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Accurately grading open-ended assignments in large or massive open onlinecourses (MOOCs) is non-trivial. Peer review is a promising solution but can beunreliable due to few reviewers and an unevaluated review form. To date, nowork has 1) leveraged sentiment analysis in the peer-review process to informor validate grades or 2) utilized aspect extraction to craft a review form fromwhat students actually communicated. Our work utilizes, rather than discards,student data from review form comments to deliver better information to theinstructor. In this work, we detail the process by which we create ourdomain-dependent lexicon and aspect-informed review form as well as our entiresentiment analysis algorithm which provides a fine-grained sentiment score fromtext alone. We end by analyzing validity and discussing conclusions from ourcorpus of over 6800 peer reviews from nine courses to understand the viabilityof sentiment in the classroom for increasing the information from andreliability of grading open-ended assignments in large courses.

Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment