Charlie

Charlie

Doctoral researcher

Posts by Collection

portfolio

publications

Methodological Considerations for Predicting At-risk Students

Published in Australasian Computing Education Conference (ACE ’22), 2022

This paper highlights a methodological flaw when using machine learning for predicting students dropping out of a course.

Recommended citation: C. Koutcheme, S. Sarsa, A. Hellas, L. Haaranen, J. Leinonen: Methodological Considerations for Predicting At-risk Students. In Australasian Computing Education Conference (ACE ’22), February 2022 https://dl.acm.org/doi/abs/10.1145/3511861.3511873

Exploring How Students Solve Open-ended Assignments: A Study of SQL Injection Attempts in a Cybersecurity Course

Published in Innovation and Technology in Computer Science Education (ITiCSE 22), 2022

In this study, we investigate how students solve open-ended assignments on a cybersecurity course.

Recommended citation: C. Koutcheme, A. Tilanterä, A. Peltonen, A. Hellas, and L. Haaranen: Exploring How Students Solve Open-ended Assignments: A Study of SQL Injection Attempts in a Cybersecurity Course. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education. July 2022 https://doi.org/10.1145/3502718.3524748

Speeding Up Automated Assessment of Programming Exercises

Published in The United Kingdom and Ireland Computing Education Research Conference, 2022

In this paper, we present an approach for reducing the computational ressources needed to assess students solutions to programming assignments.

Recommended citation: S. Sarsa, J. Leinonen, C. Koutcheme, A. Hellas. 2022: Speeding Up Automated Assessment of Programming Exercises. In Proceedings of the 2022 Conference on United Kingdom and Ireland Computing Education Research (UKICER22), September 2022 https://dl.acm.org/doi/abs/10.1145/3555009.3555013

Automated Program Repair using Generative Models for Code Infilling

Published in Artificial Intelligence in Education, 2023

In this paper, we experiment with using Code Generative Models augmented with infilling capabilities for repairing student programs.

Recommended citation: Koutcheme, C., Sarsa, S., Leinonen, J., Hellas, A., Denny, P. (2023). Automated Program Repair Using Generative Models for Code Infilling. In: Artificial Intelligence in Education. AIED 2023. https://link.springer.com/chapter/10.1007/978-3-031-36272-9_74

Training Language Models for Programming Feedback Using Automated Repair Tools

Published in Artificial Intelligence in Education, 2023

In this paper, I introduce a simple strategy to instantiate language models 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.

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

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.