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  • Published: 23 March 2018

Achievement at school and socioeconomic background—an educational perspective

  • Sue Thomson 1  

npj Science of Learning volume  3 , Article number:  5 ( 2018 ) Cite this article

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INTRODUCTION

Educational achievement, and its relationship with socioeconomic background, is one of the enduring issues in educational research. The influential Coleman Report 1 concluded that schools themselves did little to affect a student’s academic outcomes over and above what the students themselves brought to them to school—‘the inequalities imposed on children by their home, neighbourhood and peer environment are carried along to become the inequalities with which they confront adult life at the end of school’ (p. 325). Over the intervening 50 years, much has been added to the research literature on this topic, including several high-quality meta-analyses. It has become ubiquitous in research studies to use a student’s socioeconomic background, and that of the school they attend, as contextual variables when seeking to investigate potential influences on achievement.

The two articles in this issue of Science of Learning touch on aspects of this discussion rarely included in the educational research literature. The article by Smith–Wooley et al. 2 asks whether whether it is the influence of the student socioeconomic background that is the greater influence or whether the parents are passing down intellectually advantageous genes to their offspring. In contrast, the article by van Dongen et al. 3 suggests that that it is likely a combination of genetics and socioeconomic background, and they examine the effect of environment on the epigenetic status of genes that are involved in learning and memory.

What do we mean by socioeconomic background?

The definition of socioeconomic background used varies widely, even across educational research. In the Organisation for Economic Cooperation and Development’s (OECD) rigorous large-scale international assessment of more than 70 countries over 15 years, the Programme for International Student Assessment (PISA), socioeconomic background is represented by the index of Economic, Social and Cultural Status, which is a composite score derived by principal components analysis and is comprised of the International Socioeconomic Index of Occupational Status; the highest level of education of the student’s parents, converted into years of schooling; the PISA index of family wealth; the PISA index of home educational resources; and the PISA index of possessions related to 'classical' culture in the family home. 4

However, examining Sirin’s 5 meta-analysis of the research into socioeconomic status and academic achievement finds that many studies use a combination of one or more of parental education, occupation and income, others include parental expectations, and many simply use whether the student gets a free or reduced-price lunch. The latter factor is most commonly used as it is readily available from school records rather than having to ask questions about occupation and education of students or parents, yet Hauser 6 as well as Sirin have argued that it is conceptually problematic and should not be used. Other studies have used family structure, 7 , 8 family size, 9 and even residential mobility. 10

Sirin’s meta-analysis, however, found that the traditional definitions of socioeconomic background were not as strongly related to educational outcomes for students from different ethnic backgrounds, for those from rural areas, or for migrants. Its use in developing countries is particularly problematic. For example, in examining the effect of household wealth on educational achievement, Filmer & Pritchett 11 found that many poor children in developing countries either never enrol in school or attend to the end of first grade only. Even within developing countries, the gap in enrolment and achievement between rich and poor was found to be only a year or two, in other countries 9 or 10 years. Often in developing countries low achievement and enrolment is attributable to the physical unavailability of schools.

Similarly education achievement is measured in many ways—achievement on a set test in certain subject areas, completion of numbers of years of schooling, entrance to university, for example.

What does this mean for educators when they are reviewing the research? It means that they need to exercise some caution. The results and the conclusions will obviously vary, as the research is, essentially, looking at different influencers and not the same influence each time. So, when the argument is made that the relationship is not stable, this may well be because the variable under consideration is different.

School-level socioeconomic background

While the Coleman Report concluded that schools themselves added little to effect outcomes, the school environment, in particular the social background of a student’s peers at the school, has certainly been found to be positively related to student achievement. On average, a student who attends a school in which the average socioeconomic status is high enjoys better educational outcomes compared to a student attending a school with a lower average peer socioeconomic level. 12 , 13

Relationship between achievement and student socioeconomic background

There is some discussion about the size of the effect, however the relationship between a student’s socioeconomic background and their educational achievement seems enduring and substantial. Using data from PISA, the OECD have concluded that 'while many disadvantaged students succeed at school … socioeconomic status is associated with significant differences in performance in most countries and economies that participate in PISA. Advantaged students tend to outscore their disadvantaged peers by large margins' (p. 214). 14 The strength of the relationship varies from very strong to moderate across participating countries, but the relationship does exist in each country. In Australia, students from the highest quartile of socioeconomic background perform, on average, at a level about 3 years higher than their counterparts from the lowest quartile. 15 Over the 15 years of PISA data currently available, the size of this relationship, on average, has changed little, and over the now 50 years since the publication of the Coleman Report, the gap between advantaged and disadvantaged students remains.

How are these effects transmitted?

What the continued gap between advantaged and disadvantaged students highlights is that despite all the research, it is still unclear how socioeconomic background influences student attainment.

There are those that argue that the relationships between socioeconomic background and educational achievement are only moderate and the effects of SES are quite small when taking into account cognitive ability or prior achievement. 16 Cognitive ability is deemed to be a genetic quality and its effects only influenced to a small degree by schools. Much of the body of research, particularly that generated from large-scale international studies, would seem to contradict this reasoning.

Others have argued that students from low socioeconomic level homes are at a disadvantage in schools because they lack an academic home environment, which influences their academic success at school. In particular, books in the home has been found over many years in many of the large-scale international studies, to be one of the most influential factors in student achievement. 15 From the beginning, parents with higher socioeconomic status are able to provide their children with the financial support and home resources for individual learning. As they are likely to have higher levels of education, they are also more likely to provide a more stimulating home environment to promote cognitive development. Parents from higher socioeconomic backgrounds may also provide higher levels of psychological support for their children through environments that encourage the development of skills necessary for success at school. 17

The issue of how school-level socioeconomic background effects achievement is also of interest. Clearly one way is in lower levels of physical and educational resourcing, but other less obvious ways include lower expectations of teachers and parents, and lower levels of student self-efficacy, enjoyment and other non-cognitive outcomes. 15 There is also some evidence that opportunity to learn (particularly in mathematics) is more restricted for lower socioeconomic students, with ‘systematically weaker content offered to lower-income students [so that] rather than ameliorating educational inequalities, schools were exacerbating them’. 18

Conclusions

If the role of education is not simply to reproduce inequalities in society then we need to understand what the role of socioeconomic background more clearly. While much research has been undertaken in the past 50 years, and we are fairly certain that socioeconomic background does have an effect on educational achievement, we are no closer to understanding how this effect is transmitted. Until we are, it will remain difficult to address. In this edition of Science of Learning, two further contributions to this body of knowledge have been added—and perhaps indicate new paths that need to be followed to develop this understanding.

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Thomson, S. Achievement at school and socioeconomic background—an educational perspective. npj Science Learn 3 , 5 (2018). https://doi.org/10.1038/s41539-018-0022-0

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Original research article, academic achievement, self-concept, personality and emotional intelligence in primary education. analysis by gender and cultural group.

research literature academic achievement

  • 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 .

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

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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.

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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 ).

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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.

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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.

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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]

This article is part of the Research Topic

New Challenges in the Research of Academic Achievement: Measures, Methods, and Results

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In This Article Expand or collapse the "in this article" section Academic Achievement

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Academic Achievement by Ricarda Steinmayr , Anja Meißner , Anne F. Weidinger , Linda Wirthwein LAST REVIEWED: 05 August 2020 LAST MODIFIED: 30 July 2014 DOI: 10.1093/obo/9780199756810-0108

Academic achievement represents performance outcomes that indicate the extent to which a person has accomplished specific goals that were the focus of activities in instructional environments, specifically in school, college, and university. School systems mostly define cognitive goals that either apply across multiple subject areas (e.g., critical thinking) or include the acquisition of knowledge and understanding in a specific intellectual domain (e.g., numeracy, literacy, science, history). Therefore, academic achievement should be considered to be a multifaceted construct that comprises different domains of learning. Because the field of academic achievement is very wide-ranging and covers a broad variety of educational outcomes, the definition of academic achievement depends on the indicators used to measure it. Among the many criteria that indicate academic achievement, there are very general indicators such as procedural and declarative knowledge acquired in an educational system, more curricular-based criteria such as grades or performance on an educational achievement test, and cumulative indicators of academic achievement such as educational degrees and certificates. All criteria have in common that they represent intellectual endeavors and thus, more or less, mirror the intellectual capacity of a person. In developed societies, academic achievement plays an important role in every person’s life. Academic achievement as measured by the GPA (grade point average) or by standardized assessments designed for selection purpose such as the SAT (Scholastic Assessment Test) determines whether a student will have the opportunity to continue his or her education (e.g., to attend a university). Therefore, academic achievement defines whether one can take part in higher education, and based on the educational degrees one attains, influences one’s vocational career after education. Besides the relevance for an individual, academic achievement is of utmost importance for the wealth of a nation and its prosperity. The strong association between a society’s level of academic achievement and positive socioeconomic development is one reason for conducting international studies on academic achievement, such as PISA (Programme for International Student Assessment), administered by the OECD (Organisation for Economic Co-operation and Development). The results of these studies provide information about different indicators of a nation’s academic achievement; such information is used to analyze the strengths and weaknesses of a nation’s educational system and to guide educational policy decisions. Given the individual and societal importance of academic achievement, it is not surprising that academic achievement is the research focus of many scientists; for example, in psychology or educational disciplines. This article focuses on the explanation, determination, enhancement, and assessment of academic achievement as investigated by educational psychologists.

The exploration of academic achievement has led to numerous empirical studies and fundamental progress such as the development of the first intelligence test by Binet and Simon. Introductory textbooks such as Woolfolk 2007 provide theoretical and empirical insight into the determinants of academic achievement and its assessment. However, as academic achievement is a broad topic, several textbooks have focused mainly on selected aspects of academic achievement, such as enhancing academic achievement or specific predictors of academic achievement. A thorough, short, and informative overview of academic achievement is provided in Spinath 2012 . Spinath 2012 emphasizes the importance of academic achievement with regard to different perspectives (such as for individuals and societies, as well as psychological and educational research). Walberg 1986 is an early synthesis of existing research on the educational effects of the time but it still influences current research such as investigations of predictors of academic achievement in some of the large-scale academic achievement assessment studies (e.g., Programme for International Student Assessment, PISA). Walberg 1986 highlights the relevance of research syntheses (such as reviews and meta-analyses) as an initial point for the improvement of educational processes. A current work, Hattie 2009 , provides an overview of the empirical findings on academic achievement by distinguishing between individual, home, and scholastic determinants of academic achievement according to theoretical assumptions. However, Spinath 2012 points out that it is more appropriate to speak of “predictors” instead of determinants of academic achievement because the mostly cross-sectional nature of the underlying research does not allow causal conclusions to be drawn. Large-scale scholastic achievement assessments such as PISA (see OECD 2010 ) provide an overview of the current state of research on academic achievement, as these studies have investigated established predictors of academic achievement on an international level. Furthermore, these studies, for the first time, have enabled nations to compare their educational systems with other nations and to evaluate them on this basis. However, it should be mentioned critically that this approach may, to some degree, overestimate the practical significance of differences between the countries. Moreover, the studies have increased the amount of attention paid to the role of family background and the educational system in the development of individual performance. The quality of teaching, in particular, has been emphasized as a predictor of student achievement. Altogether, there are valuable cross-sectional studies investigating many predictors of academic achievement. A further focus in educational research has been placed on tertiary educational research. Richardson, et al. 2012 subsumes the individual correlates of university students’ performance.

Hattie, John A. C. 2009. Visible learning: A synthesis of over 800 meta-analyses relating to achievement . London: Routledge.

A quantitative synthesis of 815 meta-analyses covering English-speaking research on the achievement of school-aged students. According to Hattie, the influences of quality teaching represent the most powerful determinants of learning. Thereafter, Hattie published Visible Learning for Teachers (London and New York: Routledge, 2012) so that the results could be transferred to the classroom.

OECD. 2010. PISA 2009 key findings . Vols. 1–6.

These six volumes illustrate the results of the Programme for International Student Assessment (PISA) 2009—the most extensive international scholastic achievement assessment—regarding the competencies of fifteen-year-old students all over the world in reading, mathematics, and science. Furthermore, the presented results cover the effects of student learning behavior, social background, and scholastic resources. Unlimited online access.

Richardson, Michelle, Charles Abraham, and Rod Bond. 2012. Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin 138:353–387.

DOI: 10.1037/a0026838

A current and comprehensive review concerning the prediction of university students’ performance, illustrating self-efficacy to be the strongest correlate of tertiary grade point average (GPA). Cognitive constructs (high school GPA, American College Test), as well as further motivational factors (grade goal, academic self-efficacy) have medium effect sizes.

Spinath, Birgit. 2012. Academic achievement. In Encyclopedia of human behavior . 2d ed. Edited by Vilanayur S. Ramachandran, 1–8. San Diego, CA: Academic Press.

A current introduction to academic achievement, subsuming research on indicators and predictors of achievement as well as reasons for differences in education caused by gender and socioeconomic resources. The chapter provides further references on the topic.

Walberg, Herbert J. 1986. Syntheses of research on teaching. In Handbook of research on teaching . 3d ed. Edited by Merlin C. Wittrock, 214–229. New York: Macmillan.

A quantitative and qualitative aggregation of a variety of reviews and quantitative syntheses as an overview of early research on educational outcomes. Walberg found nine factors to be central to the determination of school learning.

Woolfolk, Anita. 2007. Educational psychology . 10th ed. Boston: Pearson.

Woolfolk represents a comprehensive basic work that is founded on an understandable and practical communication of knowledge. The perspectives of students as scholastic learners as well as teachers are the focus of attention. Suitable for undergraduate and graduate students. Currently presented in the 12th edition.

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  • Published: 10 February 2020

Predicting academic success in higher education: literature review and best practices

  • Eyman Alyahyan 1 &
  • Dilek Düştegör   ORCID: orcid.org/0000-0003-2980-1314 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  3 ( 2020 ) Cite this article

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Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success , through which student attributes to focus on , up to which machine learning method is more appropriate to the given problem . This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.

