发表机构
Nakamura Gakuen University; Hiroshima University（中村学园大学; 广岛大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过22门OULAD课程的同调分析，验证β0为参与敏感指标，发现截止日期引发先碎片后收敛模式，为AI学习分析提供结构依据。
AI 中文摘要
异步在线学习在结构上以牺牲社区凝聚力为代价提供时间灵活性：学习社区倾向于碎片化而非凝聚。Zigzag持久同调得到的断开行为簇数量β0作为该结构的队列级指标。两个问题在大规模上仍未验证：（1）表观β0收敛是否反映真实行为对齐还是学习者辍学？（2）评估截止日期是否产生可复现的碎片化-收敛周期？我们在全部22门OULAD课程（N>22,000；857个周对）中解决这两个问题。β0的变化与活跃学习者变化强烈共变（合并r=0.387；每课程中位数r_delta=0.459，20/22门课程），将β0识别为参与敏感指标：β0和活跃学习者计数对截止日期事件共同响应而非因果关系。截止日期在82.6%的评估中产生碎片化，完整先碎片后收敛（FFCL）周期占60.2%。三阶段分析确认结构碎片化为主导长期轨迹（90.9%的课程），受课程结构调节。这些发现确立β0为参与敏感结构指标，对AI增强学习分析设计有直接影响。
英文摘要
Asynchronous online learning offers temporal flexibility at a structural cost: learning communities tend to fragment rather than cohere. $β_0$, 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 $β_0$ 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 $β_0$ strongly co-vary with active learner changes (pooled r = 0.387; median per-course r_delta = 0.459, 20/22 courses), identifying $β_0$ as a participation-sensitive indicator: $β_0$ 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 $β_0$ as a participation-sensitive structural indicator with direct implications for AI-augmented learning analytics design.
CommentsAuthor's version, posted under the non-commercial rights retained in the APSCE copyright transfer agreement
Journal refProceedings of ICLEA 2026: 2nd International Conference on Learning Evidence and Analytics, Asia-Pacific Society for Computers in Education, 2026
