研究结对编程中协同问题解决的多模态分析

Proceedings of the Sixth International Conference on Learning Analytics & Knowledge Pub Date : 2016-04-25 DOI:10.1145/2883851.2883877

Shuchi Grover, M. Bienkowski, Amir Tamrakar, Behjat Siddiquie, David A. Salter, Ajay Divakaran

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引用次数: 47

摘要

协作解决问题(CPS)被视为K-12教育中的一项关键技能——在计算机科学和其他学科中都是如此。向孩子们介绍计算机的努力依赖于结对编程，作为一种让年轻学习者参与CPS的方式。高质量协作的特征是共同探索或理解、共同表述和共同执行。我们提出了一种数据驱动的方法，通过多模态学生行为和表现数据建模来评估和阐明协作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multimodal analytics to study collaborative problem solving in pair programming

Collaborative problem solving (CPS) is seen as a key skill in K-12 education---in computer science as well as other subjects. Efforts to introduce children to computing rely on pair programming as a way of having young learners engage in CPS. Characteristics of quality collaboration are joint exploring or understanding, joint representation, and joint execution. We present a data driven approach to assessing and elucidating collaboration through modeling of multimodal student behavior and performance data.

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Proceedings of the Sixth International Conference on Learning Analytics & Knowledge

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