Introduction

Computers have become ubiquitous, especially in the last three decades, and are significantly widespread. This has led to the collection of vast volumes of heterogeneous data, which can be utilized for discovering unknown patterns and trends (Han et al., 2011 ), as well as hidden relationships (Sumathi & Sivanandam, 2006 ), using data mining techniques and tools (Fayyad & Stolorz, 1997 ). The analysis methods of data mining can be roughly categorized as: 1) classical statistics methods (e.g. regression analysis, discriminant analysis, and cluster analysis) (Hand, 1998 ), 2) artificial intelligence (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019 ) (e.g. genetic algorithms, neural computing, and fuzzy logic), and 3) machine learning (e.g. neural networks, symbolic learning, and swarm optimization) (Kononenko & Kukar, 2007 ). The latter consists of a combination of advanced statistical methods and AI heuristics. These techniques can benefit various fields through different objectives, such as extracting patterns, predicting behavior, or describing trends. A standard data mining process starts by integrating raw data – from different data sources – which is cleaned to remove noise, duplicated or inconsistent data. After that, the cleaned data is transformed into a concise format that can be understood by data mining tools, through filtering and aggregation techniques. Then, the analysis step identifies the existing interesting patterns, which can be displayed for a better visualization (Han et al., 2011 ) (Fig.  1 ).

figure 1

standard data mining process (Han et al. 2011 )

Recently data mining has been applied to various fields like healthcare (Kavakiotis et al., 2017 ), business (Massaro, Maritati, & Galiano, 2018 ), and also education (Adekitan, 2018 ). Indeed, the development of educational database management systems created a large number of educational databases, which enabled the application of data mining to extract useful information from this data. This led to the emergence of Education Data Mining (EDM) (Calvet Liñán & Juan Pérez, 2015 ; Dutt, Ismail, & Herawan, 2017 ) as an independent research field. Nowadays, EDM plays a significant role in discovering patterns of knowledge about educational phenomena and the learning process (Anoopkumar & Rahman, 2016 ), including understanding performance (Baker, 2009 ). Especially, data mining has been used for predicting a variety of crucial educational outcomes, like performance (Xing, 2019 ), retention (Parker, Hogan, Eastabrook, Oke, & Wood, 2006 ), success (Martins, Miguéis, Fonseca, & Alves, 2019 ; Richard-Eaglin, 2017 ), satisfaction (Alqurashi, 2019 ), achievement (Willems, Coertjens, Tambuyzer, & Donche, 2018 ), and dropout rate (Pérez, Castellanos, & Correal, 2018 ).

The process of EDM (see Fig.  2 ) is an iterative knowledge discovery process that consists of hypothesis formulation, testing, and refinement (Moscoso-Zea et al., 2016 ; Sarala & Krishnaiah, 2015 ). Despite many publications, including case studies, on educational data mining, it is still difficult for educators – especially if they are a novice to the field of data mining – to effectively apply these techniques to their specific academic problems. Every step described in Fig. 2 necessitates several decisions and set-up of parameters, which directly affect the quality of the obtained result.

figure 2

Knowledge discovery process in educational institutions (Moscoso-Zea, Andres-Sampedro, & Lujan-Mora, 2016 )

This study aims to fill the described gap, by providing a complete guideline, providing easier access to data mining techniques and enabling all the potential of their application to the field of education. In this study, we specifically focus on the problem of predicting the academic success of students in higher education. For this, the state-of-the-art has been compiled into a systematic process, where all related decisions and parameters are comprehensively covered and explained along with arguments.

In the following, first, section 2 clarifies what is academic success and how it has been defined and measured in various studies with a focus on the factors that can be used for predicting academic success. Then, section 3 presents the methodology adopted for the literature review. Section 4 reviews data mining techniques used in predicting students’ academic success, and compares their predictive accuracy based on various case studies. Section 5 concludes the review, with a recapitulation of the whole process. Finally, section 6 concludes this paper and outlines the future work.

Academic success definition

Student success is a crucial component of higher education institutions because it is considered as an essential criterion for assessing the quality of educational institutions (National Commission for Academic Accreditation &amp, 2015 ). There are several definitions of student success in the literature. In (Kuh, Kinzie, Buckley, Bridges, & Hayek, 2006 ), a definition of student success is synthesized from the literature as “Student success is defined as academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge, skills and competencies, persistence, attainment of educational outcomes, and post-college performance”. While this is a multi-dimensional definition, authors in (York, Gibson, & Rankin, 2015 ) gave an amended definition concentrating on the most important six components, that is to say “Academic achievement, satisfaction, acquisition of skills and competencies, persistence, attainment of learning objectives, and career success” (Fig.  3 ).

figure 3

Defining academic success and its measurements (York et al., 2015 )

Despite reports calling for more detailed views of the term, the bulk of published researchers measure academic success narrowly as academic achievement. Academic achievement itself is mainly based on Grade Point Average (GPA), or Cumulative Grade Point Average (CGPA) (Parker, Summerfeldt, Hogan, & Majeski, 2004 ), which are grade systems used in universities to assign an assessment scale for students’ academic performance (Choi, 2005 ), or grades (Bunce & Hutchinson, 2009 ). The academic success has also been defined related to students’ persistence, also called academic resilience (Finn & Rock, 1997 ), which in turn is also mainly measured through the grades and GPA, measures of evaluations by far the most widely available in institutions.

Review methodology

Early prediction of students’ performance can help decision makers to provide the needed actions at the right moment, and to plan the appropriate training in order to improve the student’s success rate. Several studies have been published in using data mining methods to predict students’ academic success. One can observe several levels targeted:

Degree level: predicting students’ success at the time of obtention of the degree.

Year level: predicting students’ success by the end of the year.

Course level: predicting students’ success in a specific course.

Exam level: predicting students’ success in an exam for a specific course.

In this study, the literature related to the exam level is excluded as the outcome of a single exam does not necessarily imply a negative outcome.

In terms of coverage, section 4 and 5 only covers articles published within the last 5 years. This restriction was necessary to scale down the search space, due to the popularity of EDM. The literature was searched from Science Direct, ProQuest, IEEE Xplore, Springer Link, EBSCO, JSTOR, and Google Scholar databases, using academic success , academic achievement , student success , educational data mining , data mining techniques , data mining process and predicting students’ academic performance as keywords. While we acknowledge that there may be articles not included in this review, seventeen key articles about data mining techniques that were reviewed in sections 4 and 5 .

Influential factors in predicting academic success

One important decision related to the prediction of students’ academic success in higher education is to clearly define what is academic success. After that, one can think about the potential influential factors, which are dictating the data that needs to be collected and mined.

While a broad variety of factors have been investigated in the literature with respect to their impact on the prediction of students’ academic success (Fig.  4 ), we focus here on prior-academic achievement , student demographics , e-learning activity , psychological attributes , and environments , as our investigation revealed that they are the most commonly reported factors (summarized in Table  1 ). As a matter of fact, the top 2 factors, namely, prior-academic achievement , and student demographics , were presented in 69% of the research papers. This observation is aligned with the results of The previous literature review which emphasized that the grades of internal assessment and CGPA are the most common factors used to predict student performance in EDM (Shahiri, Husain, & Rashid, 2015 ). With more than 40%, prior academic achievement is the most important factor. This is basically the historical baggage of students. It is commonly identified as grades (or any other academic performance indicators) that students obtained in the past (pre-university data, and university-data). The pre-university data includes high school results that help understand the consistency in students’ performance (Anuradha & Velmurugan, 2015 ; Asif et al., 2015 ; Asif et al., 2017 ; Garg, 2018 ; Mesarić & Šebalj, 2016 ; Mohamed & Waguih, 2017 ; Singh & Kaur, 2016 ). They also provide insight into their interest in different topics (i.e., courses grade (Asif et al., 2015 ; Asif et al., 2017 ; Oshodi et al., 2018 ; Singh & Kaur, 2016 )). Additionally, this can also include pre-admission data which is the university entrance test results (Ahmad et al., 2015 ; Mesarić & Šebalj, 2016 ; Oshodi et al., 2018 ). The university-data consists of grades already obtained by the students since entering the university, including semesters GPA or CGPA (Ahmad et al., 2015 ; Almarabeh, 2017 ; Hamoud et al., 2018 ; Mueen et al., 2016 ; Singh & Kaur, 2016 ), courses marks (Al-barrak & Al-razgan, 2016 ; Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Asif et al., 2015 ; Asif et al., 2017 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Mueen et al., 2016 ; Singh & Kaur, 2016 ; Sivasakthi, 2017 ) and course assessment grades (e.g. assignment (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Mueen et al., 2016 ; Yassein et al., 2017 ); quizzes (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Mohamed & Waguih, 2017 ; Yassein et al., 2017 ); lab-work (Almarabeh, 2017 ; Mueen et al., 2016 ; Yassein et al., 2017 ); and attendance (Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Mueen et al., 2016 ; Putpuek et al., 2018 ; Yassein et al., 2017 )).

figure 4

a broad variety of factors potentially impacting the prediction of students’ academic success

Students’ demographic is a topic of divergence in the literature. Several studies indicated its impact on students’ success, for example, gender (Ahmad et al., 2015 ; Almarabeh, 2017 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Putpuek et al., 2018 ; Sivasakthi, 2017 ), age (Ahmad et al., 2015 ; Hamoud et al., 2018 ; Mueen et al., 2016 ), race/ethnicity (Ahmad et al., 2015 ), socioeconomic status (Ahmad et al., 2015 ; Anuradha & Velmurugan, 2015 ; Garg, 2018 ; Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Mueen et al., 2016 ; Putpuek et al., 2018 ), and father’s and mother’s background (Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ; Singh & Kaur, 2016 ) have been shown to be important. Yet, few studies also reported just the opposite, for gender in particular (Almarabeh, 2017 ; Garg, 2018 ).

Some attributes related to the student’s environment were found to be impactful information such as program type (Hamoud et al., 2018 ; Mohamed & Waguih, 2017 ), class type (Mueen et al., 2016 ; Sivasakthi, 2017 ) and semester period (Mesarić & Šebalj, 2016 ).

Among the reviewed papers, also many researchers used Student E-learning Activity information, such as a number of login times, number of discussion board entries, number / total time material viewed (Hamoud et al., 2018 ), as influential attributes and their impact, though minor, were reported.

The psychological attributes are determined as the interests and personal behavior of the student; several studies have shown them to be impactful on students’ academic success. To be more precise, student interest (Hamoud et al., 2018 ), the behavior towards study (Hamoud et al., 2018 ; Mueen et al., 2016 ), stress and anxiety (Hamoud et al., 2018 ; Putpuek et al., 2018 ), self-regulation and time of preoccupation (Garg, 2018 ; Hamoud et al., 2018 ), and motivation (Mueen et al., 2016 ), were found to influence success.

Data mining techniques for prediction of students’ academic success

The design of a prediction model using data mining techniques requires the instantiation of many characteristics, like the type of the model to build, or methods and techniques to apply (Witten, Frank, Hall, & Pal, 2016 ). This section defines these attributes, provide some of their instances, and reveal the statistics of their occurrence among the reviewed papers grouped by the target variable in the student success prediction, that is to say, degree level, year level, and course level.

Degree level

Several case studies have been published, seeking prediction of academic success at the degree level. One can observe two main approaches in term of the model to build: classification where CGPA that is targeted is a category as multi class problem such as (a letter grade (Adekitan & Salau, 2019 ; Asif et al., 2015 ; Asif et al., 2017 ) or overall rating (Al-barrak & Al-razgan, 2016 ; Putpuek et al., 2018 )) or binary class problem such as (pass/fail (Hamoud et al., 2018 ; Oshodi et al., 2018 )). As for the other approach, it is the regression where the numerical value of CGPA is predicted (Asif et al., 2017 ). We can also observe a broad variety in terms of the department students belongs to, from architecture (Oshodi et al., 2018 ), to education (Putpuek et al., 2018 ), with a majority in technical fields (Adekitan & Salau, 2019 ; Al-barrak & Al-razgan, 2016 ; Asif et al., 2015 ; Hamoud et al., 2018 ). An interesting finding is related to predictors: studies that included university-data, especially grades from first 2 years of the program, yielded better performance than studies that included only demographics (Putpuek et al., 2018 ), or only pre-university data (Oshodi et al., 2018 ). Details regarding the algorithm used, the sample size, the best accuracy and corresponding method, as well as the software environment that was used are all in Table  2 .

Less case studies have been reported, seeking prediction of academic success at the year level. Yet, the observations regarding these studies are very similar to the one related to degree level (reported in previous section). Similar to previous sub-section, studies that included only social conditions and pre-university data gave the worse accuracy (Singh & Kaur, 2016 ), while including university-data improved results (Anuradha & Velmurugan, 2015 ). Nevertheless, it is interesting to note that even the best accuracy in (Anuradha & Velmurugan, 2015 ) is inferior to the accuracy in (Adekitan & Salau, 2019 ; Asif et al., 2015 ; Asif et al., 2017 ) reported in previous section. This can be explained by the fact that in (Anuradha & Velmurugan, 2015 ), only 1 year of past university-data is included while in (Asif et al., 2015 ; Asif et al., 2017 ), 2 years of past university-data and in (Adekitan & Salau, 2019 ) 3 years of past university-data is covered. Other details for these methods are in Table  3 .

Course level

Finally, some studies can be reported, seeking the prediction of academic success at the course level. As already mentioned in degree level and year level sections, the comparative work gives accuracies of 62% to 89% while predicting success at a course level can give accuracies more than 89%, which can be seen as a more straightforward task than predicting success at degree level or year level. The best accuracy is obtained in course level with 93%. In (Garg, 2018 ), the target course was an advanced programming course while the influential factor was a previous programming course, also a prerequisite course. This demonstrates how important it is to have a field knowledge and use this knowledge to guide the decisions in the process and target important features. All other details for these methods are in Table  4 .

