Online Learning and Analytics

Online learning platforms, especially Massive Open Online Courses, generate enormous quantities of behavioral data — clickstreams, forum posts, assessment scores, time-on-task — that researchers use to understand how students actually learn at scale. Educational data mining and learning analytics apply statistical and machine learning methods to this data in order to identify struggling students early, predict dropout, and uncover which instructional designs lead to better outcomes. The practical stakes are high: millions of learners worldwide enroll in MOOCs, yet completion rates often hover below ten percent, making accurate engagement models a meaningful lever for improvement. Active research questions include how to build predictive systems that generalize across different courses and populations without encoding existing inequities, and how to translate algorithmic insights into interventions that instructors can realistically act on.

Works

92,514

Total citations

773,270

Keywords

Educational Data MiningLearning AnalyticsMassive Open Online CoursesPredictive AnalysisStudent EngagementMachine Learning