Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students
This research addresses the challenge of student heterogeneity in MOOCs for educators and platform designers, but it is incremental as it builds on existing methods with new data.
The study tackled the problem of understanding diverse student behaviors in MOOCs by analyzing a large dataset from FutureLearn, using clustering and comparative analysis to reveal hidden patterns in student activities and demographics, which can inform adaptive strategies for improved MOOC experiences.
Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex "big data" from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns related to student diversity. Using a large dataset from a MOOC offered by FutureLearn, we delve into a new way of investigating hidden patterns through both machine learning and statistical modelling. In this paper, we report on clustering analysis of student activities and comparative analysis on both behavioral patterns and demographical patterns between student subpopulations in the MOOC. Our approach allows for a deeper understanding of how MOOC students behave and achieve. Our findings may be used to design adaptive strategies towards an enhanced MOOC experience