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As an educational tool, peer review can be a valuable way to provide students feedback without a significant increase in instructor workload. Despite all that is currently known about our students, the best mechanism for assigning reviewers to reviewees in a peer review of artifacts is still considered to be blind, random assignment. The underlying conjecture of this research project is that “there has to be a better way”. This paper represents a follow-up to earlier work by the author [1]. That study presented the results of an attempt to develop a classification schema using a large archival database of student work. This paper takes the resulting algorithms produced from that archival dataset and applies them to new student work, identifying how well the archival-based classification works on a new data set. The implications of that application on future algorithm design will be discussed as well as the next steps for the research.

Pilot Study on Applying Natural Language Processing Techniques to Classify Student Responses to Open-Ended Problems to Improve Peer Review Assignments