homework motivation and academic achievement in a college genetics course

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

Academic Goals, Student Homework Engagement, and Academic Achievement in Elementary School

Antonio valle.

1 Department of Developmental and Educational Psychology, University of A Coruña, A Coruña, Spain

Bibiana Regueiro

José c. núñez.

2 Department of Psychology, University of Oviedo, Oviedo, Spain

Susana Rodríguez

Isabel piñeiro, pedro rosário.

3 Departmento de Psicologia Aplicada, Universidade do Minho, Braga, Portugal

There seems to be a general consensus in the literature that doing homework is beneficial for students. Thus, the current challenge is to examine the process of doing homework to find which variables may help students to complete the homework assigned. To address this goal, a path analysis model was fit. The model hypothesized that the way students engage in homework is explained by the type of academic goals set, and it explains the amount of time spend on homework, the homework time management, and the amount of homework done. Lastly, the amount of homework done is positively related to academic achievement. The model was fit using a sample of 535 Spanish students from the last three courses of elementary school (aged 9 to 13). Findings show that: (a) academic achievement was positively associated with the amount of homework completed, (b) the amount of homework completed was related to the homework time management, (c) homework time management was associated with the approach to homework, (d) and the approach to homework, like the rest of the variables of the model (except for the time spent on homework), was related to the student's academic motivation (i.e., academic goals).

Introduction

Literature indicates that doing homework regularly is positively associated with students' academic achievement (Zimmerman and Kitsantas, 2005 ). Hence, as expected, the amount of homework done is one of the variables that shows a strong and positive relationship with academic achievement (Cooper et al., 2001 ).

It seems consensual in the literature that doing homework is always beneficial to students, but it is also true that the key for the academic success does not rely on the amount of homework done, but rather on how students engage on homework (Trautwein et al., 2009 ; Núñez et al., 2015c ), and on how homework engagement is related with student motivation (Martin, 2012 ). There is, therefore, a call to analyze the process of homework rather than just the product; that is, to examine the extent to which the quality of the process of doing homework may be relevant to the final outcome.

Trautwein's model of homework

The model by Trautwein et al. ( 2006b ) is rooted in the motivational theories, namely the theory of the expectancy value (Eccles (Parsons) et al., 1983 ; Pintrich and De Groot, 1990 ), and the theory of self-determination (Deci et al., 2002 ), as well as on theories of learning and instruction (Boekaerts, 1999 ). Trautwein and colleagues' model analyzes students' related variables in two blocks, as follows: the motivational (aiming at directing and sustaining the behavior) and the cognitive and behavioral implications (cognitions and behaviors related to the moment of doing homework).These two blocks of variables are rooted in the literature. Motivational variables are related with the theory of expectancy-value by Eccles (Parsons) et al. ( 1983 ), while the variables addressing students' implication are related with the school engagement framework (e.g., Fredricks et al., 2004 ). However, as Eccles and Wang ( 2012 ) stress, both models are interrelated due to the fact that both variables are closely related and show reciprocal relationships.

Student homework engagement: the interplay between cognitive and behavioral components

Engagement is a relatively new construct with great relevance in the field of psychology and instruction (Fredricks et al., 2004 ). Generally considered, engagement has been described as the active implication of the person in an activity (Reeve et al., 2004 ). However, despite the close relation between engagement and motivation, literature clearly differentiates between them (e.g., Martin, 2012 ), stressing engagement as the behavioral manifestation of motivation (Skinner and Pitzer, 2012 ), or arguing that motivation is a precursor of engagement rather than part of it. In sum, motivation relates to the “why” whereas the engagement focuses on the “what” of a particular behavior.

Consistent with this perspective, the current research fitted a model with the variable engagement mediating the relationship between motivation and academic achievement (see Eccles and Wang, 2012 ). Engagement is a complex construct with observational and non-observational aspects (Appleton et al., 2008 ). Some researchers conceptualize engagement with two dimensions—behavior and emotions (e.g., Marks, 2000 )—while others define engagement with four dimensions—academic, behavioral, cognitive, and emotional (e.g., Appleton et al., 2006 ). In the current study, we followed Fredricks' et al. ( 2004 ) conceptualization of engagement as a construct with three dimensions: cognitive (e.g., approaches to learning), behavioral (e.g., student homework behaviors), and emotional (e.g., interest, boredom). For the purpose of the present study, the dimension of emotion was not included in the model (see Figure ​ Figure1 1 ).

General model hypothesized to explain the relationship between academic motivation, student homework engagement, and academic achievement .

Cognitive homework engagement

In the past few decades, a robust body of research has been addressing the relationship between the way students deal with their learning process and academic outcomes (Marton and Säljö, 1976a , b ; Struyven et al., 2006 ; Rosário et al., 2010a , 2013a ). Marton and Säljö ( 1976a , b ) examined how students studied an academic text and found two ways of approaching the task: a surface and a deep approach. The surface approach is characterized by learning the contents aiming at achieving goals that are extrinsic to the learning content. In contrast, the deep approach is characterized by an intrinsic interest in the task and students are likely to be focused on understanding the learning content, relating it to prior knowledge and to the surrounding environment (Entwistle, 2009 ; Rosário et al., 2010b ). The metaphor “surface vs. deep” constitutes an easy to perceive conceptual framework, both in the classroom setting and in other educational settings (i.e., doing homework at home), and has been shown to be a powerful tool for parents, teachers, and students when conceptualizing the ways students approach school tasks (Entwistle, 1991 ; Rosário et al., 2005 ). The core of the concept of approaches to studying (or to learning) is the metacognitive connection between an intention to approach a task and a strategy to implement it (Rosário et al., 2013b ).

The process of doing homework focuses on what students do when completing homework, that is, how they approach their work and how they manage their personal resources and settings while doing homework. It is likely that students' approaches to homework may influence not only the final homework outcome but also the quality of that process. Students who adopt a deep approach are likely to engage their homework with the intention of deepening their understanding of the knowledge learned in class. In this process, students often relate the homework exercises to prior knowledge and monitor their mastery of the content learned. This process involves intrinsic intention to understand the ideas and the use of strategies to build meaning (Cano et al., 2014 ). In contrast, students who approach homework with a surface approach are likely to do homework with extrinsic motivation (e.g., rewards of their parents, fear of upsetting their teacher). Their goals may target finishing homework as soon as and with the less effort possible to be able to do more interesting activities. Students using this approach are more likely to do homework to fulfill an external obligation (e.g., hand in homework in class and get a grade), than for the benefits for learning.

Behavioral homework engagement

Findings from prior research indicate that the more the implication of students in doing their homework the better the academic achievement (Cooper et al., 2006 ). Following Trautwein et al. ( 2006b ), our conceptualization of student homework engagement includes behaviors related with the amount of homework done, time spent on homework, and homework time management (e.g., concentration). In the present investigation, these three variables were included in the model (see Figure ​ Figure1 1 ).

Extant findings on the relationship between the amount of homework done and academic achievement are in need of further clarification. Some authors argue for a strong and positive relationship (e.g., Cooper et al., 2006 ), while others found that this relationship is higher throughout schooling (Cooper et al., 2001 ; Zimmerman and Kitsantas, 2005 ). Authors explained this last finding arguing that the load of homework assigned by teachers vary throughout schooling, and also that the cognitive competencies of students are likely to vary with age (Muhlenbruck et al., 2000 ). More recently, Núñez et al. ( 2015c ) found that the relationship between these two variables varied as a function of the age of the students enrolled. Particularly, this relationship was found to be negative in elementary school, null in junior high school, and positive in high school.

Moreover, the relationship between the amount of homework done and academic achievement relates, among other factors, with the students' age, the quality of the homework assigned, the type of assessment, and the nature of the feedback provided. For example, some students may always complete their homework and get good grades for doing it, which does not mean that these students learn more (Kohn, 2006 ). In fact, more important than the quantity of the homework done, is the quality of that work (Fernández-Alonso et al., 2014 ).

Another variable included in the model was the time spent on homework. Findings on the relationship between time spent on homework and academic achievement are mixed. Some studies found a positive relationship (Cooper et al., 2001 , 2006 ) while others found a null or a negative one (Trautwein et al., 2006b , 2009 ). In 2009, Dettmers, Trautwein and Lüdtke conducted a study with data from the PISA 2003 (Dettmers et al., 2009 ). Findings on the relationship between the number of hours spent on homework and academic achievement in mathematics show that the students in countries with higher grades spend fewer hours doing homework than students in countries with low academic grades. At the student level, findings showed a negative relationship between time spent on homework and academic achievement in 12 out of 40 countries.

The relationship between the amount of homework done, time dedicated to homework, and academic achievement was hypothesized to be mediated by the homework time management. Xu ( 2007 ) was one of the pioneers examining the management of the time spent on homework. Initially, Xu ( 2007 ) did not find a relationship between time management and academic achievement (spend more time on homework is not equal to use efficient strategies for time management). Latter, Xu ( 2010 ) found a positive relationship between students' grade level, organized environment, and homework time management. More recently, Núñez et al. ( 2015c ) found that effective homework time management affects positively the amount of homework done, and, consequently, academic achievement. This relationship is stronger for elementary students when compared with students in high school.

Academic motivation and student homework engagement relationship

Literature has consistently shown that a deep approach to learning is associated positively with the quality of the learning outcomes (Rosário et al., 2013b ; Cano et al., 2014 ; Vallejo et al., 2014 ). The adoption of a deep approach to homework depends on many factors, but students self-set goals and their motives for doing homework are among the most critical motivational variables when students decide to engage in homework.

Literature on achievement motivation highlights academic goals as an important line of research (Ng, 2008 ). In the educational setting, whereas learning goals focus on the comprehension and mastery of the content, performance goals are more focused on achieving a better performance than their colleagues (Pajares et al., 2000 ; Gaudreau, 2012 ).

Extant literature reports a positive relationship between adopting learning goals and the use of cognitive and self-regulation strategies (Elliot et al., 1999 ; Núñez et al., 2013 ). In fact, students who value learning and show an intention to learn and improve their competences are likely to use deep learning strategies (Suárez et al., 2001 ; Valle et al., 2003a , b , 2015d ), which are aimed at understanding the content in depth. Moreover, these learning-goal oriented students are likely to self-regulate their learning process (Valle et al., 2015a ), put on effort to learn, and assume the control of their learning process (Rosário et al., 2016 ). These students persist much longer when they face difficult and challenging tasks than colleagues pursuing performance goals. The former also use more strategies oriented toward the comprehension of content, are more intrinsically motivated, and feel more enthusiasm about academic work. Some researchers also found positive relationships between learning goals and pro-social behavior (e.g., Inglés et al., 2013 ).

Reviewing the differentiation between learning goals and performance goals, Elliot and colleagues (Elliot and Church, 1997 ; Elliot, 1999 ; Elliot et al., 1999 ) proposed a three-dimensional framework for academic goals. In addition to learning goals, performance goals were differentiated as follows: (a) performance-approach goals, focused on achieving competence with regard to others; and (b) performance-avoidance goals, aimed at avoiding incompetence with regard to others. Various studies have provided empirical support for this distinction within performance goals (e.g., Wolters et al., 1996 ; Middleton and Midgley, 1997 ; Skaalvik, 1997 ; Rodríguez et al., 2001 ; Valle et al., 2006 ). Moreover, some authors proposed a similar differentiation for learning goals (Elliot, 1999 ). The rationale was as follows: learning goals are characterized by high engagement in academic tasks, so an avoidance tendency in such goals should reflect avoidance of this engagement. Hence, students who pursue a work avoidance goal are likely to avoid challenging tasks and to put on effort to do well, only doing the bare minimum to complete the task. In general, learning goals are associated with a large amount of positive results in diverse motivational, cognitive, and achievement outcomes, whereas performance goals have been linked to less adaptive outcomes, or even to negative outcomes (Valle et al., 2009 ).

