Green et al. (2016) found that test anxiety was moderately negatively correlated with USMLE score ( r = − 0.24, p  = 0.00); high test anxiety was associated with low USMLE scores in the control group, further suggesting that anxiety can adversely affect performance. The findings that a test-taking strategy course reduced anxiety without improving test scores highlight the complex nature of anxiety’s impact on performance.

Nazir et al. (2021) found that excellent female medical students reported significantly lower test anxiety than those with low academic grades, with an odds ratio of 1.47, indicating that students with higher test anxiety are more likely to have lower academic grades. Kim’s (2016) research shows moderate correlations between test anxiety and negative achievement emotions such as anxiety and boredom, but interestingly, this anxiety does not significantly affect practical exam scores (OSCE) or GPAs. However, one study found that examination stress enhanced academic performance with a large effect size (W = 0.78), with stress levels at 47.4% among their sample, suggesting that a certain stress level before exams may be beneficial [ 30 ].

Three papers explored shame’s effect on medical students’ academic achievement [ 24 , 31 , 32 ]. Hayat et al. (2018) reported that academic feelings, like shame, significantly depend on the academic year. shame was found to have a slight negative and significant correlation with the academic achievement of learners ( r =-0.15). One study found that some medical students felt shame during simulations-based education examinations because they had made incorrect decisions, which decreased their self-esteem and motivation to learn. However, others who felt shame were motivated to study harder to avoid repeating the same mistakes [ 23 ].

Hautz (2017) study examined how shame affects medical students’ learning using a randomised controlled trial where researchers divided the students into two groups: one group performed a breast examination on mannequins and the other group on actual patients. The results showed that students who performed the clinical examination on actual patients experienced significantly higher levels of shame but performed better in examinations than in the mannequin group. In the final assessments on standardised patients, both groups performed equally well. Therefore, shame decreased with more clinical practice, but shame did not have significant statistics related to learning or performance. Similarly, Burr and Dallaghan (2019) reported that the shame level of medical students was (40%) but had no association with academic performance.

Academic performance, emotions and medical students

Three articles discussed medical students’ emotions and academic performance [ 23 , 24 , 32 ]. Burr and Dallaghan (2019) examine the relationship between academic success and emotions in medical students, such as pride, hope, worry, and shame. It emphasises the links between academic accomplishment and professional efficacy, as well as hope, pride, worry, and shame. Professional efficacy was the most significant factor linked to academic performance, explaining 31.3% of the variance. The importance of emotions on understanding, processing of data, recall of memories, and cognitive burden is emphasised throughout the research. To improve academic achievement, efforts should be made to increase student self-efficacy.

Hayat et al. (2018) found that positive emotions and intrinsic motivation are highly connected with academic achievement, although emotions fluctuate between educational levels but not between genders. The correlations between negative emotions and academic achievement, ranging from − 0.15 to -0.24 for different emotions, suggest small but statistically significant adverse effects.

Behren et al.‘s (2019) mixed-method study found that students felt various emotions during the simulation, focusing on positive emotions and moderate anxiety. However, no significant relationships were found between positive emotions and the student’s performance during the simulation [ 23 ].

This review aims to investigate the role of emotions in the academic performance of undergraduate medical students. Meta-analysis cannot be used because of the heterogeneity of the data collection tools and different research designs [ 33 ]. Therefore, narrative synthesis was adopted in this paper. The studies are grouped into four categories as follows: (1) The effect of depression and anxiety on academic performance, (2) Test anxiety and academic achievement, (3) Shame and academic performance, and (4) Academic performance, emotions and medical students. The control-value theory [ 20 ], will be used to interpret the findings.

The effect of depression and anxiety on academic performance

According to the retrieved research, depression and anxiety can have both a negative and a positive impact on the academic performance of medical students. Severe anxiety may impair memory function, decrease concentration, lead to a state of hypervigilance, interfere with judgment and cognitive function, and further affect academic performance [ 4 ]. Most of the good-quality retrieved articles found that anxiety and depression were associated with low academic performance [ 16 , 24 , 25 , 26 ]. Moreira (2018) and Mihailescu (2016) found that higher depression levels were associated with more failed courses and a lower GPA. However, they did not find any association between anxiety level and academic performance.

By contrast, some studies have suggested that experiencing some level of anxiety reinforces students’ motivation to improve their academic performance [ 16 , 34 ]. Zalihic et al. (2017) conducted a study to investigate anxiety sensitivity about academic success and noticed a positive relationship between anxiety level and high academic scores; they justified this because when medical students feel anxious, they tend to prepare and study more, and they desire to achieve better scores and fulfil social expectations. Similarly, another study found anxiety has a negative impact on academic performance when excessive and a positive effect when manageable, in which case it encourages medical students and motivates them to achieve higher scores [ 35 ].

In the broader literature, the impact of anxiety on academic performance has contradictory research findings. While some studies suggest that having some level of anxiety can boost students’ motivation to improve their academic performance, other research has shown that anxiety has a negative impact on their academic success [ 36 , 37 ]. In the cultural context, education and anxiety attitudes differ widely across cultures. High academic pressure and societal expectations might worsen anxiety in many East Asian societies. Education is highly valued in these societies, frequently leading to significant academic stress. This pressure encompasses attaining high academic marks and outperformance in competitive examinations. The academic demands exerted on students can result in heightened levels of anxiety. The apprehension of not meeting expectations can lead to considerable psychological distress and anxiety, which can appear in their physical and mental health and academic achievement [ 38 , 39 ].

Test anxiety and academic achievement

The majority of the studies reviewed confirm that test anxiety negatively affects academic performance [ 27 , 28 , 29 ]. Several studies have found a significant correlation between test anxiety and academic achievement, indicating that higher levels of test anxiety are associated with lower exam scores and lower academic performance [ 40 , 41 ]. For example, Green et al. (2016) RCT study found that test anxiety has a moderately significant negative correlation with the USMLE score. They found that medical students who took the test-taking strategy course had lower levels of test anxiety than the control group, and their test anxiety scores after the exam had improved from the baseline. Although their test anxiety improved after taking the course, there was no significant difference in the exam scores between students who had and had not taken the course. Therefore, the intervention they used was not effective. According to the control-value theory, this intervention can be improved if they design an emotionally effective learning environment, have a straightforward instructional design, foster self-regulation of negative emotions, and teach students emotion-oriented regulation [ 22 ].

Additionally, according to this theory, students who perceive exams as difficult are more likely to experience test anxiety because test anxiety results from a student’s negative appraisal of the task and outcome values, leading to a reduction in their performance. This aligns with Kim’s (2016) study, which found that students who believed that the OSCE was a problematic exam experienced test anxiety more than other students [ 9 , 22 , 42 ].

In the wider literature, a meta-analysis review by von der Embse (2018) found a medium significant negative correlation ( r =-0.24) between test anxiety and test performance in undergraduate educational settings [ 43 ] . Also, they found a small significant negative correlation ( r =-0.17) between test anxiety and GPA. This indicates that higher levels of test anxiety are associated with lower test performance. Moreover, Song et al. (2021) experimental study examined the effects of test anxiety on working memory capacity and found that test anxiety negatively correlated with academic performance [ 44 ]. Therefore, the evidence from Song’s study suggests a small but significant effect of anxiety on working memory capacity. However, another cross-sectional study revealed that test anxiety in medical students had no significant effect on exam performance [ 45 ]. The complexities of this relationship necessitate additional investigation. Since the retrieved articles are from different countries, it is critical to recognise the possible impact of cultural differences on the impact of test anxiety. Cultural factors such as different educational systems, assessment tools and societal expectations may lead to variances in test anxiety experience and expression across diverse communities [ 46 , 47 ]. Culture has a substantial impact on how test anxiety is expressed and evaluated. Research suggests that the degree and manifestations of test anxiety differ among different cultural settings, emphasising the importance of using culturally validated methods to evaluate test anxiety accurately. A study conducted by Lowe (2019) with Canadian and U.S. college students demonstrated cultural variations in the factors contributing to test anxiety. Canadian students exhibited elevated levels of physiological hyperarousal, but U.S. students had more pronounced cognitive interference. These variations indicate that the cultural environment has an influence on how students perceive and respond to test anxiety, resulting in differing effects on their academic performance in different cultures. Furthermore, scholars highlight the significance of carrying out meticulous instruments to assess test anxiety, which are comparable among diverse cultural cohorts. This technique guarantees that the explanations of test scores are reliable and can be compared across different populations. Hence, it is imperative to comprehend and tackle cultural disparities in order to create efficient interventions and assistance for students who encounter test anxiety in diverse cultural environments. Therefore, there is a need for further studies to examine the level of test anxiety and cultural context.