Data mining process model for student success prediction

This section compiles as a set of guidelines the various steps to take while using educational data mining techniques for student success prediction; all decisions needed to be taken at various stages of the process are explained, along with a shortlist of best practices collected from the literature. The proposed framework (Fig.  5 ) has been derived from well-known processes (Ahmad et al., 2015 ; Huang, 2011 ; Pittman, 2008 ). It consists of six main stages: 1) data collection, 2) data initial preparation, 3) statistical analysis, 4) data preprocessing, 5) data mining implementation, and 6) result evaluation. These stages are detailed in the next subsections.

figure 5

Stages of the EDM framework

Data collection

In educational data mining, the needed information can be extracted from multiple sources. As indicated in Table 1 , the most influential factor observed in the literature is Prior Academic Achievement. Related data, that is to say, pre-university or university-data, can easily be retrieved from the university Student Information System (SIS) that are so widely used nowadays. SIS can also provide some student demographics (e.g. age, gender, ethnicity), but socio-economic status might not be available explicitly. In that case, this could either be deduced from existing data, or it might be directly acquired from students through surveys. Similarly, students’ environment related information also can be extracted from the SIS, while psychological data would probably need the student to fill a survey. Finally, students’ e-learning activities can be obtained from e-learning system logs (Table  5 ).

Initial preparation of data

In its original form, the data (also called raw data) is usually not ready for analysis and modeling. Data sets that are mostly obtained from merging tables in the various systems cited in Table 5 might contain missing data, inconsistent data, incorrect data, miscoded data, and duplicate data. This is why the raw data needs to go through an initial preparation (Fig.  6 ), consisting of 1) selection, 2) cleaning, and 3) derivation of new variables. This is a vital step, and usually the most time consuming (CrowdFlower, 2016 ).

figure 6

Initial Preparation of Data

Data selection

The dimension of the data gathered can be significant, especially while using prior academic achievements (e.g. if all past courses are included both from high-school and completed undergraduate years). This can negatively impact the computational complexity. Furthermore, including all the gathered data in the analysis can yield below optimal prediction results, especially in case of data redundancy, or data dependency. Thus, it is crucial to determine which attributes are important, or needs to be included in the analysis. This requires a good understanding of the data mining goals as well as the data itself (Pyle, Editor, & Cerra, 1999 ). Data selection, also called “Dimensionality Reduction” (Liu & Motoda, 1998 ), consists in vertical (attributes/variables) selection and horizontal (instance/records) selection (García, Luengo, & Herrera, 2015 ; Nisbet, Elder, & Miner, 2009 ; Pérez et al., 2015 ) (Table  6 ). Also, it is worth noticing that models obtained from a reduced number of features will be easier to understand (Pyle et al., 1999 ).

Data cleaning

Data sources tend to be inconsistent, contain noises, and usually suffer from missing values (Linoff & Berry, 2011 ). When a value is not stored for a variable, it is considered as missing data. When a value is in an abnormal distance from the other values in the dataset, it is called an outlier. Literature reveals that missing values and outliers are very common in the field of EDM. Thus, it is important to know how to handle them without compromising the quality of the prediction. All things considered, dealing with missing values or outliers cannot be done by a general procedure, and several methods need to be considered within the context of the problem. Nevertheless, we try to here to summarize the main approaches observed in the literature and Table  7 provides a succinct summary of them.

If not treated, missing value becomes a problem for some classifiers. For example, Support Vector Machines (SVMs), Neural Networks (NN), Naive Bayes, and Logistic Regression require full observation (Pelckmans, De Brabanter, Suykens, & De Moor, 2005 ; Salman & Vomlel, 2017 ; Schumacker, 2012 ), however, decision trees and random forests can handle missing data (Aleryani, Wang, De, & Iglesia, 2018 ). There are two strategies to deal with missing values. The first one is a listwise deletion, and it consists in deleting either the record (row deletion, when missing values are few) or the attribute/variable (column deletion, when missing values are too many). The second strategy, imputation, that derives the missing value from the remainder of the data (e.g. median, mean, a constant value for numerical value, or randomly selected value from missing values distribution (McCarthy, McCarthy, Ceccucci, & Halawi, 2019 ; Nisbet et al., 2009 )).

Outliers data are also known as anomalies, can easily be identified by visual means, creating a histogram, stem and leaf plots or box plots and looking for very high or very low values. Once identified, outliers can be removed from the modeling data. Another possibility is to converts the numeric variable to a categorical variable (i.e. bin the data) or leaves the outliers in the data (McCarthy et al., 2019 ).

Derivation of new variables

New variables can be derived from existing variables by combining them (Nisbet et al., 2009 ). When done based on domain knowledge, this can improve the data mining system (Feelders, Daniels, & Holsheimer, 2000 ). For example, GPA is a common variable that can be obtained from SIS system. If taken as it is, a student’s GPA reflects his/her average in a given semester. However, this does not explicitly say anything about this student’s trend over several semesters. For the same GPA, one student could be in a steady state, going through an increasing trend, or experiencing a drastic performance drop. Thus, calculating the difference in GPA between consecutive semesters will add an extra information. While there is no systematic method for deriving new variables, Table  8 recapitulates the instances that we observed in the EDM literature dedicated to success prediction.

Statistical analysis

Preliminary statistical analysis, especially through visualization, allows to better understand the data before moving to more sophisticated data mining tasks and algorithms (McCarthy et al., 2019 ). Table  9 summarizes the statistics commonly derived depending on the data type. Data mining tools contain descriptive statistical capabilities. Dedicated tools like STATISTICA (Jascaniene, Nowak, Kostrzewa-Nowak, & Kolbowicz, 2013 ) and SPSS (L. A. D. of S. University of California and F. Foundation for Open Access Statistics, 2004 ) can also provide tremendous insight.

It is important to note that this step can especially help planning further steps in DM process, including data pre-processing to identify the outliers, determining the patterns of missing data, study the distribution of each variable and identify the relationship between independent variables and the target variable (see Table  10 ). Furthermore, statistical analysis is used in the interpreting stage to explain the results of the DM model (Pyle et al., 1999 ).

Data preprocessing

The last step before the analysis of the data and modeling is preprocessing, which consists of 1) data transformation, 2) how to handle imbalanced data sets, and 3) feature selection (Fig.  7 ).

figure 7

Data Preprocessing

Data transformation

Data transformation is a necessary process to eliminate dissimilarities in the dataset, thus it becomes more appropriate for data mining (Osborne, 2002 ). In EDM for success prediction, we can observe the following operations:

Normalization of numeric attributes: this is a scaling technique used when the data includes varying scales, and the used data mining algorithm cannot provide a clear assumptions of the data distribution (Patro & Sahu, 2015 ). We can cite K-nearest neighbors and artificial neural networks (How to Normalize and Standardize Your Machine Learning Data in Weka, n.d. ) as examples of such algorithms. Normalizing the data may improve the accuracy and the efficiency of the mining algorithms, and provide better results (Shalabi & Al-Kasasbeh, 2006 ). The common normalization techniques are min-max (MM), decimal scaling, Z-score (ZS), median and MAD, double sigmoid (DS), tanh, and bi-weight normalizations (Kabir, Ahmad, & Swamy, 2015 ).

Discretization: The simplest method of discretization binning (García et al., 2015 ), converts a continuous numeric variable into a series of categories by creating a finite number of bins and assigning a specific number of values to each attribute in each bin. Discretization is a necessary step when using DM techniques that allow only for categorical variables (Liu, Hussain, Tan, & Dash, 2002 ; Maimon & Rokach, 2005 ) such as C4.5 (Quinlan, 2014 ), Apriori (Agrawal, 2005 ) and Naïve Bayes (Flores, Gámez, Martínez, & Puerta, 2011 ). Discretization also increases the accuracy of the models by overcoming noisy data, and by identifying outliers’ values. Finally, discrete features are easier to understand, handle, and explain.

Convert to numeric variables: Most DM algorithms offer better results using a numeric variable. Therefore, data needs to be converted into numerical variables, using any of these methods:

Encode labels using a value between [0 and N (class-1)34 ] where N is the number of labels (Why One-Hot Encode Data in Machine Learning, n.d. ).

A dummy variable is a binary variable denoted as (0 or 1) to represent one level of a categorical variable, where (1) reflects the presence of level and (0) reflects the absence of level. One dummy variable will be created for each present level (Mayhew & Simonoff, 2015 ).

Combining levels: this allows reducing the number of levels in categorical variables and improving model performance. This is done by simply combining similar levels into alike groups through domain (Simple Methods to deal with Categorical Variables in Predictive Modeling, n.d. ).

However, note that all these methods do not necessarily lead to improved results. Therefore, it is important to repeat the modeling process by trying different preprocessing scenarios, evaluate the performance of the model, and identify the best results. Table  11 . recapitulates the various EDM application of preprocessing methods.

Imbalanced datasets

It is common in EDM applications that the dataset is imbalanced, meaning that the number of samples from one class is significantly less than the samples from other classes (e.g. number of failing students vs passing students) (El-Sayed, Mahmood, Meguid, & Hefny, 2015 ; Qazi & Raza, 2012 ). This lack of balance may negatively impact the performance of data mining algorithms (Chotmongkol & Jitpimolmard, 1993 ; Khoshgoftaar, Golawala, & Van Hulse, 2007 ; Maheshwari, Jain, & Jadon, 2017 ; Qazi & Raza, 2012 ). Re-sampling (under or over-sampling) is the solution of choice (Chotmongkol & Jitpimolmard, 1993 ; Kaur & Gosain, 2018 ; Maheshwari et al., 2017 ). Under-sampling consists in removing instances from the major class, either randomly or by some techniques to balance the classes. Oversampling consists of increasing the number of instances in the minor class, either by randomly duplicating some samples, or by synthetically generating samples (Chawla, Bowyer, Hall, & Kegelmeyer, 2002 ) (see Table  12 ).

Feature selection

When the data set is prepared and ready for modeling, then the important variables can be chosen and submitted to the modeling algorithm. This step, called feature selection, is an important strategy to be followed to mining the data (Liu & Motoda, 1998 ). Feature selection aims to choose a subset of attributes from the input data with the capability of giving an efficient description for the input data while reducing effects from unrelated variables while preserving sufficient prediction results (Guyon & Elisseeff, 2003 ). Feature selection enables reduced computation time, improved prediction performance while allowing a better understanding of the data (Chandrashekar & Sahin, 2014 ). Feature selection methods are classified into filter and wrapper methods (Kohavi & John, 1997 ). Filter methods work as preprocessing to rank the features, so high-ranking features are identified and applied to the predictor. In wrapper methods, the criterion for selecting the feature is the performance of the forecasting device, meaning that the predictor is wrapped on a search algorithm which will find a subset that gives the highest predictor performance. Moreover, there are embedded methods (Blum & Langley, 1997 ; Guyon & Elisseeff, 2003 ; P. (Institute for the S. of L. and E. Langley, 1994 ) which include variable selection as part of the training process without the need for splitting the data into training and testing sets. However, most data mining tools contains embedded feature selection methods making it easy to try them and chose the best one.

Data mining implementation

Data mining models.

Two types of data mining models are commonly used in EDM applications for success prediction: predictive and descriptive (Kantardzic, 2003 ). Predictive models apply supervised learning functions to provide estimation for expected values of dependent variables according to the features of relevant independent variables (Bramer, 2016 ). Descriptive models are used to produce patterns that describe the fundamental structure, relations, and interconnectedness of the mined data by applying unsupervised learning functions on it (Peng, Kou, Shi, & Chen, 2008 ). Typical examples of predictive models are classification (Umadevi & Marseline, 2017 ) and regression (Bragança, Portela, & Santos, 2018 ), while clustering (Dutt et al., 2017 ) and association (Zhang, Niu, Li, & Zhang, 2018 ), produce descriptive models. As stated in section 4 , classification is the most used method, followed by regression and clustering. The most commonly used classification techniques are Bayesian networks, neural networks, decision trees (Romero & Ventura, 2010 ). Common regression techniques are linear regression and logistic regression analysis (Siguenza-Guzman, Saquicela, Avila-Ordóñez, Vandewalle, & Cattrysse, 2015 ). Clustering uses techniques like neural networks, K-means algorithms, fuzzy clustering and discrimination analysis (Dutt et al., 2017 ). Table  13 shows the recurrence of specific algorithms based on the literature review that we performed.

In the process, first one needs to choose a model, namely predictive or descriptive. Then, the algorithms to build the models are chosen from the 10 techniques considered as the top 10 in DM in terms of performance, always prefer models that are interpretable and understandable such as DT and linear models (Wu et al., 2008 ). Once the algorithms have been chosen, they require to be configured before they are applied. The user must provide suitable values for the parameters in advance in order to obtain good results for the models. There are various strategies to tune parameters for EDM algorithms, used to find the most useful performing parameters. The trial and error approach is one of the simplest and easiest methods for non-expert users (Ruano, Ribes, Sin, Seco, & Ferrer, 2010 ). It consists of performing numerous experiments by modifying the parameters’ values until finding the most beneficial performing parameters.

Data mining tools

Data mining has a stack of open source tools such as machine learning tools which supports the researcher in analyzing the dataset using several algorithms. Such tools are vastly used for predictive analysis, visualization, and statistical modeling. WEKA is the most used tool for predictive modeling (Jayaprakash, 2018 ). This can be explained by its many pre-built tools for data pre-processing, classification, association rules, regression, and visualization, as well as its user-friendliness, and accessibility even to a novice in programming or data mining. But we can also cite RapidMiner and Clementine as stated in Table 4 .

Results evaluation

As several models are usually built, it is important to evaluate them and select the most appropriate. While evaluating the performance of classification algorithms, normally the confusion matrix as shown in Table  14 is used. This table gathers four important metrics related to a given success prediction model:

True Positive (TP): number of successful students classified correctly as “successful”.

False Positive (FP): number of successful students incorrectly classified as “non-successful”.

True Negative (TN): number of did not successful students classified correctly as “non-successful”.

False Negative (FN): number of did not successful students classified incorrectly as “successful”.

Different performance measures are included to evaluate the model of each classifier, almost all measures of performance are based on the confusion matrix and the numbers in it. To produce more accurate results, these measures are evaluated together. In this research, we’ll focus on the measures used in the classification problems. The measures commonly used in the literature are provided in Table  15 .

Early student performance prediction can help universities to provide timely actions, like planning for appropriate training to improve students’ success rate. Exploring educational data can certainly help in achieving the desired educational goals. By applying EDM techniques, it is possible to develop prediction models to improve student success. However, using data mining techniques can be daunting and challenging for non-technical persons. Despite the many dedicated software’s, this is still not a straightforward process, involving many decisions. This study presents a clear set of guidelines to follow for using EDM for success prediction. The study was limited to undergraduate level, however the same principles can be easily adapted to graduate level. It has been prepared for those people who are novice in data mining, machine learning or artificial intelligence.

