Responsible Institutional Analytics: Interpreting Bias with AI Support
Francielle Marques · Ariel Ortiz-Beltr\'an · Ishari Amarasinghe · Davinia Hern\'andez-Leo
机构分析（Institutional Analytics, IA）仪表盘为高等教育中的决策提供依据，但数据局限性、分析方法的约束以及上下文信息的缺失常影响其解读。为支持更负责任的 IA 解读，提出 FACTRIA 框架，将潜在偏差因素归纳为四个维度：分析流程、机构背景、课程特征与人口统计。以 FACTRIA 作为生成式 AI 聊天机器人的输入，引导用户在分析 IA 时反思这些因素。基于四个真实 IA 案例的定性研究结合转换网络分析表明，聊天机器人能促使参与者识别被忽视的因素如何影响其初始解读。研究显示，将结构化框架与 AI 引导相结合，可增强机构数据解读的上下文感知能力与责任性。
尚无解读。
arXiv:2610.07205v1 Announce Type: cross Abstract: Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and missing contextual information often affect their interpretation. To support more responsible interpretation of IA, we introduce FACTRIA, a framework that organizes potential biasing factors across four areas: the analytics pipeline, institutional context, course-level characteristics, and demographics. We used the FACTRIA framework as input to a generative-AI chatbot designed to prompt users to reflect on these factors while analyzing IA. A qualitative study with stakeholders, drawing on four authentic IA cases, and a transition network analysis showed that the chatbot prompted participants to recognize how overlooked factors influenced their initial interpretation. Findings indicated that combining a structured framework with AI-based guidance can enhance context-aware, responsible interpretation of institutional data.
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