Improving essay peer grading accuracy in massive open online courses using personalized weights from student's engagement and performance

Abstract

AbstractMost massive open online courses (MOOC) use simple schemes for aggregating peer grades, taking the mean or the median, or compute weights from information other than the instructor's opinion about the students' knowledge. To reduce the difference between the instructor and students' aggregated scores, some proposals compute specific weights to aggregate the peer grades. In this work, we analyse the use of students' engagement and performance measures to compute personalized weights and study the validity of the aggregated scores produced by these common functions, mean, and median, together with two others from the information retrieval field, the geometric and harmonic means. To test this procedure, we have analysed data from a MOOC about Philosophy. The course had 1,059 students registered, and 91 participated in a peer review process that consisted in writing an essay and rating three of their peers using a rubric. We compared the aggregation scores obtained using weighted and nonweighted versions of the functions. Our results show that the correlation between the aggregated scores and the instructor's grades can be improved in relation to peer grading, when using the median and weights are computed according to students' performance in chapter tests.