Aims of this study

Several relationships between motivational, cognitive, and behavioral variables involving self-regulated learning in the classroom have recently been studied (Rosário et al., 2013a ). However, there is a lack of knowledge of the relationships between these variables throughout the process of doing homework.

The principal purpose of this work (see Figure ​ Figure1) 1 ) is to analyze how student homework engagement (cognitive and behavioral) mediates motivation and academic performance. This study aims to provide new information about an issue that is taken for granted, but which, as far as we know, lacks empirical data. The question is: to what extent students acknowledge homework as a good way to acquire competence, improve their skills and performance? Our working hypothesis is that student value homework in this regard. Therefore, we hypothesized that the more students are motivated to learn, the more they will be involved (cognitively and behaviorally) in their homework, and the higher their academic achievement.

To address this goal, we developed a path analysis model (see Figure ​ Figure1) 1 ) in which we hypothesized that: (a) the student's motivational level is significantly related to their cognitive homework engagement (i.e., the approach to studying applied to homework), and their behavioral homework engagement (i.e., amount of time spent and homework time management, and amount of homework completed); (b) student's cognitive and behavioral homework engagement are positively associated with academic achievement; and (c) cognitive and behavioral homework engagement are related (the more deep cognitive engagement, the more time spent and time management, and the more amount of homework is done).

Participants

The study enrolled 535 students, aged between 9 and 13 ( M = 10.32, SD = 0.99), of four public schools, from the last three years of the Spanish Elementary Education (4th, 5th, and 6th grade level), of whom 49.3% were boys. By grade, 40.4% ( n = 216) were enrolled in the 4th grade, 35.1% ( n = 188) in the 5th grade, and 24.5% ( n = 131) in the 6th grade.

Learning goals

The level and type of motivation for academic learning was assessed with the Academic Goals Instrument (Núñez et al., 1997 ). Although, this instrument allows differentiating a broad range of academic goals, for the purposes of this work, we only used the subscale of learning goals (i.e., competence and control). The instrument is rated on a 5-point Likert-type scale, with responses ranging from one (not at all interested) to five (absolutely interested in learning and acquiring competence and control in the different subjects). An example item is: “I make an effort in my studies because performing the academic tasks allows me to increase my knowledge.” The reliability of the scale is good (α = 0.87).

Approach to homework

To measure the process of approaching homework, we adapted the Students' Approaches to Learning Inventory (Rosário et al., 2010a , 2013a ), taking into account both the students' age and the homework contexts. This instrument is based on voluminous literature on approaches to learning (e.g., Biggs et al., 2001 ; Rosário et al., 2005 ), and provides information about two ways of approaching homework. For the purpose of this research, we only used the deep approach (e.g., “Before starting homework, I usually decide whether what was taught in class is clear and, if not, I review the lesson before I start”). Students respond to the items on a 5-point Likert-type scale ranging from one (not at all deep approach) to five (completely deep approach). The reliability of the scale is good (α = 0.80).

Time spent on homework, homework time management, and amount of homework completed

To measure these three variables, we used the Homework Survey (e.g., Rosário et al., 2009 ; Núñez et al., 2015a , b ; Valle et al., 2015b , c ). To measure the time spent on homework , students responded to three items (in general, in a typical week, on a typical weekend) with the general formulation, “How much time do you usually spend on homework?,” with the response options 1, <30 min; 2, 30 min to 1 h; 3, 1 h to an hour and a half; 4, 1 h and a half to 2 h; 5, more than 2 h. Homework time management was measured through the responses to three items (in general, in a typical week, on a typical weekend) in which they were asked to indicate how they managed the time normally spent doing homework, using the following scale: 1, I waste it completely (I am constantly distracted by anything); 2, I waste it more than I should; 3, regular; 4, I manage it pretty much; 5, I optimize it completely (I concentrate and until I finish, I don't think about anything else). Finally, the amount of homework completed by students (assigned by teachers) was assessed through responses to an item about the amount of homework usually done, using a 5-point Likert-type scale (1, none; 2, some; 3, one half; 4, almost all; 5, all).

Academic achievement

Assessment of academic achievement was assessed through students' report card grades in Spanish Language, Galician Language, English Language, Knowledge of the Environment, and Mathematics. Average achievement was calculated with the mean grades in these five areas.

Data of the target variables was collected during regular school hours, by research assistants, after obtaining the consent of the school administration and of the teachers and students. Prior to the application of the questionnaires, which took place in a single session, the participants were informed about the goals of the project, and assured that data was confidential and used for research purposes only.

Data analysis

The model was fit with AMOS 18 (Arbuckle, 2009 ). The data were previously analyzed and individual cases presenting a significant number of missing values were eliminated (2.1%), whereas the rest of the missing values were replaced by the mean. Taking into account the analysis of the characteristics of the variables (e.g., skewness and kurtosis in Table ​ Table1), 1 ), we used the maximum likelihood method to fit the model and estimate the values of the parameters.

Means, standard deviations, skewness, kurtosis, and correlation matrix of the target variables .

A series of goodness-of-fit statistics were used to analyze our model. Beyond chi-square (χ 2 ) and its associated probability ( p ), the information provided by the goodness-of-fit index (GFI) and the adjusted goodness-of-fit index (AGFI; Jöreskog and Sörbom, 1983 ); the comparative fit index (CFI) (Bentler, 1990 ); and the root mean square error of approximation (RMSEA; Browne and Cudeck, 1993 ) was used. According to these authors, the model fits well when GFI and AGFI > 0.90, CFI > 0.95, and RMSEA ≤ 0.05.

Descriptive analysis

The relations between the variables included in the model as well as the descriptive statistics are shown in Table ​ Table1. 1 . All the variables were significantly and positively related, except for the time spent on homework, which was only related to the amount of homework done. According to the value of the means of these variables, students in the last years of elementary school: (a) reported a high level of motivation to learn and mastery; (b) used preferentially a deep approach to homework; (c) did the homework assigned by the teachers most of the times; (d) usually spent about an hour a day on homework; (e) reported to manage their study time effectively; and (f) showed a medium-high level of academic achievement.

Evaluation and re-specification of the initial model

The data obtained indicated that the initial model (see Figure ​ Figure1) 1 ) presented a poor fit to the empirical data: χ 2 = 155.80, df = 8, p < 0.001, GFI = 0.917, AGFI = 0.783, TLI = 0.534, CFI = 0.751, RMSEA = 0.186, 90% CI (0.161, 0.212), p < 0.001. Analysis of the modification indexes revealed the need to include three direct effects initially considered as null, and to eliminate a finally null effect (included in the initial model as significant). The strategy adopted to modify the initial model involved including and estimating the model each time a new effect was included. The final model comprised three effects (academic goals on homework time management, on amount of homework done, and on academic achievement) and the elimination of the initially established effect of the approach to studying on the time spent doing homework. The inclusion or elimination of the effects in the model was determined accounting for their statistical and theoretical significance. The final model resulting from these modifications is shown in Figure ​ Figure2, 2 , with an adequate fit to the empirical data: χ 2 = 12.03, df = 6, p = 0.061, GFI = 0.993, AGFI = 0.974, TLI = 0.975, CFI = 0.990, RMSEA = 0.043, 90% CI (0.000, 0.079), p = 0.567.

The results of the fit of the hypothesized model (standardized outcomes): Relations in dashed lines were found to be statistically significant, but this was not established in the initial model .

Assessment of the relationships on the final model

Table ​ Table2 2 presents the data obtained for the relationships considered in the final model (see also Figure ​ Figure2 2 ).

Fit of the hypothesized model (standardized outcomes): final model of student engagement in homework .

The data from Table ​ Table2 2 and Figure ​ Figure2 2 indicates that the majority of the relationships between the variables are consistent with the hypotheses. First, we found a statistically significant association between the learning goals (i.e., competence and control), the approach to homework ( b = 0.50, p < 0.001), two of the variables associated with engagement in homework (the amount of homework done [ b = 0.27, p < 0.001], homework time management [ b = 0.30, p < 0.001]), and academic achievement ( b = 0.34, p < 0.001). These results indicate that the more oriented students are toward learning goals (i.e., competence and control), the deeper the approach to homework, the more homework is completed, the better the homework time management, and the higher the academic achievement.

Second, a statistically significant association between the deep approach and homework time management ( b = 0.30, p < 0.001) and the amount of homework done ( b = 0.09, p < 0.05) was found. These results reflect that the deeper the students' approach to homework, the better the management of the time spent on homework, and the more the homework done. Third, there was a statistically significant association between homework time management, time spent on homework, and the amount of homework done ( b = 0.23, p < 0.001, and b = 0.10, p < 0.01, respectively). These results confirm, as expected, that the more time students spent doing homework and the better students manage their homework time, the more homework they will do. Four, we found a statistically significant relation between the amount of homework done and academic achievement ( b = 0.20, p < 0.001). This indicates that the more homework students complete the better their academic achievement.

In summary, our findings indicate that: (a) academic achievement is positively associated with the amount of homework completed; (b) the amount of homework done is related to homework time management; (c) homework time management is associated with how homework is done (approach to homework); and (d) consistent with the behavior of the variables in the model (except for the time spent on homework), how homework is done (i.e., approach to homework) is explained to a great extent (see total effects in Table ​ Table3) 3 ) by the student's type of academic motivation.

Standardized direct, indirect, and total effects for the final model .

Finally, taking into account both the direct effects (represented in Figure ​ Figure2) 2 ) and the indirect ones (see Table ​ Table3), 3 ), the model explained between 20 and 30% of the variance of the dependent variables (except for the time spent on homework, which is not explained at all): approach to homework (24.7%), time management (26.9%), amount of homework done (24.4%), and academic achievement (21.6%).

Consistent with prior research (e.g., Cooper et al., 2001 ), our findings showed that students' academic achievement in the last years of elementary education is closely related to the amount of homework done. In addition, the present study also confirms the importance of students' effort and commitment to doing homework (Trautwein et al., 2006a , b ), showing that academic achievement is also related with students' desire and interest to learn and improve their skills. Therefore, when teachers assign homework, it is essential to attend to students' typical approach to learning, which is mediated by the motivational profile and by the way students solve the tasks proposed (Hong et al., 2004 ). The results of this investigation suggest that the adoption of learning goals leads to important educational benefits (Meece et al., 2006 ), among which is doing homework.

Importantly, our study shows that the amount of homework done is associated not only with the time spent, but also with the time management. Time spent on homework should not be considered an absolute indicator of the amount of homework done, because students' cognitive skills, motivation, and prior knowledge may significantly affect the time needed to complete the homework assignment (Regueiro et al., 2015 ). For students, managing homework time is a challenge (Corno, 2000 ; Xu, 2008 ), but doing it correctly may have a positive influence on their academic success (Claessens et al., 2007 ), on homework completion (Xu, 2005 ), and on school achievement (Eilam, 2001 ).

Despite, that previous studies reported a positive relationship between the time spent on homework and academic achievement (Cooper et al., 2006 ), the present research shows that time spent on homework is not a relevant predictor of academic achievement. Other studies have also obtained similar results (Trautwein et al., 2009 ; Núñez et al., 2015a ), indicating that time spent on homework is negatively associated to academic achievement, perhaps because spending a lot of time on homework may indicate an inefficient working style and lack of motivation (Núñez et al., 2015a ). Besides, our data indicates that spending more time on homework is positively associated to the amount of homework done.

Although, some studies have found that students who spend more time on homework also tend to report greater commitment to school work (Galloway et al., 2013 ), our findings indicated that spending more time doing homework was not related to a deeper engagement on the task. A possible explanation may be that using a deep approach to school tasks subsumes engaging in homework with the aim of practicing but also to further extend the content learned in class. This approach does not depends on the time spent doing homework, rather on the students' motives for doing homework.