Shame and academic performance

The review examined three studies that discuss the impact of feelings of shame on academic performance [ 23 , 24 , 48 ]. Generally, shame is considered a negative emotion which involves self-reflection and self-evaluation, and it leads to rumination and self-condemnation [ 49 ]. Intimate examinations conducted by medical students can induce feelings of shame, affecting their ability to communicate with patients and their clinical decisions. Shame can increase the avoidance of intimate physical examinations and also encourage clinical practice [ 23 , 24 , 48 ].

One study found that some medical students felt shame during simulations-based education examinations because they had made incorrect decisions, which decreased their self-esteem and motivation to learn. However, others who felt shame were motivated to study harder to avoid repeating the same mistakes [ 23 ]. Shame decreased with more clinical practice, but shame did not affect their learning or performance [ 48 ]. The literature on how shame affects medical students’ learning is inconclusive [ 31 ].

In the broader literature, shame is considered maladaptive, leading to dysfunctional behaviour, encouraging withdrawal and avoidance of events and inhibiting social interaction. However, few studies have been conducted on shame in the medical field. Therefore, more research is needed to investigate the role of shame in medical students’ academic performance [ 49 ]. In the literature, there are several solutions that can be used to tackle the problem of shame in medical education; it is necessary to establish nurturing learning settings that encourage students to openly discuss their problems and mistakes without the worry of facing severe criticism. This can be accomplished by encouraging medical students to participate in reflective practice, facilitating the processing of their emotions, and enabling them to derive valuable insights from their experiences, all while avoiding excessive self-blame [ 50 ]. Offering robust mentorship and support mechanisms can assist students in effectively managing the difficulties associated with intimate examinations. Teaching staff have the ability to demonstrate proper behaviours and provide valuable feedback and effective mentoring [ 51 ]. Training and workshops that specifically target communication skills and the handling of sensitive situations can effectively equip students to handle intimate tests, hence decreasing the chances of them avoiding such examinations due to feelings of shame [ 52 ].

The literature review focused on three studies that examined the relationship between emotions and the academic achievements of medical students [ 23 , 24 , 32 ].

Behren et al. (2019) mixed-method study on the achievement emotions of medical students during simulations found that placing students in challenging clinical cases that they can handle raises positive emotions. Students perceived these challenges as a positive drive for learning and mild anxiety was considered beneficial. However, the study also found non-significant correlations between emotions and performance during the simulation, indicating a complex relationship between emotions and academic performance. The results revealed that feelings of frustration were perceived to reduce students’ interest and motivation for studying, hampered their decision-making process, and negatively affected their self-esteem, which is consistent with the academic achievement emotions literature where negative emotions are associated with poor intrinsic motivation and reduced the ability to learn [ 3 ].

The study also emphasises that mild anxiety can have positive effects, corroborated by Gregor (2005), which posits that moderate degrees of anxiety can improve performance. The author suggests that an ideal state of arousal (which may be experienced as anxiety) enhances performance. Mild anxiety is commonly seen as a type of psychological stimulation that readies the body for upcoming challenges, frequently referred to as a “fight or flight” response. Within the realm of academic performance, this state of heightened arousal can enhance concentration and optimise cognitive functions such as memory, problem-solving skills, and overall performance. However, once the ideal point is surpassed, any additional increase in arousal can result in a decline in performance [ 53 ]. This is additionally supported by Cassady and Johnson (2002), who discovered that a specific level of anxiety can motivate students to engage in more comprehensive preparation, hence enhancing their performance.

The reviewed research reveals a positive correlation between positive emotions and academic performance and a negative correlation between negative emotions and academic performance. These findings align with the control–value theory [ 8 , 22 ], which suggests that positive emotions facilitate learning through mediating factors, including cognitive learning strategies such as strategic thinking, critical thinking and problem-solving and metacognitive learning strategies such as monitoring, regulating, and planning students’ intrinsic and extrinsic motivation. Additionally, several studies found that extrinsic motivation from the educational environment and the application of cognitive and emotional strategies improve students’ ability to learn and, consequently, their academic performance [ 23 , 24 , 32 ]. By contrast, negative emotions negatively affect academic performance. This is because negative emotions reduce students’ motivation, concentration, and ability to process information [ 23 , 24 , 32 ].

Limitations of the study

This review aims to thoroughly investigate the relationship between emotions and academic performance in undergraduate medical students, but it has inherent limitations. Overall, the methodological quality of the retrieved studies is primarily good and fair. Poor-quality research was excluded from the synthesis. The good-quality papers demonstrated strengths in sampling techniques, data analysis, collection and reporting. However, most of the retrieved articles used cross-section studies, and the drawback of this is a need for a more causal relationship, which is a limitation in the design of cross-sectional studies. Furthermore, given the reliance on self-reported data, there were concerns about potential recall bias. These methodological difficulties were noted in most of the examined research. When contemplating the implications for practice and future study, the impact of these limitations on the validity of the data should be acknowledged.

The limitation of the review process and the inclusion criteria restricted the study to articles published from January 2013 to December 2023, potentially overlooking relevant research conducted beyond this timeframe. Additionally, the exclusive focus on undergraduate medical students may constrain the applicability of findings to other health fields or educational levels.

Moreover, excluding articles in non-English language and those not published in peer-reviewed journals introduces potential language and publication biases. Reliance on electronic databases and specific keywords may inadvertently omit studies using different terms or indexing. While the search strategy is meticulous, it might not cover every relevant study due to indexing and database coverage variations. However, the two assessors’ involvement in study screening, selection, data extraction, and quality assessment improved the robustness of the review and ensured that it included all the relevant research.

In conclusion, these limitations highlight the need for careful interpretation of the study’s findings and stress the importance of future research addressing these constraints to offer a more comprehensive understanding of the nuanced relationship between emotions and academic performance in undergraduate medical education.

Conclusion and future research

The review exposes the widespread prevalence of depression, anxiety and test anxiety within the medical student population. The impact on academic performance is intricate, showcasing evidence of adverse and favourable relationships. Addressing the mental health challenges of medical students necessitates tailored interventions for enhancing mental well-being in medical education. Furthermore, it is crucial to create practical strategies considering the complex elements of overcoming test anxiety. Future research should prioritise the advancement of anxiety reduction strategies to enhance academic performance, focusing on the control-value theory’s emphasis on creating an emotionally supportive learning environment. Additionally, Test anxiety is very common among medical students, but the literature has not conclusively determined its actual effect on academic performance. Therefore, there is a clear need for a study that examines the relationship between test anxiety and academic performance. Moreover, the retrieved literature did not provide effective solutions for managing test anxiety. This gap highlights the need for practical solutions informed by Pekrun’s Control-Value Theory. Ideally, a longitudinal study measuring test anxiety and exam scores over time would be the most appropriate approach. it is also necessary to explore cultural differences to develop more effective solutions and support systems tailored to specific cultural contexts.

The impact of shame on academic performance in medical students was inconclusive. Shame is a negative emotion that has an intricate influence on learning outcomes. The inadequacy of current literature emphasises the imperative for additional research to unravel the nuanced role of shame in the academic journeys of medical students.

Overall, emotions play a crucial role in shaping students’ academic performance, and research has attempted to find solutions to improve medical students’ learning experiences; thus, it is recommended that medical schools revise their curricula and consider using simulation-based learning in their instructional designs to enhance learning and improve students’ emotions. Also, studies have suggested using academic coaching to help students achieve their goals, change their learning styles, and apply self-testing and simple rehearsal of the material. Moreover, the study recommended to improve medical students’ critical thinking and autonomy and changing teaching styles to support students better.

Data availability

all included articles are mentioned in the manuscript, The quality assessment of included articles are located in the supplementary materials file no. 1.