A variety of factors have been investigated in the literature related to its impact on predicting students ‘academic success which was measured as academic achievement, as our investigation showed that prior-academic achievement, student demographics, e-learning activity, psychological attributes, are the most common factors reported. In terms of prediction techniques, many algorithms have been applied to predict student success under the classification technique.

Moreover, a six stages framework is proposed, and each stage is presented in detail. While technical background is kept to a minimum, as this not the scope of this study, all possible design and implementation decisions are covered, along with best practices compiled from the relevant literature.

It is an important implication of this review that educators and non-proficient users are encouraged to applied EDM techniques for undergraduate students from any discipline (e.g. social sciences). While reported findings are based on the literature (e.g. potential definition of academic success, features to measure it, important factors), any available additional data can easily be included in the analysis, including faculty data (e.g. competence, criteria of recruitment, academic qualifications) may be to discover new determinants.

Availability of data and materials

Not applicable.

Abbreviations

(Probabilistic) neural network

Classification

  • Data mining

Decision tree

Educational data mining

K-nearest neighbors

Logistic regression

Naive Bayes

Neural network

Random forest

Rule induction

Random tree

Tree ensemble

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Academic success from an individual perspective: A proposal for redefinition

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The examination of academic achievements is common in educational research literature, with most studies referring to grades (marks) as measures of success. In addition, outside the realm of research, a student’s grades are usually the main criteria for admission to education programmes, nomination for honours (passing above ordinary level), award of scholarships and so forth. However, scholars have put forward several arguments against the use of grades as the sole or most important measure of academic success. This research note focuses on a specific aspect of this problem, namely the failure to consider learners’ personal perspective regarding their own achievements. Many approaches to evaluating achievements call for their examination in light of previously defined goals. However, each learner defines her or his aspirations and goals differently, while achievements are usually measured on a uniform scale. This research note reviews this problem and considers alternative models (including both their advantages and their shortcomings) for defining academic success in terms of expectations and motivation. In addition, the author proposes a measure to enable the evaluation of academic achievements in terms of an individual student’s goals and aspirations.

La réussite des études d’un point de vue personnel : proposition d’une redéfinition – Il est courant que les ouvrages de recherche éducative se penchent sur les résultats scolaires et universitaires, ces travaux mesurant souvent la réussite des études à l’aune des notes. Cependant, en dehors du domaine de la recherche, les notes sont généralement aussi le principal critère pour être admis à des programmes d’études, se voir proposé pour des distinctions (à un niveau se situant au-delà de l’ordinaire), obtenir des bourses, etc. Toutefois, les chercheurs ont avancé différents arguments contre l’utilisation des notes comme moyen unique ou principal d’évaluation de la réussite des études. Cette note de recherche est axée sur un aspect particulier du problème, à savoir le fait que l’on omet de prendre en compte le point de vue personnel des apprenants sur leurs propres résultats. Nombre d’approches utilisées pour évaluer les résultats nécessitent d’être examinées à la lumière d’objectifs fixés au préalable. Néanmoins, chaque apprenant ne définit pas de la même façon ses aspirations et ses objectifs, alors que pour mesurer les résultats scolaires et universitaires, on applique d’ordinaire un barème uniforme. Dans cette note de recherche, l’auteur étudie ce problème et examine d’autres modèles (y compris leurs avantages et leurs inconvénients) pour définir la réussite des études en termes d’attentes et de motivation. Il propose en outre aussi une mesure pour permettre d’évaluer les résultats scolaires et universitaires à l’aune des objectifs et aspirations individuels des élèves.

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An empirical study of college students’ reading engagement on academic achievement

Xiao-wu wang.

1 Anhui Xinhua University, Hefei, China

Yu-Juan Zhu

2 Institute of Higher Education, Anhui Xinhua University, Hefei, China

Yi-Cheng Zhang

3 Quality Education Research Center, Anhui Xinhua University, Hefei, China

4 School of Management, University of Science and Technology of China, Hefei, China

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The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

With the popularity of Internet technology, reading has developed in the direction of digitalization and mobileization. And entering the metaverse era, both the subject and object of reading may be redefined, presenting a new developmental pattern. This process brings a crisis to reading, such as the fragmentation of reading, the obstruction of reading needs, and the replacement of classical reading. However, reading is still an important way for college students to acquire new knowledge, broaden their horizons and improve their skills. The existence of reading crises inevitably affects the academic achievement of college students. Therefore, from the perspective of university management, this paper conducts regression analysis on 1,155 effective samples of colleges and universities in Anhui Province, extracts the factors that affect college students’ reading engagement, and further explores the relationship between college students’ reading engagement and academic achievement. The study concluded that: (1) in terms of family reading culture, students who grow up in families with good family reading culture perform better in reading engagement. The amount of family books, family reading education and family reading atmosphere all have significant positive effects on reading time and reflective reading strategies of college students. (2) In the cultivation of reading habits in colleges and universities, the course-driven mechanism and the atmosphere stimulating mechanism have a significant positive effect on students’ reading time. The course-driven mechanism, resource supporting mechanism and atmosphere stimulating mechanism have a significant positive effect on the critical reading strategy of college students. (3) In terms of reading time, it is only found that the reading time spent on paper books has a significant positive effect on college students’ academic achievement and professional quality. (4) In terms of reading strategies, the replicative reading strategy only has a significant positive effect on the improvement of college students’ academic achievement and professional quality. The critical reading strategy has a significant positive effect on the professional quality, general ability and career planning ability of college students.

Introduction

The year 2021 is regarded as the “meta-universe year” ( Zhao et al., 2021 ), and the phenomenon of “meta-universe” has received widespread attention, reflecting the new trend of digital technology development after new technologies such as big data, blockchain, 5G and cloud computing. The metaverse is linked and created by using technology, a virtual world mapped and interacted with the real world, and a digital living space with a new social system. Combing through the literature related to metaverse reveals that many scholars focus on the impact of metaverse on games, literary travel, education and other fields, but there are few studies on the connection between metaverse and reading and how metaverse will bring changes to reading. With the popularity of Internet technology, reading has developed in the direction of digitalization and mobilization. And entering the era of metaverse, both the subject and the object of reading may be redefined and take on a new developmental shape. This process brings a crisis to reading, such as reading fragmentation, hindered reading demand, and the replacement of classical reading ( Cai and Zhao, 2022 ; Dwivedi et al., 2022 ; Zhu, 2022 ).

The acquisition of intellectual capabilities and development of high level manpower which is the goal of tertiary education implies a crop of students with good study habits because effective study habits and strategies have been attributed to the secret of success in school, graduation, entering the universities as well as the attainment of job advancement ( Musingati and Zebron, 2014 ).

Reading is an important way for contemporary college students to acquire new knowledge, expand their horizons and improve their skills, which is not only beneficial to their academic progress and quality improvement, but also plays an important role in creating a good cultural atmosphere and playing the function of educating people in colleges and universities. As an important means of educating people in colleges and universities, reading is related to the mission of cultivating talents in colleges and universities and the effectiveness of cultivating talents in universities. Therefore, reading for college students is one of the very important research topics in college education management. ( Leal-Rodriguez and Albort-Morant, 2019 ; Gao, 2021 ; Weli and Nwogu, 2022 ).

Based on the above, from the perspective of university management, this study explores: (1) What factors affect college students’ reading engagement? (2) What is the influence mechanism of college students’ reading engagement on their academic performance? Thus, the university reading management mechanism can be constructed to improve their academic achievement.

Literature review and research hypotheses

College students’ reading engagement.

Different scholars hold different views on the connotation of reading engagement, and so far, there is no unified standard. Csikszentmihalyi (1990) believes that reading engagement is a state of concentration. Pearson et al. (2016) believed that reading engagement is the interaction between students’ motivation and strategies. Different scholars have different views on the dimensions of reading engagement. PISA2009 divides reading engagement into personal reading engagement and school reading engagement. Personal reading engagement includes four dimensions: love of reading, diversity of reading, online reading activities and reading time. The school reading engagement specifically includes four dimensions: text interpretation, use of non-consecutive texts, reading activities of traditional literary works, and instrumental text use. Zhang et al. (2014) drew on the PISA2009 definition of reading engagement, and divided reading engagement into reading time, reading quantity and reading interest. Wen et al. (2016) studied reading engagement from three dimensions: length of reading time, amount of reading, and diversity of reading content.

The reading engagement proposed in this study refers to the time and energy input of college students in the process of reading, which specifically includes college students’ reading time and reading strategies ( Brookbank et al., 2018 ; McDaniel, 2018 ). See Table 1 for details.

Specific indicators of reading engagement.

Academic achievement

Research on the academic achievement of college students can be traced back to the 1960s. In 1966, the American Educational Advisory Council created the “Cooperative Program on Institutional Research.” After the 1990s, countries all over the world began to pay attention to the investigation and research on the academic achievement of college students. This is not only an effective way to judge the value growth of college students, but also an important way to explore the learning and development of college students, and an effective method to measure the quality of college education ( Douglass et al., 2012 ). Park et al. (2014) summarized the academic achievement of college students as knowledge, values and attitudes, skills or appropriate behaviors. American university scholars developed a standardized test tool CLA (Collegiate Learning Assessment), which reflects the academic achievement status of college students by measuring critical thinking ability, analytical reasoning ability, problem-solving ability and communication ability. Developmental psychology believes that there is an interaction mechanism between value orientation and behavioral choice, and there is a causal relationship between academic achievement orientation and behavior, that is, college students with higher academic achievement will have better future development ( Morgan, 2004 ; Aharony and Bar-Ilan, 2018 ). Maniaci et al. (2021) evaluated the relationship between healthy lifestyle and academic performance of 373 Italian adolescents, and found that academic achievement was conducive to healthy lifestyle and good eating habits.

Reading engagement and academic achievement of college students

According to the input-environment-output model proposed by Astin (1984) , output refers to students’ ability, that is, the target of education and teaching in colleges and universities, input refers to the personal characteristics of students before receiving higher education, including the student’s family background, academic qualifications before admission, etc., environment refers to all kinds of actual experiences that students experience in universities and colleges, including those from course teaching, campus activities, social practice, and others ( Astin, 1984 ).

A study environment therefore refers to the physical, social and psychological situations that affect a student’s well being as well his studies. A study environment must be safe, healthy and promote the study life of a student ( Dempsey, 2020 ). A satisfying learning environment is conducive to college students’ reading engagement (time and energy), which in turn enhances their academic achievement. Therefore, the more college students participate in various practical activities, the better their comprehensive ability and quality will be improved ( Camelo and Elliott, 2019 ; Wu, 2019 ; Marley and Wilcox, 2022 ).

Research hypothesis

Reading is essentially the inheritance and innovation of knowledge (object) in the interactive relationship between subject (reader) and carrier (reading material). The differences of reading subjects (readers) mainly depend on individual preconditions and post-environments. In the preconditions, family reading characteristics (mainly the pros and cons of the students’ family cultural capital) play a leading role. In the post-endowment environment, the cultivation of reading habits in colleges and universities plays a leading role. Different kinds of colleges and universities play different roles in the cultivation of reading habits, which affects reading engagement. The object of reading is composed of explicit knowledge and tacit knowledge. Explicit knowledge is mainly composed of disciplinary professional knowledge, interdisciplinary integration knowledge and fragmented knowledge, while tacit knowledge is obtained through comprehension, and in university campus, it is mainly manifested as scholarly campus and cultural influence. Reading carriers are mainly composed of paper carriers and digital carriers. Under the background of technological change, intelligent knowledge production (IR, AI), interactive knowledge transmission and lively knowledge experience (AR, VR) are the inevitable trends of the development of reading carriers.

Therefore, this study explores the influence mechanism of college students’ reading engagement on the improvement of academic achievement from the perspective of students and universities. Based on this, this study puts forward the following hypotheses:

Hypothesis 1: The characteristics of reading subjects (preconditions and college endowments) have a significant influence on the academic achievement of college students. The better the subject endowment, the better the academic achievement of college students.
Hypothesis 1a: Controlling other variables, the better the college students’ pre-existing conditions, the better their academic achievement;
Hypothesis 1b: Controlling other variables, the better the endowment of college students in colleges and universities, the better their academic achievement;
Hypothesis 2: College students’ reading engagement has a significant influence on academic achievement, that is, the more college students engage in reading, the better their academic achievement.
Hypothesis 2a: Controlling other variables, the more time college students devote to reading, the better their academic achievement;
Hypothesis 2b: Controlling other variables, the better the reading strategy of college students, the better their academic achievement;
Hypothesis 3: The cultivation of reading habits in colleges and universities has a significant impact on the academic achievement of college students by influencing their reading engagement, that is, the better the university’s curriculum driving mechanism, resource support mechanism, atmosphere stimulation mechanism and interaction promotion mechanism are played, the better the academic achievement of college students will be.

The logic of this research is to construct the relationship between reading engagement and academic achievement, and explore the influence mechanism of reading engagement on academic achievement by investigating the respective effects of reading engagement at the student level and the university level. The reading engagement (time and strategy) level of college students, from the individual level, is mainly affected by the individual’s preconditions. From the perspective of group characteristics, it is mainly affected by the endowment of colleges and the cultivation of college reading habits. The combined effect of the individual level and the university level constitutes a research system of college students’ reading engagement on academic achievement.

Research design

Questionnaire design and data collection, questionnaire design.

First of all, literature was sorted out to collect authoritative classical scales, and some mature scales were used for reference. In this study, the reading engagement questionnaire of college students draws on the questionnaire of “Research on Reading Motivation of College Students” by Chen Xiaoli of Jinan University and the questionnaire of “Chinese Reading Behavior in the Digital Age” by Li Xinxiang of Wuhan University. The college students’ academic achievement questionnaire was based on the “University Quality and Student Development Monitoring Project in Beijing” questionnaire and NSSE-China.

Secondly, through discussion among members of the research team and consultation with authoritative experts in this field, the contents of the questionnaire items and the wording of the questionnaire were modified, and the design content of the questionnaire was initially determined. In the first part, the purpose of the study was explained to the research subjects. The second part defines the background data of this study. The third part is mainly about the impact of college students’ reading engagement on academic achievement, including time reading motivation, family reading culture, college reading habit training, college organizational endowment and so on. The fourth part is the basic information of the research object, including age and other demographic variables.