Another important contribution of this study concerns learning-oriented goals—usually associated with positive outcomes in motivational, cognitive, and achievement variables (Pajares et al., 2000 ). Results indicate that the motivation to increase competence and learning is also related to approaching homework deeply and to manage homework efficiently. Consistent with previous findings (Xu, 2005 ), these results provide additional empirical support to time management goals (Pintrich, 2004 ).

There is a robust relationship between learning-oriented goals and a deep approach, and between a deep approach and the amount of homework done. All this indicates that these results are in line with prior research, meaning that the adoption of a deep approach to learning is related with high quality academic achievement (Lindblom-Ylänne and Lonka, 1999 ; Rosário et al., 2013b ).

Educational implications and study limitations

One of the major limitations of this study lies in the type of research design used. We used a cross-sectional design to examine the effects among the variables within a path analysis model. However, to establish a cause-effect relationship a temporal sequence between two variables is needed a requirement that can only be met with longitudinal designs. Future studies should consider address this limitation.

Despite the above limitation, our results can be considered relevant and show important educational implications. It is essential for teachers and school administrators to be sensitized about the effects of teachers' homework follow-up practices on students' homework engagement (Rosário et al., 2015 ), and of these variables in students' school engagement and academic success. Likewise, research on students' learning should be undertaken from the perspective of the learners to understand how students use their knowledge and skills to do homework and to solve problems posed therein. On the other hand, research should examine in-depth the use of learning strategies during homework, as well as how students' motivations at an early age may foster homework completion and increase the quality of school outcomes. For this last purpose, teachers should pay attention not only to the acquisition of curricular content but also to the development of the appropriate thinking skills and self-regulated learning strategies (Rosário et al., 2010b ; Núñez et al., 2013 ). Finally, the amount of homework done and its positive relationship with academic achievement should be considered as a final outcome of a process rooted on a comprehensive and meaningful learning. Students motivated to learn are likely to approach homework deeply and manage homework time efficaciously. As a result, they tend to do more homework and outperform. In sum, is doing homework a good way to acquire competence, improve skills, and outperform? Our data suggest a positive answer.

Author contributions

AV and BR Collect data, data analysis, writing the paper. JN and PR data analysis, writing the paper. SR and IP writing the paper.

Conflict of interest statement

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

Acknowledgments

This work was developed through the funding of the research project EDU2013-44062-P, of the State Plan of Scientific and Technical Research and Innovation 2013-2016 (MINECO) and to the financing received by one of the authors in the FPU program of the Ministry of Education, Culture, and Sport.