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Alshareef, N., Fletcher, I. & Giga, S. The role of emotions in academic performance of undergraduate medical students: a narrative review. BMC Med Educ 24 , 907 (2024). https://doi.org/10.1186/s12909-024-05894-1

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Explaining Achievement Gaps: The Role of Socioeconomic Factors

Racial achievement gaps in schools are well documented and remain a significant cause of concern in education. Troubling too is that the role of socioeconomic disparities in mediating these gaps remains unresolved. 

To better understand the relationship between race and socioeconomic status (SES) in producing achievement gaps, SUNY Albany's Paul L. Morgan and Eric Hengyu Hu examine two waves of data from the federal Early Child Longitudinal Study. Results show that a broad set of family SES factors explains a substantial portion of racial achievement gaps: between 34 and 64 percent of the Black-White gap and between 51 and 77 percent of the Hispanic-White gap, depending on the subject and grade level.

While SES accounts for much of the racial achievement disparities, closing these gaps requires a comprehensive approach, including improving school quality and supporting family stability. As essential steps toward equity, the authors recommend investments in early childhood education and income supplements, such as expanding child tax credits. Download Explaining Achievement Gaps: The Role of Socioeconomic Factors or read the full report below.

By Michael J. Petrilli

In 2004, superstar economists Roland Fryer and Steven Levitt published a seminal paper , Understanding the Black-White Test Score Gap in the First Two Years of School. Using then-brand-new data from the federal Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999 (ECLS-K), they found:

In stark contrast to earlier studies, the Black-White test score gap among incoming kindergartners disappears when we control for a small number of covariates. Real gains by Black children in recent cohorts appear to play an important role in explaining the differences between our findings and earlier research. The availability of better covariates also contributes. Over the first two years of school, however, Blacks lose substantial ground relative to other races. There is suggestive evidence that differences in school quality may be an important part of the explanation.

To say the findings were “mixed” dramatically underplays how good the good news was and how bad the bad news was.

The good news was twofold. First, as the authors wrote, Black kindergarteners at the time were making strong gains over previous cohorts. Indeed, child poverty dropped dramatically in the 1990s, particularly for Black children, and this was showing up in stronger readiness for school.

It was also good news—great, actually—that Fryer and Levitt could completely erase the racial achievement gap when controlling for “a small number of covariates.” These included some traditional measures of socioeconomic status (SES), such as family income and parental education levels, but also health-related factors, such as the child’s birthweight and births to teenage moms.

These findings are hugely consequential for America’s longstanding debates around racial inequality. They directly rebut the hateful arguments of white supremacists who posit that achievement gaps are a sign of Black Americans’ genetic inferiority. And they throw cold water on the claims by some on the far left that bigotry and racism in schools are at the heart of all racial disparities in student achievement in the U.S.

Instead, the explanation for racial achievement gaps is much more straightforward, though still tragic: The vast racial disparities in socioeconomic conditions and prenatal and early-life health experiences explain the achievement gaps we see between racial and ethnic groups, at least at school entry. That suggests, per the Fryer and Levitt analysis, that universal, race-neutral interventions designed to improve the academic, social, economic, and health conditions of the poor would lift all boats and would also narrow racial gaps. (Not that those interventions are easy or always obvious.)

But the bad news was really bad, too. Namely, once children entered school, Black students started losing ground, likely because the schools they attended were lower quality than the ones attended by White students, even after controlling for SES. Changing that fact has, of course, been a major focus of education reform.

That was twenty years ago, and those of us at the Thomas B. Fordham Institute were curious to see if anything had changed. We knew that racial achievement gaps had continued to narrow until the early to mid-2010s. And we knew that the federal government had released a newer ECLS dataset, the ECLS-K: 2011. We wondered: Had the relationship between socioeconomic achievement gaps and racial/ethnic achievement gaps shifted? Was the Black-White gap still growing during elementary school? And how did all of this look for the White-Hispanic gap (also explored by Fryer and Levitt) and for subjects beyond just reading and math?

To find out, we turned to SUNY Albany's Paul Morgan. Paul is one of the nation’s leading scholars on disparities in education and health care. He’s made a career out of shaking up conventional wisdom—for example, finding that Black students are actually less likely to be identified for many disability conditions (like specific learning disabilities) in analyses controlling for academic achievement. He understood the complex relationships between the variables we were interested in, plus had a great deal of experience with the ECLS data.

He worked with Eric Hengyu Hu, an education policy and postdoctoral researcher experienced in analyzing the two ECLS datasets. They got to work, diving into the data from the older and newer ECLS-K datasets. What they found was largely consistent with Fryer and Levitt’s study, although they were able to add some new understandings, as well.

Key Findings

At the heart of Hu and Morgan’s study is a set of “SES-Plus” variables.

Table F-1. Family SES measures included in the study

Since we were most interested in understanding the relationship between socioeconomic status and racial achievement gaps, Hu and Morgan did not look at health-related covariates, such as child’s weight at birth, or the age of the mother at first child’s birth, which Fryer and Levitt had included. As a result, the racial achievement gap did not “disappear,” as it had for Fryer and Levitt. But it did decrease significantly, just by controlling for the “SES-plus” factors.

Here’s what they found. (See the main body of the study for more details.)

Finding 1: Taken together, family SES+ factors explain between 34 and 64 percent of the Black-White achievement gap (depending on subject and grade level) and between 51 and 77 percent of the Hispanic-White achievement gap.

Figure F-1. Family SES+ explains more of the Black-White achievement gap in first grade reading than in other subjects and grade levels.

Figure F-2. Family SES+ explains more of the Hispanic-White achievement gap than the Black-White achievement gap.

Finding 2: Household income and mother’s education are the SES+ factors that best explain Black-White and Hispanic-White achievement gaps, respectively.

Figure F-3. Among individual SES+ factors related to science achievement gaps, household income best explains the Black-White gap and mother’s education best explains the Hispanic-White gap.

Finding 3: Family SES+ indicators, and the extent to which they explain racial/ethnic achievement gaps, are stable over time (1998-99 and 2010-11).

Table F-2. Various indicators of family SES+ are moderately correlated with each other across the two kindergarten cohorts.

Finding 4: The inclusion of family SES+ helps explain racial and ethnic excellence gaps.

Figure F-4. Family SES+ factors explain between 36 and 60 percent of the Black-White excellence gaps.

Figure F-5. Family SES+ factors explain between 52 and 69 percent of the Hispanic-White excellence gaps.

Making sense of the findings

These findings are generally consistent with Fryer and Levitt’s study from two decades ago. Socioeconomic factors can explain a large proportion of racial achievement gaps. But the current study adds a great amount of detail and nuance to our understanding of the relationships at play, while raising new questions:

1. How can we explain the different patterns for the Black-White achievement gap for reading, on the one hand, and math and science, on the other? Why is first grade reading such an outlier, given that it’s the only subject and grade combination where we see SES explaining a majority of the Black-White gap (about two-thirds)—especially when we combine that pattern with the finding that the Black-White reading gap continues to grow as students make their way through school? Here’s one hypothesis: As scholars, including E.D. Hirsch, Jr., have long argued, initial reading skills are more closely correlated to family SES than are math and science ones. This is likely because parents play a larger role, especially in a child’s first five years, in transmitting language abilities than they do for math and science. That can occur via behaviors, such as reading to their children, but also through their own use of verbal language. The advantages of high SES—and disadvantages of low SES—thus show up more for students’ initial reading skills than for their math and science ones. As students get older and benefit from classroom instruction, their relative advantages and disadvantages start to matter less. As Paul Morgan explains, “Children from higher SES families, who are disproportionately White and Asian, have a head start in terms of acquisition of early reading skills, so their better reading abilities show up early on the reading achievement measures. Over time, those from lower SES families acquire these early reading skills, including while attending school, and so the SES/racial gap narrows and begins to approximate those in the other subjects.” That’s good news from an equity perspective, but let’s not forget that the Black-White achievement gap (including in reading) continues to grow as students age through elementary school. Consistent with Fryer and Levitt’s paper, that likely means that we still haven’t closed the “school quality gap” between Black students and their White peers.  