Finally, a small number of qualified samples were screened, and the proposed questionnaire was used for pre-investigation, and the data of the recovered questionnaire was sorted out. At the same time, the reliability and validity of the questionnaire items were tested, and the inconsistent questions were deleted to form the final draft of the questionnaire.

Questionnaire distribution and collection

After the preliminary questionnaire survey, the re-adjustment and modification of the questionnaire were completed to improve reliability and validity, and the final questionnaire of this study was formed.

In this study, cluster sampling method was mainly adopted. This method is to merge the units in the population into several non-intersecting and non-repeating sets, that is, cluster; and then use the cluster as a sampling unit to draw samples.

Considering the representativeness and comprehensiveness of the sample, three undergraduate universities in Anhui Province were selected, one is a university of Project 985, one is a government-run undergraduate university, and one is a private undergraduate university. The student sample covers freshmen to seniors, and the majors cover humanities and social sciences, science and engineering. In the questionnaire survey, we gathered the students together and conducted the survey in the form of answering online questionnaires through smartphones. Finally, a total of 1,155 valid questionnaires were obtained.

Measurement of variables

This study takes the academic achievement of college students as the dependent variable, which includes three dimensions, namely, professional ability, general competence, and career planning ability. The reading engagement of college students was taken as the explanatory variable, and the individual characteristics of college students, reading motivation, family reading culture, college reading habit training and college organizational endowment were used as control variables. The definitions of main explanatory variables, explained variables and control variables are shown in Table 2 .

Measurement of variables.

In order to verify the mechanism of college students’ reading engagement on college students’ academic achievement proposed in this paper, the following multiple regression models were used to analyze from the student level and the university level. The specific model is as follows:

Among them, X ij represents the reading engagement of college students, A ij represents the individual characteristics of college students; B ij represents the reading motivation of college students; C ij represents the reading culture of college students’ families; D ij represents the cultivation of reading habits in colleges and universities; E ij stands for organizational endowment of colleges and universities; Y ij is for academic achievement of college students. β i is the regression coefficient.

In Model 1, the dependent variable is X ij , and other variables are independent variables, which are used to explore the factors affecting college students’ reading engagement. In Model 2, the dependent variable is Y ij , and other variables are independent variables, which are used to explore the impact of college students’ reading engagement on their academic achievement.

Analysis of empirical results

Descriptive statistical analysis.

In terms of the grade distribution of the sample, there are fewer seniors. The main reason is that the courses of seniors have finished, and many students have been practicing abroad, so the number of seniors in school is small. The sample number of freshmen, sophomores and juniors was relatively balanced, with 238 freshmen, accounting for 20.6%, 436 sophomore students, accounting for 37.7%, 359 juniors, accounting for 31.1 percent. In terms of the types of colleges and universities, 203 students are from 985 colleges and universities, accounting for 17.6%. The number of students from local public undergraduate universities is 283, accounting for 24.5%; the number of local private undergraduate students is 669, accounting for 57.9%. In terms of gender, there are 633 boys, accounting for 54.8%, and 522 girls, accounting for 45.2%. In terms of majors, 382 students are in humanities and social sciences, accounting for 28.5%, while 826 in science and engineering, accounting for 71.5%. In terms of the source of students, 606 students are from rural areas, accounting for 52.5%, and 549 from urban areas, accounting for 47.5%. In terms of the types of high schools, 788 students, accounting for 68.2%, are enrolled in key high schools, and 367 students, or 31.8 percent, are enrolled in regular high schools. From the perspective of annual family income, 58.4% (674 students) have an annual family income of 50,000 yuan or less, 25% (289 students) 60,000–100,000 yuan, and the remaining 17.6% (242 students) 100,000 yuan or more. See Table 3 for details.

Descriptive statistics of the sample.

Reliability analysis

The reliability test of the questionnaire is mainly to ensure the reliability of the questionnaire, so we conducted the reliability test of the questionnaire by using SPSS21.0. According to the structure of the questionnaire, we selected the following dimensions for reliability analysis, as shown in Table 4 .

Reliability analysis.

Cronbach’s α coefficient is used to measure the reliability of the internal consistency of the questionnaire. If the Cronbach’s α coefficient is larger, it indicates that the degree of internal consistency of the questionnaire is higher, and the reliability of the questionnaire results is stronger. It is generally believed that the reliability coefficient should be between 0 and 1. If the reliability coefficient of the questionnaire is above 0.9, it means that the reliability of the questionnaire is very good. If the reliability coefficient of the questionnaire is between 0.8 and 0.9, it indicates that the reliability of the questionnaire is acceptable. If the reliability coefficient of the questionnaire is between 0.7 and 0.8, it indicates that some items of the questionnaire need to be revised. If the reliability coefficient of the questionnaire is below 0.7, it indicates that some items in the questionnaire need to be deleted.

The reliability of this questionnaire can be found from the above table, the reliability of each dimension of the questionnaire is above 0.8, indicating that the reliability of the questionnaire in this paper is good.

Validity analysis

The paper used structural equation modeling to test the construct validity of the main variables, including academic achievement and reading engagement. As can be seen from Figure 1 , the path coefficients of the three abilities of academic achievement are all above 0.7, and other observation indicators meet the requirements, indicating that the validity of the academic achievement in the questionnaire is good. As can be seen from Figure 2 , the path coefficients of the two aspects of reading engagement are both above 0.6, and the other observed indicators are consistent, indicating that the validity of reading engagement in the questionnaire is good.

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Construct validity analysis chart of academic achievement (after normalization). Significance level * P < 0.1, ** P < 0.05, and *** P < 0.01.

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Construct validity analysis diagram of reading input (after standardization). Significance level * P < 0.1, ** P < 0.05, and *** P < 0.01.

Analysis of influencing factors of college students’ reading engagement

The above analysis shows that different individual characteristics, different reading motivation, different family reading culture, different reading habit cultivation and different organizational endowment of colleges and universities show different reading input. Therefore, the paper took college students’ reading engagement as the dependent variable and college students’ individual characteristics, reading motivation, family reading culture, college reading habit cultivation and college organizational endowment as independent variables to construct a multiple regression model and analyze the influencing mechanism of these factors on college students’ reading engagement. The regression results are shown in Table 5 .

Results of multiple linear regression of college students’ reading engagement.

Significance level * P < 0.1, ** P < 0.05, *** P < 0.01.

The influence of college students’ individual characteristics on reading engagement

As can be seen from Table 5 , in the prediction of paper reading time, the grade of college students has a positive and significant effect on reading time, that is, with the rise of grade, college students will spend more and more time on paper reading. In the prediction of reading strategies, the grade of college students plays a significant role in the replication reading strategy. With the rise of grade, college students are more and more inclined to choose the replication reading strategy. Gender of college students plays a significant role in critical reading strategies, and male students are more inclined to think and reflect when reading.

The influence of college students’ reading motivation on reading engagement

As can be seen from Table 5 , in terms of reading motivation, both practical motivation and entertainment motivation have positive and significant effects on paper reading time, which also indicates that college students spend more time on practical and entertainment paper reading materials.

In terms of reading motivation, entertainment motivation has a positive and significant effect on e-reading time, while developmental motivation has a negative and significant effect on e-reading time. This also indicates that the main purpose of e-reading is entertainment, but college students choose to read from the perspective of their own development, so they spend less time on e-reading.

In terms of the impact of reading motivation on reading strategies, practical motivation, entertainment motivation and developmental motivation all play a positive and significant role in the replication and reflective reading strategies. In other words, on the whole, the stronger the reading motivation of college students, the better they will use the replication and reflective reading strategies.

The influence of college students’ family reading culture on reading engagement

As can be seen from Table 5 , in the prediction of reading time, only the father’s education years play a negative and significant role in the paper reading time of college students. That is, the higher the father’s education level, the less reading time of college students, which is obviously not in line with our conventional cognition, and the specific reasons need to be further analyzed.

However, the amount of family books, family reading education and family reading atmosphere all have significant positive effects on the paper reading time and electronic reading time of college students, which indicates that the more books in the family, the better the family reading education, the better the family reading atmosphere, and the longer the reading time of college students.

In the prediction of reading strategies, family reading education and family reading atmosphere had positive and significant effects on college students’ duplicative reading strategies and reflective reading strategies. The amount of family books has a positive and significant effect on the reflective reading strategy, which also indicates that family reading education, family reading atmosphere and the amount of family books are an important factor affecting the reading strategy of college students.

Influence of reading habit cultivation on reading input in colleges and universities

In terms of the prediction of reading time, the course-driven mechanism and the atmosphere stimulation mechanism have positive and significant effects on paper reading time and electronic reading time, which indicates that the opening of college guidance courses and the development of college reading activities will increase the reading time of college students.

In the prediction of reading strategies, the course-driven mechanism and the atmosphere stimulating mechanism play a positive and significant role in the replication reading strategy of college students, while the course-driven mechanism, the resource supporting mechanism and the atmosphere stimulating mechanism play a positive and significant role in the reflective reading strategy of college students. Generally speaking, the cultivation mechanism of college reading habits plays a very important role in the selection of reading strategies for college students. The more attention colleges pay to the cultivation of students’ reading habits, the better college students can use reading strategies.

Influence of organizational endowment on reading input in colleges and universities

In the prediction of organizational endowment of colleges and universities, the type of colleges and universities plays a positive and significant role in paper reading time, electronic reading time, replicative reading strategy and reflective reading strategy, that is, with the improvement of college selection, the longer the reading time of college students and the better the use of reading strategies.

Analysis of the impact of college students’ reading engagement on college students’ academic achievement

Through the above analysis, we can find that college students with different levels of reading engagement show different levels of academic achievement. Therefore, taking college students’ reading engagement as the independent variable, college students’ academic achievement as the dependent variable, and college students’ individual characteristics, reading motivation, family reading culture and organizational endowment as control variables, we constructed a multiple regression model to test whether college students’ reading engagement has an impact on college students’ academic achievement. At the same time, it also examines whether the individual characteristics of college students, reading motivation, family reading culture and organizational endowment of colleges and universities affect the academic achievement of college students. The regression results are shown in Table 6 .

Results of multiple linear regression of college students’ academic achievement.

The influence of college students’ reading engagement on their academic achievement

(1) The influence of college students’ reading engagement on college students’ professional quality.

As can be seen from Table 6 , paper reading time, replicative reading strategy and speculative reading strategy all play a significant positive role in the prediction of professional literacy of college students. However, e-reading time has no significant effect on college students’ professional quality. The explanatory power of the whole model is 58.3%. The data show that paper reading time and critical reading strategy can promote the improvement of college students’ professional ability, that is, the more paper reading time of college students, the more professional quality can be promoted. The use of critical reading strategy can also significantly improve the professional quality of college students. The measurement of professional literacy includes the improvement of professional performance and self-directed learning ability. It can be seen that if college students want to improve their professional knowledge and enhance their autonomous learning ability, they must first increase their reading time of paper books. Secondly, we should use the critical reading strategy, that is, we should keep thinking while reading, so as to improve our professional ability.

(2) The influence of college students’ reading engagement on college students’ general ability.

As can be seen from Table 6 , only the critical reading strategy plays a significant positive role in predicting the general ability of college students. Paper reading time, electronic reading time and Replicative reading strategy have no significant effect on college students’ general ability. The explanatory power of the whole model is 48.3%. The data show that the critical reading strategy can promote the general ability of college students. The measurement of general ability includes two elements: expressive ability and organizational leadership ability. From the perspective of the function of reading, if college students want to improve their general ability, that is, their expressive ability and organizational leadership ability, they must strengthen the use of critical reading strategies in reading, think in reading, and constantly improve their general ability.

(3) The impact of reading engagement on college students’ career and career planning

As can be seen from Table 6 , in terms of the prediction of career and career planning ability, the critical reading strategy has a significant positive effect on college students’ career and career planning ability. Paper reading time, electronic reading time and replicative reading strategy have no significant effect on college students’ general ability. The explanatory power of the whole model is 62.7%. The data show that the critical reading strategy can improve college students’ career and career planning ability. The measurement of career and career planning ability includes two elements: determining future career direction and forming stage development goals. The more college students use critical reading strategies in reading, the more they can improve their career and career planning ability.

Path analysis of the impact of college students’ reading engagement on academic achievement

The above multiple linear regression analysis shows the degree of influence of college students’ reading engagement on academic achievement. For how each dimension of college students’ reading engagement affects college students’ academic achievement, it is necessary to construct structural equation model to conduct path analysis and analyze the specific impact mechanism of college students’ reading engagement on academic achievement. AMOSS20.0 was used for path analysis in this paper. The chi-square of the model was 135.873, the degree of freedom was 13, and the overall significance probability of the model was P&LT. 0.001, the model fit is good, and the indicators are shown in Table 7 .

Fitting index of the model.

According to the Maximum Likelihood method, the model is analyzed. Figure 3 summarizes the standardized influence mechanism between “reading engagement and academic achievement.” The effect of college students’ reading engagement on standardized academic achievement reached 0.57, and the probability of significance was P&LT. 0.001, which indicates that college students’ reading engagement can indeed have a positive role in promoting academic achievement. This also confirms the conclusion in the regression model that college students’ reading engagement has a significant promoting effect on academic achievement, and verifies the basic hypothesis of this paper.

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Structural equation model of college students’ reading engagement and academic achievement (after normalization). Significance level * P < 0.1, ** P < 0.05, and *** P < 0.01.

The impact of other factors on college students’ academic achievement

In this study, the individual characteristics, reading motivation, family reading culture of college students and the organizational endowment of colleges and universities were added into the regression model as control variables. The regression model was used to test whether college students’ individual characteristics, reading motivation, family reading culture, reading habit training in colleges and universities, and organizational endowment in colleges and universities would have an impact on college students’ academic achievement. The regression results are shown in Table 6 .

(1) The influence of college students’ individual characteristics on college students’ academic achievement.

In terms of professional literacy, it can be seen from Table 6 that both the place of origin and developmental motivation have significant positive effects on college students’ professional literacy, that is, the professional literacy of urban students is better than that of rural students, and the more college students read for their own development, the stronger their professional literacy will be.