• Appleton J. J., Christenson S. L., Furlong M. J. (2008). Student engagement with school: critical conceptual and methodological issues of the construct . Psychol. Sch. 45 , 369–386. 10.1002/pits.20303 [ CrossRef ] [ Google Scholar ]
• Appleton J. J., Christenson S. L., Kim D., Reschly A. L. (2006). Measuring cognitive and psychological engagement: validation of the student engagement instrument . J. Sch. Psychol. 44 , 427–445. 10.1016/j.jsp.2006.04.002 [ CrossRef ] [ Google Scholar ]
• Arbuckle J. L. (2009). Amos 18.0 User's Guide . Crawfordville, FL: Amos Development Corporation. [ Google Scholar ]
• Bentler P. M. (1990). Comparative fit indexes in structural models . Psychol. Bull. 107 , 238–246. 10.1037/0033-2909.107.2.238 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Biggs J., Kember D., Leung D. Y. (2001). The revised two-actor study process questionnaire: R-SPQ-2F . Br. J. Educ. Psychol. 71 , 133–149. 10.1348/000709901158433 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boekaerts M. (1999). Self-regulated learning: where are today . Int. J. Educ. Res. 31 , 445–458. 10.1016/S0883-0355(99)00014-2 [ CrossRef ] [ Google Scholar ]
• Browne M. W., Cudeck R. (1993). Alternative ways of assessing model fit , in Testing Structural Equation Models , eds Bollen K., Long J. (Newbury Park, CA: Sage; ), 136–162. [ Google Scholar ]
• Cano F., García A., Justicia F., García-Berbén A. B. (2014). Enfoques de aprendizaje y comprensión lectora: el papel de las preguntas de los estudiantes y del conocimiento previo [Approaches to learning and reading comprehension: the role of students' questions and of prior knowledge] . Rev. Psicodidáctica 19 , 247–265. 10.1387/RevPsicodidact.10186 [ CrossRef ] [ Google Scholar ]
• Claessens B. J. C., van Eerde W., Rutte C. G., Roe R. A. (2007). A review of the time management literature . Pers. Rev. 36 , 255–276. 10.1108/00483480710726136 [ CrossRef ] [ Google Scholar ]
• Cooper H., Jackson K., Nye B., Lindsay J. J. (2001). A model of homework's influence on the performance evaluations of elementary school students . J. Exp. Educ. 69 , 181–200. 10.1080/00220970109600655 [ CrossRef ] [ Google Scholar ]
• Cooper H., Robinson J. C., Patall E. A. (2006). Does homework improve academic achievement? A synthesis of research, 1987–2003 . Rev. Educ. Res. 76 , 1–62. 10.3102/00346543076001001 [ CrossRef ] [ Google Scholar ]
• Corno L. (2000). Looking at homework differently . Element. Sch. J. 100 , 529–548. 10.1086/499654 [ CrossRef ] [ Google Scholar ]
• Deci E. L., Ryan R. M. (2002). Handbook of Self-Determination Research . New York, NY: University of Rochester Press. [ Google Scholar ]
• Dettmers S., Trautwein U., Lüdtke O. (2009). The relationship between homework time and achievement is not universal: evidence from multilevel analyses in 40 countries . Sch. Eff. Sch. Improv. 20 , 375–405. 10.1080/09243450902904601 [ CrossRef ] [ Google Scholar ]
• Eccles J., Wang M. T. (2012). Part I Commentary: so what is student engagement anyway? ,in Handbook of Research on Student Engagement , eds Christenson S. L., Reschly A. L., Wylie C. (New York, NY: Springer; ), 133–145. [ Google Scholar ]
• Eccles (Parsons) J., Adler T. F., Futterman R., Goff S. B., Kaczala C. M., Meece J. L., et al. (1983). Expectancies, values, and academic choice: origins and changes , in Achievement and Achievement Motivation , ed Spence J. (San Francisco, CA: Freeman; ), 75–146. [ Google Scholar ]
• Eilam B. (2001). Primary strategies for promoting homework performance . Am. Educ. Res. J. 38 , 691–725. 10.3102/00028312038003691 [ CrossRef ] [ Google Scholar ]
• Elliot A. J. (1999). Approach and avoidance motivation and achievement goals . Educ. Psychol. 34 , 169–189. 10.1207/s15326985ep3403_3 [ CrossRef ] [ Google Scholar ]
• Elliot A. J., Church M. A. (1997). A hierarchical model of approach and avoidance achievement motivation . J. Pers. Soc. Psychol. 72 , 218–232. 10.1037/0022-3514.72.1.218 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Elliot A. J., McGregor H. A., Gable S. (1999). Achievement goals, study strategies, and exam performance: a mediational analysis . J. Educ. Psychol. 91 , 549–563. 10.1037/0022-0663.91.3.549 [ CrossRef ] [ Google Scholar ]
• Entwistle N. J. (1991). Approaches to learning and perceptions of the learning environment . High. Educ. 22 , 201–204. 10.1007/BF00132287 [ CrossRef ] [ Google Scholar ]
• Entwistle N. J. (2009). Teaching for Understanding at University: Deep Approaches and Distinctive Ways of Thinking . Basingstoke: Palgrave Macmillan. [ Google Scholar ]
• Fernández-Alonso R., Suárez-Álvarez J., Muñiz J. (2014). Tareas escolares en el hogar y rendimiento en matemáticas: una aproximación multinivel con estudiantes de Enseñanza Primaria [Homework and academic performance in mathematics: a multilevel approach with Primary school students] . Rev. Psicol. Educ. 9 , 15–29. [ Google Scholar ]
• Fredricks J. A., Blumenfeld P. C., Paris A. (2004). School engagement: potential of the concept, state of the evidence . Rev. Educ. Res. 74 , 59–109. 10.3102/00346543074001059 [ CrossRef ] [ Google Scholar ]
• Galloway M., Conner J., Pope D. (2013). Nonacademic effects of homework in privileged, high-performing high schools . J. Exp. Educ. 81 , 490–510. 10.1080/00220973.2012.745469 [ CrossRef ] [ Google Scholar ]
• Gaudreau P. (2012). Goal self-concordance moderates the relationship between achievement goals and indicators of academic adjustment . Learn. Individ. Differ. 22 , 827–832. 10.1016/j.lindif.2012.06.006 [ CrossRef ] [ Google Scholar ]
• Hong E., Milgram R. M., Rowell L. L. (2004). Homework motivation and preference: a learner-centered homework approach . Theory Pract. 43 , 197–203. 10.1207/s15430421tip4303_5 [ CrossRef ] [ Google Scholar ]
• Inglés C. J., Martínez-González A. E., García-Fernández J. M. (2013). Conducta prosocial y estrategias de aprendizaje en una muestra de estudiantes españoles de Educación Secundaria Obligatoria [Prosocial behavior and learning strategies in a sample of Spanish students of Compulsory Secondary Education] . Eur. J. Educ. Psychol. 6 , 33–53. 10.1989/ejep.v6i1.101 [ CrossRef ] [ Google Scholar ]
• Jöreskog K. G., Sörbom D. (1983). LISREL - 6 User's Reference Guide . Mooresville, IN: Scientifi c Software. [ Google Scholar ]
• Kohn A. (2006). Abusing research: the study of homework and other examples . Phi Delta Kappan 88 , 9–22. 10.1177/003172170608800105 [ CrossRef ] [ Google Scholar ]
• Lindblom-Ylänne S., Lonka K. (1999). Individual ways of interacting with the learning environment - are they related to study success? Learn. Instruct. 9 , 1–18. 10.1016/S0959-4752(98)00025-5 [ CrossRef ] [ Google Scholar ]
• Marks H. (2000). Student engagement in instructional activity: patterns in the elementary, middle, and high school years . Am. Educ. Res. J. 37 , 153–184. 10.3102/00028312037001153 [ CrossRef ] [ Google Scholar ]
• Martin A. J. (2012). Motivation and engagement: conceptual, operational, and empirical clarity , in Handbook of Research on Student Engagement , eds Christenson S. L., Reschly A. L., Wylie C. (New York, NY: Springer; ), 303–311. [ Google Scholar ]
• Marton F., Säljö R. (1976a). On qualitative differences in learning . I: outcome and process. Br. J. Educ. Psychol. 46 , 4–11. 10.1111/j.2044-8279.1976.tb02980.x [ CrossRef ] [ Google Scholar ]
• Marton F., Säljö R. (1976b). On qualitative differences in learning . II: outcome as a function of the learner's conception of the task. Br. J. Educ. Psychol. 46 , 115–127. 10.1111/j.2044-8279.1976.tb02304.x [ CrossRef ] [ Google Scholar ]
• Meece J. L., Anderman E. M., Anderman L. H. (2006). Classroom goal structure, student motivation, and academic achievement . Annu. Rev. Psychol. 57 , 487–503. 10.1146/annurev.psych.56.091103.070258 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Middleton M., Midgley C. (1997). Avoiding the demonstration of lack of ability: an unexplored aspect of goal theory . J. Educ. Psychol. 89 , 710–718. 10.1037/0022-0663.89.4.710 [ CrossRef ] [ Google Scholar ]
• Muhlenbruck L., Cooper H., Nye B., Lindsay J. J. (2000). Homework and achievement: explaining the different strengths of relation at the elementary and secondary school levels . Soc. Psychol. Educ. 3 , 295–317. 10.1023/A:1009680513901 [ CrossRef ] [ Google Scholar ]
• Ng C. H. (2008). Multiple-goal learners and their differential patterns of learning . Educ. Psychol. 28 , 439–456. 10.1080/01443410701739470 [ CrossRef ] [ Google Scholar ]
• Núñez J. C., González-Pienda J. A., González-Pumariega S., García M., Roces C. (1997). Cuestionario Para la Evaluación de Metas Académicas [Academic Goals Assessment Questionnaire] . Department of Psychology, University of Oviedo. [ Google Scholar ]
• Núñez J. C., Suárez N., Cerezo R., González-Pienda J. A., Rosário P., Mourão R., et al. (2015a). Homework and academic achievement across Spanish Compulsory Education . Educ. Psychol. 35 , 726–746. 10.1080/01443410.2013.817537 [ CrossRef ] [ Google Scholar ]
• Núñez J. C., Suárez N., Rosário P., Vallejo G., Cerezo R., Valle A. (2015b). Teachers' feedback on homework, homework-related behaviors and academic achievement . J. Educ. Res. 108 , 204–216. 10.1080/00220671.2013.878298 [ CrossRef ] [ Google Scholar ]
• Núñez J. C., Suárez N., Rosário P., Vallejo G., Valle A., Epstein J. L. (2015c). Relationships between parental involvement in homework, student homework behaviors, and academic achievement: differences among elementary, junior high, and high school students . Metacogn. Learn. 10 , 375–406. 10.1007/s11409-015-9135-5 [ CrossRef ] [ Google Scholar ]
• Núñez J., Rosário P., Vallejo G., González-Pienda J. (2013). A longitudinal assessment of the effectiveness of a school-based mentoring program in middle school . Contemp. Educ. Psychol. 38 , 11–21. 10.1016/j.cedpsych.2012.10.002 [ CrossRef ] [ Google Scholar ]
• Pajares F., Britner S. L., Valiante G. (2000). Relation between achievement goals and self-beliefs of middle school students in writing and science . Contemp. Educ. Psychol. 25 , 406–422. 10.1006/ceps.1999.1027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pintrich P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students . Educ. Psychol. Rev. 16 , 385–407. 10.1007/s10648-004-0006-x [ CrossRef ] [ Google Scholar ]
• Pintrich P. R., De Groot E. V. (1990). Motivational and self-regulated learning components of classroom performance . J. Educ. Psychol. 82 , 33–40. 10.1037/0022-0663.82.1.33 [ CrossRef ] [ Google Scholar ]
• Reeve J., Jang H., Carrell D., Jeon S., Barch J. (2004). Enhancing students' engagement by increasing teachers' autonomy support . Motiv. Emot. 28 , 147–169. 10.1023/B:MOEM.0000032312.95499.6f [ CrossRef ] [ Google Scholar ]
• Regueiro B., Suárez N., Valle A., Núñez J. C., Rosário P. (2015). La motivación e implicación en los deberes escolares a lo largo de la escolaridad obligatoria [Homework motivation and engagement throughout compulsory education] . Rev. Psicodidáctica 20 , 47–63. 10.1387/RevPsicodidact.12641 [ CrossRef ] [ Google Scholar ]
• Rodríguez S., Cabanach R. G., Piñeiro I., Valle A., Núñez J. C., González-Pienda J. A. (2001). Metas de aproximación, metas de evitación y múltiples metas académicas [Approach goals, avoidance goals and multiple academic goals] . Psicothema 13 , 546–550. [ Google Scholar ]
• Rosário P., González-Pienda J. A., Pinto R., Ferreira P., Lourenço A., Paiva O. (2010a). Efficacy of the program “Testas's (mis)adventures” to promote the deep approach to learning . Psicothema 22 , 828–834. [ PubMed ] [ Google Scholar ]
• Rosário P., Mourão R., Baldaque M., Nunes T., Núñez J. C., González-Pienda J. A., et al. (2009). Homework, self-regulation of learning and math performance . Rev. Psicodidáctica 14 , 179–192. [ Google Scholar ]
• Rosário P., Núñez J. A., Ferrando J. P., Paiva O., Lourenço A., Cerezo R., et al. (2013a). The relationship between approaches to teaching and approaches to studying: a two-level structural equation model for biology achievement in high school . Metacogn. Learn. 8 , 47–77. 10.1007/s11409-013-9095-6 [ CrossRef ] [ Google Scholar ]
• Rosário P., Núñez J. C., González-Pienda J. A., Almeida L., Soares S., Rúbio M. (2005). Academic learning from the perspective of Model 3P of J. Biggs . Psicothema 17 , 20–30. [ Google Scholar ]
• Rosário P., Núñez J. C., González-Pienda J. A., Valle A., Trigo L., Guimarães C. (2010b). Enhancing self-regulation and approaches to learning in first-year college students: a narrative-based program assessed in the Iberian Peninsula . Eur. J. Psychol. Educ. 25 , 411–428. 10.1007/s10212-010-0020-y [ CrossRef ] [ Google Scholar ]
• Rosário P., Núñez J. C., Vallejo G., Cunha J., Azevedo R., Pereira R., et al. (2016). Promoting Gypsy children school engagement: a story-tool project to enhance self-regulated learning . Contemp. Educ. Psychol . [Epub ahead of print]. 10.1016/j.cedpsych.2015.11.005. [ CrossRef ] [ Google Scholar ]
• Rosário P., Núñez J. C., Vallejo G., Cunha J., Nunes T., Suárez N., et al.. (2015). The effects of teachers' homework follow-up practices on students' EFL performance: a randomized-group design . Front. Psychol. 6 : 1528 . 10.3389/fpsyg.2015.01528 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosário P., Núñez J., Valle A., González-Pienda J., Lourenço A. (2013b). Grade level, study time, and grade retention and their effects on motivation, self-regulated learning strategies, and mathematics achievement: a structural equation model . Eur. J. Psychol. Educ. 28 , 1311–1331. 10.1007/s10212-012-0167-9 [ CrossRef ] [ Google Scholar ]
• Skaalvik E. (1997). Self- enhancing and self-defeating ego orientation: relations with task and avoidance orientation, achievement, self- perceptions, and anxiety . J. Educ. Psychol. 89 , 71–81. 10.1037/0022-0663.89.1.71 [ CrossRef ] [ Google Scholar ]
• Skinner E. A., Pitzer J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience , in Handbook of Research on Student Engagement , eds Christenson S. L., Reschly A. L., Wylie C. (New York, NY: Springer; ), 21–44. [ Google Scholar ]
• Struyven K., Dochy F., Janssens S., Gielen S. (2006). On the dynamics of students' approaches to learning: the effects of the teaching/learning environment . Learn. Instr. 16 , 279–294. 10.1016/j.learninstruc.2006.07.001 [ CrossRef ] [ Google Scholar ]
• Suárez J. M., Cabanach R. G., Valle A. (2001). Multiple-goal pursuit and its relation to cognitive, self-regulatory, and motivational strategies . Br. J. Educ. Psychol. 71 , 561–572. 10.1348/000709901158677 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trautwein U., Lüdtke O., Kastens C., Köller O. (2006a). Effort on homework in grades 5 through 9: development, motivational antecedents, and the association with effort on classwork . Child Dev. 77 , 1094–1111. 10.1111/j.1467-8624.2006.00921.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Trautwein U., Ludtke O., Schnyder I., Niggli A. (2006b). Predicting homework effort: support for a domain-specific, multilevel homework model . J. Educ. Psychol. 98 , 438–456. 10.1037/0022-0663.98.2.438 [ CrossRef ] [ Google Scholar ]
• Trautwein U., Schnyder I., Niggli A., Neumann M., Lüdtke O. (2009). Chameleon effects in homework research: the homework-achievement association depends on the measures used and the level of analysis chosen . Contemp. Educ. Psychol. 34 , 77–88. 10.1016/j.cedpsych.2008.09.001 [ CrossRef ] [ Google Scholar ]
• Valle A., Cabanach R. G., Núñez J. C., González-Pienda J. A., Rodríguez S., Piñeiro I. (2003a). Cognitive, motivational, and volitional dimensions of learning: an empirical test of a hypothetical model . Res. High. Educ. 44 , 557–580. 10.1023/A:1025443325499 [ CrossRef ] [ Google Scholar ]
• Valle A., Cabanach R. G., Núñez J. C., González-Pienda J. A., Rodríguez S., Piñeiro I. (2003b). Multiple goals, motivation and academic learning . Br. J. Educ. Psychol. 73 , 71–87. 10.1348/000709903762869923 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Valle A., Cabanach R. G., Rodríguez S., Núñez J. C., González-Pienda J. A. (2006). Metas académicas, estrategias cognitivas y estrategias de autorregulación del estudio [Academic goals, cognitive and self-regulatory strategies] . Psicothema 18 , 166–170. [ PubMed ] [ Google Scholar ]
• Valle A., Núñez J. C., Cabanach R. G., González-Pienda J. A., Rodríguez S., Rosário P., et al.. (2009). Academic goals and learning quality in higher education students . Span. J. Psychol. 12 , 96–105. 10.1017/S1138741600001517 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Valle A., Núñez J. C., Cabanach R., Rodríguez S., Rosário P., Inglés C. (2015a). Motivational profiles as a combination of academic goals in higher education . Educ. Psychol. 35 , 634–650. 10.1080/01443410.2013.819072 [ CrossRef ] [ Google Scholar ]
• Valle A., Pan I., Núñez J. C., Rosário P., Rodríguez S., Regueiro B. (2015b). Deberes escolares y rendimiento académico en Educación Primaria [Homework and academic achievement in Primary Education] . An. Psicol. 31 , 562–569. 10.6018/analesps.31.2.171131 [ CrossRef ] [ Google Scholar ]
• Valle A., Pan I., Regueiro B., Suárez N., Tuero E., Nunes A. R. (2015c). Predicting approach to homework in primary school students . Psicothema 27 , 334–340. 10.7334/psicothema2015.118 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Valle A., Regueiro B., Rodríguez S., Piñeiro I., Freire C., Ferradás M., et al. (2015d). Perfiles motivacionales como combinación de expectativas de autoeficacia y metas académicas en estudiantes universitarios [Motivational profiles as a combination of self-efficacy expectations and academic goals in university students] . Eur. J. Educ. Psychol. 8 , 1–8. 10.1016/j.ejeps.2015.10.001 [ CrossRef ] [ Google Scholar ]
• Vallejo G., Tuero E., Núñez J. C., Rosário P. (2014). Performance evaluation of recent information criteria for selecting multilevel models in behavioral and social sciences . Int. J. Clin. Health Psychol. 14 , 48–57. 10.1016/S1697-2600(14)70036-5 [ CrossRef ] [ Google Scholar ]
• Wolters C. A., Yu S. L., Pintrich P. R. (1996). The relation between goal orientation and students' motivational beliefs and self- regulated learning . Learn. Individ. Differ. 8 , 211−238. 10.1016/s1041-6080(96)90015-1 [ CrossRef ] [ Google Scholar ]
• Xu J. (2005). Purposes for doing homework reported by middle and high school students . J. Educ. Res. 99 , 46–55. 10.3200/JOER.99.1.46-55 [ CrossRef ] [ Google Scholar ]
• Xu J. (2007). Middle-school homework management: more than just gender and family involvement . Educ. Psychol. 27 , 173–189. 10.1080/01443410601066669 [ CrossRef ] [ Google Scholar ]
• Xu J. (2008). Validation of scores on the Homework Management Scale for high school students . Educ. Psychol. Meas. 68 , 304–324. 10.1177/0013164407301531 [ CrossRef ] [ Google Scholar ]
• Xu J. (2010). Predicting homework time management at the secondary school level: a multilevel analysis . Learn. Individ. Differ. 20 , 34–39. 10.1016/j.lindif.2009.11.001 [ CrossRef ] [ Google Scholar ]
• Zimmerman B. J., Kitsantas A. (2005). Homework practices and academic achievement: the mediating role of self-efficacy and perceived responsibility beliefs . Contemp. Educ. Psychol. 30 , 397–417. 10.1016/j.cedpsych.2005.05.003 [ CrossRef ] [ Google Scholar ]

Advertisement

College students’ homework and academic achievement: The mediating role of self-regulatory beliefs

• Published: 12 August 2008
• Volume 4 , pages 97–110, ( 2009 )

Cite this article

• Anastasia Kitsantas 1 &
• Barry J. Zimmerman 2  

4590 Accesses

92 Citations

Explore all metrics

The influence of homework experiences on students’ academic grades was studied with 223 college students. Students’ self-efficacy for learning and perceived responsibility beliefs were included as mediating variables in this research. The students’ homework influenced their achievement indirectly via these two self-regulatory beliefs as well as directly. Self-efficacy for learning, although moderately correlated with perceptions of responsibility, predicted course grades more strongly than the latter variable. No gender differences were found for any of the variables, a finding that extends prior research based on high school girls. Educational implications about the importance of students’ homework completion and its relationship to college students’ development of self-regulation and positive self-efficacy beliefs is discussed from a social cognitive perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

What Shapes Academic Self-efficacy?

