Identifying At-Risk Students in Virtual Learning Environment using Clustering Techniques

Palani, Kamalesh (2020) Identifying At-Risk Students in Virtual Learning Environment using Clustering Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Higher education institutions across the world have started using web-based learning platform to provide distance learning education online. These learning platforms provide opportunities for the eLearners and the working professional to learn on demand and gain knowledge remotely. This wide spread use of Virtual Learning Environments (VLEs) in universities has higher rate of students drop out percentage and difficulty in monitoring the student’s online engagements during the courses. Therefore, goal of this research is to develop a data-driven clustering model which is aimed to identify the students who are at risk during the course cycle in early stages. A publicly accessible Open University (OU) dataset which consist of more than 30,000 students for 7 different courses, is used to build clustering model based on individual student’s behaviour in Virtual Learning Environment. This research was carried out using three unsupervised clustering algorithms, namely Gaussian Mixture, Hierarchical and K-prototypes. Models efficiency is measured using clustering evaluation metric to find the best fit model. Upon comparing the different models, the K-Prototype model clustered the at-risk students more accurately than the other proposed models and generated highly partitioned clusters. The outcome of the models can help the online instructors in distance learning universities to monitor the students based on the student online engagement in the VLEs and offer extra assistance to the students who are at-risk during the course cycle in early stages in order to minimize the drop-out rate.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	20 Jan 2021 18:08
Last Modified:	20 Jan 2021 18:08
URI:	https://norma.ncirl.ie/id/eprint/4411

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