low academic performance literature review

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Original research article, defining the profile of students with low academic achievement: a cross-country analysis through pisa 2018 data.

• 1 Department of Methods of Research and Diagnosis in Education II, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
• 2 Department of Research and Psychology in Education, Universidad Complutense de Madrid, Madrid, Spain

The explanation of underachievement and the search for its associated factors have been of constant interest in educational research. In this regard, the number of variables that have been involved in its description and explanation has increased over the years, as has the number of studies at an international level on this topic. Although much research has focused on identifying the personal, family, and school aspects that exert the greatest influence on students’ low academic performance, the literature shows the need to study the differential effects of said variables according to the countries in which the studies are conducted. The objective of this article is therefore to analyse cross-national differences in the effect of personal, family, and school characteristics on students’ academic underachievement based on data derived from the Programme for International Student Assessment (PISA) 2018. Furthermore, it aims to identify the profile that characterises students with the lowest academic performance and to estimate the importance of the selected variables in explaining low achievement across countries. To reach these goals, the multivariate technique of decision trees through the binary CART (Classification and Regression Trees) algorithm was used, allowing the estimation of both a global model and nine specific models for each of the selected countries. The results show that, despite slight differences between the countries analysed, the variables that define the general profile of students with the lowest achievement and which have shown the strongest predictive capacity for low performance are mainly linked to the students themselves. These variables are followed in importance by family aspects, which present great differences between the territories that compose the sample. Finally, teacher and school variables have shown to have a low explanatory capacity in this study. It can therefore be concluded that, although personal characteristics continue to be those that best explain academic performance, a series of contextual variables, especially related to families, appear to influence academic achievement differentially and may even hide or cancel out certain personal characteristics.

Introduction

A reduction in low academic achievement, related to the non-attainment of learning goals for a student’s level, age, or ability ( Lamas, 2015 ), is one of the main objectives of current education systems. However, there is a clear lack of agreement when it comes to establishing the most appropriate standards or criteria for its definition ( Gorard and Smith, 2003 ).

These standards may refer to the students’ performance, to the performance of the group they belong to, or even to previously established external criteria ( Gutiérrez-de-Rozas and López-Martín, 2020 ). For this reason, in a particular situation of low performance, a student can present insufficient attainment—by not achieving the educational objectives established for all the students—or an unsatisfactory performance—by performing below what could be expected based on his or her abilities ( Jiménez Fernández, 2010 ). Hence, a situation of low academic performance may or may not exist depending on the standard used.

The International Association for the Evaluation of Educational Achievement (IEA) and the Organisation for Economic Co-operation and Development (OECD) apply standards based on external criteria in their assessments—that is, Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) by IEA, or the Programme for International Student Assessment (PISA) by OECD. In these assessments, students are considered to be low-performing students if they are placed at the Low International Benchmark ( Mullis et al., 2020 ) in TIMSS and PIRLS, or below Level 2 in PISA ( Organisation for Economic Co-operation and Development [OECD], 2019a ). Despite the utility of these standards for making international comparisons in the level of academic achievement between different countries, they do not capture the variability existing within countries. In this regard, results from the PISA 2018 assessment showed that around 71.8% of students in the Philippines were low-performing students in the three areas considered, while only 1.1% of the students were low-performing students in Beijing, Shanghai, Jiangsu, and Zhejiang ( Organisation for Economic Co-operation and Development [OECD], 2019b ). These unequal results may hide, among other things, different socio-cultural realities. Thus, being placed at one level or another has different implications in each territory.

Regardless of the contextualisation of student underachievement and the way of evidencing it, there is no doubt that knowing the aspects that facilitate or hinder academic performance is key to providing an adequate response to the educational needs of students. For this reason, numerous empirical studies have focused on identifying and analysing the predictive capacity of the conditioning factors of academic performance ( Kornilova et al., 2009 ).

Within these factors, and despite the interrelationship among the variables that influence learning ( Bhowmik, 2019 ; Akbas-Yesilyurt et al., 2020 ), since the past century, academic literature has been highlighting the strong influence of students’ personal characteristics, together with other contextual aspects, on their educational outcomes. As proof of this, in the review conducted by Sipe and Curlette (1997) , student characteristics had the largest effect sizes on academic achievement, followed by school variables and, finally, family aspects. Subsequently, Hattie (2003) showed that, when the interactions between variables were ignored, student characteristics predicted 50% of performance, while teacher characteristics explained 30%. The author attributed much smaller influences to school, peers, school leaders, and family characteristics (between 5 and 10%). In line with these results, the most recent meta-analytic evidence shows the effects of some specific personal aspects, such as the use of self-regulated learning strategies ( Ergen and Kanadli, 2017 ), intelligence ( Zaboski et al., 2018 ), or some personality types ( Poropat, 2009 ), on academic performance. Therefore, there is ample scientific evidence, generated since the last century, for the differential influence of personal, family, and school variables on students’ academic performance.

In this sense, Hattie’s ( 2009 , 2017 ) work should be highlighted as one of the most important international review studies in the field, since this author identified the influence of personal, family, and school variables on student academic performance by compiling the existing meta-analytical evidence. His research is of particular interest due to the vast amount of evidence that it summarises and also for its systematicity, as the author classifies these conditioning factors of academic performance into 22 categories and 66 subcategories. The results of his research show that previous high academic performance and self-efficacy are the personal variables that most positively influence academic achievement. On the contrary, some personal factors, such as boredom, depression, minority language use, superficial motivation, sleep problems, attention deficit hyperactivity disorder, or hearing difficulties showed the strongest negative effects. Among the family variables, the author demonstrated the positive influence of a favourable home environment and high socio-economic status and highlighted the negative effects of corporal punishment, excessive television viewing, or being a beneficiary of welfare policies. Finally, among school and teacher variables, this author found that teacher efficacy had the strongest positive influence, while student suspension, excessively long summer holidays, or changes of school by students were the aspects with the most negative influence on academic performance.

However, despite these general findings, the literature warns the differential effects of conditioning factors on academic performance depending on the countries in which the studies are conducted. For example, the study by Ghasemi and Burley (2019) revealed the existence of differences in the predictive capacity of gender in mathematics in the countries analysed. Also, Ning et al. (2015) , using PISA 2009 data, showed that the influence of school disciplinary climate on students’ academic performance presented cross-national differences. This is to be expected given that each territory has its own socio-economic, cultural, political, and educational characteristics; that the aspects that condition academic achievement are interrelated ( Bhowmik, 2019 ; Akbas-Yesilyurt et al., 2020 ); and that, in accordance with the ecological systems theory, inhabitants are influenced by the countries they live in ( Hampden-Thompson et al., 2013 ). It can, therefore, be deduced that any macro-level differential aspects between countries may affect the characteristics of the students, the education given by families, and the education provided in schools.

Programme for International Student Assessment: Assessment of competences and associated factors

The Programme for International Student Assessment (PISA), aims to evaluate the extent to which students in the participating countries have acquired the knowledge and skills that are required to fully participate in today’s societies by the end of compulsory education ( Organisation for Economic Co-operation and Development [OECD], 2019c ).

This assessment analyses students’ proficiency in science, mathematics, and reading—the 2018 edition also includes global competence—through a series of tests that provide an updated and comparative overview of students’ academic performance at the age of 15 years. Said performance does not only refer to the level of knowledge acquired in the areas assessed but also to the degree of skills and competence development in these domains. In each PISA edition, the OECD focuses its analysis and conclusions on one of the skills assessed, thus establishing it as the main domain. In the 2018 edition, as was the case in 2000 and 2009, the focus was placed on reading literacy—understood as “students’ ability to understand, use, evaluate, reflect on and engage with text to achieve their purposes” ( Organisation for Economic Co-operation and Development [OECD], 2019c , p. 15).

Through the PISA assessments, the OECD aims not only to provide countries with information on the performance of adolescents in their education systems but also to enable them to understand the results obtained by students in other participating countries and to analyse and compare educational policies ( Organisation for Economic Co-operation and Development [OECD], 2019a ). Therefore, context questionnaires are applied as a supplement to achievement tests to identify the characteristics of education systems, interpret the results obtained, and understand the factors that are linked to success or failure from both a national and a comparative perspective ( López-Martín et al., 2018 ).

These questionnaires collect contextual data of students—including personal, family, and school aspects—but only a small part of the contextual information is provided by teachers and families. This aspect deserves special consideration, as students’ perceptions often explain variation in learning outcomes beyond what could be attributed to background characteristics themselves ( Van Petegem et al., 2007 ).

Another issue that also deserves consideration is that the OECD has not only added new items to the contextual questionnaires over the successive editions of PISA but has also developed and implemented new full questionnaires, such as the ICT familiarity questionnaire, the educational career questionnaire, the financial literacy questionnaire, or the well-being questionnaire.

Regarding the topic of student well-being, defined as “the psychological, cognitive, social and physical functioning and capabilities that students need to live a happy and fulfilling life” ( Organisation for Economic Co-operation and Development [OECD], 2017 , p. 35), it should be mentioned that, while information on this construct was collected through certain items of the student questionnaire in previous PISA editions, the specific well-being questionnaire was applied for the first time in 2018. Thus, the importance given to this construct by the OECD is in line with the findings of current empirical evidence, which is highlighting the prominent role of adolescent well-being in both positive adolescent development and success in learning processes ( Holzer et al., 2021 ).

Present study

For all the above, this study aims to analyse cross-national differences in the effect of personal, family, and school characteristics on students’ academic underachievement based on the data derived from the PISA 2018 assessment. Also, this article aims to identify the profile that characterises students with the lowest academic performance and to estimate the importance of the selected variables in the explanation of low performance across countries.

Therefore, this research seeks to help eliminate existing knowledge gaps relating to the differential influence of personal, family, and school variables on students’ low academic performance across countries. Thus, the present study goes beyond the conventional research approach into the conditioning factors of academic performance and, more specifically, academic underachievement, in which the particularities of each territory are usually not considered.

For this purpose, the multivariate decision tree technique, through the binary CART (Classification and Regression Trees) algorithm ( Breiman et al., 1984 ), is used. This technique is considered to be particularly suitable for yielding insights into the research question posed because, as Razi and Athappilly (2005) state, being a non-parametric procedure that allows the prediction of a continuous dependent variable from categorical independent variables, it fits the data perfectly. Also, as the cited authors affirm, CART models provide better predictions than regression models when the predictors are binary or categorical and the dependent variable is continuous. In addition, this model allows the creation of subsets of homogeneous data for the dependent variable, and calculation of the relative importance of each of the independent variables in explaining said dependent variable. Moreover, this technique allows a more thorough study of the variables that influence low performance not only globally but also comparatively across countries. Finally, it is noteworthy that several studies have already used this technique satisfactorily to analyse PISA data ( Asensio Muñoz et al., 2018 ; López-Martín et al., 2018 ; Arroyo Resino et al., 2019 ; She et al., 2019 ). For all these aspects, the multivariate decision tree technique, through the binary CART algorithm, is used here to achieve the objectives proposed in this article.

After the above introduction, the rest of this article is structured as follows: first, the method is described. Then, the profiles of students with the lowest academic performance are presented together with the standardised importance of the analysed variables in explaining low performance in the selected countries. The article concludes with a discussion of the main results.

Materials and methods

Population and sample.

The study population was composed of 15-year-old students from the countries that completed all the student context questionnaires in PISA 2018. After excluding from the selection process all the territories that did not apply all context questionnaires to their students, the final selection was based on nine countries. The final sample consisted of 97,878 students from Bulgaria (5.4%), Georgia (5.7%), Hong Kong SAR (China) (6.2%), Ireland (5.7%), Mexico (7.5%), Panama (6.4%), Serbia (6.8%), Spain (36.7%), and the United Arab Emirates (19.7%). Therefore, information was available from Europe, Asia, and Latin America. The main socio-demographic, political, and economic characteristics of the selected countries are described in Appendix A .

As reflected in Table 1 , the sample was weighted using the normalised weight variable SENWT—when analysing the overall information from the set of countries considered—or the student sampling weight W_FSTUWT—when analysing the data individually for each of the countries ( Organisation for Economic Co-operation and Development [OECD], n.d. ). In this regard, it should be clarified that the SENWT variable assigns the same value to the samples of each country to ensure an equal contribution to the analysis, while the W_FSTUWT variable adjusts the samples to the population size of each country so that each contribution depends on population size.

Table 1. Sample description.

Information derived from achievement tests and context questionnaires administered to students in the PISA 2018 assessment was selected. Reliability and validity evidence of the scales can be found in the PISA 2018 Technical Report, 1 in which information about the sampling procedure, the questions included in each questionnaire, the sample items, and the response scale are provided.

As the dependent variable of this study, low academic performance in reading, which was the core subject in the PISA 2018 assessment, was considered. Taking into account that one of the main features of the PISA assessment design is that it reports students’ academic performance through 10 plausible values, the following procedure was followed to estimate the variable “low academic performance”:

• Calculation of average reading literacy performance for each country.

• Classification of students as having low academic achievement: YES—in cases where their performance was below the estimated average performance for their country—and NO—if their reading literacy score was equal to or above the average performance for their country. This classification was made for each of the 10 plausible values provided by PISA.

In this regard, it should be noted that establishing the average performance of each country as a reference point for calculating this variable intends to overcome the limitation of PISA performance levels by considering the internal variability of each territory.

Contextual information was obtained by selecting some of the indices estimated by the OECD from students’ responses to the student and well-being questionnaires. For this selection, Hattie’s ’s ( 2009 ) work was taken as a reference, as it proposes a classification of personal, family, and school aspects whose influence on performance has been analysed and demonstrated in the meta-analytical literature ( Table 2 ).

Table 2. Independent variables according to Hattie’s ’s ( 2009 ) categories and subcategories.

The response rate for all variables was above 70%—except for Learning time (in total)-minutes per week , which had a response rate of 55%.

The multivariate technique of decision trees through the binary CART algorithm ( Breiman et al., 1984 ) was used. As mentioned previously, this technique allows the creation of subsets of data that are as homogeneous as possible with respect to the dependent variable, as well as the calculation of the relative importance of each independent variable in explaining the dependent variable.

To identify the personal, family, and school factors that explain low academic performance, first, 10 global models were estimated for the set of countries in the study sample. Second, the average importance of the predictors obtained in the 10 models was calculated. Finally, the relative importance of each independent variable with respect to the predictor that emerged as most relevant in explaining academic performance was calculated. In other words, the standardised importance reflects the impact of each of the independent variables in the model, so that it is possible to observe which are the most important ( de Oña et al., 2012 ). Thus, the relative importance of the most relevant variable would be 100%, while the rest of the variables would be attributed importance proportional to that of 100%. This same procedure was performed with each of the subsamples corresponding to the different countries.

As shown in Table 1 , the data were weighted using the SENWT variable in the estimation of the global model to ensure that the contributions of each of the countries were equal, regardless of their sample size. In the models estimated for each of the countries, the values were weighted by the final student weight (W_FSTUWT).

Together with the standardised importance of each of the independent variables, we present the variables that compose the profiles of students with the lowest reading performance in the global model and in each of the nine selected countries.

SPSS Statistics version 27 was used to conduct the analyses.

The results of the global model—relative to the set of countries that comprise the sample—and of the specific models—for each of the nine countries—are presented below. As can be seen in Table 3 , the overall average classification rate of the estimated models, which reveals the models’ ability to correctly classify the variables through a percentage, is situated between 68.70% (average for the 10 general models) and 77.26% (average for the 10 Irish models).

Table 3. Overall average classification rate of the models estimated from the 10 plausible values.

Based on these decision trees—calculated for the whole sample and for each of the countries—the general most extreme profile of students with the lowest academic achievement in reading literacy is presented, along with the specific models that reflect cross-national differences in the effect of personal, family, and school characteristics on low performance.

Profile of students with the lowest reading achievement

This section shows the general most extreme profile of students with low academic achievement in reading using the decision tree estimated for the whole sample. In this estimation, a depth of six levels was established. However, since this article seeks to analyse the variables that best describe the profile of students with the lowest achievement in reading, only the variables that appear in the first three positions of the branch in which said profile is represented are displayed below ( Table 4 ).

Table 4. Variables that best describe the profile of students with the lowest academic achievement in reading literacy.

The student profile with the lowest achievement in reading is best defined by the three variables that emerged ordered by their discriminatory capacity, which decreases on descending the nodes of the tree. Thus, the variable Meta-cognition: summarising appears in first place with regard to segmentation of the sample, as it has the greatest discriminatory capacity when characterising students with the lowest performance in the 10 global models. The second variable that allows charactirisation of students with the lowest achievement is Joy/like reading , meaning that a lack of enjoyment of this activity may be another key aspect in low performance. After that, Self-concept of reading: perception of difficulty and Self-concept of reading: perception of competence emerge (eight times and twice, respectively) in the third position, indicating that students with very low reading achievement would also consider reading as a difficult task and would regard themselves as not sufficiently prepared to complete it satisfactorily.

