NDLI: BayesRank: A Bayesian Approach to Ranked Peer Grading

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BayesRank: A Bayesian Approach to Ranked Peer Grading

Content Provider	ACM Digital Library
Author	Baraniuk, Richard G. Waters, Andrew E. Tinapple, David
Abstract	Advances in online and computer supported education afford exciting opportunities to revolutionize the classroom, while also presenting a number of new challenges not faced in traditional educational settings. Foremost among these challenges is the problem of accurately and efficiently evaluating learner work as the class size grows, which is directly related to the larger goal of providing quality, timely, and actionable formative feedback. Recently there has been a surge in interest in using peer grading methods coupled with machine learning to accurately and fairly evaluate learner work while alleviating the instructor bottleneck and grading overload. Prior work in peer grading almost exclusively focuses on numerically scored grades -- either real-valued or ordinal. In this work, we consider the implications of peer ranking in which learners rank a small subset of peer work from strongest to weakest, and propose new types of computational analyses that can be applied to this ranking data. We adopt a Bayesian approach to the ranked peer grading problem and develop a novel model and method for utilizing ranked peer-grading data. We additionally develop a novel procedure for adaptively identifying which work should be ranked by particular peers in order to dynamically resolve ambiguity in the data and rapidly resolve a clearer picture of learner performance. We showcase our results on both synthetic and several real-world educational datasets.
Starting Page	177
Ending Page	183
Page Count	7
File Format	PDF
ISBN	9781450334112
DOI	10.1145/2724660.2724672
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Peer grading Bayesian methods Rank aggregation Automatic grading Adaptive recommender systems
Content Type	Text
Resource Type	Article

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