Harbingers of Collaboration? The Role of Early-Class Behaviors in Predicting Collaborative Problem Solving
Harbingers of Collaboration? The Role of Early-Class Behaviors in Predicting Collaborative Problem Solving
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DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Emma Mercier
中科院分区：
文献类型：
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作者：
Paul Hur;Nigel Bosch;L. Paquette;Emma Mercier
协作问题解决行为由于其无定形和动态的性质而难以识别和培养。在本文中，我们调查的价值，考虑早期的类时期的行为，小团体发展理论的基础上，建立预测机器学习模型的协作行为在解决问题。在12周的时间里，20个本科生小组在工程课程的50分钟讨论部分中，通过平板电脑上的数字联合问题空间工具解决了问题。我们注释了16，270组协作行为的视频剪辑，包括任务相关性，谈话内容，同伴互动，教学助理互动和平板电脑使用。我们从平板电脑日志文件数据中设计了两个特征子集：起始特征（从上课前十分钟计算出的早期协作解决问题的行为特征）和并发特征（整个上课期间的更一般的协作行为）。我们比较了机器学习模型中的onset、concurrent和onset + concurrent特征之间的准确性。结果在整个上课时间内呈现出U形的准确性模式，并表明单独的起始特征无法用于有效地建模整个上课时间内群体的协作行为。此外，分析未显示当发作特征与并发特征相结合时准确性显著增加的支持。最后，我们讨论了研究协作学习和开发软件，以促进协作的影响。
Collaborative problem solving behaviors are difficult to identify and foster due to their amorphous and dynamic nature. In this paper, we investigate the value of considering early class period behaviors, based on small group development theory, for building predictive machine learning models of collaborative behaviors during problem solving. Over 12 weeks, 20 small groups of undergraduate students solved problems facilitated by a digital joint problem space tool on tablet computers, in the 50-minute discussion component of an engineering course. We annotated 16,270 video clips of groups for collaborative behaviors including task relatedness, talk content, peer interaction, teaching assistant interaction, and tablet usage. We engineered two subsets of features from tablet log file data: onset features (early collaborative problem solving behavior characteristics calculated from the first ten minutes of the class) and concurrent features (more general collaborative behaviors from the whole class period). We compared accuracy between the onset, concurrent, and onset + concurrent features in machine learning models. Re-sults exhibited a U-shaped pattern of accuracy over class time, and showed that onset features alone could not be used to effectively model groups’ collaborative behaviors over the entire class time. Furthermore, analysis did not show support for significant gain in accuracy when onset features were combined with concurrent features. Finally, we discuss implications for studying collaborative learning and development of software to facilitate collaboration.
DOI：
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发表时间：
2017
期刊：
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影响因子：
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作者：
Zhiqiang Cai;Brendan R. Eagan;Nia Dowell;J. Pennebaker;A. Graesser;D. Shaffer
通讯作者：
Zhiqiang Cai;Brendan R. Eagan;Nia Dowell;J. Pennebaker;A. Graesser;D. Shaffer