Table 4 also shows the aspects that characterise students with the lowest achievement across countries. As can be seen, personal variables linked to meta-cognition, joy/like reading , and cognitive self-concept present a notable discriminatory capacity in the profile of students with the lowest academic achievement in most of the countries considered, which is in line with the results of the general model. In this sense, it is worth noting that Panama is the only country in which no metacognitive variable appears in the profile.

Work mastery is also a prevalent variable in relation to the personal dimension, being found in the models of three of the countries (Mexico, Panama, and the United Arab Emirates). In addition, Subjective well-being: sense of belonging to school appears as a defining variable in Bulgaria and Student’s expected occupational status does in Serbia. Finally, Attitude towards school: learning activities is found in Ireland and Learning time (minutes per week)-in total in Panama; nevertheless, each of these only appears in one of the 10 estimated models.

Home possessions emerge as a family variable with a high discriminatory capacity in Bulgaria, Georgia, Mexico, and Panama. However, it is not present in Hong Kong SAR (China), Ireland, Spain, or in the United Arab Emirates. Also, Immigration background is found in the profile of students with very low academic performance in the United Arab Emirates. Finally, Social connection to parents and Highest occupational status of parents show some presence in Georgia and Serbia and in Mexico, respectively.

School and teacher variables are poorly represented among those aspects characterising students with the lowest academic achievement, except for Teacher-directed instruction and Student’s experience of being bullied in Serbia and Perception of cooperation in Georgia, all of which show a minor presence among the first three positions of the models.

Beyond these results, it is necessary to consider that there are differences in the average level in the achievement of the selected countries. Table 5 shows the mean achievement for each country and the data on student underachievement. These data were used to estimate the models that describe the profile of students with very low academic performance in each country. Hence, the percentage of students with a low performance can be observed to be below 50% in Hong Kong SAR (China), Ireland, Serbia, and Spain, but above 50% in the remaining countries.

Table 5. Mean performance in reading for each country and number of students with low achievement.

Variables with the highest explanatory capacity for low reading achievement

To analyse the cross-national differences in the personal, family, and school characteristics of students that most contribute to explaining low academic achievement in reading, this section presents the standardised importance that these predictors have in both the overall model and each of the models estimated for the nine countries.

In this vein, it should be clarified that the models have been estimated by considering all the indices together. However, the results are presented in three sections based on each of the three ambits considered—personal, family, or school—for easier reading. It is also noteworthy that the standardised importance corresponding to each of the independent variables is established according to the variable that best contributes to explaining the dependent variable, to which a value of 100% is attributed.

Student variables

The results presented in Table 6 show that the variable Joy/like reading has the greatest explanatory capacity for low performance in the global model. Although the importance of this ability differs among the countries considered, it can be found among the top five positions in all the territories except for the United Arab Emirates, Mexico, and Panama. The rest of the variables that constitute the subcategory Attitude to school subjects present low standardised importance in the overall model.

Table 6. Standardised importance of the independent student variables.

The three metacognitive indicators can be considered to show a high explanatory capacity both in the overall model and in each of the selected countries. However, this explanatory capacity differs among territories. Hence, Meta-cognition: summarising is the variable that most contributes to explaining low performance in Spain. It can also be found among the top five positions in all the remaining countries. Moreover, Meta-cognition: understanding and remembering leads the chart in Serbia and occupies the second position in Georgia, and Meta-cognition: assess credibility occupies the first position in Hong Kong SAR (China), and Ireland.

Cognitive self-concept is another construct that plays an important role in explaining low reading achievement across countries. At least one of the two cognitive self-concept variables appears at the top of the table in all the countries considered, except for Serbia. By contrast, Body image , which is related to physical self-concept, has an unremarkable explanatory capacity in all the selected countries, ranking below the middle of the table in most of the models.

Regarding motivational variables, only Work mastery and Student’s expected occupational status appear to be remarkable in the overall model. However, differences between the nine countries in relation to both aspects are noteworthy. On one hand, Work mastery is in the top five positions in Panama, while it is situated at the bottom of the ranking in Hong Kong SAR (China). On the other hand, although Student’s expected occupational status appears in the first half of the table in all the countries analysed, it only ranks among the top five positions in Serbia. Finally, the variables Eudaimonia: meaning in life , Subjective well-being: positive affect , and Mastery goal orientation are situated outside the top positions in all the models.

Regarding personality indices, Resilience is the variable in this category with the greatest explanatory capacity in the overall model and that is best positioned in the said category in most of the selected countries. However, this set of variables plays a minor role in all the countries analysed.

The variable Learning time (minutes per week)-in total shows a standardised importance rate of 19.2% in the overall model. However, the cross-country differences in relation to this aspect are notable: while this variable is located near the centre of the table in most countries, it can be found leading the table in Panama.

Finally, the variables Body mass index of student and Duration in early childhood education and care are located in the second half of the table in most of the countries with the exception of the former in Hong Kong SAR (China), Ireland, and Panama and the latter in Panama.

Family variables

The results reflecting the standardised importance and position of the family variables ( Table 7 ) show that socio-economic and cultural factors are the aspects related to the family environment that most contribute to explaining low performance in the global model—with the exception of Educational level of parents . However, some remarkable differences between the territories must be considered.

Table 7. Standardised importance of the independent family variables.

First, the variable Household possessions is among the top positions in four of the countries (Bulgaria, Georgia, Mexico, and Panama)—being the first variable of the model in Mexico and Bulgaria—and is situated in the first half of the ranking in all the countries analysed. Also, although Highest occupational status greatly contributes to explaining academic performance in almost all the countries (especially in Mexico), it only occupies the 18th position in Georgia. In addition, Immigration background presents wide diversity across the countries, being the third most important variable in the United Arab Emirates and the second last in Hong Kong SAR (China), and Ireland. Finally, the disparity is also evident for the variable Educational level of parents , as this appears in the 24th place in Hong Kong SAR (China) but occupies the 5th position in Mexico.

In relation to parental involvement, the standardised importance values for the two variables considered ( Parents’ emotional support perceived by student and Social connection to parents ) are around 20% in the overall model. Again, the results show cross-country differences, with both variables ranking close to the 10th position in Georgia but appearing situated at the bottom of the table in Ireland.

School and teacher variables

The results in Table 8 show the low explanatory capacity of school and teacher variables in the overall model. Moreover, none of the variables within this domain are among the main predictors of low achievement in any of the countries considered. However, some differences among the territories require further analysis.

Table 8. Standardised importance of the independent teacher and school variables.

In terms of the aspects more directly related to school, having an Experience of being bullied plays an important role in explaining underachievement in some countries. However, some diversity can be observed, as this variable is in the lower half of the table in Ireland and the United Arab Emirates but reaches the upper half in all the remaining countries. Another school factor, Disciplinary climate in test language classes , ranks Considerably higher in Serbia and Hong Kong SAR (China) than in the other countries. Finally, the variables related to group cohesion have a low explanatory capacity both in the overall model and in all the countries considered.

With regards to teacher-related variables, only Teacher-directed instruction shows a standardised importance of above 6%. However, these teacher-related variables are still below the middle of the ranking in almost all the countries considered.

The explanation of underachievement and the search for its associated factors have been of constant interest in educational research. In this regard, the number of variables involved in the description and explanation of achievement—and, more specifically, of underachievement—has increased over the years, as so the number of studies. The very selection of the explanatory variables itself poses a bias in the analysis.

Therefore, to get as broad a picture as possible of this phenomenon, we have analysed the factors affecting low academic achievement using the data that—at least up to now—offer the most complete overview of performance: that is, taking the data from the international PISA assessment. For this purpose, and to adjust the explanation to the reality of each country, all available information on possible associated variables has been incorporated into the analyses. For this reason, only countries that had applied all context questionnaires were included in the sample.

The results of this study show the effects of personal, family, and school characteristics on low academic achievement. Hence, despite slight differences between the countries analysed, the variables that most influence low academic performance are mainly linked to the students themselves (low metacognition, lack of enjoyment of reading, poor self-concept, and low expectations about their future occupational status). In addition, at the family level, socio-economic aspects also play a significant role in explaining low academic achievement. These results are in line with those obtained by Sipe and Curlette (1997) and Hattie (2003) , who reported personal variables, followed by family variables, to have the highest predictive capacity for low academic performance. However, in contrast to the results obtained in the aforementioned review papers, in our research, teacher variables were shown to have a low explanatory capacity.

Focusing attention on students’ characteristics, this research shows the major role of a lack of enjoyment of reading in students’ low performance. In this vein, this variable not only presents the greatest predictive capacity in the global model but also plays a relevant role in almost all the countries considered. These results coincide with the conclusions of the meta-analysis conducted by Tze et al. (2016) , in which high levels of boredom were significantly related to low levels of academic performance, as well as to low levels of motivation and poor study strategies. In this sense, it is necessary to consider the role of emotional self-regulation when managing boredom. As this is one of the main components of emotional intelligence, it has been shown to be a good predictor of academic results ( Checa et al., 2008 ; Calero et al., 2014 ). Similarly, in the study by Chang et al. (2016) , cognitive engagement is positively correlated with academic performance. As Calero et al. (2014) describe, this could be explained by the fact that engagement is an essential element of motivation when it comes to predicting academic performance, as it is linked to the subjective value that students give to the task they are performing and thus influences their desire to carry it out and the results obtained.

Along with reading enjoyment, metacognition and self-concept have also played a major role in explaining low student achievement in all the countries analysed in our study; these results are in line with the findings of Hattie’s ( 2009 , 2017 ). Furthermore, Ohtani and Hisasaka (2018) analysed 118 papers and reported that once intelligence is controlled, metacognition appears to be a good predictor of academic performance. These results seem to be reasonable, as metacognition refers to a person’s knowledge of his or her own information processing abilities, cognitive processes, and strategies for developing said processes, and includes the executive skills responsible for monitoring and self-regulating them ( Schneider, 2010 ). Therefore, if students can recognise and understand their mental processes properly, they are likely to apply them optimally while learning.

On the contrary, the personal characteristics that consistently show a low explanatory capacity for academic underachievement across the selected countries correspond to duration in early childhood education and care, body image , and body mass index of student . Regarding the first of these variables, it is often claimed that attending early childhood education improves academic outcomes in the long term. However, although findings about this aspect seem to be contradictory in meta-analytic literature, they tend to show that the effects depend more on factors such as the quality of education than on whether this level of education is attended ( Van Huizen and Plantenga, 2018 ). In this vein, it is also worth mentioning that the results obtained by the aforementioned authors revealed that the positive effects of attending early childhood education are greater for disadvantaged children, thus demonstrating the interrelation of this predictor with the socio-economic status of the families.

Finally, results for the remaining personal variables differ depending on the countries analysed, although not substantially. A greater variability is only observed for learning time, especially in the two, especially in the two Latin American countries considered. This difference is particularly noticeable in the case of Panama, where this variable ranks first in the model. Among other aspects, this could be related to the fact that in this region, some children spend part of their time at work rather than at school or studying, with the negative consequences this has on academic performance ( Murillo and Román, 2014 ).

The importance of family factors in the explanation of academic achievement has also been analysed in this study, as the relationship between parents and children can be one of the most significant throughout a person’s life ( Vasquez et al., 2016 ). Regarding said factors, the overall results are, again, in line with the findings of existing reviews, which reveal a medium-low predictive capacity for this dimension ( Sirin, 2005 ; Castro et al., 2015 ; Pinquart, 2016 ; Vasquez et al., 2016 ; Tan, 2017 ). However, in contrast to the similar patterns which personal variables follow in each of the countries analysed, there are notable differences between the explanatory capacity of family variables across the territories.

First, the explanatory capacity of parental involvement varies greatly across countries: while the highest explanatory capacity is found in Georgia, the influence of this dimension is very small in Ireland. In this regard, Hampden-Thompson et al. (2013) state that there is a growing body of research that, in line with ecological systems theory, suggests that countries exert social, cultural, political, public, and institutional influences on their inhabitants. Due to this, cross-country differences in the association between family involvement and educational outcomes could be the consequence of national variations in relation to very diverse economic, cultural, social, or political aspects.

Meanwhile, cross-country differences in relation to families’ cultural and socio-economic status can be explained by the differences that exist between the territories analysed ( World Bank, 2022a ). In this vein, the United Arab Emirates, as a territory with very high rates of temporary labour immigration ( Möller, 2022 ), is the country where immigrant status and occupational status have shown the greatest explanatory capacity for low academic performance. In this regard, the results also probably reflect education inequalities derived from differences between locals and immigrants in the school system. Also noteworthy is the prominent role played by the educational and occupational status of families and the material household resources in Bulgaria—being a country where inequality has been rising during the last decade ( Peshev, 2015 ; Hallert, 2020 ). The same phenomenon is also observed in Mexico which, despite being the country with the lowest inequality in Latin America, is still affected by this problem ( Amarante and Colacce, 2018 ). Finally, the great importance that these household material resources and the occupational status of families have in Serbia—as well as the students’ expectations about their employment—should be highlighted. These aspects may be related in part to the unemployment rates that this country still faces despite their gradual reduction ( World Bank, 2022b ), and also to the recent economic and social stabilisation that this territory has faced after the war suffered between 1991 and 2001. Therefore, the results suggest that these variables are more important in countries where the discrimination between family socio-economic resources is greater.

Variables relating to the characteristics of schools and teachers have shown little influence both in defining the general profile of students with low achievement and in explaining underachievement in each of the countries analysed. These results are partly in line with the findings of Hattie (2003) , who, while attributing a minor role to school characteristics in explaining academic performance, found that teachers had an explanatory capacity of about 30%. However, the author did not consider the interrelationship between variables, which could explain the differential results with respect to our work. In any case, some slight differences between the analysed territories are observed in relation to school variables.

Although the explanatory capacity of the selected variables is, in general, very similar in all the countries analysed, the high standardised importance of school variables in Serbia stands out. This may be linked to the great variance found in reading achievement across the schools in this territory ( Organisation for Economic Co-operation and Development [OECD], 2019a ). Also, the fact of having suffered bullying plays a remarkable role in seven of the nine countries considered (Bulgaria, Hong Kong SAR (China), Spain, Mexico, Panama, Georgia, and Serbia). Of these territories, only Spain has lower rates of exposure to bullying than the OECD average ( Organisation for Economic Co-operation and Development [OECD], 2019d ). Finally, the variability found in the influence of classroom disciplinary climate on academic underachievement must be highlighted, which may be related to differences in cultural and behavioural standards across countries ( Ning et al., 2015 ).

In conclusion, although much research has been focused on identifying the personal, family, and school aspects that exert the greatest influence on students’ low academic performance, our findings suggest the need to examine cross-national differences in greater depth and to consider the specificities of each territory. Despite the interest in these results, the main limitations of this study should be noted. On one hand, the selection of variables was based on the indicators of the PISA context questionnaires. Hence, there could be other explanatory variables for low academic performance—such as intelligence ( Zaboski et al., 2018 ), self-regulation ( Kyriakides et al., 2013 ), or perfectionism ( Madigan, 2019 ) at the personal level, or the type of leadership of the school leaders ( Chin, 2007 ) at the school level—which have not been considered in this article. On the other hand, cross-country comparisons have only been made between territories where all the context questionnaires were applied, which has reduced the number of international comparisons.

It would therefore be desirable to explore further the realities of the countries analysed in this study, as well as to explore comparisons between the explanatory capacity of the variables in a larger number of territories. In this vein, this study lays the theoretical foundations for future research, as it demonstrates the need to go a step further in research into the conditioning factors of low academic performance, which have traditionally been addressed from an international non-comparative perspective that establishes universal conclusions. Thus, this work has demonstrated the existence of differences in the personal, family, and school variables depending on the territories analysed. The results of this research also contribute to laying the foundations to develop specific policies addressed for preventing and improving underachievement, in which the whole educational community should be involved ( Vera Sagredo et al., 2021 ). In this sense, this article demonstrates that, in addition to the development of international common policies aimed at reducing educational problems—as is the case, for example, of those conducted in the European Union—every country should develop its own robust policies aimed at improving students’ academic performance, which should be based on the specific influence that each of the variables exerts on said performance.

In conclusion, despite some common trends in the countries analysed, the variables that explain underachievement are different across them, given that socio-demographic and contextual conditions also differ. Therefore, although personal characteristics continue to be the ones that best explain academic performance, a series of contextual variables, especially related to families, exert a greater or lesser influence on performance depending on the level of development and characterisation of each country, and may even hide or annul certain personal characteristics.

In this vein, although personal variables have shown the greatest impact on students’ underachievement, there is an interrelation between all the factors that influence students’ academic performance ( Bhowmik, 2019 ; Akbas-Yesilyurt et al., 2020 ). For this reason, consideration should be given to the possibility that personal factors may, in turn, be influenced by family or school variables. For example, we should inquire whether students’ expected occupational status or attitude toward school may be conditioned by family or teachers’ expectations, which also depend on their socio-economic status. We may also ask to what extent teachers might be influencing students’ level of metacognition, self-concept, or boredom.

