Document Type

Conference Proceeding

Publication Date

7-27-2022

Department

Department of Mathematical Sciences

Abstract

Rich, relevant, and immediate student feedback is a core ingredient supporting effective student learning. Feedback is particularly important for introductory computing courses where novice programmers are still learning the basic syntax and semantics of a programming language. Our project is aimed at detecting poor solutions to common problems, termed antipatterns, in student code and providing feedback that guides the student to better solutions. This paper discusses the first year of the project, specifically, the development of a Code Critiquer to detect antipatterns in student code and generate appropriate feedback. This important first step sets-up the project to advance knowledge about novice antipatterns and their detection. The use of these antipatterns and code critiquers in future classroom interventions will help the project improve our understanding of student learning, retention, and self-efficacy.

Publisher's Statement

© 2022 Copyright is held by the authors. Publisher’s version of record: https://doi.org/10.5281/zenodo.6983498

Publication Title

Proceedings of the 6th Educational Data Mining in Computer Science Education (CSEDM)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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