In terms of general ability, it can be seen from Table 6 that grade, subject and place of origin all have negative and significant effects on college students’ general ability, that is, with the improvement of college students’ grade, their own general ability decreases. Compared with students majoring in science and engineering, students majoring in humanities and social sciences improve their general ability better. The origin of students has a positive and significant effect on the general ability of college students. Urban students improve their general ability more than rural students.

In terms of college students’ career planning ability, from Table 6 , the grade of career and career planning ability of college students has negative significant effect, namely as college students’ grade rises, the cognitive ability of college students decreases in their career planning, and the reason may be that as their grade grows, especially when they need to find a job, their self-efficacy for their career planning ability becomes even lower. Discipline has a significant negative effect on career and lifetime planning ability, that is, the improvement of career planning ability of humanities and social science students is higher than that of science and engineering students.

(2) The impact of reading motivation on the academic achievement of college students.

In terms of professional quality, Table 6 shows that developmental motivation has a significant positive effect on the professional quality of college students, which indicates that the more they pay attention to long-term development in reading, the higher their career planning ability will be.

In terms of general ability, Table 6 shows that practical motivation, recreational motivation and developmental motivation all have a significant positive effect on general ability, which indicates that the stronger the reading motivation of college students, the better they can improve their general ability.

In terms of career planning ability, Table 6 shows that practical motivation and developmental motivation have a significant positive effect on career planning ability, which shows that the more college students pay attention to practicality and long-term development in reading, the better they can improve their career planning ability.

(3) The influence of family reading culture on the academic achievement of college students.

In terms of professional quality, Table 6 shows that the schooling years of fathers have a significant negative effect on the professional quality of college students, which is contradictory to conventional understanding. The possible reason is that most of the students come from private colleges and are not enthusiastic about their major. So the more educated their father, the more they will be motivated to learn knowledge. That being said, due to their psychological inversion, their professional quality is usually not very good.

In terms of general ability and career planning ability, Table 6 shows that the variables of family reading culture have no significant impact on the dimensions of college students’ academic achievement.

The impact of the organizational endowment of colleges on the academic achievement of college students

In terms of the organizational endowment of colleges, that is, the types of colleges, Table 6 shows that the types of colleges have a significant positive impact on students’ general ability, and as college selection improves, college students can better improve their general ability. This shows that the better the organizational endowment of colleges, the more prominent the general abilities of college students, including organizational and communication skills.

Conclusion and recommendation

Factors affecting college students’ reading engagement.

(1) From the perspective of college students, in terms of college students’ reading time, grades, practical motivation, recreational motivation, family collection of books, family reading education, and family reading atmosphere have a significant positive impact on paper reading time. The same is true of the education level of fathers. College students’ grade, recreational motivation, developmental motivation, family book collection, family reading education, and family reading atmosphere have a significant positive impact on e-reading time. In terms of reading strategies, grades, practical motivation, recreational motivation, developmental motivation, family reading education, and family reading atmosphere have significant positive effects on replicative reading strategies, while gender, practical motivation, recreational motivation, developmental motivation, family book collection, family reading education, and family reading atmosphere have significant positive effects on critical reading strategies.

(2) From the perspective of colleges, the curriculum-driven mechanism, atmosphere stimulation mechanism, and organizational endowment have a significant positive impact on paper reading time and e-reading time. In terms of reading strategies, curriculum-driven mechanism, atmosphere-based incentive mechanism, and college organizational endowment have a significant positive impact on replicative reading, while curriculum-driven mechanism, resource support mechanism, atmosphere incentive mechanism, and college organizational endowment have a significant positive impact on critical reading.

Mechanism of factors affecting the academic achievement of college students

From the analysis on how college students’ reading engagement affect their academic achievement, the following two points are drawn: (1) Among others, reading time is the most important factor affecting reading behavior and reading volume. It is found that the reading time of paper books has a significant positive effect on the professional quality of college students, but has no significant effect on the general ability, career and lifetime planning ability of college students. Many students prefer paper books while studying professional knowledge, because they can take notes on them and they are conducive to systemic learning. This may be an important reason why paper-book reading time has a significant positive effect on the professional quality of college students. In addition, e-book reading time has no significant effect on the three abilities concerning college students’ academic achievement, which to some extent reflects the drawbacks of e-book reading. E-book reading can easily lead to fragmented reading, and it is difficult to form a systematic knowledge system. Moreover, students also reported that e-books are basically recreational. Therefore, it is difficult for such reading to have a significant impact on the three abilities, but this does not mean that e-reading is useless. (2) In terms of reading strategies, critical reading strategies have a significant positive effect on college students’ professional quality, general ability, career and lifetime planning. While reading, college students should not only rely on passive memory, but also learn to think and make explorations actively, which is conducive to the improvement of academic achievement.

Based on the analysis on how the individual characteristics, reading motivation, family reading culture and institutional endowment of college students on the academic achievement of college students, the following four conclusions are drawn: (1) Grades have a significant negative effect on college students’ general ability and career and lifetime planning ability. With the increase of grades, the general ability and the ability of career and lifetime planning decrease. Disciplines have a significant negative effect on the general ability and ability of career and lifetime planning: feedback from students of humanities and social sciences shows that they can better improve their general ability through reading than those of science and engineering. The source of students has a positive effect on the professional quality, general ability, career and lifetime planning of college students, that is, the self-evaluation of urban students on their academic achievement is higher than that of rural students. (2) Practical motivation has a positive and significant effect on college students’ general ability and career planning ability. Students believe that practical reading can promote the two abilities; recreational motivation has a significant impact on general ability; motivation has a significant positive effect on academic achievement overall. (3) In terms of family reading culture, only the education level of fathers has a significant negative effect on college students’ professional quality, while that of others has no significant effect, which is contrary to our cognition and requires further verification and analysis. (4) The organizational endowment of colleges has a significant positive effect on general ability. With the improvement of college selectivity, college students have a higher evaluation of their general ability.

Recommendation

This research verifies the functional mechanism of reading subject, reading object and reading carrier, and concludes that the core of the operating mechanism of college reading management lies in the management of reading input (reading time and reading strategy), and the mechanism of college reading management makes sure that the reading subject, reading object and the reading carrier are effectively integrated. The joint effect of the operation mechanism and the action mechanism of reading management in colleges can promote the effective implementation of reading management, and then improve the academic achievement of college students.

First of all, find the individual, group and epochal characteristics of college students, and based on systematically grasping their individual characteristics, encourage them to read and improve their reading efficiency. Based on the cultivation of reading content tendencies and reading habits, actively promote the application and conversion of replicative reading and critical reading. Second, give play to the role of the reading object, well manage and serve knowledge content, promote the transformation between tacit knowledge and explicit knowledge, and play the role of tacit knowledge and tacit curriculum. In particular, promote reading atmosphere and provide a comfortable reading space for students to combine the role of the environment and culture in talent cultivation. Finally, in terms of reading carriers, combine technology and reading form. The construction of resources and platforms in colleges is the foundation of reading management. The construction and application of electronic resources and online platforms has become an inevitable trend under the background of technological change. The application of new technologies to improve and promote reading, especially the method of “intelligence plus reading” will be the main trend leading the development of reading carrier management.

Improve the construction of reading system

A sound system can guarantee the effective operation of reading management. The study found that the intended purpose of reading management cannot be achieved only through encouragement and advocacy. It is also necessary to systematically build a flexible and rigid institutional system that combine universities, libraries, counselors, teachers and student.

(1) In the construction of flexible restraint system, requirements concerning the number of books that students should read (for example one hundreds Chinese and foreign classics) and thought sharing should be implemented in each term, and relevant achievements, such as the number of book sharing and essays on book reading, are directly linked to student awards, party membership, and student cadre elections.

(2) In terms of rigid system construction, the responsibility assessment system for university administrators such as librarians, counselors and teachers should consider whether they participate in and guide students’ reading activities. Libraries and curators promote reading services and counseling, extend the functions of libraries in a timely manner based on technological progress and actual needs. In particular, they are responsible for selecting and recommending classic books and offer monthly lectures on famous classics. The work assessment system for counselors should consider whether they organize and take part in reading group activities in class management activities. Teachers, especially those with senior professional titles, should regularly offer lectures on professional reading, which has become an important part of teacher assessment. At the same time, colleges have established a reading assistance system for students with financial difficulties through student scholarships and other means.

Enrich reading resources and promote platform building

Reading resources and platforms are the material basis of reading management in colleges. Efforts should be made to strengthen the information construction of libraries, encourage teachers to develop relevant reading websites, share proper reading resources among students, and students with a “reading corner” for sharing and reading, thus building a bridge connecting teachers and students and promoting communication among students. In addition, new technologies should be used to improve college students’ reading strategies. In particular, the method of “artificial intelligence plus reading” such as virtual reality and augmented reality can effectively resolve shortcomings caused by “fragmented reading,” and find a reasonable and effective way for the promotion of classic reading.

Build a better long-term reading mechanism

(1) Establish a mechanism for reading promotion. The formation of a reading culture is a long-term and accumulative process, which cannot be achieved by just one or two reading activities. Through online and offline reading tutoring and various reading promotion activities (special lectures, reading salons, etc.), colleges can effectively improve students’ reading enthusiasm and participation, and form an institutionalized and systematic reading tradition. Libraries should improve their reading service, and introduce themselves to freshmen so that they know their services. Efforts should also be made to optimize the information building, and build a recommendation column for new and good books. At the same time, we should focus on guiding students to use electronic resources, and conduct regular activities to let them know how to use electronic resources better.

(2) Build a reading interaction system. The reading interaction between teachers and students and among students is an important part of campus interaction, and also a beautiful “learning landscape” on campus. On the one hand, it is necessary to construct a benign reading interaction mechanism between teachers and students, involving the reading interaction between professional teachers and students, and between counselors and students, and the informal reading organization of teachers and students. On the other hand, it is also necessary to build an active classmate reading interaction mechanism that include formal reading interaction and informal reading activities.

(3) Build a development mechanism for college students’ reading behavior. First, increase the reading time. Through opening characteristic reading courses, building a wealth of electronic reading resources, and providing an elegant and comfortable reading environment on campus, students are encouraged to read at any time. Second, use appropriate reading strategies. Encourage teachers to offer guidance on reading in their teaching process; organize clubs and library activities; invite famous teachers to give reading strategy lectures. Third, optimize the structure of reading contents, help students choose reading contents reasonably based on their needs and major, promote classic reading, and prevent recreational reading from becoming the main body of reading.

Research outlook

(1) Expand the distribution area and number of samples. The follow-up research will cover more samples, select some colleges and universities in the eastern, central and western regions as the survey objects, expand the sample size to about 4,000, and conduct interviews with graduates and teachers to further explore factors influencing college students’ reading and their relationship with academic achievement.

(2) Systematically carry out reading management in colleges and universities. Reading management needs to implement targeted reading intervention measures for 2–3 years and quantify specific indicators, which will inevitably produce good practical results. Reading management in colleges and universities should further expand its thinking and pay attention to the reading intervention of college students before they go to college. Especially for students who come from rural areas and have a relatively weak cultural background, it is necessary to transfer reading management to the stage of compulsory education.

(3) Promote classic reading. Classical reading has always been the most important part among college students, but there are various reading difficulties in practice. How to use the reform of reading carriers brought by technological reform, especially the method of “artificial intelligence plus classics” in practice, to improve the popularity and efficiency of classic reading is one of focuses for follow-up research.

(4) Research on the relationship between e-reading and academic achievement. With the increase of electronic resources and the popularization of media such as film, television media and e-readers, television, the Internet, and mobile phones have become the major reading methods used by the Chinese people. How to effectively use the “artificial intelligence plus reading” method, especially the application of VR and AR, to improve the academic achievement of college students, is worthy of further research in the context of technological innovation.

Data availability statement

Author contributions.

X-WW proposed the research hypothesis and research design, analyzed the experimental data, and analyzed the experimental results. Y-JZ contributed to questionnaires and collected data. Y-CZ designed the framework of the manuscript and discussed the experimental results. All authors contributed to the article and approved the submitted version.

This study was supported by the major teaching and research project of Anhui Province, Construction and Exploration of Quality Culture System of Private Universities (Project No. 2020jyxm0779) and the construction of school-level scientific research team of Anhui Xinhua College (Project No. kytd202206).

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.

Publisher’s note

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

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Linking Academic Relevance and Achievement Motivation to Students’ Dishonesty. Original Research Brief Report

Preprint from Research Square , 09 Jan 2024 https://doi.org/10.21203/rs.3.rs-3845579/v1   PPR: PPR784705 

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REVIEW OF ACADEMIC ACHIEVEMENT AND INFLUENCING FACTORS

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Extensive research has been done, to study the factors influencing the Academic Achievement. Research has been going on, in the area of Academic Achievement for decades. The available literature is presented under the following subheadings.

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Wendell Glenn Sudaria

Academic performance represents how well a student is achieving tasks and studies. This study show factors associated that affects academic performance of students such as; course, study habit, learning style, motivation, professor, and social factors. Based on the investigation of 42 astronomy students, this study analyses and evaluates the factors affecting academic performance of astronomy technology students and the relationship of their general weighted average to the rated factors. It shows that there are significant differences in the factors affecting academic performance. There is a marked negative relationship between the general weighted average and the factors affecting academic performance. It appears that students are optimistic in their academic performance, confident with their study habits, assertive with their learning styles, emphatic about their motivation, expresses more compassion in their studies compared to their social life, and their professors have shown professional, ethical and moral attitudes. The department should continue reinforcing students' reasoning and thinking skills especially if it is related to astronomy.