Thus, policies and interventions should not only target students but should also consider the context in which they live, paying special attention to their families. Also, adequate pre-service and continuous training should be guaranteed for all teachers to ensure that students receive an adequate educational response, paying special attention to those who work in disadvantaged socio-educational contexts ( Fernández Batanero, 2011 ).

For all these reasons, there is a clear need to continue working toward equity as a starting point, so that once equal opportunities are achieved, other personal variables can flourish.

Data availability statement

Publicly available datasets were analysed in this study. These data can be found here: https://www.oecd.org/pisa/data/2018database/ .

Ethics statement

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

BG: conceptualization and design of the study, validation, data analysis, interpretation of results, discussion and conclusions, and reviewing and editing. EL-M: conceptualization and design of the study, validation, data analysis, interpretation of results, and reviewing and editing. ECM: conceptualization and design of the study, validation, and reviewing and editing. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved the submitted version for publication.

This research has been conducted under the support of the Ayudas para la Formación de Profesorado Universitario (FPU).

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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Appendix A. General description of the nine selected countries (2018 data).

Keywords : academic achievement, low achievement, PISA, cross-country analysis, decision trees, CART

Citation: Gutiérrez-de-Rozas B, López-Martín E and Carpintero Molina E (2022) Defining the profile of students with low academic achievement: A cross-country analysis through PISA 2018 data. Front. Educ. 7:910039. doi: 10.3389/feduc.2022.910039

Received: 31 March 2022; Accepted: 11 July 2022; Published: 05 August 2022.

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Copyright © 2022 Gutiérrez-de-Rozas, López-Martín and Carpintero Molina. 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: Belén Gutiérrez-de-Rozas, [email protected]

This article is part of the Research Topic

Evaluating Performance

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Determinants of academic achievement among higher education student found in low resource setting, A systematic review

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

* E-mail: [email protected]

Affiliations Department of psychiatry, Dilla University, Dilla, Ethiopia, Faculty of Social Sciences, Lobachevsky State, University of Nizhny Novgorod, Nizhny Novgorod, Russia

Affiliation Department of Social Security and Humanitarian Technologies, Nizhny Novgorod State University, Nizhniy Novgorod, Russia

• Chalachew Kassaw, 
• Valeriia Demareva

Published: November 20, 2023

• https://doi.org/10.1371/journal.pone.0294585
• Reader Comments

Academic success is a measure of students’ ability to attain their educational objectives, often assessed through regular evaluations or examinations. To establish effective policies and programs that align with academic accomplishments, conducting comprehensive data analysis is pivotal. Hence, this systematic review aimed to synthesize the factors impeding the academic achievements of Ethiopian students in higher education.

A comprehensive review was conducted on studies involving Ethiopian university students from 2013 to 2022. The review encompassed 24 papers that were gathered from different databases like PubMed, Google Scholar, African Journals Online, Scopus, and Web of Science.

The findings of this research revealed that inadequate classroom environments, experiencing dysmenorrhea, and engaging in excessive social media usage were all linked to a decline in academic performance. Conversely, adopting healthy sleep habits, achieving high scores in entrance exams, and avoiding recent substance abuse were all factors positively influencing academic success. In addition, there was a positive correlation between academic excellence and being a health science college student and age range of 20 to 24 years old.

To enhance academic performance, it is crucial to address the negative factors identified, such as inadequate classroom environments, dysmenorrhea, and excessive social media usage, while promoting positive factors like healthy sleep habits, high scores in exams, and avoiding substance abuse. Additionally, being a health science college student and belonging to the age range of 20 to 24 were found to be associated with academic excellence.

Citation: Kassaw C, Demareva V (2023) Determinants of academic achievement among higher education student found in low resource setting, A systematic review. PLoS ONE 18(11): e0294585. https://doi.org/10.1371/journal.pone.0294585

Editor: Mukhtar Ansari, University of Hail, SAUDI ARABIA

Copyright: © 2023 Kassaw, Demareva. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: This article does not report data and the data availability policy is not applicable.

Funding: The authors received no specific funding for this work.

Competing interests: Declaration of Competing Interest: There are no apparent conflicts of interest with this publication.

Introduction

Academic success pertains to the extent of accomplishment exhibited by a student or institution in attaining educational objectives, regardless of whether they are immediate or long-range in nature [ 1 ]. Education is a formidable catalyst for transforming a nation’s societal harmony, economic prosperity, standard of living, and overall well-being [ 2 ]. Graduation rates evaluate the performance of an institution, while GPA (Grade Point Average) measures the achievements of individual students [ 3 ]. GPA (Grade Point Average) is calculated by dividing the sum of grade points by the total units [ 4 ]. The assessment of students’ knowledge and skills attained from each subject depends on the subject-area measurement level [ 5 ].

Higher education institutions play a vital role in creating an environment that promotes learning and supports the development of global competencies in various academic disciplines. This, in turn, allows learners to effectively navigate the ever-changing global landscape. Ultimately, such efforts enhance the overall quality of education and equip students to successfully overcome challenges [ 6 ]. Learning is a lifelong and challenging process that does not guarantee the attainment of knowledge, skills, or perspectives. It requires significant effort and time [ 7 ]. In order to succeed in school, students must exhibit initiative, self-control, effective time management, focused attention, inquisitiveness, and active engagement in the classroom [ 8 ]. Good academic performance offers numerous benefits, including improved living conditions, increased productivity, and better economic prospects for society. It also provides students with a positive self-image, confidence, good mental health, social skills, and a clear vision for their future [ 9 ]. Poor academic performance in students can potentially lead to a range of psychological problems, such as substance abuse, criminal behavior, promiscuity, and conflicts in relationships [ 10 ]. There were also encounters of difficulties with timely graduation due to retakes and grade changes, strained relationships with professors and support staff, as well as conflicts with college deans and students [ 11 ]. The government’s extensive educational efforts have failed to assist many students in achieving higher academic levels [ 12 ]. The number of students enrolling in Ethiopia’s higher education institutions is not comparable to the number of graduates because a significant portion of applicants are initially rejected, then withdraw, and ultimately get readmitted [ 13 ]. Challenging situations can often result in family struggles, dependence, lack of insurance, poverty, and insufficient access to healthcare coverage [ 14 ]. The effectiveness of teaching and learning tools, along with the students’ personality, goals, and teachers’ skills, all have an impact on academic progress. Studies have shown that the environment also plays a critical role in students’ performance in school [ 15 ]. Academic achievement is influenced by several factors, including finances, study habits, time management, health, and family connections, all of which are significant [ 16 ]. Poor academic performance has been found to be linked to several factors, including sporadic school attendance, low parental education, unstable family relationships, excessive use of social media, and spending excessive amounts of time engaging in conversation [ 17 ]. Research conducted by national universities has identified specific characteristics that are consistently associated with poor academic performance [ 18 ]. For instance, a study conducted by Bahir Dar University discovered that a student’s academic status is influenced by the education level of their parents and their tendency to frequent pubs and clubs [ 19 ]. However, the results of a study at Arba Minch University showed that a student’s past academic achievement largely predicts their present performance on campus [ 20 ]. An additional examination conducted at Wolayita Sodo University discovered a correlation between present drug usage and academic achievement [ 21 ]. Currently, there are 42 public institutions in the country, all of which strive arduously to improve the quality of education [ 22 ]. Assessing the academic performance of students is crucial for ensuring quality assurance in higher education institutions. However, analyzing national averages of academic predictors is an essential tool for developing academic policies and strategies that can enhance education quality on a broader scale. This review specifically aimed to identify the main predictors of academic achievement based on studies conducted among universities located in different regions of Ethiopia. The review found that participation in a supportive academic environment, acquiring essential information, maintaining a positive outlook, and possessing subject-specific abilities are crucial factors for student success. While numerous individual studies have been conducted in various parts of the country that have identified potential factors associated with academic achievement, decision-makers who are striving to improve academic standards in Ethiopian higher education institutions will find this comprehensive review particularly beneficial. Moreover, the review also identified areas of knowledge gaps that require further exploration in order to enhance academic quality throughout the country.

Study design and setting

A systematic review covering studies conducted in Ethiopian higher education institutions between 2013 and 2022 was conducted between January and February of 2023. As of 2023, Ethiopia will have 83 private institutions, 42 public universities, and 677 study options. Additionally, more than 150,000 adults graduate annually in the country. The universities offer training for students pursuing undergraduate, graduate, and doctoral degrees [ 23 ]. We have checked the Prospero database ( http://www.library.ucsf.edu/ ) to determine if there are any published or ongoing projects related to the topic, in order to avoid any duplication. The findings revealed that there are no ongoing or published articles in the area of this topic. The current systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria ( S1 File ) [ 24 ].

Searching strategy and source of information

An extensive literature search was conducted using international databases, including PubMed, Scopus, Google Scholar, African Journals Online, and Web of Science, to retrieve relevant articles. Search terms were formulated following the Population, Intervention, Comparison, and Outcomes (PICO) framework and applied to the online databases. Medical Subject Headings (MeSH) terms and key terms were developed using various Boolean operators, such as "AND" and "OR." The following search terms were used: “Academic Achievement”, OR “Academic Performance” OR “Average Cumulative Grade Point”, OR “Performance Indicators” AND Psychological Determinants”, “Biological Determinants”, “Social Determinants”, “Higher Education”, “Competency Measures”, AND “Teaching-learning styles Predictors” AND “Ethiopia” ( S2 File ).

Eligibility criteria

The authors performed an unbiased eligibility examination based on the provided criteria. Issues were resolved through mutual agreement and the involvement of other authors. This systematic review analyzed articles written in English, published between 2013–2022 and that investigated predictors of academic achievement among higher education students. Only studies with cross-sectional designs, defined outcome variables, and covariates were included. This research aimed to identify factors that contribute to academic success among college students. Studies that did not use basic statistical analysis (Prevalence, Mean, ANOVA, T-test, adjusted odds ratio and Crude odd ratio) to establish the connection between academic performance and its influencing factors were excluded from the review.

Operational definitions

Outcome measurement (academic achievement)..

Grade Point Average (GPA): Grade point average (GPA) is a value calculated by multiplying the unit value for each course by the grade point total and then dividing the sum by the total number of units.

A checklist: An assessment tool lists the specific criteria for skills, behaviors, or attitudes that participants must demonstrate to show that they have successfully learned from training.

Writing assessment: It refers to a field of study that contains theories and practices that guide the evaluation of a writer’s performance or potential through writing tasks.

Interview assessment: An interview based test used to evaluate a student’s suitability for the particular subject they wish to pursue in a specific department.

A skills gap analysis: It is a tool used to assess the gap between a student’s current capabilities and the requirements of a particular profession, both current and future.

Determinant factors of the outcome measurement.

Psychological factors: The internal influences shape our thoughts, feelings, and behaviors.

It includes mental pain, sleep quality, self-esteem, prosocial behavior, anxiety, depression, and suicidality.

Biological factors: These are the physical and chemical influences on our bodies and minds.

It includes gender, age, and hormonal issues (dysmenorrhea). Facility-related factors: This physical and environmental conditions support student learning. It includes availability of adequate seating and studying spaces, lighting, technology, equipment and supplies, sleeping accommodations, dining halls, sports fields, green spaces and other outdoor areas.

Life style factors: These are the choices and behaviors that people make that can affect their health and well-being. It includes excessive social media usage, premarital sex, and sexual abstinence.

Study selection and data extraction.

The researchers used the reference management software Mendeley, Desktop and Endnote version 25 to remove duplicate articles from the search results. Three independent reviewers then screened the titles and abstracts of the remaining articles to determine eligibility for the review. Any disagreements between the reviewers were resolved based on pre-established criteria. Two independent reviewers then extracted data from the eligible articles using a standardized data extraction form created in Microsoft Excel. Any discrepancies during data extraction were resolved through discussion. The data that was extracted included the name of the first author, study area and region, study month and year, study design, year of publication, study population, sample size, response rate, and level of good knowledge, positive attitude, and poor practice.

Quality assessment

To assess the quality of each study included in this systematic review, we used the modified Newcastle Ottawa Quality Assessment Scale (NOS) for cross-sectional studies [ 25 ]. Both authors (Chalachew Kassaw and Valeria Demareva) independently assessed the quality of each study, considering the following factors: methodological quality, sample selection, sample size, comparability of the study groups, outcome assessment, and statistical analysis. In the case of disagreement between authors, other reviewers were involved to resolve the issue. All studies included in this systematic review were cross-sectional, quantitative, or qualitative studies ( S3 File ).

Study search and selection

This study conducted a systematic review of academic achievement and its related factors by limiting the search to full-text articles in English published between 2013 and 2022 in the following databases: PubMed, Scopus, Google Scholar, African Journals Online, and Web of Science. A total of 67 primary papers were found, and 19 and 20 publications were discarded as duplicates and unrelated to the study, respectively, after title and abstract screening. Of the remaining 28 papers, four were excluded due to inadequate evidence of the relationship between academic achievement and its factors. Finally, 24 papers that met all inclusion requirements were selected for the systematic review. The rigorous methodology used in this study highlights the importance of selecting relevant papers to establish robust findings that can support subsequent research on academic achievement and its factors ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0294585.g001

Most systematic reviews of academic achievement and its factors among college students in Ethiopia focus on average grade point and performance evaluation indicators, adapting these metrics to their research goals and regional contexts [ 26 – 32 ] However, high-quality research on the topic in Ethiopia is scarce. This study analyzed eligible peer-reviewed papers from various Ethiopian colleges published between 2013 and 2022. Most of these studies were cross-sectional and included samples of both men and women from institutions. However, two studies used a cross-sectional, qualitative design [ 33 , 34 ]. Most of the studies we reviewed [ 26 – 32 ] examined sociodemographic factors such as age, gender differences, and monthly pocket money as potential contributors to academic achievement. However, a small proportion of studies, specifically those that examined the relationship between menstruation and academic success, included interviews with women [ 35 , 36 ]. This study review included participants from all part of the nation, aged 18–35 years with an average of 21.2 years. It found that factors such as biological, psychological, social, student-teacher interaction and lifestyle characteristics are predictive of academic achievement (Tables 1 – 4 ).

https://doi.org/10.1371/journal.pone.0294585.t001

https://doi.org/10.1371/journal.pone.0294585.t002

https://doi.org/10.1371/journal.pone.0294585.t003

https://doi.org/10.1371/journal.pone.0294585.t004

Psychological predictors of academic achievement

According this study review result, sleep quality, mental distress, suicidal ideation, perceived stress, low self-esteem, depression, pro-social behavior and test anxiety were identified factors associated with academic achievement ( Table 1 ).

Biological predictors of academic achievement

Gender difference (male), age difference (20–24 years old), psychoactive substance use and menstrual related factors (dysmenorrhea and long menses period) were associated with academic achievement ( Table 2 ).

Facility and educational environment predictors of academic achievement

This study review revealed that Dormitory crowdedness, inadequate anatomy-teaching model, low internet access, Past English achievement, Entrance exam result, students’ perception of teachers, students’ academic self -perception and students’ social self-perception were associated with academic achievement ( Table 3 ).

Life style predictors of academic achievement

Student’s life style factors such as pre-marital sex, sexual abstinence and excessive social media (Facebook, What up and telegram) use were associated with academic achievement ( Table 4 ).

Summary of predictors of academic achievement

Several studies have found a significant negative correlation between academic achievement and factors. This include facility related factors such as large class sizes, insufficient internet access, poor classroom amenities, and teaching methods. Lifestyle style related factors such as excessive social media usage, premarital sex, and sexual abstinence have also been shown to have an impact on academic achievement. Psychlogical factors such as perceived stress, a lack of social support, and low self-esteem have also been found to influence academic success. Finally, Biological factors such as gender, age above 24, and hormonal issues, especially dysmenorrhea, are other factors that have been shown to have a significant impact on academic performance ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0294585.g002

This systematic review explored the factors affecting academic success in Ethiopian higher education. Both modifiable and non-modifiable characteristics were identified as predictors of academic achievement.

Socio-economic factors

This study review found that individuals with low socioeconomic status, specifically low monthly income and inadequate social support, were more likely to experience poor academic performance. This review result is similar with a systematic review done in Belgium [ 50 ], Pakistan [ 51 ], and Netherlands [ 52 ]. Financial constraints can prevent students from accessing basic educational tools, such as pencils, paper, laptops, and notebooks, which are necessary to attend classes efficiently and achieve good academic results. Students with strong social support are highly motivated to succeed in their studies, cope with challenges, manage stress and anxiety, and have opportunities to collaborate with others, discuss ideas, and get feedback on their work. Providing daily necessities such as financial and emotional support is essential for students’ physical and mental well-being.

