Task Assignment of Peer Grading in MOOCs

Yong Han, Wenjun Wu, Yanjun Pu

Abstract: In a massive online course with hundreds of thousands of students, it is unfeasible to provide an accurate and fast evaluation for each submission. Currently the researchers have proposed the algorithms called peer grading for the richly-structured assignments. These algorithms can deliver fairly accurate evaluations through aggregation of peer grading results, but not improve the effectiveness of allocating submissions. Allocating submissions to peers is an important step before the process of peer grading. In this paper, being inspired from the Longest Processing Time (LPT) algorithm that is often used in the parallel system, we propose a Modified Longest Processing Time (MLPT), which can improve the allocation efficiency. The dataset used in this paper consists of two parts, one part is collected from our MOOCs platform, and the other one is manually generated as the simulation dataset. We have shown the experimental results to validate the effectiveness of MLPT based on the two type datasets.