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.
JZ2655/CSCL23
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