Biological factors

The study also found that biological factors, such as gender, age, and menstrual cycle-related hormone changes, are associated with academic success. These findings are consistent with research conducted in low- and middle-income countries [ 53 ] and Saudi Arabia [ 54 ] and China [ 55 ]. Advanced age may cause changes in all parts of the body, including the brain. Certain parts of the brain shrink, especially those that are important for learning and other complex mental processes [ 56 ]. Neuronal transmission may become less efficient in some areas of the brain as people age, which can lead to a decline in working memory and make tasks like making decisions and resolving problems more challenging [ 57 ]. Students with dysmenorrhea may miss classes, assignments, and tests, be reluctant to participate in class discussions or activities, experience pain and discomfort during class and study, and find it difficult to focus and concentrate, all of which can lead to lower academic achievement.

Estrogen increases the production of acetylcholine, a brain enzyme essential for memory, and strengthens neuronal connections in the hippocampus, a region of the brain critical for language recall. Estradiol, a hormone prevalent in women, is particularly important for word memory, focus, and rapid information processing [ 58 , 59 ].

Psychological factors

This study review found that mental and psychological conditions such as melancholy, anxiety, suicidal thoughts, low self-esteem, perceived stress, prosocial behavior, and sleep disorders are important indicators of academic achievement in higher education. This finding was consistent with a research conducted in Italy [ 60 ], Australia [ 61 ], Nepal [ 62 ] and China [ 63 ]. Mental health issues can negatively influence students’ academic success by reducing their energy, focus, motivation, cognitive abilities, and optimism. Depressed or anxious students may find it difficult to socialize and participate in class, which can lead to a decline in their academic performance. Students struggling with mental health concerns may also become less engaged and proactive in their studies [ 64 , 65 ].

Life style factors

The systematic review found that premarital sex, social media use, and sexual abstinence could affect a person’s way of life. The same conclusion was reached in a Latin American study [ 66 ] and China [ 67 ]. Students who engage in premarital sex may be more likely to experience academic failure for a number of reasons. They may spend more time with their partners, miss more classes, and get distracted. They may also feel guilty, low self-esteem, and susceptible to physical illnesses. All of these factors can contribute to academic problems [ 68 ]. A major drawback of technology is that social media use can distract students from their academic work. When students are bombarded with both educational and entertainment messages, it can be difficult for them to concentrate on their lectures. Additionally, students may prioritize online chatting and building relationships on social media over reading books in their free time, which can further harm their academic performance [ 69 , 70 ].

This study also found that certain facility-related factors, such as crowded dorm quarters, large class sizes, inadequate classroom amenities, and restricted internet access, are associated with academic achievement. This result was supported with a study done United Kingdom [ 71 ], Malaysia [ 72 ] and Korea [ 73 ]. This may be explained by inadequate school infrastructure, which can distract, tire, and disengage students, making it difficult for them to learn effectively. Examples of poor school facilities include loud noises, crowding, poor lighting, and difficulty accessing instructional materials [ 74 ].

This review found that the field of study, a good student-teacher relationship, the absence of breaks, and the performance of previous students are all academically linked criteria, consistent with reviews and studies conducted in the United Kingdom [ 75 ], Unites States of America [ 76 ], Nepal [ 77 ] and China [ 78 ]. Students are more likely to study when they feel positive about their learning environment. This is because they are more motivated to learn when they feel a sense of belonging, competence, and autonomy in their academic setting [ 79 ]. Factors in your classroom environment can influence student motivation. Motivated students put more effort into learning activities, such as paying attention, overcoming challenges, interacting with others, forming friendships, and managing their emotions (e.g., sadness and anxiety) [ 80 ].

Strengths of the study review

The review searched five databases to retrieve relevant articles.

The review strictly followed PRISMA flow charts.

More than one assessor evaluated the quality of the studies.

The review used the appraisal process developed by the Joanna Briggs Institute (JBI).

The review included studies from all parts of the country, ensuring good representativeness.

Limitations of the study review

The measurements for academic achievement and operational definitions may have differed between the primary studies.

This systematic review analyzed the predictors of academic achievement in Ethiopian higher education students, as identified in primary studies conducted over the past 10 years. The review identified many factors that affect academic achievement, including controllable factors such as facility-related variables, emotional factors, and lifestyle factors. These include large class sizes, poor internet connections, inadequate classroom facilities, poor teaching strategies, perceived stress, lack of social support, and substance use.

Recommendation

Based on the findings of this systematic review, it is recommended that universities and colleges in Ethiopia take steps to improve facility-related resources, provide support for student emotional and mental well-being, educate students about healthy lifestyle choices, and develop and implement interventions to address specific predictors of academic achievement.

Improving facility-related resources includes reducing class sizes, improving internet access, and providing adequate classroom facilities and teaching materials. This can help to create a more conducive learning environment for students and support their academic success.

Providing support for student emotional and mental well-being can be done by offering counseling services, creating a supportive campus environment, and raising awareness of the importance of mental health. This can help to reduce stress and anxiety among students, which can improve their academic performance. Educating students about healthy lifestyle choices can be done through workshops, seminars, and other educational programs. This can help students to make informed decisions about their health and well-being, which can indirectly lead to improved academic achievement. Developing and implementing interventions to address specific predictors of academic achievement can involve a variety of strategies. For example, interventions could be designed to reduce stress, improve social support, and prevent substance use. These interventions can be tailored to the specific needs of the student population and can be delivered in a variety of settings, such as classrooms, residence halls, and student health centers. By taking these steps, universities and colleges in Ethiopia can help to improve the academic achievement of their students and create a supportive and inclusive learning environment.

Supporting information

S1 file. preferred reporting items for systematic reviews and meta-analyses (prisma) guideline..

https://doi.org/10.1371/journal.pone.0294585.s001

S2 File. Search strategy.

https://doi.org/10.1371/journal.pone.0294585.s002

S3 File. Newcastle-Ottawa Quality assessment scale.

https://doi.org/10.1371/journal.pone.0294585.s003

S4 File. Microsoft excel document.

https://doi.org/10.1371/journal.pone.0294585.s004

Acknowledgments

We would like to thank all authors of the studies included in this systematic review.

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Internet use and academic performance: An interval approach

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• Published: 21 May 2022
• Volume 27 , pages 11831–11873, ( 2022 )

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• María Ladrón de Guevara Rodríguez   ORCID: orcid.org/0000-0002-5087-422X 1 , 2 ,
• Luis Alejandro Lopez-Agudo   ORCID: orcid.org/0000-0002-0906-3206 2 ,
• Claudia Prieto-Latorre   ORCID: orcid.org/0000-0002-6510-3057 2 &
• Oscar David Marcenaro-Gutierrez   ORCID: orcid.org/0000-0003-0939-5064 2  

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As children spend more and more time on electronic devices and social networks, there is a growing concern about the influence that these activities may have on their development and social well-being. In this context, the present research is aimed at analysing the influence that Internet use may have on 6 th grade primary school students’ academic performance in Spain. In order to do so, we have employed a methodological approach that combines econometric and interval multiobjective programming techniques, which has let us identify the traits and Internet use patterns that allow students to maximise their academic performance in terms of scores in four competences. Our results show that, while daily use of the Internet to listen to music or search for information about other topics of interest can favor the maximization of educational outcomes, the use of social networks should be limited as much as possible to avoid hindering the educational process.

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1 Introduction

The Internet has become an indispensable tool, particularly for young people. For those who have been surrounded by digital technology since birth, it is not only an information tool, but a major innovation that has conditioned how they spend their leisure time and engage in non-leisure activities. The Internet has been fundamental in society’s development; it created a new dimension for digital natives (Prensky, 2001 ) and has also made it possible to digitise social and paperwork structures that were traditionally “physical” by promoting “virtual mobility” and allowing certain daily activities to be carried out through telework, telehealth and e-learning (Mouratidis & Papagiannakis, 2021 ).

In this context, the Internet has not only enabled young people around the world to stay in touch with each other, but has also provided them with new learning opportunities, as it is an endless source of information that can speed up the educational process. It allows the creation of “educational environments” that complement the traditional system and fill educational gaps that may be left by face-to-face education. The Internet can even promote an active and independent type of learning adapted to students’ characteristics and their own learning pace (O’Flaherty & Phillips, 2015 ).

In addition to learning, the Internet is a means of entertainment and communication (Zhang et al., 2018 ) that has enabled young people around the world to develop and nurture relationships while strengthening their sense of community (Pendry & Salvatore, 2015 ) and social well-being (Castellacci & Tveito, 2018 ; Alivernini et al., 2019 ). Furthermore, using different mobile devices from an early age enables the acquisition of so-called digital skills, such as the ability to search for and evaluate information (Van Deursen & Van Dijk, 2008 ), which can be extremely useful when writing reports or completing assignments (Pagani et al., 2016 ).

The Internet has become indispensable in teenagers’ lives, but its excessive or inappropriate use also has undesirable consequences for young people, especially if we consider that they are more likely to develop a certain degree of Internet addiction than adults (Fineberg et al., 2018 ; Ko et al., 2012 ). Excessive use can lead to withdrawal and weaker social skills as well as mental health and family problems (O’Day & Heimberg, 2021 ; Song et al., 2019 ; Twenge, 2017 ). Besides, such dependence can negatively interfere with the educational process and consequently reduce academic performance (Azizi et al., 2019 ; Kates et al., 2018 ; Koca & Berk, 2019 ; Sengupta et al., 2018 ; Wammes et al., 2019 ).

Given the relevance of the Internet in our lives, it seems reasonable to question whether its use from an early age can negatively affect a person’s psycho-emotional educational and professional development and, specially, if we take into account that late childhood and adolescence are critical stages in human life, as teenagers are supposed not only to develop educational and career goals, but also to ask themselves who they are and who they want to be (Verhoeven et al., 2019 ).

Therefore, the aim of our study is to identify the profile of students who are able to maximise their academic performance in reading, mathematics, science and English given the different ways in which they can use the Internet. That said, we focus on Spain, where 91.4% of households had a fixed or mobile broadband Internet connection and 92.9% (INE, 2019 ) of children aged 10–15 use it in 2019, with the average time spent on the Internet being 3 or more hours per day in 2019 (Qustodio, 2019 ). Particularly, within Spain, we will use a recent database from the Spanish region of the Canary Islands which collected the census of primary school students in 6 th grade in 2018–2019.

We have to bear in mind that, according to the latest Programme for International Student Assessment (PISA) report (2018), the Canary Islands are at the bottom of the Spanish educational ranking. For instance, Canarian students scored 19 points below the OECD average in science and 13 points below Spain (MEPF, 2018 ). In this sense, given the increase in both poor academic results and Internet use by the younger generations, in our study we seek to analyse the influence that Internet use can have on academic performance. To do so, we will make use of interval multiobjective programming, specifically the algorithm proposed in Henriques et al. ( 2019 ). This methodology has been used in applications to analyse workers’ well-being (Henriques et al., 2020 , 2021 ) and in the educational context (Prieto-Latorre et al., 2021 ).

In short, this study aims to enrich the existing literature in, at least, three aspects. Firstly, it assesses how social networks (and additionally WhatsApp) influence on students’ academic performance in different cognitive domains in late childhood, while studies usually focus on secondary school and university students. Secondly, we provide up-to-date evidence for Spain, to the extent that previous literature is limited and the databases used are outdated. Thirdly, by using interval multiobjective programming, we offer a potentially useful tool in the design of educational policies and parental guidance.

The article is structured as follows. First, we provide a brief review of the relevant literature on the influence of Internet use on academic performance. Then, we present the main characteristics of the dataset. Sections  4 and 5 describe the methodology employed and the results obtained. Finally, we discuss and present the main findings, including implications for socio-economic policies.

2 Literature review

The digital dependency and the effect it may have on young people’s academic performance and personal development has sparked an increased interest among researchers. Specifically, some studies have shown how using the Internet can improve academic performance (Çebi & Güyer 2020 ; Chen et al., 2014 ; Hou et al., 2021 ; Gil, 2012 ; Naqshbandi et al., 2017 ; Zhu et al., 2011 ). On the one hand, Chen et al. ( 2014 ) analysed the relationship between Internet information seeking, academic performance and academic self-efficacy, with the latter being the mediator between the first two. The authors distinguished between educational and leisure-oriented Internet use, concluding that both types had a positive impact on twelfth-grade students’ academic self-efficacy, indirectly improving their academic performance. On the other hand, Hou et al. ( 2021 ) examined the impact that the Chinese social network, WeChat, may have on university students’ academic performance. The authors concluded that the impact of using WeChat was largely due to students’ self-control, with the effect of sharing information through the application being positive when students had high self-control.

Likewise, researchers have found a positive relationship between ICT use and academic performance (Mo et al., 2014 ; Cabras & Tena Horrillo, 2016 ; Gubbels et al., 2020 ; Lei et al., 2021 ). In particular, Cabras and Tena Horrillo ( 2016 ), using data provided by PISA 2012, found a causal effect of ICT use on Spanish students’ mathematics performance, with the effect being stronger for lower-income students. Recently, Gubbels et al. ( 2020 ) showed that moderate ICT use was positively related to the reading achievement of 15-year-old Dutch students, with a negative impact when ICT was overused. Similarly, Machin et al. ( 2007 ) highlight that the increase of ICT investment at schools in England caused a positive impact on reading competence and science, but it had no significant influence in mathematics, while Villafuerte and Romero ( 2017 ) found that watching videos and using social networks help to improve English skills, as motivation and engagement facilitate English learning, both in writing and listening skills. This result is opposed to most empirical evidence, which usually finds a negative effect of social networks on educational attainment (see meta-analysis by Liu et al., 2017 ).

However, far from reaching the same conclusion, other studies have found a negative relationship between Internet/ICT use and academic performance (Azizi et al., 2019 ; Chang et al., 2019 ; Hsiao et al., 2017 ; Junco, 2015 ; Karpinski et al., 2013 ; Kim et al., 2017 ; Koca & Berk, 2019 ; Michikyan et al., 2015 ; Sengupta et al., 2018 ; Vigdor et al., 2014 ). Particularly, Kim et al. ( 2017 ) considered confounding factors such as gender, drug use or parental education level, and found that using the Internet for general purposes was negatively correlated with higher school performance, in contrast to when the Internet was used for study.

Finally, some studies point to the lack of significant effect of Internet and ICT on educational outcomes (Cristia et al., 2017 ; Fairlie & Robinson, 2013 ; Leuven et al., 2007 ; Mbaeze et al., 2010 ; Raines, 2012 ; Spiezia, 2011 ; Woessmann & Fuchs, 2004 ).

The influence that the Internet has on the teaching–learning process may depend on the type of analysis conducted, the potential existence of selection bias (Bulman & Fairlie, 2016 ), how it is used and whether it is more or less academically oriented (Chang et al., 2019 ; Gil, 2012 ; Kim et al., 2017 ; Lau, 2017 ; Torres-Díaz et al., 2016 ). In particular, while online information seeking tools and word processing are associated with higher academic performance in 15-year-old students (Gil, 2012 ), video games or streaming entertainment hinder the educational process (Lopez-Agudo & Marcenaro-Gutierrez, 2020 ; Rideout et al., 2010 ).

In this regard, it seems that the problem lies in excessive or inappropriate use of the Internet (Zhou et al., 2020 ) resulting in situations where users are unable to control the time they spend on online activities and neglect their daily activities (Wąsiński & Tomczyk, 2015 ). This “addiction”, which some studies refer to as “Internet use disorder” (Peterka-Bonetta et al., 2019 ; Sha et al., 2019 ), can lead to socioemotional problems among young people (Pontes et al., 2015 ), as well as negatively affect their academic performance (Berte et al., 2021 ; Flisher, 2010 ; Siciliano et al., 2015 ) by reducing academic engagement and increasing disaffection with learning activities (Feng et al., 2019 ; Karpinski et al., 2013 ; Zhang et al., 2018 ).

Focusing on Spain, the studies that have analysed this issue are limited (Fernández-Gutiérrez et al., 2020 ; García-Martín & Cantón-Mayo, 2019 ; Gómez-Fernández & Mediavilla, 2021 ). For instance, García-Martín and Cantón-Mayo ( 2019 ) assessed how different types of Internet use might affect academic skills, concluding that each type of Internet use was associated with different cognitive domains. Fernández-Gutiérrez et al. ( 2020 ) employ PISA data from three waves (2009, 2012 and 2015) to evaluate the use of ICT at secondary school, finding significant effects in students’ outcomes in science, but not in reading and mathematics. Particularly relevant is the study carried out by Prieto-Latorre et al. ( 2021 ), in which they analysed the effect that Internet use may have on school grades (content-based knowledge) and test scores (competences) of a cohort of 8 th grade students in 2011–2012. Footnote 1 The authors concluded that using the internet academically and for hobbies should be prioritised over continued use of social networks.

In any case, the evidence in Spain is still scarce and further up-to-date research is needed, as the use of the Internet and new technologies is increasing among young people.

