Automating transparent learner profiling through explainable AI
摘要
With the continuous development of digital learning environments, the demand for providing transparency and explanations about learner profiles (LPs) in the digital era is constantly increasing. Digital learning traces play a fundamental role in understanding LP categories and justifying the reasoning behind their behavior, such as learners in difficulty, active/inactive learners, those in progress, and success-oriented vs. at-risk learners. However, effectively identifying LPs from the massive data generated by these environments is challenging due to its vulnerability to misunderstandings and biases. Moreover, stakeholder may not know what queries to ask or what patterns to look for in the data, while traditional Learning Analytics tools struggle to bridge the gap between granular digital traces and human-interpretable conceptual insights. To address this gap, we propose XAI-Profile, a framework that leverages Explainable AI (XAI) to transform raw learner data into interpretable, actionable profiles. XAI-Profile combines explainable machine learning models with interactive visual workflows to bridge between low-level data traces and high-level pedagogical insights. The framework is built on three pillars: (1) Goal-Oriented Requirements for capturing stakeholders’ needs by refining a problem into sub-problems using goal-oriented AND/OR refinement, and mapping them to the target LP with their data indicators. (2) Visual Analytics Design for mapping LP categories by automatically correlating them with trust objects (e.g., rule-based models, frequent sequences in the data), visualization goals, and analysis types. (3) Interaction and Actionability, a guided process enabling stakeholders to discover interesting and unexpected patterns, validate hypotheses, and drill into context-specific explanations. A case study using data from the