"Your click decides your fate": Leveraging clickstream patterns from MOOC videos to infer students' information processing & attrition behavior

TL;DR

This paper introduces a behavioral metric derived from clickstream data in MOOCs to better understand student engagement, predict attrition, and improve learning outcomes by analyzing video interaction patterns.

Contribution

It operationalizes clickstream data into a cognitive-inspired index and models student dropout behavior using machine learning, offering new insights into MOOC student engagement.

Findings

Clickstream-based behavioral actions reveal distinct student engagement profiles.

The information processing index correlates with student retention and dropout.

Predictive models achieve improved accuracy in forecasting course attrition.

Abstract

With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. The hope is that this new surge of development will bring the vision of equitable access to lifelong learning opportunities within practical reach. MOOCs offer many valuable learning experiences to students, from video lectures, readings, assignments and exams, to opportunities to connect and collaborate with others through threaded discussion forums and other Web 2.0 technologies. Nevertheless, despite all this potential, MOOCs have so far failed to produce evidence that this potential is being realized in the current instantiation of MOOCs. In this work, we primarily 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 behavioral actions, and construct a quantitative information processing index, that can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illuminate the effectiveness of developing such a metric inspired by cognitive psychology, towards answering critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video dropouts. We leverage recurring click behaviors to differentiate distinct video watching profiles for students in MOOCs. Additionally, we discuss about prediction of complete course dropouts, incorporating diverse perspectives from statistics and machine learning, to offer a more nuanced view into how the second generation of MOOCs be benefited, if course instructors were to better comprehend factors that lead to student attrition.