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Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques

Al-Shabandar, R, Hussain, A, Liatsis, P and Keight, R Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques. IEEE Access. ISSN 2169-3536 (Accepted)

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Abstract

Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in at-risk student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-risk students.

Item Type: Article
Additional Information: © 20X19 IEEE.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 24 Sep 2019 09:33
Last Modified: 24 Sep 2019 09:33
URI: http://researchonline.ljmu.ac.uk/id/eprint/11373

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