Your click decides your fate: Inferring Information Processing and Attrition Behavior from MOOC Video Clickstream Interactions
Tanmay Sinha, Patrick Jermann, Nan Li, Pierre Dillenbourg
TL;DR
This paper develops a cognitive-inspired metric from MOOC video clickstream data to understand student engagement, predict dropout behavior, and improve learning outcomes.
Contribution
It introduces a novel information processing index based on clickstream data, linking cognitive psychology to online learning analytics.
Findings
The index correlates with student engagement levels.
It predicts future click interactions and dropout risk.
Insights can inform targeted interventions.
Abstract
In this work, we explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into cognitively plausible higher level behaviors, and construct a quantitative information processing index, which can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illustrate how such a metric inspired by cognitive psychology can help answer critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video & course dropouts. Implications for research and practice are discussed
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