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Assessing program-level learning strategies in MOOCs

机译:评估MOOCS中的计划级学习策略

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摘要

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.
机译:大规模开放的在线课程为研究人员提供了相当大的机会,可以在技术介导的在线环境中评估学生学习。然而,对于大多数报道的研究,分析是从单个MOOC或多个MOOC与不同学习者入学进行的。因此,这限制了评估学习者的变化行为的机会随着时间的推移。在本文中,审查了175名学生的行为参与,他被纳入了由四种不同的MOOCS组成的专业开发研究计划,包括完整的学习计划。进行了全面的分析,以了解每个课程中学习者的不断变化的行为,并跨MOOC计划。为此,我们使用了潜在的类分析,一种广泛用于其他领域的聚类技术,但在教育技术研究中大多是未开发的。我们的业绩根据学生课程参与和三种不同的计划级别学习策略揭示了六个每周学习策略,而且其学习行为不仅不同,而且还有其最终的学术成果。我们的研究还表明MooC课程设计对课程中的学生参与水平的实质性影响。进一步讨论了结果和影响。

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