Computer programming MOOCs attract people who have different motivations. Previous studies have hypothesized that the motivation declared before starting the course can be an important predictor of distinctive dropout rates. The aim of this study was to outline the main motivation clusters of participants in a computer programming MOOC, and to compare how these clusters differed in terms of intention to complete and actual completion rate. The sample consisted of 1,181 respondents to the pre-course questionnaire in the Introduction to Programming MOOC. A validated motivation scale, based on expectancy-value theory and k-means cluster analysis, was used to form the groups. The four identified clusters were named as Opportunity motivated (27.7%), Over-motivated (28.6%), Success motivated (19.6%) and Interest motivated (24.0%). Comparison tests and chi-square test were used to describe the differences among the clusters. There were statistically significant differences among clusters in self-evaluated probability of completion. Also, significant differences emerged among three clusters in terms of percentages of respondents who completed the MOOC. Interestingly, the completion rate was the lowest in the Over-motivated cluster. A statistically significant higher ratio of completers to non-completers was found in the Opportunity motivated, Success motivated, and Interest motivated clusters. Our findings can be useful for MOOC instructors, as a better vision of participants’ motivational profiles at the beginning of the MOOC might help to inform the MOOC design to better support different needs, potentially resulting in lower dropout rates.

中文翻译：

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积极性高的学习者是否更有可能完成计算机编程 MOOC？

计算机编程 MOOC 吸引了具有不同动机的人。以前的研究假设，在开始课程之前宣布的动机可能是独特辍学率的重要预测因素。本研究的目的是概述计算机编程 MOOC 参与者的主要动机集群，并比较这些集群在完成意图和实际完成率方面的差异。样本由 1,181 名参与编程入门 MOOC 课前问卷调查的受访者组成。使用基于期望值理论和 k 均值聚类分析的经过验证的动机量表来形成组。四个确定的集群被命名为机会动机 (27.7%)、过度动机 (28.6%)、成功动机 (19.6%) 和兴趣动机 (24.0%)。比较检验和卡方检验用于描述聚类之间的差异。集群之间的自我评估完成概率存在统计学上的显着差异。此外，就完成 MOOC 的受访者百分比而言，三个集群之间出现了显着差异。有趣的是，完成率在过度激励的集群中最低。在机会动机、成功动机和兴趣动机集群中，完成者与非完成者的比例在统计学上显着提高。我们的研究结果可能对 MOOC 教师有用，因为在 MOOC 开始时更好地了解参与者的动机概况可能有助于为 MOOC 设计提供信息，以更好地支持不同的需求，从而可能降低辍学率。