3 Data and institutional background

In recent years, the Spanish education system has adopted a formative assessment model in line with European standards that aims to promote lifelong learning and to move away from an exam-centred educational culture. To implement this formative assessment scheme, in addition to a continuous assessment throughout the academic year, Spanish students take an individual assessment test at the end of each educational stage. Specifically, for primary education, Spanish students take the test at the end of 3 rd (LOMCE, art. 20.3; BOE, 2013 ) and 6 th grades (LOMCE, art. 21; BOE, 2013 ). These tests are designed to assess students’ numeracy skills, as well as their oral and written comprehension skills, in order to provide them with individualised attention according to their needs and to prevent them from failing at a later stage of their education.

In this study we have used the data collected by the Canarian Agency for University Quality and Educational Assessment. Specifically, as mentioned above, we have used data from the cohort of 6 th grade students in 2018–2019 (in the Autonomous Community of the Canary Islands). In total, our sample collects data from 13,296 students who completed tests in reading, mathematics, science and English. In addition, to avoid losing observations, we have introduced a set of missing flag variables.

In the database under scrutiny, to facilitate comparison with other research studies and between different measures of educational performance, the variables regarding test scores in the different competences have been standardised (through statistical normalisation) to have mean 0 and standard deviation 1 and, consequently, the results can be interpreted as effect sizes. Along with educational competences, by having both a student questionnaire and a parent questionnaire, we have information about parents’ educational level, income and occupation, among other variables. Moreover, to capture the students’ socio-economic level, the Canarian Agency provides us with an indicator of socio-economic and cultural status (ESCS). This index, which is a continuous variable, has also been standardised to facilitate comparisons.

Since our goal is to analyse how Internet use influences academic performance, this analysis is mainly based on the information collected by the student questionnaire. Concretely, we have based our analysis on the following question:

“How often do you use the Internet for the following activities?

Searching for information for your studies (Google, Wikipedia, etc.).

Searching for information about games or playing games.

Searching for information about sports.

Searching for information about music or movies (YouTube, Spotify, etc.).

Searching for information on other topics that interest you.

Communicating with other people (WhatsApp, Telegram, Hangout, etc.).

For using social networks (Facebook, Twitter, Instagram, Musically, etc.).

For these 7 Internet-related variables, the answer is one of the following options: “never or almost never”, “once or twice a month”, “once or twice a week”, “every day or almost every day”. Descriptive statistics regarding these variables are shown in Table 4 ( Appendix ). We can observe that 6 th grade students mainly use the Internet to communicate with others through applications such as WhatsApp, with 40% of students using it every day. This appears to be consistent with new trends, as the younger generation represents the new wave of users of these applications which can be broadly considered as social networks. Similarly, 32.9% of students use applications such as YouTube or Spotify on a daily basis, while they use the Internet for other topics of interest and to play games once or twice a week (36.1% and 24.1%, respectively). This underlines the aim of our analysis, as younger students tend to use the Internet more for non-academic purposes.

Since WhatsApp or Telegram can be considered social networks, we only include the variables related to social network use as a category that encompasses not only Facebook or Instagram, but also other communication channels. This will help us to avoid multicollinearity problems. However, as we will show below, the estimates corresponding to the model including the variables related to WhatsApp use have been run as a robustness check.

4 Methodology

The main goal of our analysis is to explore the influence that Internet use during late childhood may have on students’ academic performance. In doing so, we aim to provide a student profile that combines both academic and non-academic Internet use to maximise academic achievement. To this end, we will combine econometric analysis with multi-objective programming techniques.

The econometric analysis is based on the estimation by ordinary least squares (OLS) of different regression models in which academic performance is regressed on a set of variables. In these variables we include both a set of control variables and those relating to Internet use. Therefore, our base model would be defined as:

where \(Z\) is the standardised academic performance, \(i\) is the student, \(k=\mathrm{1,2},\mathrm{3,4}\) represents reading, mathematics, science and English, respectively; \({x}_{1}\) to \({x}_{4}\) represent student characteristics; \({x}_{5}\) is a school characteristic; \({x}_{6}\) to \({x}_{23}\) indicate Internet use (see Table 5 , Appendix ); \({\varepsilon }_{k}\) is the error term; \({\widehat{\beta }}_{j}\) represents estimated regression coefficients for the \(j=1,\dots , 23\) variables. Since this is a point estimate of the influence that each independent variable has on the dependent variable, there is a margin of error and, therefore, we have estimated the lower and upper bounds of the estimated coefficients at the 99% confidence level as follows:

where \(\widehat{\beta }\) is the estimated average coefficient; \(SE\) is the standard error; \(n\) is the number of observations; \(k\) is the number of conditioning variables; \(\alpha\) is the significance level and \(t\) are the probability values of the t-distribution.

Table 1 shows the lower and upper bounds of estimated coefficients for each of the educational outcomes, i.e. reading, mathematics, science and English. Footnote 2 Those coefficients that are not statistically significant, at least at 10%, are shown as “0”.

The results obtained show that females perform better than males in reading and English, but perform worse in mathematics. Specifically, girls obtain on average 0.27 standard deviations (SD) and 0.19 SD more in reading and English, respectively, than boys, while their mathematics performance drops by 0.08 SD. Far from being surprising, these findings are consistent with the evidence collected in the literature. On the one hand, females tend to perform better in reading; the Progress in International Reading Literacy Study (PIRLS) 2016 showed that, in 48 of the 50 participating countries, female students’ reading achievement was higher than boys’ and this female dominance has persisted since this assessment was implemented (Mullis et al., 2016 ). This gender gap in favour of females can be the result of certain gender stereotypes attributing greater mathematical ability to males (Cvencek et al., 2011 ; Plante et al., 2013 ; Spencer et al., 1999 , 2016 ). The corollary of this is that girls tend to have a higher linguistic self-concept than boys (Heyder et al., 2017 ; Jacobs et al., 2002 ; Wigfield et al., 1997 ) and to value reading highly, which favours their academic performance in this competence. On the other hand, females’ higher achievement in English is closely linked to reading skills (Grabe, 2010 ; Oxford, 2011 ), as developed skills in interpreting texts and processing information enable females to learn a foreign language more quickly (Wightman, 2020 ).

Similarly, we find that students’ socio-economic status is positively associated with their academic performance. This relationship has been strongly supported by the literature (Cedeño et al., 2016 ; Hanushek & Woessmann, 2011 ; Kim et al., 2019 ; Liu et al., 2020 ; Martins & Veiga, 2010 ; von Stumm, 2017 ) and is explained by the fact that students from disadvantaged backgrounds are constrained by their economic resources and cannot access tutoring and other educational resources that help to improve their educational outcomes (Crosnoe & Cooper, 2010 ; Lareau, 2011 ). In contrast, the proportion of poor students in school has a negative influence on academic performance. Children from poor socio-economic backgrounds are more likely to develop behavioural problems (Hendriks et al., 2020 ; Peverill et al., 2021 ; Piotrowska et al., 2015 ); this can negatively affect classroom climate and academic performance via peer effects (Busching & Krahé, 2020 ; Jerrim et al., 2021 ).

Regarding Internet use, we observe that using the Internet to study and complete school tasks positively correlates with academic performance in all four subjects. In contrast, using the Internet to play video games is negatively related to reading performance. This negative influence goes from 0.05 SD, on average, when the frequency of use is once or twice a month, to 0.10 SD, on average, when it is used every day. This pattern seems to be in line with previous literature (Lopez-Agudo & Marcenaro-Gutierrez, 2020 ).

Similarly, using the Internet to search for information on sports is negatively related to reading, mathematics, science and English performance, with the influence also being greater as the frequency of use increases. In contrast, using apps like YouTube or Spotify seems to have a positive influence on academic performance. However, the average influence in mathematics is lower than in the other disciplines. For example, daily use of these apps is positively related to mathematics performance (0.12 SD), while the association is 0.18 SD and 0.16 SD in science and English, respectively. Since using music with lyrics or certain genres can be more distracting (Avila et al., 2012 ; Perham & Currie, 2014 ) the level of concentration required for mathematics tasks may not be achieved.

Meanwhile, using social networks has a negative influence on academic performance, with the greater the frequency of use, the greater this correlation. In this sense, its overuse can cause students to adopt less efficient and more superficial study techniques, given a greater number of distractions (Alt, 2018 ).

Following the econometric analysis, our study is carried out on the basis of interval multiobjective programming. This methodology has been applied because, as we know, there are many factors involved in the educational process that are not always controllable and that can affect students’ academic performance. Consequently, results cannot be interpreted as causal effects, but rather as conditional associations. In this context, using interval multiobjective programming models is particularly useful. Interval multiobjective programming and, specifically, the algorithm proposed in Henriques et al. ( 2019 ) and applied in Henriques et al., ( 2020 , 2021 ) will allow us to overcome the uncertainty inherent to the coefficients in multiobjective problems. This methodology, which solves Multiobjective Linear Problems (MOLP), combines the reference-point approach with the concept of interval programming (Oliveira & Antunes, 2009 ) and allows us to use objective functions that take into account the confidence intervals of the regression coefficients. Therefore, by using this methodological approach, we will be able to analyse the potential trade-offs between different students outcomes while obtaining robust results.

That said, we start from a maximisation problem of educational outcomes subject to constraints:

where each \({Z}_{k}\) stands for:

\({Z}_{k}\left(x\right)\) are the objective functions to be maximized; \({\varvec{x}}={\left({x}_{1},\dots ,{x}_{n}\right)}^{T}\) is the vector of decision variables; \({\varvec{x}}\boldsymbol{ }\in X\) is the feasible region; \({\widehat{\beta }}_{kj}^{L}\) and \({\widehat{\beta }}_{kj}^{U}\) are the lower and upper bounds of the estimated coefficients, respectively.

In this sense, the estimated coefficients will be given by the correlation coefficients in Table 1 and our objective functions will be defined as follows:

Competences in reading.

Competences in mathematics.

Competences in science.

Competences in English.

In order to obtain realistic solutions, a set of technical constraints have been defined for Internet use variables. These constraints will guarantee that the solutions are not simultaneously 1 for all binary variables in a group:

Using the Internet to study (Google, Wikipedia, etc.).

Using the Internet to play games.

Using the Internet to search for information about sports.

Using the Internet to search for information about music or cinema (YouTube, Spotify, etc.).

Using the Internet to search for information about hobbies.

Using social networks (Facebook, Twitter, Instagram, Musically, etc.)

Besides the technical constraints, we define another set of constraints reflecting the relationships between those independent variables that have significantly stronger associations. To illustrate the creation of these restrictions, an example is given below using the variables “Proportion of poor students” ( \({x}_{5}\) ) and “Immigrant status” ( \({x}_{3}\) ):

Dependence between the two variables is defined as:

To incorporate this linear regression into the model, we use 99% confidence intervals for each parameter as follows:

which implies:

This expression can be broken down into two inequalities:

By following this procedure, we can obtain the set of constraints:

Relationship between proportion of poor students (1 st quartile ESCS) and students’ ESCS.

Relationship between proportion of poor students (1 st quartile ESCS) and immigrant status.

Relationship between proportion of poor students (1 st quartile ESCS) and repeater.

Relationship between students’ ESCS and immigrant status.

Relationship between students’ ESCS and repeater.

Relationship between students’ ESCS and using the Internet to find information for study (Google, Wikipedia, etc.).

Relationship between students’ ESCS and using the Internet to search for information about other topics.

Relationship between students’ ESCS and using social networks (Facebook, Twitter, Instagram, Musically, etc.)

Therefore, our multiobjective problem has 23 decision variables (binary and continuous), 4 objective functions and 22 constraints. The type of variables and their bounds (which are considered as constraints of the multiobjective problem) are specified in Table 5 ( Appendix ).

In order to solve our interval multiobjective problem we must first solve each one of the problems for the objective functions individually. In this way, we will obtain the individual optimal values (Chinneck & Ramadan, 2000 ) and we will be able to check whether there are trade-offs between the four educational competences.

where \(k\) is the number of objective functions; \(c\) is the number of constraints; \(j\) is the number of decision variables. Consequently, the optimal solution of our multiobjective problem will be somewhere between the ideal solutions of the upper and lower bounds \({Z}_{k}^{*}=[\) \({Z}_{k}^{U*},{Z}_{k}^{L*}] ,\) where \({Z}_{k}^{U*}={{Z}_{k}^{U}(\mathrm{x}}_{k}^{U*})\) and \({Z}_{k}^{L*}={{Z}_{k}^{L}(\mathrm{x}}_{k}^{L*})\) .

Then, and to obtain the solution of the interval multiobjective problem, we use the following surrogate scalarizing problem proposed in Henriques et al. ( 2019 ):

where the term \(\rho >0\) is an augmentation coefficient that guarantees the uniqueness of the obtained solution; \(k\) is the number of objective functions; \(c\) is the number of constraints; \(j\) is the number of decision variables.

This approach considers the Tchebychev distance to the interval ideal values \({Z}_{k}^{L*}\) and \({Z}_{k}^{U*}\) , as well as the relevance of each objective function to reach these values through the weights \({\mu }_{k}^{L},{\mu }_{k}^{U}>0\) for all \(k=1,\dots ,p\) . In addition, it provides “possibly” efficient solutions, which implies that the solution will be efficient for a linear combination of the parameters \({\overline{\beta }}_{kj}\in \left[{\beta }_{kj}^{L}, {\beta }_{kj}^{U}\right]\) .

On the other hand, the algorithm proposed in Henriques et al. ( 2019 ) assumes that \({\mu }_{k}^{L}+{\mu }_{k}^{U}=1\) for all \(k=1,\dots ,p\) , which enables to assign different importance to upper or lower bounds and, therefore, the decision maker (DM) will be able to define the importance of achieving each objective function according to their preferences. However, in our analysis we have considered the same importance for reaching each corresponding ideal solution.

5.1 Main results

First, we have obtained the individual optimal values for each function. To simplify the interpretation of the results, Table 2 shows the optimal values ( \({Z}_{k}^{*}\) ) for each function instead of the ideal solutions of the upper and lower bounds ( \({Z}_{k}^{L*}\) and \({Z}_{k}^{U*}\) ).

The last row of Table 2 shows the optimal values in terms of standardised scores. In this sense, the optimal value for English is higher than for the rest of the subjects, with 0.644 SD, compared to 0.549 and 0.472 SD for science and reading, respectively. Meanwhile, the optimal value in mathematics is 0.339 SD.

Along with the individual optimal values, Table 2 shows the profile of the student that maximises their performance in each one of the competences. As we can see, there is a certain trade-off between the different objective functions. Firstly, female students are the highest achievers in reading and English. In contrast, male students are the ones who maximise their performance in science and mathematics. This is hardly surprising and is consistent with the existing literature, as these are male-dominated fields (Spencer et al., 1999 , 2016 ).

Regarding students’ socio-economic level we observe that, in order to maximise students’ academic performance, the ESCS index should not be particularly high, although higher than the sample average value. Specifically, students’ socio-economic index should be 0.149 if they want to maximise their performance in reading, mathematics and science, and 0.068 when it comes to English. This is – to some extent – unexpected, as students’ socio-economic status is positively related to their academic performance. However, given that students interact with peers from diverse socio-economic backgrounds, it seems that academic achievement is maximised when students from low socio-economic backgrounds are surrounded by a higher proportion of poor students. This proportion of poor students will be 23.6% for reading and 22.11% for English performance. In mathematics and science this proportion will be slightly lower (21.11%).

On the other hand, performance in all four subjects is maximised for those students who are not repeaters, while immigrant students are more likely to maximise their performance in English, which may be explained by their language background.

In terms of the Internet use, we can observe a more marked trade-off between the different objective functions. Specifically, students maximise their mathematics performance by continuously using the Internet to study, while it must be limited for the other academic competences. In addition, Internet use for gaming should be limited to once or twice a month to maximise students’ performance in mathematics, science and English, and reduced as much as possible to maximise their reading achievement. In the same vein, using the Internet to search for information on sports should be reduced to zero if students want to maximise their academic performance.

Alike, using the Internet for listening to music or for other hobbies daily contributes to maximise their performance in reading, science and English. In contrast, they should reduce these uses to once or twice a week to perform well in mathematics.

Finally, regarding social networks, we see that students should keep their use to the barest minimum. Given that using social networks can lead to addiction, especially in late childhood and adolescence, students should practically not use them if they want to ensure their academic performance.

Once the individual optimal values have been obtained, we have run the algorithm considering equal importance to reach each objective function and obtain “possibly” efficient solutions, presenting these results in Table 3 . This table shows that the range of variation of the achieved value in science is larger (between -0.009 and 0.922 points) than in the other subjects. In contrast, the range of variation in mathematics is the smallest (between -0.115 and 0.602 points), with the maximum value being much smaller than that achieved in the other 3 competences.

As we can see, the profile of the student who maximises her academic performance is female, non-immigrant and has not repeated before 6 th grade. Moreover, as in the mono-objective problem, it appears that academic performance is maximised when students from a low socio-economic background are surrounded by a higher proportion of poor students, with students’ socio-economic index being 0.149 and the proportion of poor students being 21.13%.

