arXiv：cs.LG（机器学习，全量分类）· Hitoshi Inoue, Koichi Yasutake·· 15 小时前AI 评分25
TopoLA：面向解释不确定性的拓扑学习分析仪表盘架构与设计原则
Designing for Interpretation Uncertainty: Architecture and Principles for Topological Learning Analytics Dashboards
AI 导读
研究者开发了 TopoLA 仪表盘系统，将 Zigzag Persistent Homology 应用于学习管理系统数据，并提出三条面向新兴分析的解释支持设计原则：客观测量与情境解释分离、从指标到模式再到反思提示的渐进披露、明确承认方法论不确定性。系统采用特征提取、拓扑计算、解释支持三阶段模块化流水线，可扩展至其他分析方法。
正文
Abstract:Topological Data Analysis (TDA) offers novel methods for understanding temporal dynamics in complex systems, yet its application in information systems design faces a fundamental challenge: how should systems present analytical outputs when interpretation frameworks are still developing? This paper reports on the development of TopoLA, a dashboard system applying Zigzag Persistent Homology to learning management system data, and proposes three early design principles for interpretation support in emerging analytics: (1) separation of objective measurement from contextual interpretation, (2) graduated disclosure from metrics through patterns to reflective prompts, and (3) explicit acknowledgment of methodological uncertainty. The system implements a modular three-stage pipeline--feature extraction, topological computation, and interpretation support--enabling extension to additional analytical methods. This work contributes to information systems research by articulating preliminary design knowledge for systems that must communicate analytical insights from methods lacking established interpretation norms--a challenge increasingly common as novel computational techniques enter applied domains.
| Comments: | Author's version, posted under the preprint/reprint distribution rights retained in the IADIS copyright transfer agreement |
| Subjects: | Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01749 [cs.CY] |
| (or arXiv:2610.01749v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01749 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | Proceedings of the IADIS International Conference Information Systems 2026, pp. 506-510, IADIS, 2026 |
Submission history
From: Hitoshi Inoue [view email]
[v1]
Thu, 1 Oct 2026 14:16:53 UTC (414 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org