Toward Trait-Aware Learning Analytics

Abstract

Learning analytics (LA) draws from the learning sciences to interpret learner behavior and inform system design. Yet, past personalization remains largely at the content or performance level (during learner-system interactions), overlooking relatively stable individual differences such as personality (unfolding over long-term learning trajectories such as college degrees). The latter could bring underappreciated benefits to the design, implementation, and impact of LA. In this position paper, we conduct an ad hoc literature review and argue for an expanded framing of LA that centers on learner traits as key to both interpreting and designing close-the-loop experiments in LA. We show that personality traits are relevant to LA's central outcomes (e.g., engagement and achievement) and conducive to action, as their established ties to human-computer interaction (HCI) inform how systems time, frame, and personalize support. Drawing inspiration from HCI, where psychometrics inform personalization strategies, we propose that LA can evolve by treating traits not only as predictive features but as design resources and moderators of analytics efficacy. In line with past position papers published at LAK, we present a research agenda grounded in the LA cycle and discuss methodological and ethical challenges.