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Published in International Conference on Artificial Intelligence in Education (AIED23), 2023
In this paper, I present a simple strategy to instantiate sequence-to-sequence model for repairing student programs. The strategy consists in finetuning existing open models, such as those available on HuggingFace, using as ground truth the repairs found by Automated Repair Tools. We use CodeT5 as the sequence-to-sequence model.
Recommended citation: C. Koutcheme. Training Language Models for Programming Feedback Using Automated Repair Tools, Artificial Intelligence in Education (AIED ’23), June 2023 https://link.springer.com/chapter/10.1007/978-3-031-36272-9_79
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Published in Artificial Intelligence in Education, 2023
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Published in Artificial Intelligence in Education, 2023
Recommended citation: Koutcheme, C. (2023). Training Language Models for Programming Feedback Using Automated Repair Tools. In Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023 https://link.springer.com/chapter/10.1007/978-3-031-36272-9_79
Published in ACM Conference on International Computing Education Research, 2023, 2023
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Published in 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), 2024
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Published in International Conference on Innovation and Technology in Computer Science Education, 2024
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Published in 56th ACM Technical Symposium on Computer Science Education, 2025
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Published in 20th Workshop on Innovative Use of NLP for Building Educational Applications, 2025
Published in 9th Educational Data Mining in Computer Science Education (CSEDM) Workshop, 2025
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Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.