קובי גל

Massive Open Online Courses (MOOCs) are often plagued by a low level of student engagement and retention, with many students dropping out before completing the course. In an effort to improve student retention, educational researchers are increasingly turning to the latest Machine Learning (ML) models to predict student learning outcomes, based on which instructors can provide timely support to at-risk students as the progression of a course. Though achieving a high prediction accuracy, these models are often “black-box” models, making it difficult to gain instructional insights from their results, and accordingly, designing meaningful and actionable interventions remains to be challenging in the context of MOOCs. To tackle this problem, we present an innovative approach based on Hidden Markov Model (HMM). We devoted our efforts to model students’ temporal interaction patterns in MOOCs in a transparent and interpretable manner, with the aim of empowering instructors to gain insights about actionable interventions in students’ next-step learning activities. Through extensive evaluation on two large-scale MOOC datasets, we demonstrated that, by gaining a temporally grounded understanding of students’ learning processes using HMM, both the students’ current engagement state and potential future state transitions could be learned, and based on which, an actionable next-step intervention tailored to the student current engagement state could be formulated to recommend to students. These findings have strong implications for real-world adoption of HMM for promoting student engagement and preempting dropouts.