Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/7297
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dc.contributor.authorWinograd, Blair A.
dc.contributor.authorDood, Amber J.
dc.contributor.authorFinkenstaedt-Quinn, Solaire A.
dc.contributor.authorGere, Anne R.
dc.contributor.authorShultz, Ginger V.
dc.coverage.spatialBochum, Germanyen_US
dc.date.accessioned2021-10-09T15:44:05Z
dc.date.accessioned2021-10-09T19:46:24Z-
dc.date.available2021-10-09T15:44:05Z
dc.date.available2021-10-09T19:46:24Z-
dc.date.issued2021-06
dc.identifier.citationWinograd, B. A., Dood, A. J., Finkenstaedt-Quinn, S. A., Gere, A. R., & Shultz, G. V. (2021). Automating Characterization of Peer Review Comments in Chemistry Courses. In Hmelo-Silver, C. E., De Wever, B., & Oshima, J. (Eds.), Proceedings of the 14th International Conference on Computer-Supported Collaborative Learning - CSCL 2021 (pp. 11-18). Bochum, Germany: International Society of the Learning Sciences.en_US
dc.identifier.urihttps://doi.dx.org/10.22318/cscl2021.11
dc.identifier.urihttps://repository.isls.org//handle/1/7297-
dc.description.abstractWhile writing-to-learn (WTL) pedagogies are a promising way for students to construct knowledge, one limiting factor to implementation is time the instructor spends grading. We conducted two WTL assignments in two undergraduate general chemistry courses combined with collaborative peer review. We used a previously developed scheme to classify peer review comments generated by 1,732 students enrolled in two undergraduate chemistry courses as praise, problem/solution, and verification/summary. Problem/solution comments were further separated into greater-level, mid-level, and word-sentence descriptors. Using the SciBERT language model we then developed a classifier which accurately identifies comments where human coding was considered the ground truth. In the future, this model can provide an efficient way for instructors to monitor peer review collaborations and help instructors use peer-generated insights to guide their instruction.en_US
dc.format.extentpp. 11-18
dc.language.isoen_US
dc.publisherInternational Society of the Learning Sciencesen_US
dc.relation.ispartofProceedings of the 14th International Conference on Computer-Supported Collaborative Learning - CSCL 2021en_US
dc.subjectCSCLen_US
dc.titleAutomating Characterization of Peer Review Comments in Chemistry Coursesen_US
dc.typeConference Paperen_US
dc.typeLong Paperen_US
Appears in Collections:ISLS Annual Meeting 2021

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