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Utilizing Multimodal Data Through fsQCA to Explain Engagement in Adaptive Learning

机译：通过FSQCA利用多模数据数据来解释自适应学习的参与

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摘要

Investigating and explaining the patterns of learners’ engagement in adaptive learning conditions is a core issue towards improving the quality of personalized learning services. This article collects learner data from multiple sources during an adaptive learning activity, and employs a fuzzy set qualitative comparative analysis (fsQCA) approach to shed light to learners’ engagement patterns, with respect to their learning performance. Specifically, this article measures and codes learners’ engagement by fusing and compiling clickstreams (e.g., response time), physiological data (e.g., eye-tracking, electroencephalography, electrodermal activity), and survey data (e.g., goal-orientation) to determine what configurations of those data explain when learners can attain high or medium/low learning performance. For the evaluation of the approach, an empirical study with 32 undergraduates was conducted. The analysis revealed six configurations that explain learners’ high performance and three that explain learners’ medium/low performance, driven by engagement measures coming from the multimodal data. Since fsQCA explains the outcome of interest itself, rather than its variance, these findings advance our understanding on the combined effect of the multiple indicators of engagement on learners’ performance. Limitations and potential implications of the findings are also discussed.
机译：调查和解释学习者在自适应学习条件下的参与模式是提高个性化学习服务质量的核心问题。本文在自适应学习活动期间收集来自多个来源的学习者数据，并采用模糊集的定性比较分析（FSQCA）方法来揭示学习者的参与模式，以及他们的学习表现。具体而言，本文通过融合和编译单击流（例如，响应时间），生理数据（例如，眼睛跟踪，脑电平衡，电台活动）和调查数据（例如，目标导向）来衡量学习者的参与，以确定什么这些数据的配置解释了学习者可以获得高或中/低学习性能。为了评估该方法，进行了3​​2人本科生的实证研究。该分析揭示了六种配置，解释了学习者的高性能和三个，解释了学习者的中/低性能，由来自多模式数据的参与措施驱动。由于FSQCA解释了利益本身的结果，而不是其差异，这些调查结果推进了我们对学习者绩效的多个指标的综合影响的理解。还讨论了调查结果的限制和潜在影响。

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