利用过程挖掘技术识别聊天数据中的群体决策模型

P. Reimann, Jimmy Frèrejean, K. Thompson
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引用次数: 36

摘要

本文介绍了过程建模和挖掘作为CSCL过程分析的一种方法。这种方法特别适用于以项目为基础的协作学习,并在本研究中应用于处理复杂任务的团队的在线聊天数据。这些小组在成员数量和针对小组流程和任务需求的脚手架数量方面有所不同。使用启发式miner算法生成的模型表明,在任务要求方面接受更多指导的成员较少的小组比在小组过程中接受指导的小组具有更线性的决策过程,但两者都不是线性的,单一阶段模型的例子。这种方法既适用于CSCL的研究方法,也适用于为学生的决策过程提供反馈。
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Using process mining to identify models of group decision making in chat data
This paper introduces process modeling and mining as an approach to process analysis for CSCL. This approach is particularly relevant for collaborative learning that takes a project-based form, and is applied in this study to online chat data from teams working on a complex task. The groups differed in terms of the number of members and the amount of scaffolding aimed at group processes and task requirements. The models, produced using the HeuristicsMiner algorithm, showed that the group with fewer members that received more instruction in the task requirements had a more linear decision-making process than the group that received instruction in group processes, however neither were an example of a linear, unitary phase model. This approach has relevance both for CSCL research methods and for providing feedback to students on their decision-making processes.
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