| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 105 === Due to huge participant sizes in Massive Open Online Courses (MOOCs), peer grading is practically the only existing solution to grading high-level assignments. One of the main issues of utilizing peer grading in MOOCs is that learners are not motivated and do not spend enough effort in grading. To modify current peer grading mechanism to induce better grading, we focus on the idea of making the learners'' grade depend on the accuracy of their grading of others'' work. We built a game theoretical model to characterize the rational behavior of learners in such a mechanism. We found a set of conditions which guarantees existence of pure-strategy equilibria. When the conditions are satisfied, the course designer can encourage the learners to spend more time on grading through tuning the mechanism parameters. Furthermore, when the learners are assumed to be homogeneous, we proved that in any pure equilibrium, any submitted work will be graded with identical effort by the relevant graders. With this property all the possible pure equilibria are theoretically calculable. We also extended our result to the case where some of the learners are not strategic or rational. We discussed applications of our results in practical situations.
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