As for Internet use we can observe that, in order to achieve higher academic performance, students should reduce their use of the Internet both for studying and for searching for information about sports or playing video games. In this regard, it is worth noting that, to maximise their academic performance, students should reduce their Internet use for studying to once or twice a month, which may be the result of multitasking (Feng et al., 2019 ; Junco, 2015 ). Since younger students may lack self-control, using the Internet for schoolwork or study may lead to a non-academic use. This could hinder the educational process and encourage procrastination from an early age (Aznar-Díaz et al., 2020 ).

In contrast, our results show that students can maximise their academic performance by using the Internet daily to listen to music or to search for information on other topics of interest. In this sense, listening to music can improve not only mood, but also arousal levels and, consequently, enhance the performance of certain simple cognitive tasks (Goltz & Sadakata, 2021 ).

Finally we can observe that, if students want to maximise their academic performance, social networks use should be non-existent. Adolescents and pre-adolescents are heavy users of social networks and their desire to be constantly interacting with others can lead them to misuse their time efficiently and neglect academic work (Qiaolei, 2014 ).

5.2 Robustness check

To check the robustness of our results, we have replicated our analysis using the variables that only refer to the use of applications such as Telegram or WhatsApp (see Tables 7 , Footnote 3   9 and 10 , Appendix ).

In this sense, if we look at the results obtained for this new mono-objective problem (Table 9 ), we can observe that the optimal values for each of the subjects are pretty similar to those obtained in our main model. For example, in reading the optimal value decreases just by 0.09 points in terms of standardised scores. As for the student profile, Table 9 shows a very similar pattern to Table 2 . In order to maximise their performance in each one of the subjects, students should reduce their use of the Internet to search for information about sports, as well as to use applications such as WhatsApp. These applications should be used minimally to enhance academic performance.

On the other hand, Table 10 shows the “possibly” efficient solutions. In this sense, we can observe that the student who manages to maximise her academic performance is an immigrant female who has not repeated a grade. Her socio-economic index should be around 0.067, while the proportion of poor students (in the school) should be 22.11%.

Regarding the frequency of Internet use, we observe a pattern of use very similar to the one shown in Table 3 . Students should use the Internet as little as possible to search for information about sports, while they can listen to music and search for information about other topics of interest daily without harming their academic achievement. As for the use of applications such as WhatsApp or Telegram, this should be limited to once or twice a week.

In summary, as we have seen through the tables, the individual optimal values and the “possibly” efficient solution are pretty close to the ones obtained in our main model, which shows the consistency of our results.

6 Discussion and conclusions

Throughout this paper we have tried to analyse the influence that using the Internet may have on academic performance. In order to do so, using a database that provides us with information on a cohort of 6 th grade students in 2018–2019, we have implemented interval multiobjective programming. With this methodology, we have tried to profile those students who are able to maximise their academic performance evenly – and simultaneously – among different subjects.

In detail, we first carried out an econometric analysis to subsequently proceed with the multiobjective programming. From these estimates we observed how, e.g., using social networks had a negative influence on reading, mathematics, science and English performance, with the negative influence being greater as the frequency of use increased. The results of the interval programming model allow to assert that the profile of the student who manages to maximise her academic performance in all four subjects is a female student who has not repeated, from a medium socio-economic level and who attends a school where the proportion of students from a low socio-economic background is 21.11% (i.e. slightly below the sample average).

In addition, the results show that using the Internet from an early age has its ups and downs. To maximise academic performance, students should use the Internet to study or search for information about sports once or twice a month. Similarly, they should use the Internet to play video games 1 or 2 times a week, while they can daily use applications such as Spotify. In contrast, the use of social networks should be practically zero.

Thus, while the Internet can be helpful for the teaching–learning process, its inappropriate use can prevent students from reaching their best balanced performance. Therefore and, given the possibility that constant use of the Internet and social networks may lead to “addiction” that may affect their academic performance, it is necessary to establish some control. Hence, it would be desirable for both the family and the school to offer students some guidance to encourage them to use the Internet appropriately. In addition to promoting awareness and self-control among students, other measures should also be implemented to control both the content and frequency of use by young people. This would prevent exposure to undesirable content, as well as procrastination, that can ultimately lead young people to adopt unhealthy study techniques.

Besides, schools could find more effective ways of integrating ICT into the educational process by providing students with learning environments that are in line with the needs and trends of the twenty-first century. In this sense, while getting students to reduce their use of the Internet to play video games or to practically stop using social networks may be beyond the school’s scope of action, balancing academic performance with the ICT used in the classroom can be an objective of the school. In recent years, students’ use of computers and tablets during lessons has become increasingly common. However, in order to make it compatible with good academic performance, it is essential that schools set guidelines and control measures such as e.g. applications that limit students’ access to non-academic content during class time, in order to avoid multitasking problems.

In any case, it should be noted that our study is not free of limitations. First, as mentioned above, we are working with correlational rather than causal econometric estimates, as far as we cannot control for all variables that may affect academic performance. However, by using interval multiobjective programming, we are able to deal with this state of uncertainty. Second, Internet variables are self-reported, which may lead to measurement errors in the model. Third, our results may not be extrapolated to the rest of Spain, to the extent that we are using students’ information from only one Spanish region.

The database used dates from around 10 years ago, which limits the results obtained considerably to the extent that the Internet use in 2011 has nothing to do with internet use today.

Alternatively, Table 1 estimations have been replicated adding school fixed-effects and results are pretty similar. These results are presented in Table 6 ( Appendix ).

Table 7 ( Appendix ) estimations have been replicated adding school fixed-effects and results are pretty similar. These results are presented in Table 8 ( Appendix ).

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This work has been partly supported by FEDER funding (under Research Project PY20-00228-R); Ministerio de Ciencia e Innovación (under Research Project PID2020-119471RB-I00) and the Andalusian Regional Government (SEJ-645). We also acknowledge the scholarship FPU20/01509 of the Ministerio de Universidades and the training received from the Programa de Doctorado en Economía y Empresa of the Universidad de Malaga . The authors also acknowledge the data provided by the Agencia Canaria de Calidad Universitaria y Evaluación Educativa .

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How Can Bullying Victimisation Lead to Lower Academic Achievement? A Systematic Review and Meta-Analysis of the Mediating Role of Cognitive-Motivational Factors

Muthanna samara.

1 Department of Psychology, Kingston University London, Penrhyn Road, Kingston upon Thames KT1 2EE, UK; [email protected] (A.E.-A.); moc.liamtoh@adummaharas (S.H.)

Bruna Da Silva Nascimento

2 Department of Psychology, Brunel University London, London UB8 3PH, UK; [email protected]

Aiman El-Asam

Sara hammuda, nabil khattab.

3 Department of Sociology, Doha Institute, Doha, Zone 70, Qatar; [email protected]

Associated Data

Data is contained within the article and supplementary material .

Bullying involvement may have an adverse effect on children’s educational outcomes, particularly academic achievement. However, the underlying mechanisms and factors behind this association are not well-understood. Previous meta-analyses have not investigated mediation factors between bullying and academic achievement. This meta-analysis examines the mediation effect of cognitive-motivational factors on the relationship between peer victimization and academic achievement. A systematic search was performed using specific search terms and search engines to identify relevant studies that were selected according to specific criteria resulting in 11 studies encompassing a sample total of 257,247 children (10 years and younger) and adolescents (11 years and older) (48–59% female). Some studies were longitudinal and some cross sectional and the assessment for each factor was performed by various methods (self, peer, teacher, school and mixed reports). Children involved in bullying behaviour were less likely to be academically engaged (k = 4) (OR = 0.571, 95% CI [0.43, 0.77], p = 0.000), to be less motivated (k = 7) (OR = 0.82, 95% CI [0.69, 0.97], p = 0.021), to have lower self-esteem (k = 1) (OR = 0.12, 95% CI [0.07, 0.20], p = 0.000) and lower academic achievement (k = 14) (OR = 0.62, 95% CI [0.49, 0.79], p = 0.000). Bullying involvement was also significantly related to overall cognitive-motivational factors (k = 17, OR = 0.67, 95% CI [0.59, 0.76], p = 0.000). Cognitive-motivational factors, taken together, mediated the association between bullying victimisation and academic achievement (k = 8, OR = 0.74, 95% CI (0.72, 0.77), p = 0.000). Bullying victimisation was negatively related to cognitive-motivational factors, which, in turn, was associated with poorer academic achievement. These findings were moderated by the design of the studies, assessment methods for the bullying reports, mediators and outcomes, country, age of children in the sample and/or types of bullying. The findings are of relevance for practitioners, parents, and schools, and can be used to guide bullying interventions. Interventions should focus on improving internal and external motivational factors including components of positive reinforcement, encouragement, and programs for enhancing academic engagement and achievement amongst children and adolescents.

1. Introduction

Involvement in bullying has been associated with short and long-term negative consequences including physical health issues and behavioural and emotional problems [ 1 , 2 , 3 ]. Such consequences also vary according to the role played in the bullying experience and whether the child is a victim, a perpetrator, or both (bully–victim). Externalising problems such as hyperactivity and conduct disorder have been typically reported among bullies, whereas internalising problems such as anxiety and mood disorders have been mainly observed among victims (e.g., [ 4 , 5 ]). In turn, bully–victims experience a more severe combination of internalising and externalising problems in comparison to victims or bullies only [ 1 , 6 , 7 ].

Bullying is prevalent in the school context and as such, on top of its consequences for mental health, bullying involvement has also been found to have a negative impact on children’s educational outcomes, particularly academic performance [ 8 ]. For example, a study in Norway demonstrated that bullying involvement in adolescents was associated with lower academic grades at an individual and school level [ 9 ]. In the United States, these findings were confirmed in a nationally representative sample of 7,304 students, after controlling for poverty, school size, and personal victimisation [ 10 ]. In a meta-analytical review of 33 studies, Nakamoto and Schwartz [ 8 ] confirmed the negative association between bullying victimisation and academic performance. Liu et al. [ 11 ] in a longitudinal study found that being bullied in 3rd grade predicted poor academic outcomes in 5th grade. In another longitudinal study, Juvonen et al. [ 12 ] found that for each 1-point (out of 4-point scale) increase in self-perceived victimisation, students’ Grade Point Average (GPA) decreased by 0.3-grade points. Wang et al. [ 13 ] also found that for every 1-point (out of 5-point scale) increase in peer victimisation, students’ GPA decreased by 0.44 units. Similar findings were noted by Van der Werf [ 14 ] who studied the effect of bullying on academic achievement. It was found that a 1 standard deviation (SD) increase in school bullying incidents resulted in a 0.55 SD standardised test score decrease in the short-term and 0.4 SD decrease in the long-term (two years) for affected students.

As well as being repeatedly associated with poor academic achievement (e.g., [ 8 , 15 , 16 ]), bullying victimisation has been associated with low self-esteem [ 17 , 18 , 19 , 20 ], low educational motivation [ 21 ], reduced academic self-concept (reading and mathematics) and lower commitment to study, and higher extrinsic motivation and test anxiety rates [ 22 ]. Some studies also found a negative association between peer victimisation and academic self-efficacy [ 23 ] and self-concept [ 24 ]. In addition, children who are victimised by their peers tend to have negative attitudes toward school [ 25 ], negative perceptions of school climate [ 13 , 26 ], and difficulties concentrating on school work [ 27 ].

Although the negative association between bullying and academic performance has been well documented, the underlying mechanism for this association is yet to be fully understood. Among the various mechanisms that may link bullying victimisation with academic achievement, cognitive–motivational variables such as academic motivation and aspirations have been recognised as important, but as yet under-researched, domains. For the purpose of this study, academic motivation is defined as the student’s interest and desire to engage in their school and learning activities [ 28 ], whilst academic aspirations are a student’s educational goals and choices [ 29 ]. In fact, students with high academic motivations and aspirations tend to succeed academically [ 30 ]. However, bullying victimisation has been found to reduce students’ motivations and aspirations [ 21 , 31 ]. As such, motivation and aspirations offer a plausible mediational path between bullying victimisation and academic achievement. Thus, this study aims to explore and review the literature that has investigated the indirect effect of bullying victimisation on academic achievement through cognitive-motivational variables, particularly academic motivation and aspirations.

1.1. Bullying Victimisation, Cognitive-Motivational Factors, and Academic Achievement

The expectancy-value theory [ 32 , 33 ] and the achievement goal theory [ 34 , 35 ] propose that individuals are more likely to engage in a particular task when such a task has some value to them and when they believe they are likely to do well. From this perspective, cognitive–motivational factors such as academic motivation and aspirations play an important role in explaining academic achievement. Consistent with this view, previous studies have demonstrated that students with higher motivation and higher aspirations are more likely to succeed academically than those with low motivation and low aspirations (e.g., [ 30 , 36 , 37 ]). Given that motivation and aspirations are important mechanisms for academic success, understanding how these factors can buffer against the damaging implications of bullying victimisation for academic achievement would be informative for the development of intervention programs.

The Self-Determination Theory (SDT) [ 38 ] offers a useful framework to understand the association between bullying victimisation and cognitive–motivational factors such as motivation and aspirations in the educational context. SDT postulates that relatedness, autonomy, and competence are three important factors to maintain positive well-being. Relatedness refers to the need for being connected to others [ 39 ], autonomy refers to the need for self-endorsement of an individual’s behaviour [ 40 ], whereas competence refers to the need for achieving attained goals [ 38 ]. From this perspective, in order to feel motivated and achieve their highest academic potential, all these three needs must be supported in students. Negative school conditions such as peer rejection, social exclusion, and bullying may undermine these needs [ 41 , 42 , 43 ].

In fact, bullying victimisation has been found to negatively influence students’ school relatedness, such that bullied students tend to feel less connected to their school and, in turn, tend to achieve poorly academically [ 43 ]. On top of this, students who have suffered bullying victimisation present lower academic motivation, reduced perceived academic competence [ 21 ] and lower educational aspirations [ 31 ] in comparison to their non-bullied peers. These consequences may also be long-lasting. For example, Goodboy et al. [ 44 ] found that students who were bullied in high school presented a low level of self-determined motivation, high levels of amotivation, and emotional, social, and institutional problems in their first semesters at university, which is likely to affect their academic achievement. Studies exploring the mechanism behind the association between bullying and academic achievement have found that bullying victimisation leads to higher psychological distress, which in turn reduces student engagement leading to lower academic achievement [ 45 ]. Fan and Dempsey [ 46 ], while controlling for gender and socioeconomic status, found that students that are victimised by their peers report lower academic motivation and self-efficacy, which results in lower academic achievement. Taken together these findings suggest that bullying victimisation reduces academic achievement by decreasing students’ motivation and aspirations.

1.2. The Present Study

Meta-analysis studies have demonstrated a negative association between bullying and academic achievement [ 8 ]; however, these studies fail to identify the potential underlying mechanisms for this association. In order to design effective intervention strategies to minimise the impact of bullying on academic achievement, there is a need for studies that investigate the mediators and mechanisms of this relationship. The factors linking bullying with academic achievement have only been tested empirically to a limited extent (e.g., [ 45 , 46 ]). However, an explanation for a phenomenon cannot emerge from the findings of a single study. Therefore, the main aim of this meta-analysis is to address this gap and identify and quantify the extent to which cognitive–motivational factors such as motivation and aspirations mediate the association between bullying involvement and academic achievement. In addition, although the academic achievement of bullies and bully–victims are also generally lower than that of those uninvolved in bullying, meta-analytical studies have mainly focused on bullying victimisation (e.g., [ 8 ]). Thus, this meta-analysis also aims to identify whether bullying subgroips and type of bullying involvement plays a role in the association between bullying, motivation and aspirations, and academic achievement.

Therefore, the current study will focus on addressing the gaps in the literature regarding the indirect effect of bullying involvement of all types on academic achievement through motivation and aspiration factors in children and adolescents. We hypothesise that the relationship between bullying victimisation and lower academic achievement is mediated by cognitive-motivational factors.

2. Materials and Methods

The meta-analysis followed PRISMA guidelines [ 47 ] ( Supplementary Table S1 ).

2.1. Information Sources and Database Search

A literature search of all studies on bullying and academic motivation or aspirations, and bullying and academic achievement, published between January 2000 and January 2020, was undertaken. The following databases were selected as they incorporate pertinent disciplines. These include CINAHL (Current Index to Nursing and Allied Health Literature), Education Abstract, Education Research Complete, ERIC (Education Resources Information Center), PsychInfo, PubMed, and Web of Science. To identify all published and unpublished studies empirically analysing school bullying, academic achievement, and motivation and aspiration factors, we conducted systematic searchers by combining three different sets of keywords. The first set of keywords comprised terms describing “education” (i.e., education* OR academic* OR school), while the second set of keywords comprised terms describing achievement (i.e., achievement OR performance OR attainment OR success* OR motivation OR aspiration*), and the third group of keywords described “bullying” (i.e., bully* OR victim* OR bullied*). Studies were then selected based on specific inclusion and exclusion (see below). Both published and unpublished articles were selected and then further coded according to the variables examined by each study. More specifically, the studies were divided into: (1) studies that looked at mediation factors between bullying and academic achievement; (2) studies that looked at the association between bullying and academic achievement; and (3) studies that looked at the association between bullying and motivation or aspiration. Some studies belonged to more than one group. The final included mediation studies are the ones for which we could calculate the mediation.

