Enabling In-Class Peer Feedback on Introductory Computer Science Coding Exercises
Abstract
Instructors often implement active learning in CS1 by giving students in-class coding problems. Students need feedback on their work to improve. While some systems provide automated feedback, human feedback is more effective for novice learners. However, instructors cannot provide feedback quickly at a large scale. Peer feedback systems help students get prompt feedback during class. Existing CS peer feedback systems usually support feedback on completed code rather than work in progress, which limits opportunities to reflect on the feedback and correct their work. We introduce a novel system for giving peer feedback on code in progress during CS1 classes, as well as a pilot test of the peer feedback process in CS1. Our initial experience has implications for the delivery of in-class instruction and for teaching growth mindset in order to take full advantage of peer feedback.
Publication Title
SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V.2
Recommended Citation
Zaman, A., Phan, V., & Cook, A. (2022). Enabling In-Class Peer Feedback on Introductory Computer Science Coding Exercises. SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V.2, 1163. https://doi.org/10.1145/3478432.3499109