An examination of social and psychological influences on academic learning: a focus on self-esteem, social relationships, and personal interest.

The Triumph of Homework Completion: Instructional Approaches Promoting Self-regulation of Learning and Performance Among High School Learners

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory . Englewood Cliffs, NJ: Prentice-Hall.

Google Scholar  

Bandura, A. (2005). Guide for constructing self-efficacy scales. In F. Pajares, & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich, CT: Information Age.

Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2000). Handbook of self-regulation: Theory, research, and applications . San Diego, CA: Academic.

Campbell, J. R., Hombo, C. M., & Mazzeo, J. (2000). NAEP 1999 trends in academic progress: Three decades of student performance . Washington DC: U. S. Department of Education/NCES 2000-469.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Cool, V., & Keith, T. Z. (1991). Testing a model of school learning: direct and indirect effects on academic achievement. Contemporary Educational Psychology , 16 , 28–44. doi: 10.1016/0361-476X(91)90004-5 .

Article   Google Scholar  

Cooper, H., & Valentine, J. C. (2001). Using research to answer practical questions about homework. Educational Psychologist , 36 , 143–154. doi: 10.1207/S15326985EP3603_1 .

Corno, L., & Xu, J. (2004). Homework as the job of childhood. Theory into Practice , 43 (3), 227–233.

Crandall, V., Katovsky, W., & Crandall, V. (1965). Children's beliefs in their own control of reinforcements in intellectual-academic achievement situations. Child Development , 36 , 91–109.

George, D., & Mallery, P. (2003). SPSS for windows step by step: A simple guide and references 11.0 update (4th ed.). Boston, MA: Allyn and Bacon.

Hoover-Dempsey, K. V., Bassler, O. C., & Burow, R. (1995). Parents' reported involvement in students' homework: strategies and practices. The Elementary School Journal , 95 (5), 435–450.

Joreskog, J. C., & Sorbom, D. (1996). LISREL 8: User’s reference guide . Chicago: Scientific Software International.

Karabenick, S. A., & Knapp, J. R. (1991). Relationship of academic help seeking to the use of learning strategies and other instrumental achievement behavior in college students. Journal of Educational Psychology , 83 , 221–230. doi: 10.1037/0022-0663.83.2.221 .

Keith, T. Z., Diamond-Hallam, C., & Fine, J. G. (2004). Longitudinal effects of in-school and out-of-school homework on high school grades. School Psychology Quarterly , 19 , 187–211. doi: 10.1521/scpq.19.3.187.40278 .

Newman, R. S. (1994). Academic help-seeking: A strategy of self-regulated learning. In D. H. Schunk, & B. J. Zimmerman (Eds.), Self-regulation of learning and Performance: Issues and educational applications (pp. 283–301). Hillsdale, NJ: Lawrence Erlbaum.

National Center for Higher Education Management Systems (NCHEMS). (2007). Retention rates- First-time college freshmen returning their second year (ACT) Four-Year colleges/universities-2002 . Retrieved February 5, 2007, from http://www.higheredinfo.org/dbrowser/index.php?measure=67 .

Pajares, F., Hartley, J., & Valiante, G. (2001). Response format in writing self-efficacy assessment: greater discrimination increases prediction. Measurement and Evaluation in Counseling and Development , 38 (4), 214–221.

Pintrich, P. (Ed.) (1995). New directions in college teaching and learning: Understanding self-regulated learning . San Francisco, CA: Jossey-Bass.

Pressley, M., & McCormick, C. B. (1995). Advanced educational psychology for educators, researchers, and policy makers . New York: Harper Collins.

Schunk, D. H., & Zimmerman, B. J. (Eds.) (1994). Self-regulation of learning and performance: Issues and educational applications . Hillsdale, NJ: Erlbaum.

Schunk, D. H., & Zimmerman, B. J. (1998). Self-regulated learning: From teaching to self-reflective practice . New York: Guilford.

Trautwein, U., & Köller, O. (2003). The relationship between homework and achievement—still much of a mystery. Educational Psychology Review , 15 , 115–145. doi: 10.1023/A:1023460414243 .

Trautwein, U., Köller, O., Schmitz, B., & Baumert, J. (2002). Do homework assignments enhance achievement? A multilevel analysis in 7th-grade mathematics. Contemporary Educational Psychology , 27 , 26–50. doi: 10.1006/ceps.2001.1084 .

Trautwein, U., Lüdtke, O., Schnyder, I., & Niggli, A. (2006). Predicting homework effort: support for a domain-specific, multilevel homework model. Journal of Educational Psychology , 98 , 438–456. doi: 10.1037/0022-0663.98.2.438 .

Valentine, J. C., DuBois, D. L., & Cooper, H. (2004). The relation between self-beliefs and academic achievement: a meta-analytic review. Educational Psychologist , 39 (2), 111–133. doi: 10.1207/s15326985ep3902_3 .

Warton, P. M. (1997). Learning about responsibility: lessons from homework. The British Journal of Educational Psychology , 67 , 213–221.

Webster’s new twentieth century dictionary of the English language Unabridged (1980). (2nd ed.). United States: William Collins.

Winne, P. H., & Jamieson-Noel, D. (2002). Exploring students’ calibration of self reports about study tactics and achievement. Contemporary Educational Psychology , 27 , 551–572.

Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 532–566). Orlando, FL: Academic.

Xu, J. (2006). Gender and homework management reported by high school students. Educational Psychology , 26 (1), 73–91. doi: 10.1080/01443410500341023 .

Zimmerman, B. J. (1994). Dimensions of academic self-regulation: A conceptual framework for education. In D. H. Schunk, & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 3–21). Hillsdale, NJ: Erlbaum.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: an overview. Theory into Practice , 41 (2), 64–71. doi: 10.1207/s15430421tip4102_2 .

Zimmerman, B. J. (2006). Enhancing students’ academic responsibility and achievement: A Social-cognitive self-regulatory account. In R. J. Sternberg, & R. Subotnik (Eds.), Optimizing student success in school with the other three Rs: Reasoning, resilience, and responsibility (pp. 179–197). Greenwich, CT: Information Age.

Zimmerman, B. J. (2008). Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. American Educational Research Journal , 45 (1), 166–183. doi: 10.3102/0002831207312909 .

Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal , 29 , 663–676.

Zimmerman, B. J., & Kitsantas, A. (1999). Acquiring writing revision skill: shifting from process to outcome self-regulatory goals. Journal of Educational Psychology , 91 (2), 241–250. doi: 10.1037/0022-0663.91.2.241 .

Zimmerman, B. J., & Kitsantas, A. (2005). Homework practices and academic achievement: the mediating role of self-efficacy and perceived responsibility beliefs. Contemporary Educational Psychology , 30 , 397–417. doi: 10.1016/j.cedpsych.2005.05.003 .

Zimmerman, B. J., & Kitsantas, A. (2007). Reliability and validity of Self-Efficacy for Learning Form (SELF) scores of college students. The Journal of Psychology , 215 , 157–163.

Zimmerman, B. J., & Martinez-Pons, M. (1986). Development of a structured interview for assessing students’ use of self-regulated learning strategies. American Educational Research Journal , 23 , 614–628.

Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

Download references

Author information

Authors and affiliations.

George Mason University, MS 4B3, Fairfax, VA, 22030, USA

Anastasia Kitsantas

City University of New York, New York, NY, USA

Barry J. Zimmerman

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anastasia Kitsantas .

Items of the SELF

When you miss a class, can you find another student who can explain the lecture notes as clearly as your teacher did?

When your teacher’s lecture is very complex, can you write an effective summary of your original notes before the next class?

When a lecture is especially boring, can you motivate yourself to keep good notes?

When you had trouble understanding your instructor’s lecture, can you clarify the confusion before the next class meeting by comparing notes with a classmate?

When you have trouble studying your class notes because they are incomplete or confusing, can you revise and rewrite them clearly after every lecture?

When you are taking a course covering a huge amount of material, can you condense your notes down to just the essential facts?

When you are trying to understand a new topic, can you associate new concepts with old ones sufficiently well to remember them?

When another student asks you to study together for a course in which you are experiencing difficulty, can you be an effective study partner?

When problems with friends and peers conflict with schoolwork, can you keep up with your assignments?

When you feel moody or restless during studying, can you focus your attention well enough to finish your assigned work?

When you find yourself getting increasingly behind in a new course, can you increase your study time sufficiently to catch up?

When you discover that your homework assignments for the semester are much longer than expected, can you change your other priorities to have enough time for studying?

When you have trouble recalling an abstract concept, can you think of a good example that will help you remember it on the test?

When you have to take a test in a school subject you dislike, can you find a way to motivate yourself to earn a good grade?

When you are feeling depressed about a forthcoming test, can you find a way to motivate yourself to do well?

When your last test results were poor, can you figure out potential questions before the next test that will improve your score greatly?

When you are struggling to remember technical details of a concept for a test, can you find a way to associate them together that will ensure recall?

When you think you did poorly on a test you just finished, can you go back to your notes and locate all the information you had forgotten?

When you find that you had to “cram” at the last minute for a test, can you begin your test preparation much earlier so you won’t need to cram the next time?

Rights and permissions

Reprints and permissions

About this article

Kitsantas, A., Zimmerman, B.J. College students’ homework and academic achievement: The mediating role of self-regulatory beliefs. Metacognition Learning 4 , 97–110 (2009). https://doi.org/10.1007/s11409-008-9028-y

Download citation

Received : 30 November 2007

Accepted : 23 July 2008

Published : 12 August 2008

Issue Date : August 2009

DOI : https://doi.org/10.1007/s11409-008-9028-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Self-regulated learning
• College students
• Self-efficacy
• Find a journal
• Publish with us
• Track your research

• < Previous

Home > Faculty Publications > 1188

Faculty Publications

Homework, motivation, and academic achievement in a college genetics course.