2.2. Eligibility Criteria and Study Selection

2.2.1. inclusion criteria.

The inclusion criteria required that the study examine the link between bullying involvement, academic motivation or aspiration, and academic achievement in the same study. Second, the methodology had to be of a quantitative nature. Third, studies relating to traditional bullying (i.e., face-to-face bullying), including relational (i.e., purposeful damage and manipulation of peer relationships leading to social exclusion, spreading rumours) and/or direct bullying (i.e., physical such as hitting and pushing, and verbal such as making fun or insulting someone), and cyberbullying (i.e., bullying through digital electronic communication tools) were all included. Studies that referred to specific forms of bullying such as bullying focused on sexual orientation, where sexuality or gender are used against another person, were also included. Although the main aim of the study is to look at the mediation effect, those studies that did not necessarily explore mediation factors for the relationship between bullying and academic achievement, but that looked at either academic achievement or motivation or aspirations separately, were also retained. Fourth, the measures of bullying relationships, outcomes and mediators had to have been conducted through observational studies and/or various reporting methods (self, teacher, peer or school). Fifth, sufficient statistical information needed to be available in the study or provided by the authors for effect size calculation (e.g., means and standard deviations, odds ratio with 95% Confidence Interval, correlations, event rates and sample size, etc.). Sixth, participants needed to be children or adolescents (under 18 years of age). Lastly, articles in English, Portuguese, and Spanish were included.

2.2.2. Exclusion Criteria

Studies that were qualitative, retrospective, intervention-based, meta-analyses or exclusively examined a clinical population were not included. Reference lists from meta-analyses studies were examined in order to ensure all relevant studies had been included.

2.3. Coding

There were two independent coders that categorised variables as relevant and any disparities were discussed and duly revised. Based on the search result, the studies were allocated to three categories: mediation studies, studies on academic achievement, and studies on cognitive-motivational factors. No studies that looked at the association between bullying and aspirations and academic achievement were found.

2.4. Coding of Study Characteristics and Moderators

The percentage by gender and age range of the participants were extracted from each study. One study [ 48 ] provided the school grade of the children instead of the age range and this was converted into age range according to the school system in the respective country that the study was performed in. The age was then categorised into childhood (5–10 years of age) and adolescence (11–18 years of age) or mix of both. The age range of the study that reported the grade will not be affected even if some students have repeated one year or more as this study was put in the adolescence group and repeating years would still have put these students in that category. The age category of the participants; the assessment method (child-report, peer-report, peer nomination, teacher-report, school-report, or mixed) of the predictor (bullying), the outcome (academic achievement) and the mediator (cognitive-motivational); the type of bullying (traditional, relational, general and cyber bullying, or mixed); bullying subgroups (bullies, victims, and bully/victims); the country in which the study was conducted; and the design of the studies (cross sectional vs. longitudinal) were all included in the meta-analysis as potential moderators.

Comprehensive Meta-Analysis (CMA) [ 49 ] was used to perform the analysis. Some articles did not report some of the essential data for the analysis of the indirect effect of bullying on academic achievement [ 15 , 50 , 51 , 52 ]. Studies whose authors were uncontactable or who did not reply to the initial email and the reminders within two-weeks were not included in the mediation analysis. However, because these papers reported univariate associations between both bullying and academic achievement and bullying and cognitive-motivational factors, they were included in the univariate meta-analysis.

2.5. Statistical Analysis

2.5.1. summary measures.

The extracted data was presented in a range of formats (e.g., correlations, odds ratios, log odds ratio, means and standard deviations). The adopted effect size format for the pooled effect size of each meta-analysis was Odds Ratio (OR) with 95% confidence intervals for each study, which was evaluated against the overall weighted effect size. A random effect model was used where the within and between study variability is taken into account, and thus is more generalisable than the fixed effects model [ 4 , 53 ].

For the studies that provided multiple effect sizes for the same variable (e.g., child-report and peer-report), the aggregated mean was calculated. This is to avoid duplication of results for the same samples. In addition, the weight, which is the inverse of variance, of each study will be shown in each meta-analysis. This will give the precision of each study [ 49 ].

2.5.2. Heterogeneity and Moderators Analysis

The presence of significant heterogeneity indicates that variations in effect sizes is due to specific factors and moderators rather than errors in sampling ( Qb ). I 2 was used to measure the variability across studies [ 54 ] where values above 75% indicate that the variance between studies is due to moderators, while values below 25% are due to random error [ 55 ]. Moderator analyses were performed for categorical variables using ANOVAs for all moderators (design, assessment method for each variable, age, country, bullying types and subgroups) separately for each predictive model (bullying–motivational factors; bullying–academic achievement; and the mediation effect of motivational factors between bullying and academic achievement).

2.5.3. Publication Bias

Four methods were used to calculate publication bias, each method giving a different indication. First, the Rosenthal’s Failsafe Number [ 56 ] will specify the number of further studies that need to be published in order to nullify the significant results. If the reported Failsafe N exceeds the outcome of the equation 10 (5k + 10) (k: number of reported studies) then the results are not biased [ 57 ]. Secondly, the Begg and Mazumbar Rank Correlation Test (Kendall’s tau b) [ 58 ] examines study sample size where small studies and large effect sizes indicate large variances. Thus, no publication bias means that the relationship between the effect size and variance is not significant. Like correlation, Kendall tau b with a value of zero indicates no correlation and the deviation from zero means that there is an association [ 59 ]. Thirdly, the Egger’s test [ 60 ] uses linear regression to calculate deviations from zero in the funnel plot. The higher the deviation from zero the larger the systematic difference between larger and smaller studies. Finally, Duval and Tweedie’s Trim and Fill Test [ 61 ] removes asymmetric studies from one side in order to identify the unbiased effect. These studies are then reinserted to create a symmetric funnel plot, and then an adjusted effect size is calculated for this symmetric plot [ 62 ]. The deviation between effects sizes will give an indication of the severity of publication bias.

One study removal analysis was also performed for each meta-analysis to show whether any study’s removal would affect the significance level of the pooled effect size.

3.1. Search Results

EndNote program [ 63 ] was used for importing studies from different databases. The first databases search produced 401 articles. Duplicates and articles that did not meet the inclusion criteria were removed firstly according to titles and abstracts and then according to full text reviews. All reference lists in the included articles and meta-analyses were also reviewed. The final number of articles that investigated the three main factors (bullying, one cognitive–motivational factor and academic achievement) and were included in the meta-analysis was 11 ( Figure 1 ).

Description of the systematic search stages.

3.2. Study Characteristics

The 11 studies included 257,247 children and adolescents (number ranged between 140–235,064) aged between 5 and 17 years-old ( Table 1 ). All studies included both genders, such that 52.07% of the participants included in the meta-analysis were female. Most studies only looked at bullying victimisation (N = 7), whereas two studies also reported findings on victims, bullies, and bully–victims, and one study reported findings on both bullies and victims. Additionally, most studies were cross-sectional (N = 6), and were conducted in North America (N = 8) and Europe (N = 3).

Study characteristics for the studies included in the meta-analysis.

* For longitudinal studies the duration of the study is mentioned between brackets. † Definitions: traditional bullying included face-to-face bullying including relational bullying (i.e., purposeful damage and manipulation of peer relationships leading to social exclusion, spreading rumours) and direct bullying (i.e., physical such as hitting, pushing, and verbal such as making fun, insulting someone); cyberbullying included bullying through digital electronic communication; general bullying included violence and intimidation based on peer nomination of up to three students in the class that are perpetrators based on three items (starts fights, says unpleasant things, and gets upset easily) and victims (gets teased, gets picked on, gets pushed or hit). ‡ The predictor (bullying) and the mediator variables were measured through standardised measures in all studies. Academic achievement was measured either through the students’ self-report GPA, or through schools’ or teachers’ reports.

Among the studies included in the meta-analysis, only one study directly tested the indirect effect of bullying on academic achievement through motivation that also included data on academic engagement [ 46 ]. In addition, seven studies tested the indirect effect of bullying on academic achievement through academic engagement, academic self-concept, self-esteem, or psychological distress [ 45 , 46 , 48 , 50 , 64 , 65 , 66 ]. These variables reflect students’ general motivation levels and thus we decided to include them in this meta-analysis, referring to them as cognitive-motivational factors. One study [ 50 ] did not test the indirect effect of victimisation on academic achievement, but because the direct effect of bullying on academic engagement and the direct effect of engagement on academic achievement were provided, we used these coefficients to calculate the indirect effect of victimisation on academic achievement. Therefore, seven studies (eight mediation results) were included in the meta-analysis of the indirect effect of bullying on academic achievement through cognitive-motivational factors. There were an additional four studies [ 15 , 51 , 52 , 67 ] that did not test mediation nor provide data that allowed for the calculation of the indirect effect. However, because such studies looked at both the association between bullying and academic achievement and bullying and motivational factors, we decided to present their effect size findings in the meta-analysis.

The informant regarding bullying, academic achievement, and cognitive-motivational factors was also reported (children, peers, school-report or a mixture of respondents and methods). Most papers (N = 9) relied on children’s self-reports to assess bullying, whereas a limited number of studies used peer nomination (N = 1), and mixed informants (N = 1). In turn, most studies relied on teacher-report or school-report to assess academic achievement (N = 6), whereas the remaining studies used children’s self-reports (N = 5). For motivational factors (mediator), most studies relied on children’s self-reports (N = 5), whereas two relied on teacher-report and the rest of the studies did not report (N = 4).

3.3. Bullying and Victimisation as Predictors of Motivational Factors and Academic Achievement: Meta-Analysis

The studies that included motivational factors as mediators as well as academic achievement as an outcome were included in the analysis. Some studies looked at cognitive–motivational factors and academic achievement separately without calculating the mediation effects (N = 5), which were excluded from the analysis.

Firstly, we will present the analysis of the relationship between bullying involvement and cognitive-motivational factors. Secondly, the analysis of the relationship between bullying involvement and academic achievement will be presented and finally we will present the mediation effect of motivational factors on the relationship between bullying involvement and academic achievement. For all categories, a pooled effect size across studies of Odds Ratio (OR) was calculated.

3.3.1. Motivational Factors

The combined effect size showed that children who are involved in any bullying behaviour were significantly less likely to be academically engaged (k = 4) (OR = 0.571, 95% CI (0.43, 0.77), p = 0.000), to have less motivation (k = 7) (OR = 0.82, 95% CI (0.69, 0.97), p = 0.021), and to have lower self-esteem (k = 1) (OR = 0.12, 95% CI (0.07, 0.20), p = 0.000). However, neither self-concept (k = 3) (OR = 0.74, 95% CI (0.53, 1.03), p = 0.072) nor self-efficacy (k = 2) (OR = 0.73, 95% CI (0.41, 1.27), p = 0.264) were significantly associated with bullying involvement. The heterogeneity assessments were significant for all except self-esteem (academic engagement: Q (3) = 14.39, p = 0.002; I 2 = 79.16%; motivation: and Q (6) = 119.05, p = 0.000; I 2 = 94.96%; self-concept: Q (2) = 21.78, p = 0.000; I 2 = 90.82%; self-efficacy: Q (1) = 5.64, p = 0.018; I 2 = 82.27%). The pooled effect size for the overall cognitive–motivational factors was significant (k = 17; OR = 0.67, 95% CI (0.59, 0.76), p = 0.000) with a significant heterogeneity between groups ( Q (16) = 442.71, p = 0.000; I 2 = 96.39%) (See Figure 2 ).

Meta-analysis for the relationship between bullying involvement and cognitive-motivational factors.

As for victims only, they were also significantly less likely to be motivated (k = 4) (OR = 0.74, 95% CI (0.61, 0.89), p = 0.002). The pooled effect size for the overall cognitive-motivational factors for victims only was significant (k = 13) (OR = 0.63, 95% CI (0.55, 0.72), p = 0.000) with a significant heterogeneity between groups ( Q (12) = 424.96, p = 0.000; I 2 = 97.18%). On the other hand, the results for bullies only were not significant in relation to motivation (k = 2) (OR = 1.03, 95% CI (0.84, 1.27), p = 0.762). Figure 2 shows other individual relationships for each bullying subgroup ( Figure 2 ).

3.3.2. Academic Achievement

The combined pooled effect size showed that children who are involved in bullying behaviour were significantly more likely to have low academic achievement (k = 14) (OR = 0.61, 95% CI (0.47, 0.79), p = 0.000). The heterogeneity assessment was also significant ( Q (13) = 974.27, p < 0.000, I 2 = 98.66%).

The results for victims only also showed significant results where victims were more likely to have low academic achievement (k = 10) (OR = 0.62, 95% CI (0.47, 0.83), p = 0.001) with a significant heterogeneity between groups ( Q (9) = 966.67, p = 0.000; I 2 = 99.07%). The results for bully/victims only and bullies only were not significant (bully/victims: k = 2, OR = 0.58, 95% CI (0.18, 1.89), p = 0.367); bullies: k = 2, OR = 0.55, 95% CI (0.26, 1.19), p = 0.128) ( Figure 3 ).

Meta-analysis of the relationship between bullying involvement and academic achievement.

3.4. Mediation Analysis between Victimisation and Academic Achievement Pooled Effect Size

From the above, only seven studies reported mediation results (one of which reported two mediations) or have enough data to calculate the mediation effect between victimisation and academic achievement. The studies that were included in the mediation analysis showed different mediation factors between victimisation and academic achievement, while three studies showed similar mediation factor (academic engagement).

There were significant mediation effects between victimisation and academic achievement for psychological distress and academic engagement combined as one mediation factor (k = 1) (OR = 0.69, 95% CI (0.50, 0.97), p = 0.031), self-concept (k = 1) (OR = 0.26, 95% CI (0.16, 0.43), p = 0.000), self-efficacy (k = 1) (OR = 0.65, 95% CI (0.61, 0.68), p = 0.000), motivation (k = 1) (OR = 0.87, 95% CI (0.82, 0.91), p = 0.000) and academic engagement (k = 3) (OR = 0.77, 95% CI (0.59, 0.99), p = 0.044). On the other hand, no significant mediation was found for self-esteem and self-efficacy combined as one mediation factor (k = 1) (OR = 0.87, 95% CI (0.54, 1.39), p = 0.546).

The overall pooled effect size for all motivational factors as mediators was significant (k = 8) (OR = 0.74, 95% CI (0.72, 0.77), p = 0.000) with a significant heterogeneity between groups ( Q (7) = 79.30, p = 0.000; I 2 = 91.17%) (See Figure 4 ).

Meta-analysis showing the mediation effect of cognitive–motivational factors between victimisation involvement and academic achievement.

3.5. Moderator Analysis

As the I 2 variance for all analyses was above 75%, this indicates that the differences were due to moderators and not a random error. Meta-ANOVAs were conducted for moderation analyses for the categorical moderators: assessment method of bullying (child-report, peer nomination or mixed); assessment method of the outcomes or mediators (child report, teachers’ report, or schools’ report); country; age (childhood: 5–10; adolescence: 11–18 or mixed); bullying type; and design (longitudinal or cross-sectional).

3.5.1. Moderator Analysis of Motivational Factors

The heterogeneity analysis was significant for some of the analyses. Heterogeneity assessment was conducted for children who were involved in bullying in relation to cognitive–motivational factors.

The heterogeneity assessment indicated that motivational factors were significantly moderated by country ( Qb = 28.85, p = 0.000). This indicated that bullying involvement had a stronger effect on motivational factors in Canadian studies (k = 2, OR = 0.46, p = 0.000) followed by studies in Spain (k = 4, OR = 0.57, p = 0.000), then American studies (k = 8, OR = 0.75, p = 0.000) and finally Austrian studies (k = 3, OR = 0.98, p = 0.86).

Finally, the heterogeneity assessment indicated that motivational factors were also significantly moderated by the type of bullying ( Qb = 24.66, p = 0.000). This indicated that relational bullying had a stronger effect on motivational factors (k = 2, OR = 0.52, p = 0.014) followed by cyberbullying involvement (k = 4, OR = 0.57, p = 0.000), then sexual bullying (k = 1, OR = 0.72, p = 0.000) and traditional bullying (k = 10, OR = 0.81, p = 0.000).

On the other hand, heterogeneity was not significant for design, age category and assessment method for the mediator and bullying. However, when looking specifically at design, it was found that longitudinal studies (k = 12, OR = 0.70, p = 0.000) and cross-sectional studies (k = 5, OR = 0.68, p = 0.001) were significant. For age, studies that included adolescents (k = 11, OR = 0.74, p = 0.000), children (k = 1, OR = 0.69, p = 0.047) and mixed (children and adolescents) (k = 5, OR = 0.64, p = 0.000) were significant. In addition, studies that included children’s reports (k = 16, OR = 0.70, p = 0.000) and mixed reports (k = 1, OR = 0.69, p = 0.047) for bullying data were significant. Finally, studies that included children’s reports (k = 15, OR = 0.73, p = 0.000) and teachers’ reports (k = 2, OR = 0.52, p = 0.014) for the data on mediator factors were significant.

