arXiv cs.CY· David Otoo-Arthur, Terence L. van Zyl·· 4 天前AI 评分12
在线学习中的纵向学习者参与画像：不等观察窗口下的异质共识聚类
Longitudinal Learner Participation Profiling in Online Learning: Heterogeneous Consensus Clustering under Unequal Observation Windows
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研究基于南非一所公立大学 152 门课程、4,599 名学习者的 16,448 条完整学习者—课程序列，评估了八种参与度指标，发现即便表示为比率或占比，所有指标仍与观察窗口长度相关。
正文 · 原文
Abstract:Learning management system traces are clustered to identify participation profiles, but unequal observation windows may cause profiles to reflect duration rather than participation alone. Using 16,448 complete learner--course sequences from 4,599 learners across 152 courses at a South African public university, we evaluated eight participation measures. All remained related to observation-window length even when expressed as rates or shares. Course-grouped adjustment reduced these relationships. Five retained measures were evaluated using K-means, Gaussian mixtures, Ward agglomerative clustering and K-medoids, with standardised measures and principal-component summaries. Evidence supported a broad two-profile grouping and a more detailed adjusted four-profile consensus: Gradually declining, Early-concentrated, Cyclical variable and Sustained active. Profile membership accounted for 2.87% of the variation in duration in the primary consensus, versus 38.18% using original measures. Mean assignment support was 0.739; 8.35% of assignments fell below 0.50, with greatest uncertainty in Sustained active. Two week-by-week sequence analyses agreed strongly with each other but only modestly with the four-profile consensus, indicating that the profiles combined multiple aspects of participation rather than distinct sequence patterns. Profiling should therefore assess duration effects, compare clustering methods and participation summaries, and report assignment consistency.
| Comments: | Accepted for presentation at SACAIR 2026 and publication in the conference proceedings |
| Subjects: | Machine Learning (cs.LG); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.17494 [cs.LG] |
| (or arXiv:2610.17494v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17494 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: David Otoo-Arthur [view email]
[v1]
Mon, 12 Oct 2026 23:33:20 UTC (61 KB)
来源：arXiv cs.CY · arxiv.org