Detecting at-risk students with early interventions using machine learning techniques

Raghad Al-Shabandar, Abir Jaafar Hussain, Panos Liatsis, Robert Keight

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageBritish English
Article number8847304
Pages (from-to)149464-149478
Number of pages15
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • area under curve
  • Machine learning
  • massive open online courses
  • receiver operator characteristics

Fingerprint

Dive into the research topics of 'Detecting at-risk students with early interventions using machine learning techniques'. Together they form a unique fingerprint.

Cite this