Matthew Planchard , University of Southern Mississippi Kristy L. Daniel , Texas State University Jill Maroo , University of Northern Iowa Follow Chandrani Mishra , University of Southern Mississippi Tim McLean , Tulane University

Document Type

Academic achievement, College student performance, Genetics education, Homework, Motivation

Journal/Book/Conference Title

We conducted a mixed methods study in an upper-level genetics course exploring the relationships between student motivation, homework completion, and academic achievement at the college level. We used data from an open-ended questionnaire, homework grades and completion reports, and exam scores. We used these data sources to measure self-perceived motivating/demotivating factors and then tested these factors for correlation with homework completion and academic achievement. We found no significance in homework completion when considering credit or extra credit as a motivating factor. According to student reports they completed significantly more homework when considering reinforcement of content as a motivating factor. However, we found discrepancies between students’ reported motivation and actual completion rates. Self-reported study style, self perceived conscientiousness, intelligence, attitude, time commitments, and complexity of assignments had significant impacts on whether or not students completed homework assignments and impacted students’ academic achievement. Overall, we found a positive relationship between homework completion and academic achievement within this upper-level college genetics course and provide implications for increasing student motivation.

Department of Earth and Environmental Sciences

Original Publication Date

Recommended citation.

Planchard, Matthew; Daniel, Kristy L.; Maroo, Jill; Mishra, Chandrani; and McLean, Tim, "Homework, Motivation, And Academic Achievement In A College Genetics Course" (2015). Faculty Publications . 1188. https://scholarworks.uni.edu/facpub/1188

This document is currently not available here.

Since November 16, 2021

Advanced Search

• Notify me via email or RSS
• Collections
• Disciplines

Author Corner

• Submit Research
• Faculty Projects Home
• Research and Sponsored Programs
• Rod Library
• University Archives
• Offensive Materials Statement
• UNI ScholarWorks ISSN 2578-3637

Home | About | FAQ | My Account | Accessibility Statement | Contact Us

Privacy Copyright

University of Northern Iowa Rod Library 1227 W. 27th Street Cedar Falls, IA 50614-3675 www.library.uni.edu

• Utility Menu

Harvard FAS Course Evaluations

Assignment Self Assessment

Assignments provide students with opportunities to apply what they are learning in class and impart useful information to instructors on how to improve their teaching practices. Creating effective and meaningful assignments takes careful planning and skill. Students complain about assignments for a variety of reasons such as when they struggle with course material and feel threatened by being graded, when they are not clear about the criteria used to judge their performance, when they do not understand the assignment’s sense of purpose and view it as busy work, or when there are too many assignments in the class and they struggle to keep up. Below we provide a checklist informed by best practices in the literature on how to design effective assignments. See where you may improve.

Download the fillable PDF here

Balan, P., & Manickam, G. (2013, August). Promoting holistic education through design of meaningful and effective assignments in sustainable engineering. In Proceedings of 2013 IEEE international conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 382-385). IEEE.

Barre, E. (2016, July 15). How much should we assign? Estimating out of class workload. Rice University Center for Teaching Excellence. Retrieved September 20, 2022, from https://cte.rice.edu/blogarchive/2016/07/11/workload

Brooks, C. F., & Young, S. L. (2011). Are Choice-Making Opportunities Needed in the Classroom? Using Self-Determination Theory to Consider Student Motivation and Learner Empowerment. International Journal of Teaching and Learning in Higher Education, 23(1), 48-59.

Columbia University. (n.d.). Designing Assignments for Learning. Columbia Center for Teaching and Learning. Retrieved September 20, 2022, from https://ctl.columbia.edu/resources-and-technology/teaching-with-technolo...

Hutchings, P., Jankowski, N. A., & Schultz, K. E. (2016). Designing effective classroom assignments: Intellectual work worth sharing. Change: The Magazine of Higher Learning, 48(1), 6-15.

Planchard, M., Daniel, K. L., Maroo, J., Mishra, C., & McLean, T. (2015). Homework, Motivation, and Academic Achievement in a College Genetics Course. Bioscene: Journal of College Biology Teaching, 41(2), 11-18.

Riddle, R. (2022, March 14). How Much Homework is Too Much? Duke Learning Innovation. Retrieved September 20, 2022, from https://learninginnovation.duke.edu/blog/2018/10/how-much-homework-is-to...

Rosário, P., Núñez, J. C., Vallejo, G., Nunes, T., Cunha, J., Fuentes, S., & Valle, A. (2018). Homework purposes, homework behaviors, and academic achievement. Examining the mediating role of students’ perceived homework quality. Contemporary Educational Psychology, 53, 168-180.

Schroeder, C. (2011). Scaffolded assignments: designing structure and support. History, 22, 23.

Stevens, J. (2018). Finding the balance: Creating meaningful assignments without overwhelming instructional workload. Journal of Educators Online, 15(3), n3.

University of Chicago. (n.d.). Why Assignments? Chicago Center for Teaching and Learning. Retrieved September 20, 2022, from https://teaching.uchicago.edu/resources/evaluating-student-learning/assi...

Vatterott, C. (2010). 5 Hallmarks of Good Homework. Educational Leadership, 68 (1), 10-15.

Improve your Q scores

Take our self-assessments and view our list of resources:

• Feedback Self-Assessment
• Assignment Self-Assessment
• Lecture Self-Assessment
• Leading Discussion Self-Assessment

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Academic achievement prediction in higher education through interpretable modeling

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

Affiliation School of Foreign Languages, Wuhan Business University, Wuhan, Hubei, People’s Republic of China

Roles Investigation, Software, Writing – review & editing

* E-mail: [email protected]

• Sixuan Wang, 

• Published: September 5, 2024
• https://doi.org/10.1371/journal.pone.0309838
• Reader Comments

Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combines Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The model was applied to a dataset from a public university in Wuhan, encompassing the academic records of 87 students who were enrolled in a Japanese course between September 2021 and June 2023. The findings indicate the model excels in accuracy, achieving a Mean absolute error (MAE) of approximately 6 and an R-squared value near 0.82, surpassing three other ML models. The model further uncovers how different instructional modes influence the factors that contribute to student achievement. This insight supports the need for a customized approach to feature selection that aligns with the specific characteristics of each teaching mode. Furthermore, the model highlights the importance of incorporating self-directed learning skills into student-related indicators when predicting academic performance.

Citation: Wang S, Luo B (2024) Academic achievement prediction in higher education through interpretable modeling. PLoS ONE 19(9): e0309838. https://doi.org/10.1371/journal.pone.0309838

Editor: Shahid Akbar, Abdul Wali Khan University Mardan, PAKISTAN

Received: May 30, 2024; Accepted: August 20, 2024; Published: September 5, 2024

Copyright: © 2024 Wang, Luo. 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: All relevant data are within the manuscript and its Supporting Information files.

Funding: Fund recepient:Sixuan Wang Funder name: Hubei Provincial Department of Education Grant No: 2022GB087 Project name: A Study on the Curriculum Connection between College Japanese and High School Japanese from the Perspective of Core Literacy. https://jyt.hubei.gov.cn/ The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Introduction

Context and motivation.

Academic achievement is of paramount importance in educational contexts, serving as a key indicator of both learning ability and the effectiveness of school administration and teaching standards [ 1 ]. The prediction of academic achievement is a continuously evolving topic in educational management. The integration of predictive models in education empowers educators to make well-informed choices, offer specific support, and enhance teaching strategies, thereby improving student learning outcomes [ 2 ].

Previous research on achievement prediction primarily utilized statistical analysis methods to process data and forecast outcomes, with data mainly derived from educational management systems, student identification cards, or surveys [ 3 ]. ML techniques, known for their ability to tackle complex, nonlinear problems without presuppositions, are adept at identifying connections between various parameters [ 4 ]. The state-of-the-art ML techniques for prediction [ 5 ] include K-Nearest Neighbors (KNN), Decision Trees, Random Forests (RF), Support Vector Machines (SVM), Neural Networks, and Naive Bayes. Recent scholarly efforts, both domestically and internationally, have been geared towards increasing the precision of student achievement predictions through technological innovations in algorithms [ 6 – 8 ].

Despite these developments, challenges remain in the domain of achievement prediction. A primary issue is the limited alignment between the outcomes produced by ML algorithms and the foundational principles of education and instruction, leading to hesitancy among educators in relying on these models. Additionally, there is a gap in thorough data analysis, examination of relationships, and investigation into variables that impact student academic performance patterns.

Contribution of the study

In addressing these challenges, our study delivers distinctive contributions to the field of interpretable machine learning within the context of higher education. We delineate these contributions as follows:

• Theoretical contribution: this study introduces ML models coupled with game theory-based SHAP analysis which aims to develop and validate the XGB-SHAP model, a novel approach for interpreting machine learning-based predictions of student achievement, and explore its efficacy across various teaching modalities.
• Practical contributions: It evaluates the significance of different indicators and their positive or negative impacts on prediction outcomes, thus shedding light on the educational implications of achievement prediction models. The findings of this study provide empirical data support for teachers and educators, facilitating the refinement of their instructional strategies.
• Comparative analysis: It explores student achievement prediction models in three distinct educational settings: online, offline, and blended teachings. This exploration reveals variances in teaching patterns across these modes, yielding practical advice for educators in applying these prediction models.

Structure of the article

This paper is organized as follows: Section ‘Literature review’ presents a review of related literatures, providing a comprehensive review of the existing literature on student achievement prediction, examines the prevailing issues and identifies the gaps within the current body of research. Section ‘Methodology’ details the methodology employed in this study, introduces the interpretable performance prediction framework and the indicators system used in this paper and outlines the methodology used to conduct the data analysis for this paper. The findings and their implications are discussed in Sections ‘Case study’ and ‘Results’ respectively. The paper concludes with a summary of our key findings in the final Section ‘Discussion and Conclusions’. Table 1 illustrates the list of abbreviations.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0309838.t001

Literature review

Previous research, student achievement prediction indicators..

Prediction accuracy largely depends on the careful selection of indicators. The initial and most critical step is the selection of appropriate input data. Previous research has identified three key groups of student-related features as pertinent input parameters: historical student performance, student engagement, and demographic data (Tomasevic et al., 2020).

Historical student performance has been consistently identified as a reliable predictor. For instance, DeBerard et al. [ 9 ] demonstrated that high school GPA is a strong predictor of college academic success. Similarly, Shaw et al. [ 10 ] found that combined SAT scores explain about 28% of the variance in first-year college GPA. Moreover, test scores have been used to predict future academic performance in various studies [ 11 ].

Regarding student engagement, a notable correlation with academic achievement has been observed [ 12 ]. Hussain et al. [ 13 ] identified a moderately strong positive correlation between student engagement and academic achievement. With evolving teaching formats like Massive Open Online Courses and the flipped classrooms, several studies have developed predictive models by analyzing student behaviors in learning management systems, such as video interactions, assignment submissions, and forum discussions [ 14 ]. With the innovation of modern educational technology tools, including artificial intelligence tools (such as ChatGPT) and virtual reality, significant roles have been played in enhancing student learning outcomes by integrating with educational theories like constructivism, experiential learning, and collaborative learning. These technologies, by offering immersive and interactive learning experiences, have increased student engagement, motivation, and critical thinking skills, thereby positively impacting academic performance [ 15 , 16 ].

Studies have also considered demographic factors. Research indicates that demographic factors play a moderate role in predictive accuracy, with relevance around 60% in some studies, while others suggest that these variables have a limited impact on prediction precision [ 5 , 17 ]. Additional indicators, such as student collaboration, teacher-student communication, and psychological factors like motivation and attitude, have also been explored. Recent studies emphasize the importance of considering learners’ psychological well-being and cognitive processes in educational settings [ 18 , 19 ].These motivational and coping strategies remarkably influence students’ learning approaches and overall educational outcomes [ 20 ].

The above discussion shows that student achievement is a composite of cognitive, behavioral, skill-based, and emotional outcomes derived from educational experiences [ 21 ]. Although there is a consensus on the selection of certain important indicators, the selection of the dataset for student achievement prediction varies from study to study. Selecting the most suitable dataset depends largely on the specific goals and objectives of the researchers, with no universally accepted guidelines.

