Predicting academic performance from behavioural and learning data

Villagrá-Arnedo, Carlos; Gallego-Duraìn, Francisco J.; Compañ, Patricia; Llorens-Largo, Faraoìn; Molina-Carmona, Rafael

doi:10.2495/dne-v11-n3-239-249

Cited by 22 publications

(8 citation statements)

References 19 publications

(16 reference statements)

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“…Therefore, before applying the ML algorithms, we conducted a statistical analysis (Spearman correlation) to determine the significance between the dependent variable of the study (level of engagement) and the independent variables ( score on the assessment and the number of clicks on VLE activities, namely, dataplus , forumng , glossary , oucollaborate , oucontent , resource , subpage , homepage , and URL ). A spearman correlation is appropriate for both continuous and discrete features [ 78 ].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

Computational Intelligence and Neuroscience

251

167

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Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study included highest education level, final results, score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which included dataplus, forumng, glossary, oucollaborate, oucontent, resources, subpages, homepage, and URL during the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

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Section: Experiments and Resultsmentioning

confidence: 99%

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Hussain

Zhu

Zhang

et al. 2018

Computational Intelligence and Neuroscience

251

167

View full text Add to dashboard Cite

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“…Bayesian Additive Regressive Trees (BART) was used to predict the final grade of students in the sixth week [30]. A model based on SVM weekly predicted the probability of each student belonging to one of these three types: high, medium or low performance [31]. Latent Dirichlet Allocation (LDA) predicted student grades according to how the students described their learning situations after each lesson [32].…”

Section: Supervised Learningmentioning

confidence: 99%

“…It is possible to predict final students' performance beforehand thanks to behavioural data supplemented with other more relevant data (related to learning results). The system proposed in [31] obtained a weekly ranking of each student's probability of belonging to one of these three classification levels: high, medium or low performance. This performance could have something to do with non-cognitive characteristics which can have a significant impact on the students [9].…”

Section: Student Performancementioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

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“…In order to verify the hypothesis that complementing behavioral data with other more relevant data (related to the learning results) can lead to a better analysis of the learning process, making it possible to predict the student's final performance in advance. The study showed satisfactory results and classified users with the probability of belonging to one of the three classes: high, medium and low performance (Villagrá-Arnedo et al, 2016). Studies in computational technologies also stand out, for addressing the development and application of assessment tools in serious games, such as the EngAGe tool, which is an integrated assessment tool for teachers and developers designed to separate the serious game from the system evaluation (Chaudy & Connolly, 2019).…”

Section: Studies With An Emphasis On Computational Technologiesmentioning

confidence: 97%

“…The collection and analysis of data proves to be an extremely relevant tool among computational technologies, presenting behaviors of great significance for the learning process. In the study carried out in the Learning Management System, a learning platform was developed to collect data not only on the use of the system, but also related to the way students learn and progress training activities (Villagrá-Arnedo et al, 2016). In order to verify the hypothesis that complementing behavioral data with other more relevant data (related to the learning results) can lead to a better analysis of the learning process, making it possible to predict the student's final performance in advance.…”

Section: Studies With An Emphasis On Computational Technologiesmentioning

confidence: 99%

Use of Learning Analytics Interactive Dashboards in Serious Games

Scheneider

Lemos

2020

Int J Innov Educ Res

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The learning analytics in serious games, corresponds to a subject in increasing demand in the educational field. In this context, there is a need to study how data visualizations found in the literature are adopted in learning analytics in serious games. This paper presents a Systematic Literature Review (SLR) on how the evolution of studies associated with the use of learning analytics interactive dashboards in serious games is processed, seeking to investigate the characteristics of using dashboards for viewing educational data. A bibliometric analysis was carried out in which 75 relevant studies were selected from the Scopus, Web of Science, and IEEExplore databases. From the data analysis, it was observed that in the current literature there is a reduced number of studies containing the main actors in the learning process, as follows: teachers/instructors, students/participants, game developers/designers, and managers/researchers. In the vast majority of investigated studies, data visualization algorithms are used, where the main focus takes into account only actors, such as teachers/instructors and students/participants.

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Predicting academic performance from behavioural and learning data

Cited by 22 publications

References 19 publications

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Use of Learning Analytics Interactive Dashboards in Serious Games

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