Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/9198
Title: Automated Multi-Dimensional Analysis of Peer Feedback in Middle School Mathematics
Authors: Zhang, Jiayi
Baker, Ryan S.
Andres, J. M. Alexandra
Hutt, Stephen
Sethuraman, Sheela
Keywords: CSCL
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Zhang, J., Baker, R. S., Andres, J. M., Hutt, S., & Sethuraman, S. (2023). Automated multi-dimensional analysis of peer feedback in middle school mathematics. In Damșa, C., Borge, M., Koh, E., & Worsley, M. (Eds.), Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023 (pp. 221-224). International Society of the Learning Sciences.
Abstract: Peer review, a commonly-used pedagogy in contemporary education has been found to positively influence student learning, benefitting both feedback provider and recipient. However, the quality of the feedback may vary, and lower-quality feedback (e.g., lacking specificity), is less likely to be implemented by the recipient, leading to suboptimal outcomes. Although recent work has used criteria to scaffold feedback to ensure quality, it is often difficult to monitor whether students follow these criteria. In this study, we develop models that automatically detect the attributes of student feedback, reflecting the presence of three pedagogically relevant constructs: 1) commenting on the process, 2) commenting on the answer, and 3) relating to self. We find models employing sentence embeddings produce the best results, with AUC ROCs ranging from .90-.96, and are robust to algorithmic bias.
Description: Short Paper
URI: https://doi.org/10.22318/cscl2023.470012
https://repository.isls.org//handle/1/9198
Appears in Collections:ISLS Annual Meeting 2023

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