Student achievement prediction models.

Originally, conventional statistical methods such as Discriminant Analysis and Multiple Linear Regression were the predominant approaches in the early stages of educational research [ 22 ]. Furthermore, Structural Equation Modeling (SEM) has been widely adopted in the social sciences. However, these traditional methods have often fallen short of delivering consistent and precise predictions or classifications [ 23 ].

Recently, an array of machine learning algorithms has been employed, including Multiple Regression, Probabilistic and Logistic Regression, Neural Networks, Decision Trees, Random Forests (RF), Genetic Algorithms, and Bayesian algorithms. These have shown varied levels of success in achieving high predictive accuracy [ 24 ]. Comparative studies of machine learning methods have been conducted, with Caruana et al. [ 25 ] exploring the performance evaluation of these models. Their research underscores a fundamental point: no single model or method universally excels across all problems and metrics. Tomasevic et al. [ 5 ] used the Open University Learning Analytics Dataset for a regression problem, finding that Artificial Neural Networks (ANN) and Decision Trees were the most effective, while KNN, SVM, and Bayesian linear regression were less successful.

While previous approaches using machine learning models for predicting student achievement have focused on model optimization [ 26 ], there are growing concerns regarding the opaque nature of complex models, which may hinder their broader application [ 27 ].

Interpretable machine learning models.

Nowadays, with the rapid development of artificial intelligence (AI) technology, ML models are being applied in many critical fields, such as education [ 28 , 29 ], healthcare [ 30 – 32 ]. However, as the number of parameters soars, the ’black-box’ nature of neural networks has raised concerns. Interpretable machine learning is a promising tool to alleviate concerns regarding the opacity of machine learning models. It equips ML models with the capability to articulate their processes in a manner comprehensible to humans [ 33 ].

Broadly, interpretable machine learning methods are divided into two categories: self-interpretation models and post-hoc interpretation methods [ 34 ]. Self-interpreting models typically have a simpler structure and include Linear models, Logistic Regression, and Decision Trees. Post-hoc interpretation methods involve either model-independent or model-specific techniques, applicable to various models but may require additional computational resources and analytical expertise.

Post-hoc or model-independent interpretation methods are extensively used in different scenarios. These include Partial Dependence Plot [ 35 ], Individual Conditional Expectation [ 36 ], Permutation Feature Importance [ 37 ], Local Interpretable Model-agnostic Explanations, and the SHAP method. The survey in the field of information resource management revealed that 83.7% of explainable ML applications utilize post-hoc explanation methods, with SHAP (51.2%) and feature importance analysis (34.1%) being the most common. Unlike traditional feature importance which indicates the significance of features without clarifying their impact on predictions, SHAP offers detailed explanations on both sample and feature levels through various visualizations like waterfall diagrams and feature dependency diagrams.

These interpretative approaches have been applied in diverse fields such as medicine, policymaking, and science, aiding in auditing predictions under circumstances like regulatory pressures and the pursuit of fairness [ 35 ]. However, the critical aspect of interpretability in machine learning models within the domain of educational management research remains underexplored.

Research gap

Given the aforementioned limitations, the interpretability of ML is a contentious issue. The various ML algorithms employed often fail to effectively elucidate the relationship between factors influencing students’ academic performance and their grades. Additionally, they struggle to quantify the impact of each feature on the target value and to determine the positive or negative influence of each characteristic. To address these gaps in the literature, our study delves into the following areas:

• Feature Importance Analysis: Our research will quantify the influence of each feature on the prediction of student performance. This involves a detailed examination of the weight and significance of various factors in determining academic outcomes.
• Impact Assessment: We will assess the positive or negative impact of each feature on the target variable. This is crucial for understanding not only the magnitude of the influence but also its direction.
• Model Comparison: By comparing the interpretability and performance of different ML models, our study seeks to identify the most effective approaches for student achievement prediction.
• Practical Implications: We will discuss the practical implications of our findings, focusing on how increased interpretability can enhance educational practices and inform policy-making.

Through this comprehensive approach, our study seeks to bridge the gap in the current research by providing a clearer understanding of the mechanisms behind student achievement prediction models and their implications for educational stakeholders.

Methodology

Development of an interpretable performance prediction framework.

As shown in Fig 1 , we have developed an interpretable framework for performance prediction. The framework’s core involves extracting five key features: academic factors, student engagement, demographic factors, psychological aspects, and self-directed learning abilities. These features form an input vector that accurately represents factors relevant to achievement prediction. The data for this study is sourced from three main systems: the Education Administration System (EAS), the Chaoxing Xuexitong System, and various questionnaires.

https://doi.org/10.1371/journal.pone.0309838.g001

The methodology progresses in three phases. The initial phase involves creating an indicator system from these features. In the subsequent phase, we focus on constructing and elucidating performance prediction models. Four different ML algorithms are applied to our “learning” dataset. Their effectiveness is evaluated using two standard ML metrics: Mean Absolute Error (MAE) and R-squared ( R 2 ). The optimal model is then selected based on these evaluations. The final stage of our methodology is the model interpretability phase, which accounts for the educational significance of the model by analyzing the importance and directional influence of the indicators. This phase aims to provide educators with insights to refine their teaching strategies.

Development of the indicator system

As mentioned in ‘Literature review’ section, prior research insights advocate categorizing student-related features into historical student performance, engagement, and demographic data [ 5 ]. To capture a holistic view of learner characteristics, we have expanded this system to include psychological factors and self-directed learning capabilities to form a student achievement prediction indicator system, as shown in Table 2 . Considering the minimal variation in age, gender, and other demographic factors in our case study, we have chosen to focus solely on the major as the demographic data point.

https://doi.org/10.1371/journal.pone.0309838.t002

Model training

As SHAP is a model-agnostic interpretation framework, which enables it to be applied across a spectrum of common predictive models. This versatility allows SHAP to provide insights into the decision-making process of these models by quantifying the contribution of each feature to the prediction, thereby enhancing our understanding of the model’s behavior regardless of its underlying structure or algorithmic approach. Commonly used ML models for academic achievement prediction include RF, BPNN, SVM, and XGboost. The rationale for selecting these four models is their proficiency as data-driven prediction methods. RF, an ensemble learning technique, amalgamates numerous decision trees, thereby reducing variance relative to individual trees. It is known for its superior average prediction performance. BPNN, a supervised learning algorithm, builds multi-layer neural networks inspired by biological neurons and employs a back-propagation algorithm for training, excelling in handling non-linear relationships and high-dimensional data. SVM has gained recognition for its effectiveness in classification, regression, and time-series prediction. XGBoost, enhancing the Gradient Boosting Decision Tree algorithm, stands out for its accuracy and flexibility.

In this research, a 5-fold cross-validation approach was implemented to fine-tune the hyperparameter to avoid overfit, optimizing them according to the mean value derived from each test set.

Model interpretability

Addressing the opaque nature of ML models, our research employs the SHAP method for interpretability. Developed by Lundberg and Lee in 2017 [ 39 ], SHAP merges various existing approaches to provide a reliable and intuitive explanation of model predictions. It does so by illustrating how predictions shift when certain variables are omitted. The Python SHAP package ( https://github.com/slundberg/shap ), enables the calculation of SHAP values for any selected model, and it is extensively utilized due to its versatility.

SHAP is characterized by three fundamental properties: local accuracy (the sum of feature attributions equals the model output), missingness (zero attribution for non-present features), and consistency (no decrease in feature attribution despite an increased marginal contribution). A notable advantage of SHAP is its model-agnostic nature, making it applicable to any machine learning model.

Data for this study was obtained from the EAS of a Wuhan-based public university. This system provided access to students’ personal information, such as majors and academic grades. In addition, we gathered course-related learning data from the Chaoxing Xuexitong system, a widely used online education platform in China. To obtain data on self-study hours, learning attitudes, and self-directed learning indicators, we employed questionnaires as the methodological instrument. The learning attitude questionnaire adapted from the English-learning Motivation Scale developed by a Chinese scholar Meihua Liu [ 40 ] who is from Tsinghua University, a tool commonly utilized in in EFL teaching and learning in the Chinese context. For assessing self-directed learning capabilities, we used a questionnaire adapted from Jinfen Xu ‘s [ 41 ] self-directed learning capability scale. These questionnaires were administered in class under instructor supervision and lasted approximately 10 minutes each, aiming to evaluate students’ learning attitudes and their aptitude for independent learning. The surveys were conducted midway through each semester. Our dataset encompasses data from 87 students enrolled in the Japanese course for the class of 2021, spanning three different learning modes. It includes nine indicators linked to student grades, amounting to a total of 2349 data entries. Table 3 shows the types of nine indicators.

https://doi.org/10.1371/journal.pone.0309838.t003

While analyzing the datasets, an imbalanced data pattern was noted. To address this, we grouped students into three broad specialty categories: Arts, Science and Technology, and Arts and Sports. This categorization reduced data sparsity by assigning discrete values (1, 2, 3) to these groups.

Ethical considerations

The study was approved by the institutional review board, and the study runs from September 2021 to June 2023. All participants were not at risk if they chose or declined to participate. Parental consent is not required for undergraduate students participating in the study. Additionally, we explained the purpose of the study in the questionnaire, clarified that it was their right to participate or not to participate in the study, and informed all the participants that ‘submitting answers’ is considered informed consent for researchers to use their questionnaire responses and related data retrieved from EAS and Chaoxing platform in publications of the research.

Experimental setup

In this study, we conducted experiments employed PyCharm version 2022.3.3 as the compilation software, and implemented the algorithmic model using Python. The dataset was randomly partitioned into training and test sets in a 4:1 ratio for robust training and evaluation.

As state in the Methodology Section, we employ four classic ML models as our predictive model for academic performance. Table 4 presents the pseudo-code outlining the experimental procedures.

https://doi.org/10.1371/journal.pone.0309838.t004

Comparison of models

To obtain the optimal model parameters, the hyperparameters of the aforementioned four models were optimized separately. Table 5 displays the optimal hyperparameter combinations for the aforementioned four models.

https://doi.org/10.1371/journal.pone.0309838.t005

Table 6 presents the comparison of the task performance of four models. Both BPNN and XGBoost show higher task performance compared to RF, while SVM lags in terms of task performance. The comparison indicates that XGBoost slightly surpasses BPNN, establishing XGBoost as the model with the best predictive performance. Therefore, this study selects the XGBoost model to fit all the data. SHAP values are used for interpretation.

https://doi.org/10.1371/journal.pone.0309838.t006

Exploratory analysis utilizing XGBoost and SHAP

Given the effectiveness of the XGBoost model, it was selected for further analysis using SHAP to explore teaching patterns within the model across various teaching modes. SHAP offers insights into the influence of each indicator per sample, highlighting both positive and negative effects. In the associated figures, color coding is used to represent the magnitude of eigenvalues, with red indicating high values and blue representing low values.

