A conceptual predictive analytics model for the identification of at-risk students in VLE using machine learning techniques
Document Type
Conference Item
Publication Date
1-1-2022
Abstract
With the rapid growth and enhancement in technology-based learning platforms, students generate abundant digital footprints that are useful to mine and analyse their learning behaviours through Learning Analytics techniques. Student dropout is a pressing issue that many universities are currently facing, and it is increasing especially in e-learning systems. The prediction of at-risk students as early as possible is the recent phenomenon in the fields of LA and Educational Data Mining (EDM). Predicting failing students in Virtual Learning Environment (VLE) can benefit institutions and instructors in making data-driven decisions as well as enhancing their pedagogical methods. In this study, a predictive analytics model is proposed using Machine Learning (ML) clustering techniques to identify at-risk students in the Open University (OU). This research aims to evaluate whether unsupervised ML approaches can predict students at-risk with higher accuracy than supervised ML. The model also addresses the current research gaps based on the recent literature.
Keywords
Learning Analytics, At-Risk Students, Educational Data Mining, Machine Learning, Clustering, Virtual Learning Environment (VLE)
Divisions
infosystem
Funders
Taylor's University [IVERSON/2018/SOCIT/001]
Publisher
IEEE
Publisher Location
345 E 47TH ST, NEW YORK, NY 10017 USA
Event Title
2022 14TH INTERNATIONAL CONFERENCE ON MATHEMATICS, ACTUARIAL SCIENCE, COMPUTER SCIENCE AND STATISTICS (MACS)
Event Location
SMI Univ, Karachi, PAKISTAN
Event Dates
NOV 12-13, 2022
Event Type
conference
Additional Information
14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), SMI Univ, Karachi, PAKISTAN, NOV 12-13, 2022