This study presents how predictive analytics can be used to inform the formulation of adaptive collaborative learning groups in the context of Computer Supported Collaborative Learning considering across-spaces learning situations. During the study we have collected data from different learning spaces which depicted both individual and collaborative learning activity engagement of students in two different learning contexts (namely the classroom learning and distance learning context) and attempted to predict individual student’s future collaborative learning activity participation in a pyramid-based collaborative learning activity using supervised machine learning techniques. We conducted experimental case studies in the classroom and in distance learning settings, in which real-time predictions of student’s future collaborative learning activity participation were used to formulate adaptive collaborative learner groups. Findings of the case studies showed that the data collected from across-spaces learning scenarios is informative when predicting future collaborative learning activity participation of students hence facilitating the formulation of adaptive collaborative group configurations that adapt to the activity participation differences of students in real-time. Limitations of the proposed approach and future research direction are illustrated.

中文翻译：

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跨空间学习情况下自适应协作脚本的数据通知设计参数

本研究介绍了在考虑跨空间学习情况的计算机支持协作学习的背景下，如何使用预测分析为自适应协作学习小组的制定提供信息。在研究期间，我们从不同的学习空间收集了数据，这些数据描述了学生在两种不同学习环境（即课堂学习和远程学习环境）中的个人和协作学习活动参与情况，并试图预测单个学生未来的协作学习活动参与情况。使用监督机器学习技术的基于金字塔的协作学习活动。我们在课堂和远程学习环境中进行了实验案例研究，其中实时预测学生未来的协作学习活动参与情况，用于制定适应性协作学习小组。案例研究的结果表明，从跨空间学习场景中收集的数据在预测学生未来的协作学习活动参与时是有用的，因此有助于实时适应学生活动参与差异的自适应协作小组配置的制定。说明了所提出的方法和未来研究方向的局限性。案例研究的结果表明，从跨空间学习场景中收集的数据在预测学生未来的协作学习活动参与时具有信息性，从而有助于实时适应学生活动参与差异的自适应协作小组配置的制定。说明了所提出的方法和未来研究方向的局限性。案例研究的结果表明，从跨空间学习场景中收集的数据在预测学生未来的协作学习活动参与时具有信息性，从而有助于实时适应学生活动参与差异的自适应协作小组配置的制定。说明了所提出的方法和未来研究方向的局限性。