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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Peer Grading Tool for MOOCs on Programming
verfasst von : Zhaodong Wei, Wenjun Wu
Erschienen in: Intelligent Computation in Big Data Era
Verlag: Springer Berlin Heidelberg
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In massive open online courses (MOOCs), peer grading will play an important role to promote MOOCs development. In this paper, we develop a peer grading tool for programming courses on MOOCs. It is capable of dealing with large and diverse student population, and providing them with targeted subjective assessment. This tool firstly partition the submissions into small chunks to reduce the task of reviewers and give us flexibility to scale the code review process. Next we use code normalization and chunks clustering to assign similar chunks to the same student for increasing reviewer efficiency. Besides, the tool use a random allocation strategy and workload classification to assure reviewers workload balance while every student can get diverse feedback. Finally our evaluation experiments on a number of students in school indicate that the tool has achieved a significant improvement over the peer grading on MOOCs.