Multimodal analytics to study collaborative problem solving in pair programming

Shuchi Grover, M. Bienkowski, Amir Tamrakar, Behjat Siddiquie, David A. Salter, Ajay Divakaran
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引用次数: 47

Abstract

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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研究结对编程中协同问题解决的多模态分析
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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