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

By Mushtaq Hussain, Wenhao Zhu, Wu Zhang, Syed Muhammad Raza Abidi. Computational Intelligence and Neuroscience, 2018

330 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/519ce9c91d8a9fd5dfbb220dde6cb049935779b1

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Citations in the corpus

Predicting academic performance of students from VLE big data using deep learning models (2020, background): “Assessment engagement pattern is considered to be another parameter that effectively captures the behavior of students and induces a positive impact on their performance (Hussain et al., 2018; Jung & Lee, 2018).”
Empowering educators to be AI-ready (2022, methodology): “Many higher education institutions use these systems, including St Mary ’ s University in Mary-land (Zeide, 2019), the University of Osijek in Croatia (Babi ´ c, 2017), and the Open University in the United Kingdom (Hussain, Zhu, Zhang, & Abidi, 2018).”
Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019, methodology): “Hussain et al. (2018) applied several machine learning algorithms to analyse student behavioural data from the virtual learning environment at the Open University UK, in order to predict student engagement, which is of particular importance at a large scale distance teaching university, where it is…”