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

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
completionratearemajorconcerns.Inthispaper,theearlyidentificationofstudentswho areatriskofwithdrewandfailure 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 resultrevealsthat allclassifiersgaingood accuracyacrossboth models, the highestperformance yield by GBM
withthevalueof0.894,0.952 forfirst,second modelrespectively,whileRFyieldthevalueof0.866,inat-riskstudent framework achieved
the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-
risk students.