2. Why does SES explain so much more of the Hispanic-White gap than the Black-White gap? One explanation might be that Hispanic children being raised in Spanish-speaking families have latent potential that is obscured by their lack of English skills (which become stronger as the grade level increases). It may also be helpful to ponder what might be included in the “not SES” category. As explained earlier, possibilities include health-related factors, such as low-birth weight and being the child of a teenage mom—factors related to poverty that affect Black children more than their Hispanic peers. [1] It might also include various forms and effects of racism and bias, which might affect Black children at higher rates. For lower-income Black children, who are more likely to experience deep, persistent poverty than other groups, the combination of “adverse childhood experiences” might exacerbate inequalities. And for middle class Black children, bias, stereotype threat, and related factors might be especially at play. This might also be why the Black-White achievement gap grows over the course of elementary school, while the Hispanic-White gap shrinks. As Eric Hengyu Hu pointed out, “research by von Hipper et al. (2018) using both old and new ECLS data found school years tend to equalize early-grade Hispanic-White gaps but not Black-White gaps.” That might be because of the greater challenges Black students face outside of school, but it is likely also because of their inequitable access to effective schools.  

3. What’s the role of household structure in the Black-White and Hispanic-White gaps? Hu and Morgan find that “family structure explains between 1 and 22 percent of the gaps, but is more important for explaining the Black-White achievement gap (10 to 22 percent of the gap explained) than the Hispanic-White achievement gap (1 to 4 percent of the gap explained).” That makes sense, given that Hispanic students are far more likely than their Black peers to live in two-parent families (74 percent versus 40 percent, respectively)—a rate which is much closer to that for White children (84 percent).

But these findings likely understate the role of family structure, especially for Black children, given the relationship between the number of parents in the household and household income. As shown in Table F-2, there’s a correlation of 0.32 between these two variables for the latest ECLS cohort, which is quite strong. On top of the many non-material benefits of growing up with two loving parents, it’s clearly the case that two incomes are usually better than one when it comes to boosting families out of poverty. And increasing the proportion of two-parent, two-income families in the Black community would thus help to narrow the Black-White achievement gap, as well.

None of this lends itself to simple takeaways, but the authors’ recommendations in the report—especially their suggestion to invest in early childhood education and to supplement families’ incomes, perhaps via an expanded child tax credit—deserve serious consideration.

As has been clear since the Coleman Report, when it comes to the interplay between race, poverty, and schooling, the honest read is that it’s complicated. What’s undeniable, though, is that much hard work remains, especially when it comes to providing effective schools to marginalized students, especially those who are Black. Let’s keep at it.

Introduction

Significant racial and ethnic achievement gaps exist between students in the U.S. by elementary school. [2] However, the underlying causes for these achievement gaps differ. [3] Thus, a better understanding of why racial/ethnic achievement gaps occur can help inform policies that promote educational and societal opportunities for all students. 

One factor for racial/ethnic achievement gaps is between-group differences in socioeconomic status (SES), particularly exposure to poverty. For example, Black and Hispanic students perform, on average, at significantly lower levels academically than Asian and White students, which is primarily because Black and Hispanic students are more likely to grow up in less-resourced homes and neighborhoods. [4]   According to this explanation, racial/ethnic achievement gaps result from socioeconomic factors; therefore, addressing these gaps would emphasize race-neutral policies and practices that lessen the negative effects of economic adversity.

Moreover, other factors contributing to racial and ethnic achievement gaps include bias, cultural insensitivity, stereotypes, and individual and systemic racism. Here, socioeconomic factors are simply one part of the story. [5]   For example, why else would upper-middle-class Black students tend to perform worse than upper-middle-class White and Asian students? Or why do achievement gaps among fourth graders persist even when accounting for exposure to economic adversity? [6] This all suggests the need not for race-neutral but race-conscious policies (e.g., ensuring that Black or Hispanic students are taught by Black or Hispanic teachers and introducing ethnic studies curricula during K–12 schooling, using affirmative action in higher education) to address racial and ethnic achievement gaps.

Our understanding of the extent to which SES explains racial and ethnic achievement gaps during elementary school is limited in several important aspects. That is, available research mainly analyzed cross-sectional data rather than longitudinal data, used imprecise measures of SES (e.g., receipt of free or reduced-price lunch status), examined achievement gaps in certain academic subjects while excluding others (e.g., reading but not mathematics and science), and did not assess how SES may have changed as an explanatory factor across different cohorts of U.S. elementary students. [7]

Our study examines the extent to which socioeconomic factors explain gaps in reading, mathematics, and science achievement among racial and ethnic groups of U.S. elementary students. We use four macro- and eleven micro-level measures of family background to identify factors that best explain these achievement gaps. Our analyses include descriptive statistics and regression models. The results provide nonexperimental evidence of factors that might be the focus of experimentally assessed policies and practices attempting to lessen racial and ethnic achievement gaps in U.S. elementary schools. They also help determine the extent to which SES explains these gaps. Furthermore, we expect that the findings of this study will provide insights into the ongoing discussion of whether race-neutral or race-conscious policies are more effective in addressing these gaps.

We examine the following research questions:

• To what extent does a broad set of family SES indicators explain initially observed racial and ethnic achievement gaps?
• To what extent do specific family SES indicators explain racial and ethnic achievement gaps?
• To what extent do the family SES indicators correlate, and how have they changed over time?
• To what extent does family SES help explain racial and ethnic disparities among high achievers?

A Broader View of Socioeconomic Status

Researchers often used receipt of free or reduced-price lunch or household income to represent a family’s SES. [8]   However, SES is most certainly a much broader factor, encompassing social patterns and aspects of family life that may relate to, but are not solely dependent on, household income. In this study, we used federal data on two cohorts of kindergarten students. Accordingly, our report included eleven indicators of a student’s family life (which can be aggregated into four key factors) for a more detailed view of the relationship between SES and student racial or ethnic background and academic achievement, including but not limited to the family’s household income (Table 1). 

Table 1. Family SES measures included in the study

Initial Racial and Ethnic Gaps in Academic Achievement

Before delving into the role of SES in academic achievement, it’s essential to first understand the existing racial and ethnic gaps in general. Figure 1 presents data from the federally administered Early Child Longitudinal Study (2010-11 kindergarten cohort). The figure demonstrates the racial and ethnic gaps in assessment scores using the largest student group (White students) as the reference group. On average, Black and Hispanic students score substantially lower than White students in all subjects, whereas Asian students score slightly higher than White students. Figure 2 illustrates the disparities between different ethnic groups in terms of math and reading scores. Regarding math scores, the Black-White gap tended to grow throughout elementary school, whereas the Hispanic-White gap narrowed slightly.

Figure 1. Racial/ethnic gaps in student achievement in fifth grade are substantial.

Figure 2. The Black-White achievement gap grows across elementary grades.

Disentangling Race and Class

SES factors, including parental education, income, and occupation, strongly predict children’s academic achievement, [9] with higher SES consistently associated with greater academic achievement. [10] Being from a higher SES family undoubtedly provides students with many advantages, such as greater access to higher-quality educational resources, enriched learning environments, and increased parental time and involvement in their education. [11] Prior studies suggested that parental education plays an outsized role in shaping children’s academic trajectories. This could be because parental education is often associated with a stronger emphasis on the value of education, which may lead to more positive learning environments for children. [12] There’s also the possibility that adults who have the skills—cognitive and otherwise—to persist in their own educational attainment are likely to bequeath similar skills to their children.

A challenge often encountered during the analysis of social patterns is the presence of many factors that may correlate with a given outcome, as well as explanatory factors that may correlate with each other and other factors. Analyzing differences in academic achievement by race, ethnicity, or family SES background highlights this problem. For example, Figure 3 depicts how household income varies for students in the 2010-11 ECLS-K kindergarten cohort. About half of White students (49 percent) and Asian students (49 percent) are being raised in families who are in the top three income categories. In contrast, less than one in five Black students (17 percent) or Hispanic students (17 percent) come from such families.

Instead, Black and Hispanic students are much more likely to live in poverty than their White and Asian peers. Most Black students (58 percent) and Hispanic students (56 percent) come from families in the bottom three income categories. Only one in five White students (19 percent) and one-fourth of Asian students (24 percent) come from families with the lowest income levels.

