Game Theory based Peer Grading Mechanisms for MOOCs
Game Theory based Peer Grading Mechanisms for MOOCs
复制标题
基于博弈论的 MOOC 同行评分机制
DOI：
10.1145/2724660.2728676
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Matthew Weinberg
中科院分区：
文献类型：
--
作者：
William Wu;C. Daskalakis;N. Kaashoek;Christos Tzamos;Matthew Weinberg
提出了一种有效的同行评分机制，用于在线课程中大量作业的评分。这种新的方法是基于博弈论和机制设计。通过一系列假设和数学模型，模拟了学生在特定机制下的优势策略行为。建立了一个考虑成绩准确性和工作量的基准函数，以定量地比较各种机制的有效性和可扩展性。在越来越现实的假设下，经过多次迭代的机制，提出了三个：校准，改进的校准和演绎。校准机制在在线众包实验中进行测试时，正如博弈论所预测的那样，但在假设学生进行交流时失败了。改进的校准机制解决了这一假设，但代价是花费更多的精力进行评分。扣除机制在基准测试中表现相对较好，优于校准，改进校准，传统的自动化和传统的同行评分系统。数学模型和基准为未来的衍生作品的执行和比较开辟了道路。
An efficient peer grading mechanism is proposed for grading the multitude of assignments in online courses. This novel approach is based on game theory and mechanism design. A set of assumptions and a mathematical model is ratified to simulate the dominant strategy behavior of students in a given mechanism. A benchmark function accounting for grade accuracy and workload is established to quantitatively compare effectiveness and scalability of various mechanisms. After multiple iterations of mechanisms under increasingly realistic assumptions, three are proposed: Calibration, Improved Calibration, and Deduction. The Calibration mechanism performs as predicted by game theory when tested in an online crowd-sourced experiment, but fails when students are assumed to communicate. The Improved Calibration mechanism addresses this assumption, but at the cost of more effort spent grading. The Deduction mechanism performs relatively well in the benchmark, outperforming the Calibration, Improved Calibration, traditional automated, and traditional peer grading systems. The mathematical model and benchmark opens the way for future derivative works to be performed and compared.