Sylvester Onyeka

International Journal of Statistics and Applied Mathematics

Katleho Makatjane

In this paper, the PLS-PM model has been estimated as to directly and indirectly identify factors that influence academic performance of the first year students at NUL. Sample used to utilise the task was 46. The estimated PLS-PM model was found stable and satisfying the SEM conditions. Several measure were established and found that 63% of variation of OWM is been explained by all those factors that are found to be significant. Also, seven factors were retained with factor loadings in the range of 0.4 to 0.81. Furthermore, the results of the discriminant analysis revealed that, 54% of female students are enrolled to the university while only 46% is for male students each year. 1. Introduction In all countries of the world, education is the most important sector of living; hence the major resources are plunged into it as an investment to human resource and the development of the country. The educational performance is influenced by various components including admission points, socio economic status and school foundation. Acato (2006) [1] ; Geiser and Santelices (2007) [18] all contend that admission points which are a reflection of the past performance has some impact on future performance of students. Tertiary institutions in Austria have found that a selection rank based on a student's overall performance is a predictor of success for most courses. As documented by Berthelot, Ross, and Tremblay (2001) [5] , the study agrees with the literature that admission points really distress the performance of university students and that is why the basic university entry admission points is a diploma points or mature age points. However, Berg (2012) [4] defines education as the conveyance of learning, aptitudes and information from teachers to students is lacking to capture what is truly vital about being and getting to be educated. Learning is taken to mean any change in behavior, knowledge, understanding, skills or capabilities which the greenhorn retains which cannot be ascribed simply to the physical growth or to the development of inherited behavior patterns. In the current study, two techniques are used to check two different issues. The first technique is the use of the structural equation model (SEM) through employment of the Partial Least Square Path Model (PLS-PM) to identify the factors that influences the academic performance of first year students at the National University of Lesotho (NUL) directly and indirectly. And lastly, in assessing the enrollment rate at the university, the K th nearest neighbor discriminant analysis with the discriminating factor as the sex structure of the student is engaged.

JOSEPHAT NCHUNGO

Ahmad Thawabieh

Aims: This study aimed to investigate the factors that affect students' achievement. Study Design: Quantitative descriptive & qualitative designs were employed in this study. Place and Duration of Study: The study was conducted in Tafila Technical University (TTU), Jordan, during Feb – May 2015. Methodology: The sample of the study consisted of 488 students (219 males and 269 females). The researcher used two methods to collect data; a questionnaire was developed to collect quantitative data, it consisted of 5 sections; the first section includes items for demographic information (gender, academic year, college and students' accumulated average). The other 4 sections were the questionnaire domains; each domain represents the achievement problems from students perspectives related to that domain; domain1 represents achievement problems related to students (10 items), domain 2: problems related to the faculties (7 items), domain 3: Problems related to courses (9 items), domain 4 problems related to test administration (13 items). In order to collect a qualitative data about factors affecting students' achievement, the researcher used focus group discussion (FGD). Results: The results indicated that the following factors affect students' achievement: courses, test administration, students, and faculties. The results indicated also statistical significant differences 2 (P = .05) attributed to gender on the achievement problems associated with test administration, courses and faculties; female students had higher mean in problems associated with courses and test administration, while male students were suffering more from problems associated with faculties. Finally, there are statistically significant differences (P = .05) attributed to colleges on the achievement problems associated with students and faculties; humanity college students have more problems related to students domain, while scientific colleges students have more problems associated with faculty domain. Conclusion: This study is aimed to determine the key factors that influencing students' achievement, the study showed that students' achievement was affected by the factors identified by the researcher; faculties, courses, students and test administration. Students vary in the degree of the effect of these factors according to their gender and the college they study in. The student performance would be improved if the academic institution leaders minimize the influence of the proposed factors and taking care of the psychological factors that influence students' achievement by increasing the role of counseling centers at the universities, providing better environment for assessing students' achievement, faculties must be more fair in assessing their students, Faculties Development Centers at Jordanian universities may need to focus on developing the methods of assessment that used by faculties, and faculties and administrators should advise the students about the factors that affect their achievement and how to overcome these factors. The academic achievement of the students depends on many factors; only 4 of them have been identified by this study. There may be other factors which may have a direct effect on students' achievement, such as; the influence of socioeconomic factors, teacher-student ratio, students attendance in the class, and mother and father education. Based on the findings of this study and in order to generalize the results, the researcher suggests that research should be extended to all Jordanian universities.

Arun Christopher T

Introduction In this era of globalization and technological revolution, education is considered as a first step for every human activity. It plays a vital role in the development of human capital and is linked with an individual's well-being and opportunities for better living (Battle & Lewis, 2002). It ensures the acquisition of knowledge and skills that enable individuals to increase their productivity and improve their quality of life. Hence school education plays a major part of every child's life. Academic Achievement is the focal point of school education system. Parents want their children to perform well leading to increased focus on academic achievement. Moreover, the quality of students' performance remains at top priority for government and educators. Researchers have long been interested in exploring variables contributing effectively for quality of performance of learners. These variables are inside and outside school that affect students' quality of academic achievement. Some factors may be termed as student factors, family factors, school factors and peer factors (Crosnoe, Johnson & Elder, 2004). This paper highlights some of the demographic factors viz. gender, family size, family income, parents' education, socioeconomic status (SES), and type of institution. Unfortunately, defining and measuring the quality of education is not a simple issue and the complexity of this process increases due to the changing values of quality attributes associated with the different stakeholders' view point (Blevins, 2009; Parri, 2006).

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  • Published: 10 January 2024

School feeding in Ethiopia: a scoping review

  • Samson Mideksa 1 ,
  • Tsegaye Getachew 1 ,
  • Firmaye Bogale 1 ,
  • Ermias Woldie 1 ,
  • Desalegn Ararso 1 ,
  • Aregash Samuel 2 ,
  • Meron Girma 2 ,
  • Masresha Tessema 2 &
  • Mamuye Hadis 1  

BMC Public Health volume  24 , Article number:  138 ( 2024 ) Cite this article

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Introduction

Undernutrition is a major public health problem in developing countries, especially in Sub-Saharan Africa. Undernourished children are smaller and have low weight. To solve this issue, school feeding (corn-soya blend, vegetable oil) started in 1994 in Ethiopia. Thus, this scoping review aims to map the evidence relating to school feeding programs and their potential role in managing children`s nutrition in Ethiopia.

This scoping review is informed by the methodological framework of Arksey & O’Malley for scoping reviews and recommendations on the framework by Levac and colleagues. The databases searched included the Education Resources Information Centre, International Initiative for Impact Evaluation, Cochrane Library, MEDLINE, and Google Scholar. To ensure its comprehensive search, grey literature sources were searched. The search was undertaken on 26 April 2023. Studies on school feeding, such as coverage, and studies that evaluate the educational and nutritional impacts of school feeding in Ethiopia, regardless of study designs, were included. Reports (publications) about school feeding without scientific methodology were excluded.

Twenty-seven studies were included in this review. It includes cross-sectional, prospective cohort, laboratory-based analysis, experimental, case study, and qualitative study designs. The school feeding program results were inconclusive, while some indicate a positive effect on body mass index, height, thinness, anemia, weight, dropout rate, class attendance, and enrollment. The others showed that the school feeding program did not affect stunting, thinness, weight, hemoglobin level, enrollment, attendance, dropout rate, and academic achievement. Factors affecting school feeding programs negatively include poor quality food and financial constraints. However, no literature on school feeding program coverage was found.

School feeding programs improved nutritional status, and academic performance, although some studies show any effect. Poor-quality food provisions and financial constraints affect school feeding programs. There are mixed findings, and further research is required to determine the effect of school feeding programs conclusively. To ensure the program's sustainability, it should be supported by a national policy, and budget allocation is needed. In addition, more evidence should be generated to show the coverage of school feeding programs in Ethiopia.

Peer Review reports

Undernutrition causes eight million child deaths worldwide every year [ 1 ] It is a major public health problem in developing countries, especially in Sub-Saharan Africa [ 2 ] Despite some positive progress in economic growth observed in many developing countries, undernutrition continues to be highly prevalent and is associated with poor health status and poor academic performance [ 3 ]. Undernutrition inhibits academic attainment through poor growth and mental development, reduced motivation and poor cognitive development [ 4 ]. On the other hand, being overweight and obese is reported to have the potential to impair academic performance via social pathways such as discrimination and stigma [ 5 ]. According to the Global School-Based Student Health Survey, the mean body mass index (BMI) estimates among adolescents in South Asia, Southeast Asia, East Africa, West Africa and Central Africa are < 20. The lowest age-standardized mean BMIs were seen in Ethiopia, Niger, Senegal, India, Bangladesh, Myanmar, and Cambodia [ 6 ]. The World Bank report also indicated that, academic performance in students of Sub-Saharan African countries is less than half of what is expected for their age based on the Africa Student Learning Index (ASLI) [ 7 ]. Ethiopia is among the countries where adolescent students’ academic achievement is low according to the ASLI score measured [ 7 , 8 ]

School age provides an opportunity to remedy nutritional and developmental deficits that were not addressed during early childhood [ 9 ]. Nutritional interventions in school-aged children have been reported to result in improved cognitive function [ 10 , 11 , 12 ]. The School Feeding Program (SFP) has been recognized as a platform for nutritional, health and educational intervention programs [ 13 ]. The contribution of SFP with regard to outcomes of energy intake, micronutrient status, enrollment and school attendance and academic achievement displayed relatively consistent positive effects [ 14 , 15 ]. Its positive effect on physical growth, cognitive and academic performance was less conclusive in some countries while substantial effect was seen elsewhere [ 16 , 17 ]. The SFP is also believed to pave the way to achieve sustainable development goals and to reduce inequalities in education. In sub-Saharan African countries, SFP showed an encouraging effect on learning outcomes and a small average effect on attendance [ 18 ]. In Ethiopia, evidence on the nutritional and educational effects of SFP is minimal with some pocket studies conducted at the sub-national level [ 19 ] The Ethiopian school feeding programme, which has been in operation for 30 years, is expanding its reach and putting more strategic emphasis on developing a pilot project that connects school feeding with regional agricultural production. The Ethiopian government is actively working to change the nation's agricultural sector, including its approaches to school feeding, through effective policies and projects [ 20 ].

This scoping review aims to map the evidence relating to school feeding programs and their potential role in managing children under nutrition. Thus, its main focuses are to explore coverage of school feeding, quality of school feeding, nutritional impacts, funding issues, and effects on educational achievements among students in Ethiopia. The study results will inform the public, donors, academia, policy makers and other stakeholders. To make judicious use of evidence regarding school feeding, scientific evidence regarding school feeding in the country should be summarized, analyzed and presented in an accessible format.

The scoping review used the methodological framework of Arksey & O’Malley for scoping reviews [ 21 ]. and recommendations on the framework by Levac and colleagues [ 22 ]. This framework has six stages: 1) identifying the research question; 2) identifying relevant studies; 3) study selection; 4) charting the data; 5) collecting, summarizing and reporting the results; and 6) consultation with relevant stakeholders. A protocol was developed and registered on Open Science Framework (OSF) on March 30, 2023 https://osf.io/5m6dh/ as OSF preregistration.

Identifying relevant studies

The following databases were searched to find relevant studies: Education Resources Information Centre (ERIC), International Initiative for Impact Evaluation (3ie), Cochrane Library, and MEDLINE. In addition, Google Scholar was used. Furthermore, to make the search as comprehensive as possible, grey literature sources were searched. These sources include databases of relevant organizations such as the World Health Organization (WHO), the World Food Programme (WFP), the Food and Agriculture Organization (FAO), China Foundation for Poverty Alleviation, United Nations International Children's Emergency Fund (UNICEF), United Nations Educational, Scientific and Cultural Organization (UNESCO), Addis Ababa University electronic library, and website of the Ministry of Education. Only studies published or written in English without date limits were considered. The search strategy included all identified keywords and index terms from MESH terms for the included database and/or information source. The reference lists of all included sources of evidence were screened for additional studies. The first search was undertaken on 30 April 2022 and updated on 26 April 2023.

Study selection

Studies on school feeding program or its impacts among Ethiopian students regardless of study design were included. Studies on both sexes and any form of school feeding intervention in the school compound were included. Surveys related to school feeding were also included. Reports that lacked scientific/systematic information and outcomes in terms of nutritional and educational outcomes were excluded.

The review process had two levels of screening: title and abstract review and full-text review. Articles retrieved were screened independently by two groups of reviewers (SM & EW) and (TG & DA) to assess eligibility, as determined by the inclusion criteria. Full copies of all potentially eligible papers were retrieved. Disagreements at any of the eligibility assessment processes were resolved through discussions and consultation with the team (MH & FB) where necessary.

Data extraction

Relevant information from selected studies was extracted using a form developed by the team. The data collection form included information on: author(s), year of publication, study design, and key findings as they relate to the scoping review question. Data extraction was independently conducted by two groups of reviewers. Differences among reviewers were resolved by involving the other reviewers from the team.

Literature search

The literature search in this review yielded 430 records from all the databases (Fig.  1 ). After removing duplicates and screening the titles and abstracts, 405 studies were excluded. Before going into full text screening, nine articles were additionally included from the reference of selected studies. Hence, the full texts of 34 potentially eligible studies were retrieved, and, seven studies were excluded.

figure 1

PRISMA flow diagram for the scoping review process

Excluded studies and reasons for exclusion

The studies excluded and their reasons for exclusion are summarized in Table  1  [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Seven studies were excluded among the selected full text articles. Studies were excluded for not looking at outcome of interest (2), not looking at intervention of interest (4) and a study that was only at a protocol stage (1).

Characteristics of included studies

All included studies were carried out between 2011 and 2022. The majority of the studies (19) were cross-sectional, while the remaining four were cohort studies, one was an experimental study, one was a case study, and another was a laboratory- based study. This scoping review examined the range and nature of literature on school feeding. The studies on school feeding in the Ethiopian context have covered different aspects including coverage of school feeding, quality of school feeding, nutritional outcome, funding issues, and effect on educational achievements among students in Ethiopia. The majority of the identified studies were focused on the educational impact of SFP.

Studies on nutritional impact

Ten of the studies included in the review had the nutritional status of SFP. These studies included cross sectional, cohort and lab-based analysis studies. The studies showed mixed results. Two cross sectional studies showed that SFP increased BMI, [ 30 , 31 ] another two studies found that SFP decreased thinness, [ 32 , 33 ]. two studies found that children who consumed school meals had reduced anemia, [ 34 , 35 ] one study found that SFP reduced underweight, [ 33 ] and another study found that height was increased among children in SFP [ 30 ]. In contrast, a study reported that the prevalence of stunting was greater among students who ate lunch at school, even though this difference was not statistically significant once the relevant confounders were taken into account [ 32 ]. Similarly, a study reported that caloric and nutritional contributions were less than two-thirds of the daily reference nutrient intakes (RNIs) needed from school meals, except those of fiber, thiamine, calcium (for early adolescents), and iron [ 36 ]. Other studies found no effect on stunting, [ 35 , 37 ] thinness, [ 37 ] anemia, [ 38 ] weight, [ 33 ] and height [ 35 ]. Generally, seven of the studies reported favorable results on the importance of SFP for nutritional status [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ] Table  2 summarizes the main aspects of the studies.