Figure 3. Household income varies across racial and ethnic groups from the kindergarten cohort of 2010-11.

As stated above, economic factors such as household income are only one aspect of SES, as there are other SES factors associated with race and ethnicity. For example, household structure is a factor that measures whether a child lives with a single parent, two parents, or other guardians. Figure 4 illustrates the significant variation in household structure among different racial and ethnic student groups. That is, 93 percent of Asian students and 86 percent of White students live in two-parent households. On the other hand, just 48 percent of Black students do. Moreover, Hispanic students, who are nearly as likely as their Black peers to live at low-income levels (Figure 3), have a significantly higher probability of living in two-parent families than those peers (79 percent versus 48 percent, respectively).

Figure 4. Household structure varies across racial and ethnic groups, as per the kindergarten cohort of 2010-11.

The correlations among family SES variables are often strong, but not always. Table 2 lists the correlation coefficients between family SES factors and additional home environment measures (collectively, we refer to the SES factors and the home environment factors as “SES+”). These coefficients have a possible range from +1 (perfect direct correlation) to -1 (perfect inverse correlation). All the (bolded) statistically significant correlation coefficients in Table 2 are positive except for the relationship between household structure and parental warmth, which indicates a very weak negative correlation (-0.06). Apart from the parental warmth factor, all relationships between SES+ factors are positive, but they range from practically and statistically insignificant positive correlations (e.g., the 0.01 coefficient for household structure and family rules for TV) to strong positive correlations (e.g., the 0.65 correlation for mother’s educational background and father’s educational background).

Table 2. Various indicators of family SES+ are positively associated with each other.

These analyses suggest that students’ race and ethnicity, family SES+, and academic achievement are interrelated in multiple ways. Throughout the rest of this study, we will examine to what extent accounting for family SES+ helps explain initially observed racial and ethnic achievement gaps. 

Data and Methods

This report uses federal data from the public-use version of the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 [13] (ECLS-K:1998-99), and the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 [14] (ECLS-K:2010-11). The former is a longitudinal study that tracks the same cohort of children from kindergarten through the eighth grade. The latter follows a different cohort of children from kindergarten through the fifth grade. Both datasets have extensive information on student-level academic achievement, sociodemographic characteristics, and home environments for children who entered kindergarten during the fall of 1998 and spring of 1999, as well as the fall of 2010 and spring of 2011. The total number of students included in ECLS-K:1998-99 was 21,409, while that in ECLS-K:2010-11 was 18,174.

Table 3 displays the frequency at which data were gathered for students participating in each study.

Table 3: Data gathering intervals for ECLS-K:1998-99 and ECLS-K:2010-11

The ECLS-K datasets include individually assessed reading, mathematics, and science achievement measures in each grade reported on a consistent scale. [15] We used scores from first, third, and fifth grade reading, mathematics, and science assessments as continuous measures of academic achievement. See the  Appendix for a description of the content assessed on each of the three subject-specific tests.

The ECLS-K datasets include the parent-reported race and ethnicity of individual students. Possible responses included the following: White, non-Hispanic; Black/African American, non-Hispanic; Hispanic, race specified; Hispanic, no race specified; Asian, non-Hispanic; Native Hawaiian or Other Pacific Islander, non-Hispanic; American Indian or Alaska Native, non-Hispanic; and more than one race, non-Hispanic. We combined the responses of Hispanic, race specified, and Hispanic, no race specified, into one Hispanic group. We also merged the categories of American Indian or Alaska Native, non-Hispanic, and Native Hawaiian or Other Pacific Islander into one American Indian, Native American, Pacific Islander (AINAPI) group.

We constructed family SES measures from parent surveys in the fall or spring of the student’s kindergarten year. The highest education level for mothers and fathers included five categories. These categories are as follows: (a) not high school graduate (reference group); (b) high school graduate or equivalent (e.g., GED); (c) some college; (d) bachelor’s degree; and (e) master’s degree or higher. The occupational prestige scores for mothers and fathers were measured based on occupations coded using the “Manual for Coding Industries and Occupations,” which was created for the National Household Education Surveys Program and uses an aggregated version of occupation codes. [16] Household income was derived from parental reports and divided into 18 categories ranging from $5,000 or less to $200,001 or more. [17] The household structure variable included four categories that refer to who raises the student: (a) two parents (b) single mother; (c) single father; and (d) other guardians (e.g., grandparents). [18]

Additionally, we considered five broad measures of “home opportunity factors.” These factors are used to assess parent engagement and aspects of the home environment, including (a) cognitively stimulating activities (e.g., playing games or doing arts and crafts); (b) emergent literary activities (e.g., reading to your child; number of books the child owns); (c) parent-child activities (e.g., visits to the zoo, bookstore, or library); (d) parental warmth (e.g., expressions of love and affection); and (e) family TV rules (e.g., how much time the child is allowed to watch TV and when). Additional description of these measures can be found in the Appendix .

We also created a composite variable called “SES+” by combining the family SES variables, including household income and mother’s occupational status, with the home environment variables, such as parental warmth and emergent literacy activities.

This report includes an analysis of student race and ethnicity, family SES and home environment measures, and three measures of academic achievement. Here, we analyzed reading, mathematics, and science scores separately from the spring of first, third, and fifth grade across the two cohorts. The fall or spring kindergarten measurements are the primary predictors. The findings are derived from correlation and regression analyses. [19] We used sampling weights to ensure that the results were nationally representative. Additional details of the analyses can be found in the Appendix .

A vital aspect of this analysis involves examining the reduction of the racial and ethnic achievement gaps after including family SES factors in the regression models. Each regression was run twice: once without family SES+ factors and again with SES+ factors included in the model. We refer to these in shorthand as “reduction rates,” which is synonymous with “percentage of achievement gap explained by SES+” and represents the coefficient for the race/ethnicity variable in the second model divided by the coefficient for the race variable in the first model.

For instance, for the 2010-11 kindergarten cohort, the Black-White reading gap in first grade is -0.45 SD and statistically significant (p < .001). Including the mother’s educational background in the regression reduces the estimated Black-White reading gap in first grade to -0.29 SD, which continues to be considerably significant (p < .001). We calculated this reduction as a percentage decrease as follows: (0.45 - 0.29) / 0.45 * 100 = 36 percent reduction. Thus, statistically adjusting for the mother’s education background reduces the estimated Black-White reading gap in first grade by 36 percent for the kindergarten cohort of 2010-11. 

This section examines the degree to which family SES+, including all eleven indicators, explains the racial and ethnic achievement gaps in analyses across grades, subjects, and ECLS-K cohorts. We focus on gaps among the three largest racial and ethnic student groups, including the Black-White and Hispanic-White gaps, for which we compare reduction rates across ECLS cohorts and grades. (Again, these rates refer to the difference in the effect size of racial and ethnic categories before and after including family SES+ factors in the regression models.)

Figure 5 shows that the inclusion of SES+ factors explains nearly two-thirds of the first grade Black-White reading achievement gap but less than half of those gaps in fifth grade reading and other subjects, regardless of grade level.

For the Black-White reading achievement gap, the reduction rate is notably decreased from first to fifth grade (64 percent to 48 percent). This suggests that SES+ is somewhat less influential in later grades for reading. This could be either because of a lengthening time interval between the measurement of the two factors or because family SES+ became increasingly less predictive of reading achievement as Black students age.

For the Black-White mathematics and science achievement gaps, the role of SES+ remains stable across grades. (For analysis of the role of SES+ in explaining Black-White achievement gaps in the earlier ECLS-K cohort, see the  Appendix , Figure A1 .)

Figure 5. Family SES+ explains more of the Black-White achievement gap in reading than in other subjects.

Figure 6 shows that the Hispanic-White achievement gap is considerably better explained by SES+ factors than the Black-White achievement gap. All the analyses show that SES+ factors explain more than half of the achievement gap, and in some analyses, SES+ factors explain about three-fourths of the gaps, namely, in first grade reading (74 percent) and fifth grade reading (77 percent).

Figure 6. Family SES+ explains more of the Hispanic-White achievement gap than the Black-White achievement gap.

