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Mapping Learner Engagement in Neuro-Adaptive Systems: A Q Methodology Study of Flow, Load, and Stress

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  • Mohamed Amir Omezzine

    (EESC-GEM - Grenoble Ecole de Management)

  • Maissa Obay

Abstract

This study investigates how adaptive learning systems, powered by Bayesian Knowledge Tracing and augmented with physiological monitoring, regulate cognitive effort and engagement across digital and non-digital quiz formats. We introduce the Cognitive Elasticity Model, extending Flow Theory and Cognitive Load Theory, to conceptualize how adaptive systems stretch learner effort toward an optimal zone of engagement. A mixed-methods design combines a controlled experiment (N = 180) with Q methodology to map subjective learner profiles across three quiz modalities: adaptive Moodle, static Moodle, and paper-based. Physiological indicators—EEG (frontal theta/alpha ratio) and ECG (heart rate variability)—complement self-reports of flow, cognitive load, and stress. Q-sorting reveals distinct engagement-effort configurations, offering new insight into how learners perceive and respond to adaptive systems. Our contribution is both theoretical—refining non-linear models of cognitive regulation—and pedagogical—proposing a replicable framework for mediating scientific insight into classroom decision-making through neuro-instrumentation and learner-centered profiling.

Suggested Citation

  • Mohamed Amir Omezzine & Maissa Obay, 2026. "Mapping Learner Engagement in Neuro-Adaptive Systems: A Q Methodology Study of Flow, Load, and Stress," Post-Print hal-05789339, HAL.
    • Handle: RePEc:hal:journl:hal-05789339
    Note: View the original document on HAL open archive server: https://hal.science/hal-05789339v1
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