Modeling Temporal Association of Cognition-Topic in MOOC Discussion to Track Learners' Cognitive Engagement Dynamics

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI:10.1145/3430895.3460170

Zhi Liu, R. Mu, Shiqi Liu, Xian Peng, Sannyuya Liu

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引用次数: 2

Abstract

In the discussion forums of massive open online courses (MOOCs), cognitive processing (e.g., insight, certain) is considered an essential factor that can affect learners' learning outcomes, but the relationship between them has not been thoroughly investigated. Especially the dynamic nature of cognitive processing is still a significant research gap. In this study, we proposed an unsupervised topic model named Temporal Cognitive Topic Model (TCTM) to automatically classify cognitive processes and obtain the conditional probability with topics over time. The results indicated that completers had more active and timely cognitive engagement as time went on and tended to use certain cognitive words to discuss the topics related to the examination and certificates, which showed that they had explicit learning goals and plans. Non-completers often used exclusive cognitive words to discuss some off-task content that pointed out a distractive learning process. Using the model, teachers can capture learners' dynamic cognitive states and associated topics to improve teaching methods and increase course completion rates.

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模拟MOOC讨论中认知-话题的时间关联，追踪学习者的认知参与动态

在大规模在线开放课程(MOOCs)的论坛中，认知加工(如insight、certain)被认为是影响学习者学习成果的重要因素，但它们之间的关系尚未得到深入的研究。特别是动态性质的认知加工仍然是一个显著的研究空白。在本研究中，我们提出了一种无监督主题模型——时间认知主题模型(Temporal Cognitive topic model, TCTM)，用于对认知过程进行自动分类，并获得随时间变化的主题条件概率。结果表明，随着时间的推移，完成者的认知投入更加积极和及时，并且倾向于使用一定的认知词汇来讨论与考试和证书相关的话题，这表明他们有明确的学习目标和计划。未完成者经常使用专有的认知词汇来讨论一些任务外的内容，这些内容指出了一个分散注意力的学习过程。使用该模型，教师可以捕捉学习者的动态认知状态和相关主题，从而改进教学方法，提高课程完成率。

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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Proceedings of the Eighth ACM Conference on Learning @ Scale

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