Evaluating Learner Representations for Differentiation Prior to Instructional Outcomes

• URL: http://arxiv.org/abs/2604.05848v1
• Date: Tue, 07 Apr 2026 13:13:47 GMT
• Title: Evaluating Learner Representations for Differentiation Prior to Instructional Outcomes
• Abstract summary: This work examines how to evaluate learner representations based on whether they retain separation between learners under a shared comparison rule.<n>We introduce distinctiveness, a representation-level measure that evaluates how each learner differs from others in the cohort using pairwise distances.<n>Results show that learner-level representations yield higher separation, stronger clustering structure, and more reliable pairwise discrimination than interaction-level representations.
• Score: 1.6637373649145608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learner representations play a central role in educational AI systems, yet it is often unclear whether they preserve meaningful differences between students when instructional outcomes are unavailable or highly context-dependent. This work examines how to evaluate learner representations based on whether they retain separation between learners under a shared comparison rule. We introduce distinctiveness, a representation-level measure that evaluates how each learner differs from others in the cohort using pairwise distances, without requiring clustering, labels, or task-specific evaluation. Using student-authored questions collected through a conversational AI agent in an online learning environment, we compare representations based on individual questions with representations that aggregate patterns across a student's interactions over time. Results show that learner-level representations yield higher separation, stronger clustering structure, and more reliable pairwise discrimination than interaction-level representations. These findings demonstrate that learner representations can be evaluated independently of instructional outcomes and provide a practical pre-deployment criterion using distinctiveness as a diagnostic metric for assessing whether a representation supports differentiated modeling or personalization.

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