ABSTRACT Researchers have been interested in classifying massive open online course (MOOC) students based on their learning behaviors. However, less attention has been paid to the cognitive attributes associated with various learning behaviors. In this study, we propose a conceptual model that links MOOC students’ observable learning behaviors to their latent attributes (i.e., individual learning versus interactive learning). Using students’ behavior data from a MOOC, we performed a cognitive diagnostic analysis to identify the students’ learning profiles and to determine how these profiles related to their course achievement. We found that a large portion of the students performed individual learning whereas only a very small portion of them overtly performed interactive learning. In addition, the students who performed interactive learning were more likely to pass the course with distinction than the students who did not show this attribute. The results of this study have important implications for improving students’ learning in MOOCs. Further, the study provides a good demonstration of how to use clickstream process data for psychometric analysis.

中文翻译：

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个人学习对比 互动学习：MOOC学生学习行为的认知诊断分析

摘要 研究人员一直有兴趣根据学生的学习行为对大规模开放在线课程 (MOOC) 学生进行分类。然而，对与各种学习行为相关的认知属性的关注较少。在这项研究中，我们提出了一个概念模型，将 MOOC 学生的可观察学习行为与其潜在属性（即个人学习与互动学习）联系起来。使用来自 MOOC 的学生行为数据，我们进行了认知诊断分析，以确定学生的学习概况，并确定这些概况如何与他们的课程成绩相关。我们发现很大一部分学生进行了个人学习，而只有很小一部分学生公开进行了互动学习。此外，进行互动学习的学生比没有表现出这种属性的学生更有可能以优异的成绩通过课程。本研究的结果对提高学生在 MOOC 中的学习具有重要意义。此外，该研究很好地展示了如何使用点击流过程数据进行心理测量分析。