Massive Open Online Courses have provided researchers with considerable opportunities to assess student learning within technically mediated online environments. However, for most reported studies, analyses are conducted from a single MOOC or over multiple MOOCs with different learner enrolments. Thus, this limits the opportunities to assess the changing behavior of learners over time. In this paper, the behavioral engagement of 175 students was examined, who were enrolled in a professional development study program consisting of four different MOOCs, comprising a complete program of study. A comprehensive analysis was conducted to understand the changing behavior of learners within each course and across the MOOC program. To do this, we used latent class analysis, a clustering technique widely used in other fields, but mostly unexplored within educational technology research. Our results revealed six weekly learning strategies based on student course engagement and three different program-level learning strategies, which not only differed in their learning behavior but also their final academic outcomes. Our study also showed substantial effects of MOOC course design on the level of student engagement in the courses. The results and implications are further discussed.

大规模的在线公开课程为研究人员提供了大量机会，可在技术上介导的在线环境中评估学生的学习情况。但是，对于大多数已报道的研究，分析是从单个MOOC或具有不同学习者注册的多个MOOC进行的。因此，这限制了评估随时间变化的学习者行为的机会。在本文中，对175名学生的行为参与进行了检查，他们参加了由四个不同的MOOC组成的专业发展研究计划，其中包括完整的学习计划。进行了全面的分析，以了解每个课程内以及整个MOOC计划中学习者的行为变化。为此，我们使用了潜在类别分析，这是一种在其他领域广泛使用的聚类技术，但在教育技术研究中却大多尚未得到开发。我们的结果显示了基于学生课程参与度的六种每周学习策略以及三种不同的程序级学习策略，这些策略不仅在学习行为上有所不同，而且在最终的学业上也有所不同。我们的研究还表明，MOOC课程设计对学生参与课程的水平有实质性影响。结果和含义将进一步讨论。