Refocusing the lens on engagement in MOOCs

R. W. Crues, Nigel Bosch, M. Perry, Lawrence Angrave, Najmuddin Shaik, S. Bhat
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引用次数: 23

Abstract

Massive open online courses (MOOCs) continue to see increasing enrollment and adoption by universities, although they are still not fully understood and could perhaps be significantly improved. For example, little is known about the relationships between the ways in which students choose to use MOOCs (e.g., sampling lecture videos, discussing topics with fellow students) and their overall level of engagement with the course, although these relationships are likely key to effective course implementation. In this paper we propose a multilevel definition of student engagement with MOOCs and explore the connections between engagement and students' behaviors across five unique courses. We modeled engagement using ordinal penalized logistic regression with the least absolute shrinkage and selection operator (LASSO), and found several predictors of engagement that were consistent across courses. In particular, we found that discussion activities (e.g., viewing forum posts) were positively related to engagement, whereas other types of student behaviors (e.g., attempting quizzes) were consistently related to less engagement with the course. Finally, we discuss implications of unexpected findings that replicated across courses, future work to explore these implications, and relevance of our findings for MOOC course design.
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重新聚焦mooc的参与度
大规模在线开放课程(MOOCs)的注册人数和被大学采用的人数不断增加,尽管人们还没有完全理解它,或许还可以大幅改进。例如,学生选择使用mooc的方式(例如,抽样讲座视频,与同学讨论主题)与他们对课程的整体参与程度之间的关系知之甚少,尽管这些关系可能是有效实施课程的关键。在本文中,我们提出了mooc学生参与的多层次定义,并探讨了五个独特课程的参与与学生行为之间的联系。我们使用具有最小绝对收缩和选择算子(LASSO)的有序惩罚逻辑回归对敬业度进行建模,并发现了几个跨球场一致的敬业度预测因子。特别是,我们发现讨论活动(例如,查看论坛帖子)与参与度呈正相关,而其他类型的学生行为(例如,尝试测验)始终与课程参与度降低相关。最后,我们讨论了跨课程复制的意外发现的含义,未来探索这些含义的工作,以及我们的发现与MOOC课程设计的相关性。
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