基于博弈论的mooc同伴评分机制

William Wu, C. Daskalakis, N. Kaashoek, Christos Tzamos, S. Weinberg
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引用次数: 13

摘要

针对网络课程中大量作业的评分问题,提出了一种有效的同伴评分机制。这种新颖的方法是基于博弈论和机制设计。本文提出了一套假设和数学模型来模拟学生在特定机制下的优势策略行为。建立了考虑等级准确性和工作量的基准函数,定量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,提出了三种机制:校准,改进校准和演绎。当在一个在线众包实验中测试时,校准机制表现出博弈论的预测,但当学生被假设进行交流时,校准机制就失效了。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分级。扣除机制在基准测试中表现相对较好,优于校准、改进校准、传统自动化和传统同行评分系统。该数学模型和基准为今后衍生产品的执行和比较开辟了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Game Theory based Peer Grading Mechanisms for MOOCs
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.
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