促進大型開放式線上課程中的同儕互評

Abstract

在大型開放式線上課程 (MOOCs) 當中， 由於學習者數量極為龐大，高階學習表現通常只能透過同儕互評 (Peer Grading) 的方式來評量。 在MOOCs中實施同儕互評時，學習者通常缺乏為其他人評分的動機，因而沒有付出足夠的心力。 為改善此現象，我們考慮讓學習者的成績與其評量他人的準確度相關的機制，並建立相關的賽局理論模型，以分析學習者在此機制下的理性行為。 我們發現一組能保證純粹均衡存在的條件，在此條件下，課程設計者將可透過調整機制參數的方式，促進學習者投資更多的心力在評分之上。 更進一步，若學習者之間具有同質性時，我們證明在所有純粹均衡當中，所有為同一份作業評分的學習者都會付出恰相等的時間。 藉由這個性質，我們能夠計算所有可能的純粹均衡點。 我們將上述結果推廣到某些學習者並非採取理性策略的狀況，並討論如何在實際情況中應用本研究的結果。

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.