SES+ factors better explain the Hispanic-White achievement gaps in fifth grade than in first grade, regardless of subject. For mathematics and science achievement gaps, the difference across grades is more substantial than in reading. SES+ factors explain 59 percent and 51 percent of the math and science gaps, respectively, in first grade, but they explain 67 percent and 66 percent of those gaps, respectively, in fifth grade. (For analysis of the role of SES+ in explaining Hispanic-White achievement gaps in the earlier ECLS-K cohort, see the   Appendix , Figure A2 .)

To summarize, including the SES+ explanatory factors in an analysis of racial and ethnic achievement gaps reduces the estimated size of the gaps, but they remain. This is particularly evident in the Black-White achievement gap, where SES+ factors generally explain less than half of the gap in mathematics, science, and to some extent, reading.

Finding 2: Household income and mother’s education are the SES+ factors that best help explain the Black-White and Hispanic-White achievement gaps, respectively.

Next, we break down the measures of SES+, evaluating the extent to which they individually explain racial and ethnic achievement gaps. These analyses are designed to determine which of the study’s different family SES+ indicators best explains the observed racial and ethnic achievement gaps. Each measure of SES+ was incorporated separately into our regression model. Continuing our focus on the three largest student racial/ethnic groups, we examined the achievement of first-grade students in the more recent ECLS-K cohort. (Similar findings were observed for other grade levels and the earlier ECLS cohort.)

Overall, household income and mother’s education are the two SES+ factors that best help explain the achievement gaps. Figure 7, Figure 8, and Figure 9 depict the extent to which each SES+ factor accounts for the racial and ethnic achievement gap in first grade for each subject, respectively. Household income is the primary SES+ factor for explaining the Black-White achievement gap in all analyses , explaining between 30 percent to 56 percent of the Black-White gap, depending on the subject. It also important for explaining the Hispanic-White achievement gap, explaining between 29 and 45 percent of the gap, depending on the subject. Family opportunity factors, such as emerging literacy activities and family rules for television, explain very little of the racial and ethnic achievement gaps in all analyses.

Mother’s education is the most critical SES+ factor for explaining the Hispanic-White achievement gap in all analyses , accounting for 37 percent to 55 percent, depending on the subject. Moreover, this factor significantly explained the Black-White achievement gap, with values ranging between 20 and 36 percent, depending on the subject.

Father’s education is also an essential family SES+ factor for explaining these gaps . It is just as important as the mother’s education for explaining the Black-White achievement gap, explaining 20 to 36 percent of the gap, depending on the subject. It is also of similar significance to household income for explaining the Hispanic-White achievement gap, explaining 29 to 43 percent of the gap, depending on the subject.

Compared to the other four SES+ factors, parent occupational prestige and household structure are less influential in explaining racial and ethnic achievement gaps . Mother’s occupational prestige explains between 13 and 25 percent of the gaps, depending on race and subject. In contrast, father’s occupational prestige explains 9 to16 percent of the gaps, depending on race and subject. Family structure explains between 1 and 22 percent of the gaps . However, it explains the Black-White achievement gap (10 to 22 percent) better than the Hispanic-White achievement gap (1 to 4 percent).

Figure 7. Among individual SES+ factors, household income best explains the Black-White gap in reading achievement and mother’s education best explains the Hispanic-White gap.

Figure 8. Among individual SES+ factors, household income best explains the Black-White gap in math achievement and mother’s education best explains the Hispanic-White gap.

Figure 9. Among individual SES+ factors, household income best explains the Black-White gap in science achievement and mother’s education best explains the Hispanic-White gap.

Overall, the differences in the results between the two ECLS-K cohorts, which are 12 years apart, are relatively minor, implying that the relations between the variables of interest did not change substantially over this period. These similarities are shown in the analysis above, and they also apply to individual elements of SES+ and the relationships between the SES+ factors.

Table 4 displays the similarities in each component of SES+ across students in the ECLS-K:1998-99 and ECLS-K:2010-11 cohorts. For example, the share of students whose parents had “some college” education is identical across cohorts. However, there were some slight differences. Parental education levels increased between cohorts, with the share of mothers with bachelor’s degrees rising from 16 percent to 20 percent and the share of parents with graduate degrees rising even more (e.g., 5 percent to 10 percent for mothers). The percentage of students living in two-parent households increased from 74 percent to 79 percent between the two cohorts.

Table 4. Family SES+ components did not change substantially across the two cohorts.

Table 5 depicts the correlation coefficients demonstrating the relationships between SES+ factors for both cohorts. As discussed above, these coefficients can range from +1 (perfect positive relationship) to -1 (perfect negative relationship). The coefficients in Table 5 range from weak positive correlations (e.g., for household structure and parent educational background) to strong positive correlations (e.g., for mother’s educational background and father’s educational background).

These relations are quite similar across the cohorts, with all correlations between SES+ factors in a similar range from one cohort to the other. For example, the relationship between household structure and mother’s education is 0.16 in the later cohort and 0.19 in the earlier cohort (column 1). The reduction rates, such as those shown in the analysis above, are also stable across cohorts ( see Appendix , Figures A1 and A2 ).

Table 5. Various indicators of family SES+ are moderately correlated with each other across the two kindergarten cohorts.

Finally, we examine whether the racial/ethnic achievement gaps are moderated by SES+ factors differently based on student performance levels. Differences in the proportions of student groups within the highest education levels are often termed “excellence gaps.” [20] This analysis uses the top quartile as the cutoff point to determine whether each student is an advanced achiever in reading, mathematics, or science in the first and fifth grades. (The analysis is limited to the kindergarten 2010-11 cohort.)

Figure 10 illustrates that family SES+ factors explain 60 percent of the reading excellence gap in the first grade and half in the fifth grade. SES+ factors account for 38 to 45 percent of the math excellence gaps and 36 to 45 percent of the science excellence gaps for Black and White students, depending on the grade.

Figure 10. Family SES+ factors explain 36 to 60 percent of the Black-White excellence gaps.

Figure 11 shows that the inclusion of SES+ factors explains a larger share of Hispanic-White excellence gaps than Black-White excellence gaps across the board. More than half of the Hispanic-White excellence gap is explained by SES+ in every subject and grade. Furthermore, more than two-thirds of these gaps are explained by SES+ in reading.

Figure 11. Family SES+ factors explain between 52 and 69 percent of the Hispanic-White excellence gaps.

Our findings suggest that students’ SES and home factors help to explain initially observed racial and ethnic achievement gaps. In many cases, the analyzed SES+ factors explain more than half of racial and ethnic achievement gaps. At the same time it is evident that SES, no matter how broadly construed, does not fully explain the racial gaps. SES+ can be less predictive over time, and it was found to be a less explanatory factor for racial than ethnic achievement gaps.

Educational policy solutions should reflect this complexity, as well as the comprehensive nature of the problem. Any number of well-executed policies would likely narrow achievement gaps of all kinds. Below, we present a few ideas, none of which are novel but all of which might help. Most are not cost-neutral. Moreover, we emphasize race-neutral policies in light of our findings, which reveal that family SES+ helps to substantially or fully explain racial and ethnic disparities in achievement. Race-conscious policies might also be helpful in further reducing these achievement gaps.

Whatever the approach, there is no denying the urgency of making the U.S. educational system more equitable.

The following are the proposed solutions:

1. Support programs to help parents earn their high school diplomas or higher education credentials: Because parental education, especially that of mothers, strongly correlates with children’s academic success, policymakers should consider increasing access to adult education and lifelong learning opportunities. This could include funding for adult education classes, online learning platforms, and community college courses. [21]

2. Focus on early childhood education: Because achievement gaps are already evident by elementary school, including as early as kindergarten, investing in high-quality early childhood education programs, especially in underprivileged communities, may be beneficial in mitigating the effects of socioeconomic disparities. [22]

3.  Provide economic support and financial aid for low-income families: Income support programs that provide financial assistance should be implemented or enhanced to ensure that low-income families have the necessary resources to support their children’s education. [23]

4. Address racial and ethnic disparities: Policies that directly address the racial and ethnic achievement gaps should be developed and implemented, including the adoption of curricula that reflect diverse cultures and programs that specifically support underrepresented students. There is some evidence to indicate that student-teacher racial and ethnic matching may be of benefit, although whether such matching will address racial and ethnic disparities in achievement during elementary school is still unclear. [24]  

The time to act is now. By enacting comprehensive and inclusive policies, we can narrow achievement gaps and create a more just educational landscape for the next generation.

