Economic Behavior of Information Acquisition: Impact on Peer Grading in Massive Open Online Courses
研究了大规模开放在线课程中同伴评分系统的有效性，发现存在向均值靠拢的系统性评分偏差，并提出一种简单的尺度调整方案来同时提高评分准确性和纠正偏差。
A critical issue in operating massive open online courses (MOOCs) is the scalability of providing feedback. Because it is not feasible for instructors to grade a large number of students’ assignments, MOOCs use peer grading systems. Yoo and Zhan investigate the efficacy of that practice when student graders are considered rational economic agents. Using an economic model that characterizes the behavior of student graders, they analyse the accuracy of current peer grading scheme. Interestingly, they identify a systematic grading bias toward the mean, which discourages students from learning. To improve current practice, they propose a simple scale-shift grading scheme, which can simultaneously improve grading accuracy and adjust grading bias. They discuss how it can be readily implemented in practice with moderate involvement of the instructors and MOOCs.