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.

中文翻译：

---

通过fsQCA利用多模态数据来说明参与式自适应学习

调查和解释学习者在适应性学习条件下的参与方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中从多个来源收集学习者数据，并采用模糊集定性比较分析（fsQCA）方法为学习者的学习方式提供了参考。具体而言，本文通过融合和汇总点击流（例如，响应时间），生理数据（例如，眼动，脑电图，皮肤电活动）和调查数据（例如，目标定向）来确定并确定学习者的参与度，并以此为编码标准这些数据的配置说明了学习者何时可以达到高或中/低学习性能。为了评估方法，对32名大学生进行了实证研究。该分析揭示了六种配置，这些配置解释了学习者的高性能，而三种配置则解释了学习者的中/低性能，这是由多模态数据中的参与度指标驱动的。由于fsQCA解释了兴趣本身而不是差异的结果，因此这些发现提高了我们对参与度多个指标对学习者表现的综合影响的理解。还讨论了发现的局限性和潜在含义。这些发现而不是其差异，使我们对参与度的多个指标对学习者表现的综合影响有了更深入的了解。还讨论了发现的局限性和潜在含义。这些发现而不是其差异，使我们对参与度的多个指标对学习者表现的综合影响有了更深入的了解。还讨论了发现的局限性和潜在含义。