A data-driven analysis of student efforts and improvements on a SPOC experiment
ACM TUR-C(2017)
摘要
In recent years, Massive Open Online Courses (MOOCs) increasingly attract the attention from all circles. MOOCs provide students with abundant learning resources, which are also utilized by colleges to improve the effect of teaching. Moreover, Small Private Online Course (SPOC) and flipped classroom have been proved to be effective in some STEM courses. In this paper, we conduct a SPOC experiment on Data Structures and Algorithms . We leverage both online and offline data to analyze the efforts and improvements of students, including comparing test scores between different classes, collecting student information from questionnaires and exploring the online learning behaviors of students. We also discuss and explain our findings according to the experience which we get from the teaching experience.
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