This appendix provides further information related to the methodology and supplementary documentation of findings.

Additional Notes on Methodology

Description of Assessment Measures

Reading Achievement. The reading assessment included questions measuring basic skills (print familiarity, letter recognition, beginning and ending sounds, rhyming words, word recognition), vocabulary knowledge, and reading comprehension. Reading comprehension questions asked the child to identify information specifically stated in the text (e.g., definitions, facts, supporting details), make complex inferences within and across texts, and consider the text objectively and judge its appropriateness and quality.

Mathematics Achievement. The mathematics assessment was designed to measure skills in conceptual knowledge, procedural knowledge, and problem-solving. The assessment consisted of questions on number sense, properties, and operations; measurement; geometry and spatial sense; data analysis, statistics, and probability; and patterns, algebra, and functions.

Science Achievement . The science assessment included questions about physical sciences, life sciences, Earth and space sciences, and scientific inquiry. Meanwhile, for ECLS-K:1998-99 dataset, in the spring of first grade, student’s general knowledge was measured, which consisted of items that assessed knowledge in the natural sciences and social studies on a single scale. The social studies subdomain included questions that measured children’s knowledge in a wide range of disciplines, such as history, government, culture, geography, economics, and law. The science subdomain included questions from the fields of earth, space, physical, and life sciences.

Description of Home Opportunity Factors

Cognitive stimulation was a standardized sum of nine questions that assessed the frequency that parents engaged in activities with their children during a typical week. The activities included storytelling, singing, arts and crafts, playing games or puzzles, engaging in science projects or discussing nature, playing with construction toys, performing household chores, exercising or playing sports, and practicing reading, writing, or numeracy skills.

Emergent literacy was a standardized composite score of five items that evaluated literacy activities. Three items assessed the frequency of parental engagement in book reading and picture book reading with their children, as well as the children’s reading activities outside of school. Two items reported the number of books the children owned and the amount of time parents spent reading to their children. We combined the standardized scores of the first three items with those of the final two items to create the standardized composite score.

Parent-child activities was a standardized composite score of six items that assessed the frequency of parent-child engagement in activities over the prior month, including visits to libraries, bookstores, art galleries, concerts, zoos, and sports events. Twelve additional questions evaluated whether children participated in extracurricular activities, such as academic programs (e.g., tutoring or math lab), lessons in dance, music, drama, art, or crafts, organized athletic or club programs, volunteer work, and other forms of instruction (e.g., non-English language classes or religious instruction).

Family TV rules was a standardized composite of three binary questions indicating whether the family had established rules regarding: allowable TV programs, the maximum number of hours children could watch TV, and what time of day children could watch TV.

Parental warmth was a four-item scale that asked parents to self-assess their relationship with their children, specifically assessing their expressions of love, affection, quality time spent together, and child-parent closeness. These items were originally scaled from one to four, indicating “completely true” to “not at all true.” We reverse-coded the responses so that higher scores indicated greater warmth.

Description of Statistical Analyses

We conducted ordinary least squares (OLS) regression using the continuous version of outcomes, where we regressed student’s reading, mathematics, and science achievement from first, third, and fifth grade on student’s race or ethnicity. Then we added different family SES indicators separately, then together, through a serious of models for each grade level and subject. Each model incrementally adds variables to parse out their unique contributions. Model 1 begins with race or ethnicity as the main predictor. Models 2 to 12 added each SES indicator of mother/father’s education background, mother/father’s occupational prestige, household income, and household structure, as well as five indicators of home opportunity factors, separately. Model 13 added all SES+ indictors together. The following equation represents the fully adjusted Model 13:

Then we used 25 percent as the cut-off point to identify students who were high or low achievers. We considered as high achievers those students scoring above the 75th percentile of the academic achievement distribution in a specific grade. We considered as low achievers those students scoring below the 25th percentile of the academic achievement distribution. In this way, our outcomes became dummy variables, and we conducted both OLS and logistic regression models. The modeling strategy is the same as above.

Additional Documentation of Findings

Figure A1. Similar reduction rates of Black-White achievement gaps appear across cohorts.

Figure A2. Similar reduction rates of Hispanic-White achievement gaps appear across cohorts.

[1] “Low birth-weight babies by race and ethnicity in United States.” Kids Count Data Center. Accessed: 12 August 2024. https://datacenter.aecf.org/data/tables/9817-low-birth-weight-babies-by…

[2] Barshay, Jill. “Proof Points: Tracing Black-White Achievement Gaps since the Brown Decision.” The Hechinger Report, May 13, 2024. https://hechingerreport.org/proof-points-black-white-achievement-gaps-since-brown/ ; “Racial and Ethnic Achievement Gaps.” The Educational Opportunity Monitoring Project: Racial and Ethnic Achievement Gaps. Accessed July 29, 2024. https://cepa.stanford.edu/educational-opportunity-monitoring-project/achievement-gaps/race/ .

[3] Roland G. Fryer, Jr. and Steven D. Levitt, “Understanding the Black-White Test Score Gap in the First Two Years of School,” The Review of Economics and Statistics 86, no. 2 (May 2004): 447-464, https://doi.org/10.1162/003465304323031049 ; Megan Kuhfeld, Elizabeth Gershoff, and Katherine Paschall, “The Development of Racial/Ethnic and Socioeconomic Achievement Gaps During the School Years,” Journal of Applied Developmental Psychology 57 (July 2018): 62-73, https://doi.org/10.1016/j.appdev.2018.07.001 ; Paul L. Morgan, George Farkas, Marianne M. Hillemeier, and Steve Maczuga, “Science Achievement Gaps Begin Very Early, Persist, and Are Largely Explained by Modifiable Factors,” Educational Researcher 45, no. 1 (January 2016): 18-35, https://doi.org/10.3102/0013189X16633182 ; David M. Quinn, “Kindergarten Black–White Test Score Gaps: Re-examining the Roles of Socioeconomic Status and School Quality with New Data,” Sociology of Education 88, no. 2 (April 2015): 120-139, https://doi.org/10.1177/0038040715573027 ; David M. Quinn and North Cooc, “Science Achievement Gaps by Gender and Race/Ethnicity in Elementary and Middle School: Trends and Predictors,” Educational Researcher 44, no. 6 (Aug/Sept 2015): 336-346, https://doi.org/10.3102/0013189X15598539 ; Sean F. Reardon, Joseph P. Robinson-Cimpian, and Ericka S. Weathers, “Patterns and Trends in Racial/Ethnic and Socioeconomic Academic Achievement Gaps,” in Handbook of Research in Education Finance and Policy , 2nd ed., eds. Helen F. Ladd and Margaret E. Goertz (New York: Routledge, 2015); Sean F. Reardon and Claudia Galindo, “The Hispanic-White Achievement Gap in Math and Reading in the Elementary Grades,” American Educational Research Journal 46, no. 3 (Sept 2009): 853-891, https://doi.org/10.3102/0002831209333184

[4] Although Hispanic students may be of any race, throughout this report we simplify the student groupings by referring to Hispanic students of any race as “Hispanic” and non-Hispanic students of other races by their racial category.

[5] Fryer and Levitt, 2004; Reardon et al., 2015; Reardon and Galindo, 2009; F. Chris Curran, “Income-Based Disparities in Early Elementary School Science Achievement,” The Elementary School Journal 118, no. 2 (Oct 2017): 207-231, https://doi.org/10.1086/694218 ; Daphne A. Henry, Laura Betancur Cortés, and Elizabeth Votruba-Drzal, “Black-White Achievement Gaps Differ by Family Socioeconomic Status from Early Childhood through Early Adolescence,” Journal of Educational Psychology 112, no. 8 (Nov 2020): 1471-1489, https://doi.org/10.1037/edu0000439 ; Jung-Sook Lee and Natasha K. Bowen, “Parent Involvement, Cultural Capital, and the Achievement Gap Among Elementary School Children,” American Educational Research Journal 43, no. 2 (Summer 2006): 193-218, https://doi.org/10.3102/00028312043002193

[6] Burchinal, Margaret, Kathleen McCartney, Laurence Steinberg, Robert Crosnoe, Sarah L. Friedman, Vonnie McLoyd, Robert Pianta, and NICHD Early Child Care Research Network. "Examining the Black–White achievement gap among low‐income children using the NICHD study of early child care and youth development."  Child development  82, no. 5 (2011): 1404-1420. https://doi.org/10.1111/j.1467-8624.2011.01620.x ;

[7] See above note 2.