Figs 2 and 3 shows the importance of indicators and a summary plot for offline teaching. The average SHAP value (horizontal axis) indicates the significance of each indicator, with their order of importance shown on the vertical axis in Fig 2 . Key findings include classroom performance, previous exam grades, and student major as the most influential indicators. The impact of eigenvalues on each sample is depicted in Fig 3 , where each row represents an indicator, each dot signifies a sample, and the SHAP value is plotted on the horizontal axis. Further analysis revealed a positive relationship between prior exam grades, self-directed learning ability, learning attitudes, and their effect on academic achievement predictions. Interestingly, occasional absences did not show a substantial negative influence on predicted grades, hinting at a divergence in the dynamics of college classrooms from high school settings. This might be attributed to the independent learning skills prevalent among college students. Moreover, it was noted that students majoring in Arts and Sports tend to have a slightly negative impact on predicted grades.

https://doi.org/10.1371/journal.pone.0309838.g002

https://doi.org/10.1371/journal.pone.0309838.g003

Analysis of online teaching using XGBoost and SHAP

Figs 4 and 5 presents the indicator importance and summary plot for online teaching. A key observation is the increased influence of previous exam grades on the predicted values in comparison to offline settings. This suggests that students with a strong academic foundation tend to be more self-directed, thereby enhancing their predicted performance more remarkably. The disparity in self-directed learning abilities is more evident in online courses, highlighting the detrimental effect of inadequate self-learning skills on performance. Students struggling with self-learning might not receive timely support, leading to poorer outcomes. In this context, classroom performance becomes a less critical predictor, and the influence of a student’s major on predicted scores also diminishes. Interestingly, self-study time shows a positive correlation with predicted grades, while the relationship between quiz scores and performance prediction remains insignificant.

https://doi.org/10.1371/journal.pone.0309838.g004

https://doi.org/10.1371/journal.pone.0309838.g005

Blended teaching: Insights from XGBoost and SHAP

Figs 6 and 7 examines the indicator importance and summary plot for blended teaching. In this teaching mode, the impact of self-directed learning skills is more notable compared to other teaching methods, possibly due to the adoption of flipped classroom techniques. Self-directed learning shows a stronger positive correlation with both previous exam grades and quiz scores. Furthermore, the relevance of attitude towards learning is accentuated, suggesting its growing importance in blended learning environments where independent study is emphasized.

https://doi.org/10.1371/journal.pone.0309838.g006

https://doi.org/10.1371/journal.pone.0309838.g007

Discussion and conclusions

The prediction of academic achievement in higher education has become an increasingly prominent topic within the field of education [ 42 ]. In today’s information age, the tremendous growth of educational institutions’ electronic data “…can be utilized for discovering unknown patterns and trends” [ 43 ].Recent researches on predicting student performance are frequently spearheaded by educators identifying as "AI" educators to identify features that can be used to make predictions [ 44 ], to identify algorithms that can improve predictions [ 45 ], and to quantify aspects of student performance. However, analyzing performance, providing high-quality education strategies for evaluating the students’ performance from these abundant resources are among the prevailing challenges universities face [ 46 ].

In this research, we have developed the XGB-SHAP model, integrating XGBoost with SHAP, to systematically explore the relationship between grade prediction and diverse indicators across various teaching methods. Focused on university Japanese language classes, our study demonstrated XGBoost’s superior performance over other models, as evidenced by R 2 and MAE metrics. The integration of SHAP offered a clear visual representation, highlighting the mode and directional influence of each indicator and sheds light on the educational implications of ML structures in pedagogy. The study also supported that the XGB-SHAP model can be effectively used in the field of educational management research.

The results reveal that, the study of student achievement prediction, using student-related features, such as student historical achievement, student engagement and demographic data, which have been used as important input features in the previous literature, is not sufficient. With the development of society and the diversification of teaching and learning modes, the importance of self-directed learning skills in the prediction of university students’ performance has been demonstrated in this study. Psychological factors such as attitude towards learning should also be taken into account. The impact of a student’s major on foreign language learning is considerable, which indicate differences in learning environments, cultural factors, motivation to learn foreign languages. While classroom response accuracy and attendance appeared less critical. This suggests a potential shift in focus within higher education classrooms, advocating for a tailored approach to characteristic selection based on teaching modes. This methodology provides educators with a quantitative view of how educational processes affect student achievement.

Our study also shows that the factors influencing student performance vary: offline teaching values classroom performance, while online teaching and blended teaching emphasize independent learning. In blended teaching, quiz scores have a remarkable positive impact, differing from the trends in other modes. This could be attributed to quizzes acting as formative assessments in blended learning, enhancing student participation and providing continual feedback. Consequently, teaching strategies and support systems should be adapted to meet the distinct needs of each teaching mode to optimize learning outcomes.

Acknowledging the formidable technical challenges associated with interpretable machine learning models in practical educational contexts, it is imperative to recognize their substantial contributions in enhancing our comprehension and utility of achievement prediction models. Additionally, they play a pivotal role in mitigating the skepticism harbored by educators towards machine learning models deployed for achievement prediction. Moving forward, there exist several promising avenues for exploration within the realm of interpretable machine models that merit thorough investigation: first, expand the dataset to cover more academic areas, different institutions, and varied student groups. This will test the model’s effectiveness in diverse settings. Second, the refinement and augmentation of existing interpretable models to enhance their accuracy and utility. These directions offer promising avenues for furthering the application and acceptance of interpretable machine learning in educational settings.

Supporting information

S1 file. original data..

https://doi.org/10.1371/journal.pone.0309838.s001

• View Article
• Google Scholar
• PubMed/NCBI
• 20. Boekaerts M, Niemivirta M. Self-regulated learning: Finding a balance between learning goals and ego-protective goals. Handbook of self-regulation. Academic Press, 2000.
• 23. Kyndt E, Musso M, Cascallar E, et al. Predicting academic performance: The role of cognition, motivation and learning approaches. A neural network analysis.Methodological challenges in research on student learning. Antwerp: Garant, 2015.
• 26. Shalev-Shwartz S, Ben-David S. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.
• 42. Hellas A, Ihantola P, Petersen A, et al. Predicting academic performance: a systematic literature review[C]//Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education. 2018: 175-199.doi:10.1145/3293881.3295783.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Factors affecting assignment completion in higher education

2020, Journal of Applied Research in Higher Education

Purpose-The purpose of this paper is to simultaneously investigate a variety of factors related to assignment completion (AC) (i.e. task orientation, cooperation, teacher feedback, time management and time spent on AC). Design/methodology/approach-The study relied on a self-report survey to assess students' perceptions in relation to six variables. Participants included 1,106 undergraduate students from six public Thai universities. Analysis involved structural equation modeling. Findings-This study provided new results related to task orientation as the strongest predictor of AC and time management. Cooperation and feedback improved AC with time management as an intervening variable. Time management and feedback did not predict time spent on AC. Research limitations/implications-Future studies might explore the potential range of assignments that, for example, count for a higher portion of the grade versus those that are less or unimportant in terms of the course. Future studies might also look at the role of group assignments in relation to completion. Semistructured interviews or observations might provide insights into how students manage their time and why task orientation has the most effect on AC. Future research might investigate more specifically at what point time management does or does not affect completion. In general, given the growth of online learning and contexts in which students may be increasingly called on to complete assignments independently, factors such as those investigated in this study will require more attention in varying countries and contexts, generically and for individual subjects. Practical implications-Instructional designers and instructors can promote task orientation through reliance on strategic scaffolding. For designing a task-oriented environment, instructors need to offer challenging assignments. Instructors should also assign work that encourages motivation, effort and achievement. To ensure that cooperative learning positively affects time management, instructors and designers can allot specific in-class time for completion of tasks, reliance on flipped classroom activities and student conversations regarding time restrictions and time-management skills. Instructors can be supported to help them provide appropriate types of feedback, as well as ideas for implementing the feedback in practice. Originality/value-Little research has been conducted on AC in higher education. Those studies that have been conducted have focused on the elementary and secondary levels. Furthermore, studies have not always taken into account the complex relationships between different factors that can potentially influence AC.

Related Papers

International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI)

Jane Yin-Kim Yau

Common factors, which are related to study success include students’ sociodemographic factors, cognitive capacity, or prior academic performance, and individual attributes as well as course related factors such as active learning and attention or environmental factors related to supportive academic and social embeddedness. In addition, there are various stages of a learner’s learning journey from the beginning when commencing learning until its completion, as well as different indicators or variables that can be examined to gauge or predict how successfully that journey can or will be at different points during that journey, or how successful learners may complete the study and thereby acquiring the intended learning outcomes. The aim of this research is to gain a deeper understanding of not only if learning analytics can support study success, but which aspects of a learner’s learning journey can benefit from the utilisation of learning analytics. We, therefore, examined different ...

Journal of Chemical Education

Shawn Simonson

Journal of Stem Education Innovations and Research

Susan Shadle

INSTRUCTIONAL TECHNOLOGY

Sheila Bolduc-Simpson

Barbara L McCombs

Reagan Curtis

Journal of Computer Assisted Learning

Mike Hanyak

Teresa Foulger

In this paper researchers investigate the ways in which redesigning a course to be delivered in a hybrid format that blends face-to-face and online course delivery instigated changes in instructional practice. In addition, researchers explored instructor perceptions about the relative strengths and weaknesses of teaching a face-to-face versus hybrid course. Analyses of self-reported instructor practices with respect to seven principles of effective instruction in undergraduate education suggest that integrating face-to-face teaching with online learning provides an opportunity to enrich instructional activities and pedagogical practices. Practical implications for instructors and suggestions for future research are also described.

Dr. Douglas Dean

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

2014 ASEE North Midwest Section Conference: Engineering Something More

M. Dorneich

Contemporary Educational Technology

Bulent Ertas

Publication of National University

kamilah nurlaeli

Joshua Shannon-Chastain

Randall Davies

Topics in Cognitive Science

Thomas Shipley

Ioan Gelu Ionas

Computers in Human Behavior

Erhan Şengel

Alina Goldman

Decision Sciences Journal of Innovative Education

Marzie Astani

Meredith Toth

Jessica Burr

د.عبدالرحمن الجعيد

Asian Journal of Education and Training

melike özüdoğru

The Journal of Computers in Mathematics and Science Teaching

Angelo Segalla

Maha Al-Freih , Robin B

Information & Management

Marcin Bartosiak

Antonette L McCaster

Higher Education

Mohamed Nabil

Richard A McCue

Diane Robison

Jessica Ellis , Chris Rasmussen , Kady Hanson , Gina Nuñez

Pau Castell Granados

Dianna Newman

Larry Michaelsen

International Journal of Technology in Education and Science

Farouq Almeqdadi

Teaching Psychology Online

Heather Hussey

The American Biology Teacher

Stephen Traphagen

Rebecca Bott

Journal for Research Scholars and Professionals of English Language Teaching (jrsp-elt)

Prof. Reima Al-Jarf

Meaza Atile

sciepub.com SciEP , Salman Alsaqri

The Asia-Pacific Education Researcher

Murod Ismailov

William Neff

IALLT Journal of Language Learning Technologies

Richard Dabrowski

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• DOI: 10.1901/JABA.2005.123-03
• Corpus ID: 5286169

Effects of the contingency for homework submission on homework submission and quiz performance in a college course.

• C. Ryan , N. S. Hemmes
• Published in Journal of Applied Behavior… 1 March 2005
• Education, Psychology

Figures from this paper

45 Citations

The effect of randomized homework contingencies on college students' daily homework and unit exam performance, the effects of homework sessions on undergraduate students' homework performance, a point contingency for homework submission in the graduate school classroom., a comparison of group-oriented contingencies and randomized reinforcers to improve homework completion and accuracy for students with disabilities, engage engineering students in homework: attribution of low completion and suggestions for interventions, effects of a contingency for quiz accuracy on exam scores, homework, motivation, and academic achievement in a college genetics course., the effects of guided notes on pre-lecture quiz scores in introductory psychology.

• Highly Influenced

18 References

Using student-managed interventions to increase homework completion and accuracy., homework assignments, consequences, and classroom performance in social studies and mathematics., do homework assignments enhance achievement a multilevel analysis in 7th-grade mathematics, relationships among attitudes about homework, amount of homework assigned and completed, and student achievement, the relationship between homework and achievement—still much of a mystery, improving students' exam performance by introducing study strategies and goal setting, using research to answer practical questions about homework, the instructional assistants program: a potential entry point for behavior analysis in education., computer-based precision learning: achieving fluency with college students, think or click student preference for overt vs. covert responding in web-based instruction, related papers.

Showing 1 through 3 of 0 Related Papers