Improving Peer Evaluation Quality in Massive Open Online Courses
Master of Science
As several online course providers such as Coursera, Udacity and edX emerged in 2012, Massive Open Online Courses (MOOCs) gained much attention across the globe. While MOOCs provide learning opportunities for many people, several challenges exist in the context of MOOC and one of those is how to ensure the quality of peer grading. Interactive Programming in Python course (IPP) that Rice has offered for a number of years on Coursera has suffered from the problem of low-quality peer evaluations. In this thesis, we propose our solution to improve the quality of peer evaluations by motivating peer graders. Specifically, we want to answer the question: when a student knows that his or her own peer grading efforts are being examined and they are able to grade other peer evaluations, do those tend to motivate the student to do a better job when grading assignments? We implemented a web application where students can grade peer evaluations and we also conduct a series of controlled experiments. Finally, we find a strong effect on peer evaluation quality simply because students know that they are going to be studied using a software that is supposed to help with peer grading. In addition, we find strong evidence that by grading peer evaluations students tend to give better peer evaluations. However, the strongest effect seems to be obtained via the act of grading others’ evaluations, and not from the knowledge that one’s own peer evaluation will be examined.
MOOC; Peer Evaluation; Education