[8] See discussion of using free or reduced school lunch measure as SES here: Domina, Thurston, Nikolas Pharris-Ciurej, Andrew M. Penner, Emily K. Penner, Quentin Brummet, Sonya R. Porter, and Tanya Sanabria. "Is free and reduced-price lunch a valid measure of educational disadvantage?."  Educational Researcher  47, no. 9 (2018): 539-555. https://doi.org/10.3102/0013189X18797609 .

[9] Reardon et al., 2015; Henry et al., 2020; Pamela E. Davis-Kean, “The Influence of Parent Education and Family Income on Child Achievement: The Indirect Role of Parental Expectations and the Home Environment,” Journal of Family Psychology 19, no. 2 (June 2005): 294-304, https://doi.org/10.1037/0893-3200.19.2.294 ; Amy J. Orr, “Black-White Differences in Achievement: The Importance of Wealth,” Sociology of Education 76, no. 4 (Oct 2003): 281-304, https://doi.org/10.2307/1519867

[10] Quinn, 2015; Curran, 2017; Nikki L. Aikens and Oscar A. Barbarin, “Socioeconomic Differences in Reading Trajectories: The Contribution of Family, Neighborhood, and School Contexts,” Journal of Educational Psychology 100, no. 2 (May 2008): 235-251, https://doi.org/10.1037/0022-0663.100.2.235 ; Selcuk R. Sirin, “Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research,” Review of Educational Research 75, no. 3 (Fall 2005): 417-453, https://doi.org/10.3102/00346543075003417

[11] See the factsheet provided by American Psychological Association (APA) with additional resources: https://www.apa.org/pi/ses/resources/publications/education

[12] Davis-Kean, 2005; Annette Lareau, “Invisible Inequality: Social Class and Childrearing in Black Families and White Families,” American Sociological Review 67, no. 5 (Oct 2002): 747-776, https://doi.org/10.2307/3088916  

[13] ECLS-K: 1998-99; https://nces.ed.gov/ecls/kindergarten.asp

[14] ECLS-K: 2010-11; https://nces.ed.gov/ecls/kindergarten2011.asp

[15] Trained field personnel individually assessed reading, mathematics, and science achievement in each grade using untimed and item response theory (IRT) scaled measures. The assessment process consisted of two stages. The first stage included items of varying difficulty levels that determined the student's initial performance level. This was followed by one of three second-stage assessments that included additional low-, middle-, or high-difficulty items. We used scores from these measures of academic achievement as continuous variables.

[16] Centers for Disease Control and Prevention (CDC).  Census 2010 Occupation and Industry Coding Instructions . National Institute for Occupational Safety and Health (NIOSH). August 11, 2011.  https://www.cdc.gov/niosh/ topics/coding/pdfs/ Census2010CodingInstruction. pdf . https://www.cdc.gov/niosh/topics/coding/pdfs/Census2010CodingInstruction.pdf . There are 22 occupation codes in this coding scheme. If an occupation could not be coded using this manual, the Standard Occupational Classification Manual—1980 was used to identify the appropriate code. Then based on these occupation categories, they were recoded to reflect the average of the 1989 General Social Survey (GSS) prestige scores. Although the GSS prestige scores are from 1989, they are still being used by the current GSS survey and matched to 1980 census codes.

[17] For ECLS-K:2011, the household income included 18 categories: 1) $5,000 or less, 2) $5,001 to $10,000, 3) $10,001 to $15,000, 4) $15,001 to $20,000, 5) $20,001 to $25,000, 6) $25,001 to $30,000 … 16) $75,001 to $100,000, 17) $100,001 to $200,000, and 18) $200,001 or more. For ECLS-K, the household income included 13 categories: 1) $5,000 or less, 2) $5,001 to $10,000, 3) $10,001 to $15,000, 4) $15,001 to $20,000, 5) $20,001 to $25,000, 6) $25,001 to $30,000, 7) $30,001 to $35,000, 8) $35,001 to $40,000, 9) $40,001 to $50,000, 10) $50,001 to $75,000, 11) $75,001 to $100,000, 12) $100,001 to $200,000, and 13) $200,001 or more. We treated them as a continuous variable in our analyses.

[18] Very little research on racial and/or socioeconomic gaps considers the role of family structure, so inclusion of this variable fills a hole in the literature that tends to be focused on race, class, and gender differences. For more, see https://www.city-journal.org/article/measure-what-matters

[19] Ordinary least squares (OLS) regression models include adjusted robust standard errors with clustering at the school (kindergarten) level. Multiple imputation is used to address missing data.

[20] Jonathan A. Plucker and Scott J. Peters, Excellence Gaps in Education: Expanding Opportunities for Talented Students (Cambridge, MA: Harvard Education Press, 2016).

[21] U.S. Department of Education provides different kinds of resources under Division of Adult Education & Literacy: https://aefla.ed.gov/ .

[22] Schoch, Annie D., Cassie S. Gerson, Tamara Halle, and Meg Bredeson. "Children's Learning and Development Benefits from High-Quality Early Care and Education: A Summary of the Evidence. Research Highlight. OPRE Report 2023-226."  Office of Planning, Research and Evaluation  (2023). https://www.acf.hhs.gov/opre/report/childrens-learning-and-development-benefits-high-quality-early-care-and-education

[23] Gennetian, Lisa A., and Katherine Magnuson. "Three Reasons Why Providing Cash to Families With Children Is a Sound Policy Investment." Journal of Human Resources 55, no. 2 (2018): 387-427. Sherman, Arloc, and Tazra Mitchell. "Economic security programs help low-income children succeed over long term, many studies find."  Washington: Center on Budget and Policy Priorities .” https://www. cbpp. org/sites/default/files/atoms/files/7-17-17pov. pdf  (2017).

[24] Paul L. Morgan and Eric Hengyu Hu, “Fixed Effect Estimates of Student-Teacher Racial or Ethnic Matching in U.S. Elementary Schools,”  Early Childhood Research Quarterly 63 (Nov 2022): 98-112, https://doi.org/10.1016/j.ecresq.2022.11.003

Acknowledgments

This report was made possible through the generous support of the Achelis and Bodman Foundation and the Thomas B. Fordham Foundation. We are especially grateful to Paul L. Morgan and Eric Hengyu Hu for conducting the analysis and authoring the report. In addition, we extend our appreciation to Andrew Conway, professor at New Mexico State University, and Jing Liu, assistant professor at the University of Maryland, College Park, for their timely and helpful feedback on a draft of the report. We also extend our gratitude to Super Copy Editors for copyediting and Dave Williams for designing the figures and tables. At Fordham, we would like to thank Chester E. Finn, Jr., Michael J. Petrilli, Amber M. Northern and Adam Tyner for reviewing drafts; Heena Kuwayama for assisting with editing; Elainah Elkins for handling funder communications; Victoria McDougald for her role in dissemination; and Stephanie Distler for developing the report’s cover art and coordinating production. 

Eric Hengyu Hu, Ph.D., is a Postdoctoral Associate at the Institute of Social and Health Equity, University at Albany, SUNY. Dr. Hu’s research focuses on sociodemographic disparities in the academic, cognitive, and social-behavioral development of early elementary students. He can be reached through email at [email protected] and Twitter/X @Eric_hhy.

Paul L. Morgan, Ph.D., is the Empire Innovation Professor, Social and Health Equity Endowed Professor in the Department of Health Policy, Management and Behavior, School of Public Health, and Inaugural Director of the Institute for Social and Health Equity, University at Albany, SUNY. He can be reached through email at [email protected] and Twitter/X @PaulMorganPhD.

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