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<p>Educational research on student affect, particularly math anxiety, has long depended on static, subjective self-report questionnaires. However, these methods are insufficient for capturing the dynamic, real-time fluctuations of learning and affective interaction. This paper addresses this limitation by proposing a novel conceptual framework that re-conceptualizes Technology-Enhanced Learning (TEL) environments as multimodal “sensors.” We advocate for a methodological shift from static outcome measurement to Dynamic Systems Modeling. Specifically, the framework integrates behavioral data streams (e.g., system log-files, response latency) with objective psychophysiological data (e.g., Galvanic Skin Response [GSR], Heart Rate Variability [HRV]) to model affective states as they unfold. We introduce a strategy where physiological sensors serve as a “ground truth” calibration tool for training algorithms to detect anxiety solely through digital traces. Using math anxiety as a case study, this paper outlines how this dynamic approach provides a granular understanding of the interplay between cognition and emotion. This contribution is critical for the development of next-generation, context-aware adaptive learning systems, enabling personalized pedagogical interventions that respond to a student’s affective state in real-time.</p>

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From static reports to dynamic sensors: a conceptual framework for modeling math anxiety in technology-enhanced learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Livanina Moukatzi,
  • Georgios Eleftherakis,
  • Nicholas Zaranis

摘要

Educational research on student affect, particularly math anxiety, has long depended on static, subjective self-report questionnaires. However, these methods are insufficient for capturing the dynamic, real-time fluctuations of learning and affective interaction. This paper addresses this limitation by proposing a novel conceptual framework that re-conceptualizes Technology-Enhanced Learning (TEL) environments as multimodal “sensors.” We advocate for a methodological shift from static outcome measurement to Dynamic Systems Modeling. Specifically, the framework integrates behavioral data streams (e.g., system log-files, response latency) with objective psychophysiological data (e.g., Galvanic Skin Response [GSR], Heart Rate Variability [HRV]) to model affective states as they unfold. We introduce a strategy where physiological sensors serve as a “ground truth” calibration tool for training algorithms to detect anxiety solely through digital traces. Using math anxiety as a case study, this paper outlines how this dynamic approach provides a granular understanding of the interplay between cognition and emotion. This contribution is critical for the development of next-generation, context-aware adaptive learning systems, enabling personalized pedagogical interventions that respond to a student’s affective state in real-time.