3.5.2. Moderator Analysis for Academic Achievement

The heterogeneity analysis was significant for some of the analyses. Heterogeneity assessment was conducted for children who were involved in bullying in relation to academic achievement. The heterogeneity assessment indicated that this relationship was significantly moderated by age categories ( Qb = 7.30, p = 0.026). This indicates that bullying involvement had a stronger effect on academic achievement for studies amongst children (k = 1, OR = 0.58, p = 0.003) and adolescents (k = 12, OR = 0.58, p = 0.005) followed by a study that included both children and adolescents (k = 1, OR = 0.83, p = 0.000).

The heterogeneity assessment indicated that the relationship was also significantly moderated by the assessment method for bullying data ( Qb = 12.62, p = 0.002). This indicated that bullying involvement had a stronger effect on academic achievement in studies that had peer nomination (k = 3, OR = 0.23, p = 0.000) followed by a study where bullying was reported by a mix of informants (k = 1, OR = 0.56, p = 0.003) and finally by children’s reports (k = 10, OR = 0.71, p = 0.013).

The heterogeneity assessment indicated that the relationship was also significantly moderated by country ( Qb = 21.41, p = 0.000). This indicated that bullying involvement had a stronger effect on academic achievement in studies in Portugal (k = 3, OR = 0.23, p = 0.000), followed by American studies (k = 6, OR = 0.64, p = 0.020), then Canadian studies (k = 2, OR = 0.66, p = 0.000) and finally Austrian studies (k = 3, OR = 0.85, p = 0.038).

The heterogeneity assessment indicated that the relationship was also significantly moderated by study design ( Qb = 11.26, p = 0.001). This indicated that bullying involvement had a stronger effect on academic achievement in longitudinal studies (k = 7, OR = 0.43, p = 0.000) compared with cross-sectional studies (k = 7, OR = 0.85, p = 0.006).

Finally, the heterogeneity assessment indicated that the relationship was also significantly moderated by the type of bullying ( Qb = 26.10, p = 0.000). This indicated that studies that investigated general bullying had a stronger effect on academic achievement (k = 3, OR = 0.23, p = 0.000), followed by studies that reported relational bullying involvement (k = 2, OR = 0.66, p = 0.000), then traditional bullying (k = 8, OR = 0.70, p = 0.119) and finally sexual bullying (k = 1, OR = 0.83, p = 0.000).

On the other hand, when looking specifically at assessment methods for the outcome (academic achievement), it was found that schools’ reports (k = 6, OR = 0.51, p = 0.005) and teachers’ reports (k = 1, OR = 0.69, p = 0.001) were significant, but not children’s reports (k = 7, OR = 0.67, p = 0.059).

3.5.3. Moderator Analysis for Mediation

The heterogeneity analysis was significant for some of the analyses. Heterogeneity assessment was conducted for children who were involved in victimisation. The heterogeneity assessment indicated that the mediation analysis was significantly moderated by the assessment of the outcome (academic achievement) ( Qb = 5.31, p = 0.070). This indicated that mediational factors had a stronger effect on academic achievement for teachers’ reports (k = 2, OR = 0.83, p = 0.267), compared to children’s reports (k = 6, OR = 0.66, p = 0.000).

On the other hand, when looking specifically at design, it was found that longitudinal mediation studies were significant (k = 5, OR = 0.76, p = 0.004), but not cross-sectional mediation studies (k = 3, OR = 0.55, p = 0.069). For age, mediation studies that included adolescents (k = 7, OR = 0.70, p = 0.000) and children (k = 1, OR = 0.67, p = 0.029) were significant. In addition, studies that included children’s reports (k = 7, OR = 0.70, p = 0.000) and mixed reports (k = 1, OR = 0.67, p = 0.029) for the bullying data were significant, while studies that included children’s reports (k = 4, OR = 0.72, p = 0.002) on academic achievement were significant, but not schools’ reports (k = 3, OR = 0.55, p = 0.069) or teachers’ reports (k = 1, OR = 0.96, p = 0.658). Mediation data studies that included children’s reports (k = 6, OR = 0.66, p = 0.000) were significant while teachers’ reports were not significant (k = 2, OR = 0.83, p = 0.267). Studies that reported traditional bullying were significant (k = 6, OR = 0.66, p = 0.000) but not studies on relational bullying (k = 2, OR = 0.83, p = 0.274).

3.6. Publication and Risk Bias

Four publication bias methods were employed (see Table 2 ). The studies included in each analysis are reflected in each meta-analysis figure shown above. For example, when the motivational factors are the outcome and the predictor is any bullying involvement then the analysis was done for all studies shown in Figure 2 , while when the predictor is the victims only subgroup then the studies that investigated victimisation only are included, and so on.

Publication bias analysis using four methods.

1 Only one study and thus cannot be performed; 2 Only two studies and thus cannot be performed.

3.6.1. Cognitive-Motivational Factors as Outcomes of Bullying Involvement

The ‘5k + 10’ benchmark using the Rosenthal’s Failsafe N analysis was not reached for bullies only, indicating that the found effects are open for future disconfirmation. The rest Rosenthal’s Failsafe N analyses indicated no publication bias for any bullying involvement and victimization only. The Kendall’s Tau calculations and Egger’s Test for all indicated an absence of publication bias. Lastly, the Trim-and-Fill analysis did not show different effect sizes for any of the results.

3.6.2. Academic Achievement as an Outcome of Bullying Involvement

For bullying involvement and victimisation there was no publication bias as the ‘5k + 10’ benchmark was reached. The Kendall’s Tau calculations indicated publication bias for any bullying involvement, but not for victims only. The Egger’s Test showed no publication bias. Lastly, the Trim-and-Fill analysis showed exactly the same effect sizes for both.

3.6.3. Mediation

For the mediation studies there was no publication bias as the ‘5k + 10’ benchmark was reached. The Kendall’s Tau calculation and the Egger’s Test did not find any publication bias. The Trim-and-Fill procedure showed exactly the same effect size for the mediation.

In addition, the academic engagement studies showed publication bias, as the ‘5k + 10’ benchmark was not reached. No publication bias was found in the rest of the tests (See Table 2 ).

3.6.4. One Study Removed

We repeated the meta-analyses by removing each study one by one. The results show that when removed none of the studies changed the pooled effect sizes for the relationship between bullying and/or victimisation involvement and academic achievement (See Figure 5 ), for the relationship between bullying and/or victimisation involvement and cognitive motivational factors (See Figure 6 ), and for the mediation analysis (See Figure 7 ). The pooled effect sizes remained significant as shown in the original analysis before removing any of the studies, indicating that none of the studies could change the results when removed.

‘One study removed’ analysis: meta-analysis showing the pooled effect size of the relationship between bullying involvement and academic achievement with each study removed.

‘One study removed’ analysis: meta-analysis showing the pooled effect size of the relationship between bullying involvement and cognitive-motivational factors with each study removed.

‘One study removed’ analysis: meta-analysis showing the pooled effect size of the mediation effect of cognitive-motivational factors on the relationship between victimisation involvement and academic achievement with each study removed.

4. Discussion

The purpose of this meta-analysis was to investigate the effect of cognitive–motivational factors on the association between bullying involvement and academic achievement. To our knowledge this is the first meta-analysis that investigated the mediation between bullying involvement and academic achievement. The overall findings showed that the relationship between victimisation and academic achievement was significantly mediated by cognitive–motivational factors. Specifically, victimisation was associated with low scores on the cognitive–motivational factors evaluated (e.g., motivation, student engagement), which were, in turn, associated with low academic achievement. These relationships were moderated by country, where American studies from the US had stronger mediation than one Canadian study. In addition, longitudinal studies and studies that included traditional victimisation had a significant mediation effect while cross-sectional studies and studies on relational victimisation did not.

The overall finding that bullying involvement and specifically victimisation is associated with low motivation, which is linked to low academic achievement, is supported partially by the literature [ 8 , 36 , 37 ]. The previous literature looked at the relationship between bullying involvement and academic achievement separately without taking into account the cognitive-motivational factors. The cognitive-motivational factors in this study included motivation, academic engagement, self-esteem, self-efficacy and self-concept, which have been found in the literature to be affected by victimisation.

The question is: why do victims perform more poorly in their academic achievement compared to other children who are not bullied? What are the mechanisms that are behind this? This meta-analysis study points out to an important mediation of this relationship, namely cognitive–motivational factors. These mechanisms are supported by some theories. These include the self-determination theory (SDT) [ 38 ], the expectancy–value theory [ 32 , 33 , 68 ], and the achievement goal theory [ 34 , 35 ], that may explain these relationships. Firstly, those who are bullied are more likely to be less motivated [ 21 ] and have lower aspiration [ 31 ] to engage in a particular goal such as academic success and achievement. Secondly, victims may have a negative view of themselves [ 19 ], have low self-efficacy (the belief in one’s ability to succeed in specific situations or accomplish a task) [ 23 , 69 ] and have low self-concept [ 24 ] that can in turn affect their scholastic achievement [ 36 ]. Thirdly, victimisation may lead to isolation, school adjustment problems including loneliness, and school avoidance [ 41 ] and as a result their self-esteem and their self-efficacy are also affected. These in turn put these children at risk of school absenteeism [ 70 ], truancy (e.g., [ 71 ]), and dropping out of school [ 72 ] as they may view their school as an unsafe place (e.g., [ 73 ]). For example, Jan and Husain [ 74 ] found that bullied students were more likely to miss school for fear of being criticized by their peers and Buhs et al. [ 75 ] found that chronically abused children were more likely to engage in school avoidance behaviour. Fourth, peer victimisation may also result in internalizing problems [ 41 , 76 ] and somatic and psychological problems [ 41 , 77 ] that result in problematic levels of school absenteeism [ 73 , 78 , 79 ], which, in turn, results in poor academic outcomes [ 80 ]. This could also lead to less engagement as they are afraid of being mocked and made fun of and as a result perform more poorly. In a longitudinal study, Juvonen et al. [ 81 ] found that peer harassment led to psychological maladjustment (low self-worth, loneliness, and depressive symptoms), which led to poor school functioning. Children with depressive symptoms may exhibit poor concentration and memory, and consequently, have low academic achievement [ 82 ]. Finally, victims may also have a negative perception of their school climate [ 13 , 73 ], which may, in turn, cause school absenteeism [ 83 ], and poor academic outcomes [ 9 , 84 , 85 ].

4.1. Implications

The finding that victimisation affects both motivational factors and academic achievement has great implications for educational practice. Educational interventions that aim to improve academic success and achievement need to take into account these aspects. The first step of an intervention program should therefore be decreasing victimisation [ 86 , 87 ] and particularly focusing on improving motivation, self-esteem, self-efficacy and self-concept. The second step is to increase students’ academic achievement and enhance their educational engagement.

In addition, high levels of support from family and friends [ 42 , 88 , 89 ] and a positive teacher–child relationship that can have a positive effect by impacting their sense of school connectedness [ 90 ] can protect bullied children and adolescents from poor academic outcomes.

Studies that looked at the mediation relationship are very few and those few studies looked at different motivational factors as discussed above. Given that the findings of the meta-analysis showed that there is an indirect negative effect of victimisation on academic success and achievement through cognitive–motivational factors, this is an area of research yet to be explored further with these factors and to include all bullying subgroups (bullies, victims, and bully/victims) and types (direct, relational, and cyber). There is a particular need for longitudinal studies examining whether bullying in fact precedes changes in the cognitive–motivational factors examined in the current study that in turn impair academic achievement. Despite the increasing number of studies on bullying and victimisation, most of the mediation studies were based in only a few countries. Therefore, there is also a need for studies in multiple countries, particularly in developing countries. This is important as different countries and cultures deal and define bullying and victimisation differently [ 91 , 92 ] and have different educational, school and grading systems, and thus interventions may differ in each country accordingly.

4.2. Strengths and Limitations

This is the first meta-analysis study that looked at the mediation effect of cognitive-motivational factors on the relationship between bullying victimisation and academic achievement. The study pointed out the lack of studies in this area and the need for more studies on these mediation factors. The study also gave a good insight into the mechanism for why victimisation can lead to lower academic achievement. This can inform policy makers, practitioners (psychologists, educationalists) and future interventions of the best way to improve victims’ school achievement by concentrating on these factors.

The process of coding and grouping of related terms is a key factor in a meta-analysis. Studies usually differ slightly in terminology and methodologies [ 91 , 92 ]; nevertheless, groups need to be formed, in order to study them in a meta-analysis. Vast methodological differences between cognitive–motivational factors were also observed. However, the study indicated that these factors negatively mediated the relationship between victimisation and academic achievement. This should be further investigated with more studies on these factors. Similarly, the studies usually included victimisation without looking at bullying others and bully/victims.

The term ‘motivation’ is quite broad and the grouping of related yet distinctive terms (e.g., self-esteem, self-efficacy, self-concept, motivation and academic engagement) might have overshadowed underlying types of motivation. The multifaceted dynamics of motivation need to be explored in greater detail. However, the findings of the current study are an important platform for this type of investigation.

This study utilised four methods to investigate possible publication biases. There were some publication biases especially in relation to bullies and bully/victims simply due to the small number of studies, while for some cases, publication bias was not performed as there were less than three studies (bullies only and bully/victims) and thus these areas should be further investigated. However, our results can be perceived as relatively robust especially with regards to victimisation. In addition, one study removal did not affect the final pooled effect sizes and all results remained significant.

5. Conclusions

The current study is the first meta-analysis that examined the mediation effect of different cognitive–motivational factors on the relationship between bullying victimisation and academic achievement, including moderators. These results showed that motivational factors negatively mediated these relationships. Additionally, the effect sizes were moderated by some moderators including the design of the studies, age, assessment methods for reporting bullying, mediators and outcomes, countries and/or bullying types. Only few studies as shown here looked at the mediation effect of motivation, while none of the studies included aspiration as a mediator. In addition, these studies looked at different cognitive–motivational factors, which shows the need for more studies in this area.

The findings of this meta-analysis are important for educational and psychological practitioners, parents and schools [ 79 , 93 ]. Based on these findings intervention programs and anti-bullying policies [ 94 , 95 ] need to be implemented in schools and parents and family dynamics should play a central role in these interventions. In addition, interventions can concentrate on internal and external motivational and academic factors. Motivational factors can serve as protective factors in these situations, therefore positive enforcement, encouragement, and programs for engaging these children and adolescents should be designed. Furthermore, the findings highlight the need for further studies on each cognitive-motivational factor including several moderators.

Acknowledgments

This work was supported by the Qatar National Research Fund (QNRF) a member of Qatar Foundation Doha, Qatar, National Priority Research Programs (NPRP) under Grant (NPRP9-061-5-006). The authors would like to thank QNRF for their support. We would like also to thank the authors who supplied the relevant data for the study.

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/18/5/2209/s1 , Table S1: PRISMA checklist for meta-analysis studies.

Author Contributions

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

This research was funded by the Qatar National Research Fund (QNRF) a member of Qatar Foundation Doha, Qatar, National Priority Research Programs (NPRP) under Grant (NPRP9-061-5-006).

Institutional Review Board Statement

NA as this is a meta-analysis study that did not require active recruitment of participant and thus no ethical approval required.

Informed Consent Statement

NA. This is a meta-analysis study that did not require consent of participant.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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

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Chapter II REVIEW OF RELATED LITERATURE AND STUDIES REVIEW LITERATURE

Given the fact that any student can have poor academic performance. The trouble appears critical when this becomes a trend. There are various reasons for students' cultural and historical background, the educational approaches and the common knowledge of students. The concept of Poor Academic Performance Poor academic performance according to A remu (2000) is a performance that is adjudged by the examinee/testee and some other significant as falling below an expected standard. The interpretation of this expected or desired standard is better appreciated from the perpetual cognitive ability of the evaluator of the performance. The evaluator or assessor can therefore give different interpretations depending on some factors. A study by (Bakare 1994) described poor academic performance as any performance that fails below a desired standard. The criteria of excellence can be form 40 to 100 depending on the subjective yardstick of the evaluator or assessor. For example, a 70% performance of senior secondary 3; students in junior secondary English language examination is by all standard a very good performance. However a cursory look at the performance and the individual examined and the standard of the examination he or she took could reveal that the performance is a very poor one. On the other hand, a JSS2 student's performance of 37% in SS3 mathematics can said to be a poor performance when in actual fact the performance is by all standards a very good one. This shows that the concept of poor academic performance is very relative and this depends on so many intervening variables. Causes of Poor Academic Performance among Secondary School Students According to Aremu and Sokan (2003) submit that the search for the causations of poor academic achievement is unending and some of the factors they put forward are: motivational orientation, self-esteem/self-efficacy, emotional problems, study habits, teacher

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