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arXiv Machine Learning By Hitoshi Inoue, Koichi Yasutake

Participation-Sensitive Convergence and the Fragment First, Converge Later Pattern in Asynchronous Online Learning: A Topological Analysis Across 22 OULAD Courses

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The study analyzes 22 OULAD asynchronous online courses (over 22,000 learners) using Zigzag Persistent Homology to track the number of disconnected behavioral clusters, denoted $eta_0$. It finds that changes in $eta_0$ strongly co‑vary with active learner counts, indicating that $eta_0$ is a participation‑sensitive indicator rather than a cause of dropout. Assessment deadlines trigger fragmentation in 82.6% of cases and the full Fragment First, Converge Later cycle in 60.2%, with long‑term fragmentation dominating 90.9% of courses.

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