arXiv cs.CY· Hitoshi Inoue, Koichi Yasutake·· 3 天前AI 评分15
OULAD 22 门课程研究：异步在线学习中的参与敏感收敛与“先分裂后收敛”模式
Participation-Sensitive Convergence and the Fragment First, Converge Later Pattern in Asynchronous Online Learning: A Topological Analysis Across 22 OULAD Courses
AI 导读
一项覆盖 OULAD 全部 22 门课程（N > 22,000，857 个周对）的研究发现，Zigzag Persistent Homology 得到的 β₀ 与活跃学习者数量强共变（合并 r = 0.387，逐课程 r_delta 中位数 0.459，20/22 门课程），是参与敏感的结构指标。
正文 · 原文
Abstract:Asynchronous online learning offers temporal flexibility at a structural cost: learning communities tend to fragment rather than cohere. $\beta_0$, the number of disconnected behavioral clusters from Zigzag Persistent Homology, serves as a cohort-level indicator of this structure. Two questions remained unverified at scale: (1) does apparent $\beta_0$ convergence reflect genuine behavioral alignment or learner dropout? and (2) do assessment deadlines produce reproducible fragmentation-convergence cycles? We address both across all 22 OULAD courses (N > 22,000; 857 week-pairs). Changes in $\beta_0$ strongly co-vary with active learner changes (pooled r = 0.387; median per-course r_delta = 0.459, 20/22 courses), identifying $\beta_0$ as a participation-sensitive indicator: $\beta_0$ and active learner counts co-respond to deadline events rather than one causing the other. Deadlines produced fragmentation in 82.6% of assessments and the full Fragment First, Converge Later (FFCL) cycle in 60.2%. 3-phase analysis confirmed structural fragmentation as the dominant long-term trajectory (90.9% of courses), moderated by curriculum structure. These findings establish $\beta_0$ as a participation-sensitive structural indicator with direct implications for AI-augmented learning analytics design.
| Comments: | Author's version, posted under the non-commercial rights retained in the APSCE copyright transfer agreement. v2: method description corrected - epsilon = 1.5 (not 1.00), connected components computed directly (no persistent-homology library), beta0 over weekly-active learners (not a fixed cohort). Results unchanged |
| Subjects: | Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01738 [cs.CY] |
| (or arXiv:2610.01738v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01738 arXiv-issued DOI via DataCite |
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| Journal reference: | Proceedings of ICLEA 2026: 2nd International Conference on Learning Evidence and Analytics, Asia-Pacific Society for Computers in Education, 2026 |
Submission history
From: Hitoshi Inoue [view email]
[v1]
Thu, 1 Oct 2026 14:10:36 UTC (340 KB)
[v2]
Wed, 14 Oct 2026 11:25:52 UTC (888 KB)
来源：arXiv cs.CY · arxiv.org