Studies on educational impact

Nineteen studies reported on educational impact (Table  3 ). The studies showed mixed results. Some of the findings conclude that the implementation of a school feeding program enhances academic achievement, [ 35 , 40 , 41 , 42 ] raises class attendance, [ 30 , 31 , 40 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ] reduces dropout rates, [ 42 , 43 , 44 , 49 ] and increases enrollment. [ 40 , 42 , 48 , 50 ] On the other hand, a study reported that stopping SFP increases male enrollment, and decreases class repetition [ 49 ]. In contrast, there were also studies that reported that, there is no significant effect of SFP on the dropout rate, [ 34 , 48 , 51 , 52 ] academic achievement, [ 46 , 53 , 54 ] and attendance [ 34 , 48 , 51 , 53 , 54 ]. The majority of studies (12 studies) have reported favorable results on the importance of school feeding programs for educational outcomes.

School feeding on other variables

Few studies have reported on the quality of food [ 39 , 45 , 55 ] in SFP programs and financial or funding constraints [ 50 , 55 ]. These studies included qualitative studies conducted to explore the advantages and challenges of the program. One of the indicated advantages of the SFP program is the contribution of the program in saving parents money and time as the SFPs were making use of (purchasing) local food and agricultural development. Despite the advantage of SFP, studies highlighted challenges related to food provision, infrastructure, and administration.

Challenges related to food provision included a lack of hygienic, adequate, regular, and quality food. Infrastructure challenges, including a lack of independent SFP structures at various levels making implementation and sustainability challenging; a lack of training for cooks; a lack of physical capital, such as feeding utensils, electricity, and water, exacerbated by administration problems, such as inadequate stakeholder engagement, absence of clear policy and financial constraints were reported. Table 4 .

This scoping review aims to map the evidence relating to school feeding programs and their potential role in managing children under nutrition. The main objectives are to explore school feeding programs coverage, quality, nutritional impacts, funding issues, and its impact on educational achievements among students in Ethiopia. The finding of the study indicates mixed results.

School feeding shows an improvement in BMI among underweight students. Additionally, body fat is increased among thin students. Furthermore, the hemoglobin status increased among anemic students. There are also increased weight and height [ 30 , 31 , 32 , 33 , 34 , 35 ]. This shows that school feeding programs improve the overall anthropometric status. A study from South Africa also obtainedsimilar results. The school breakfast programme improved anthropometric measurements with a 10% increase in the number of children within the healthy BMI range for their age [ 56 ] Similarly, a study in Kenya reported that the school feeding programme clearly had a positive effect on children’s nutritional status. The programme reduced anemia and malnutrition and improved child growth [ 57 ] However, some studies have shown that SFP has no effect on stunting, thinness, weight, and hemoglobin level [ 33 , 35 , 37 , 38 ]. Similarly, a study reported that caloric and nutritional contributions were less than two-thirds of the daily RNIs needed from school meals [ 36 ]. This signifies that there is a need for further study.

School feeding programs improve academic performance, [ 34 , 40 , 41 , 45 ] increase class attendance, [ 30 , 31 , 40 , 43 , 44 , 45 , 46 , 47 , 49 , 50 ] decrease the dropout rate, [ 42 , 43 , 44 , 49 ] and increase enrollment. [ 40 , 42 , 48 , 50 ] The majority of studies have reported favorable results on the importance of school feeding programs for educational outcomes [ 30 , 31 , 34 , 40 , 41 , 42 , 43 , 44 , 45 , 47 , 48 , 50 ]. Different studies in different countries also show consistent results with this study. A study in Uganda revealed that, SFP had large impacts on school attendance, and reduced grade repetition [ 58 ]. Nutritional interventions in school-aged children have been reported to result in improved cognitive function [ 9 , 10 , 11 ]. Furthermore, this study found that meals provided in the morning help students better than those provided at the end of school [ 41 ]. A similar study in South Africa revealed that meals served at breakfast are also shown to have a positive impact [ 56 ] Children who consume a meal before learning have better short-term memory function, as the brain activated differently based on nutrient supply [ 59 ] However, some studies indicate that, SFP has no effect on attendance, dropout rate and academic achievement [ 34 , 46 , 48 , 51 , 52 , 53 , 54 ]. If loss of teaching time could be the factor for no significant difference in achievement test scores, it might be simpler to offer a take home ration program to prevent disturbance during the school day [ 60 ]. This indicates the need for additional research.

There were also advantages and challenges of the school feeding program. Advantages of the SFP programs are the contribution of the program in saving parents money and time, as the SFPs were making use of (purchasing) local food and agricultural development. Nevertheless, the challenges related to food provision included a lack of hygienic, adequate, regular, and quality food. Infrastructure challenges, including a lack of independent SFP structures at various levels making implementation and sustainability challenging; a lack of training for cooks; and a lack of physical capital, such as feeding utensils, electricity, and water exacerbated by administration problems, such as inadequate stakeholder engagement; and the absence of clear policy and financial constraints [ 50 , 55 ].

There is a shortage of evidence on SFPs in Ethiopia. The majority of the studies identified were cross-sectional studies. Therefore, there is a need to conduct more research using higher quality study designs and quantitative research for decision and policy-making.

The limitations of this study were low quality of study design, primarily, cross sectional. Although the study results are mainly from low study designs, the findings of this review have the first hand information to inform the importance of school feeding programs and their effect on nutritional status and academic performance.

School feeding programs in Ethiopia showed mixed findings on nutritional status and academic performance. Besides, poor-quality food provisions and financial or funding constraints affect school feeding programs. The SFP should take into account the nation's diversity, including its geography, climate, agrarian and pastoral areas. Although there are studies conducted in different areas of the country and schools, it is important to conduct nationwide study to conclusively determine the coverage, nutritional, and educational effect of the SFP in Ethiopia.

Strengths and limitations of this study

This is the first scoping review on school feeding in Ethiopia. The search strategy included four electronic databases, including ancestor searching and grey literature (both government and organization websites). Additional literature was sought from relevant bodies such as the ministry of education and experts on the area. This scoping review provides information on the range and nature of evidence on school feeding in Ethiopia and identifies research gaps. Nevertheless, the study was limited to reviewing findings only from publications and grey literature with scientific methods and might therefore miss important information from other sources.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Africa Student Learning Index

Body Mass Index

Education Resources Information Centre

Food and Agriculture Organization

International Initiative for Impact evaluation

School Feeding Program

United Nations Educational, Scientific and Cultural Organization

United Nations International Children's Emergency Fund

World Food Programme

World Health Organization

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  4. PDF THE FACTORS AFFECTING ACADEMIC ACHIEVEMENT: A SYSTEMATIC REVIEW OF ...

    The study aimed to identify the factors and to demonstrate their effects on academic achievement in various publications that utilized meta-analyses. For this purpose, the meta- analyses publications on the Web of Science-All Database till 2018 were reviewed. In the study, the systematic review method was adopted.

  5. The Importance of Students' Motivation for Their Academic Achievement

    Achievement motivation energizes and directs behavior toward achievement and therefore is known to be an important determinant of academic success (e.g., Robbins et al., 2004; Hattie, 2009; Plante et al., 2013; Wigfield et al., 2016 ).

  6. Academic Achievement: Influences of University Students' Self

    Literature Review and Hypotheses Development. ... 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 ...

  7. Motivation-Achievement Cycles in Learning: a Literature Review and

    1 Mention Explore all metrics Abstract The question of how learners' motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education.

  8. The effect of cognitive ability on academic achievement: The mediating

    Academic achievement. In the current research, in order to reduce the influence due to the level of students' test performance, the average of the students' four test scores in the semester when the cognitive ability was tested to be worthy was used as the academic score for each subject, and the raw scores were standardized (scores were ...

  9. Full article: The self-efficacy and academic performance reciprocal

    Understanding the determinants of academic achievement in higher education contexts has been a significant focus of research for several decades (Richardson et al., Citation 2012; Robbins et al, Citation 2004; Schneider & Preckel, Citation 2017).Among these determinants, self-efficacy has consistently emerged as a highly influential motivational variable (Honicke & Broadbent, Citation 2016 ...

  10. Personality Traits and Academic Achievement

    To address this gap in the educational research literature, we conducted a meta-analysis based on 78 studies, with 1491 effect sizes representing data from 500,218 students and 110 samples from elementary to high school. ... Students' academic achievement is a central predictor of a long list of important educational outcomes, such as access ...

  11. Achievement at school and socioeconomic background—an ...

    However, examining Sirin's 5 meta-analysis of the research into socioeconomic status and academic achievement finds that many studies use a combination of one or more of parental education,...

  12. The Importance of Students' Motivation for Their Academic Achievement

    The few existing studies that investigated diverse motivational constructs as predictors of school students' academic achievement above and beyond students' cognitive abilities and prior achievement showed that most motivational constructs predicted academic achievement beyond intelligence and that students' ability self-concepts and task values...

  13. Full article: Academic performance and assessment

    Scholars agree that students' academic achievement is a 'net result' of their cognitive and non-cognitive attributes (Lee & Shute, 2010; Lee & Stankov, 2016) as well as the sociocultural context in which the learning process takes place (Liem & McInerney, 2018; Liem & Tan, 2019 ).

  14. Frontiers

    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.

  15. (PDF) Academic Achievement

    Academic achievement as measured by the GPA (grade point average) or by standardized assessments designed for selection purpose such as the SAT (Scholastic Assessment Test) determines whether a...

  16. Academic Achievement

    Academic achievement as measured by the GPA (grade point average) or by standardized assessments designed for selection purpose such as the SAT (Scholastic Assessment Test) determines whether a student will have the opportunity to continue his or her education (e.g., to attend a university).

  17. Predicting academic success in higher education: literature review and

    As indicated in Table 1, the most influential factor observed in the literature is Prior Academic Achievement. Related data, that is to say, pre-university or university-data, can easily be retrieved from the university Student Information System (SIS) that are so widely used nowadays. ... Croatian Operational Research Review, 7(2), 367-388 ...

  18. Academic success from an individual perspective: A proposal for

    The examination of academic achievements is common in educational research literature, with most studies referring to grades (marks) as measures of success. In addition, outside the realm of research, a student's grades are usually the main criteria for admission to education programmes, nomination for honours (passing above ordinary level), award of scholarships and so forth. However ...

  19. Parent involvement and student academic performance: A multiple

    It was hypothesized that parent involvement would predict academic performance, as measured by both the WIAT-II achievement score and teacher ratings of a child's classroom academic performance. As shown in Table 2 , parent involvement was a significant predictor of the child's WIAT-II score F (3, 154) change = 9.88, p < .01, β = .20, over and ...

  20. (PDF) Academic Achievement

    1.50. Sadegh Zare* et al. International Journal Of Pharmacy & Technology. Discussion. The aim of this study was investigating the r elationship between academic achievement motivation and academic ...

  21. An empirical study of college students' reading engagement on academic

    Combing through the literature related to metaverse reveals that many scholars focus on the impact of metaverse on games, literary travel, education and other fields, but there are few studies on the connection between metaverse and reading and how metaverse will bring changes to reading. ... Academic achievement. Research on the academic ...

  22. PDF A Literature Review on The Academic Achievement of College Students

    The study of academic achievement of college students is an effective way to promote the quality of higher education process. Through a literature review related to academic achievement of college students, it is revealed that the measurement indexes of academic achievement tend to be diversified and the measurement tools are more perfect.

  23. Linking Academic Relevance and Achievement Motivation to Students

    Europe PMC is an archive of life sciences journal literature. Linking Academic Relevance and Achievement Motivation to Students' Dishonesty. ... Linking Academic Relevance and Achievement Motivation to Students' Dishonesty. Original Research Brief Report. Koscielniak M. Preprint from Research Square, 09 Jan 2024 https: ...

  24. Review of Academic Achievement and Influencing Factors

    Academic performance represents how well a student is achieving tasks and studies. This study show factors associated that affects academic performance of students such as; course, study habit, learning style, motivation, professor, and social factors.

  25. The Effects of Academic Press on Student Learning and Its Malleability

    Purpose: The purposes of this study were to: (a) meta-analyze the effects of academic press (AP) on K-12 student achievement in aggregate and in each examined learning subject; (b) meta-analyze the effect of school leadership of different leadership styles on AP; and (c) examine whether school level, subjects, and leadership or AP measures moderate these above-mentioned effects.

  26. School feeding in Ethiopia: a scoping review

    The others showed that the school feeding program did not affect stunting, thinness, weight, hemoglobin level, enrollment, attendance, dropout rate, and academic achievement. Factors affecting school feeding programs negatively include poor quality food and financial constraints. However, no literature on school feeding program coverage was found.

  27. Eduardo Esteban Bustamante is senior author of paper published in PLOS ONE

    Eduardo Esteban Bustamante is senior author of "Sociodemographic disparities in sedentary time among US youth vary by period of the day" published in PLOS ONE. María Enid Santiago-Rodríguez '21 PHD KN is lead author of the study.David Marquez is among the authors.. The group analyzed accelerometry data in a representative sample of US youth, and found that sex disparities in sedentary ...

  28. Saif Islam Receives Technology Achievement Award from SPIE

    Saif Islam, an electrical and computer engineering professor and director of the Center for Information Technology Research in the Interest of Society and Banatao Institute at the University of California, Davis, has received the 2024 SPIE Aden and Marjorie Meinel Technology Achievement Award.. The annual award recognizes outstanding technical accomplishments in optics, electro-optics ...

  29. Spring 2024 Important Dates

    Thursday, March 7: Midterm grades available for 000 and 100 level courses. Friday, March 8: Last day for students enrolled in 8 week Part of Term B course to add/drop a course. Friday, March 15: Last day for undergraduate students to request a late course drop for a spring 16 week course. Monday, March 18 : Application for Summer 2024 and Fall ...