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I am a Postdoctoral Research Fellow in the Department of Mechanical Engineering at Imperial, working on automated feedback with the Lambda Feedback Team. I completed my PhD at King’s College London, where my research focused on Automated Assessment of Code Quality, Prior to that, I obtained my MSci in Computer Science from Royal Holloway, University of London.

My research focuses automated assessment and feedback, student engagement and curricula alignment with industry expections.

Before my PhD, I worked as a full-stack software engineer within a data analytics company and was responsible for various projects, including data ingestion and web-based data visualization.

Research Topics

  • Automated Feedback
  • Automated Assessment
  • STEM Education
  • Computer Science Education
  • Software Tools
  • Human-Computer Interaction

Key Projects

Menagerie: A Dataset of Graded CS1 Assignments

The Menagerie dataset consists of a second semester CS1 assignment that ran over four academic years (18/19 - 21/22). It consists of 667 total submissions, with 273 of those being subsequently graded post hoc as part of a study into the consistency of human graders, and includes final grades and feedback for correctness, code elegance, readability and documentation.

OpenScienceFoundation

Grants

  • College Teaching Fund - King’s College London - £10,222.50

Publications

Journal Articles

  1. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2025). How Consistent Are Humans When Grading Programming Assignments? ACM Trans. Comput. Educ. https://doi.org/10.1145/3759256
  2. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2024). Automated Grading and Feedback Tools for Programming Education: A Systematic Review. ACM Trans. Comput. Educ., 24(1). https://doi.org/10.1145/3636515

Conference Articles

  1. Su, X., Song, Y., Messer, M., Savelka, J., Cutumisu, M., & Wang, A. (2025). Can GPT4 Generate Effective Feedback on Code Readability? Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2, 773. https://doi.org/10.1145/3724389.3730771
  2. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2025). Menagerie: A Dataset of Graded Programming Assignments. Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2, 1547–1548. https://doi.org/10.1145/3641555.3705129
  3. Messer, M., Shi, M., Brown, N. C. C., & Kölling, M. (2024). Grading Documentation with Machine Learning. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial Intelligence in Education (pp. 105–117). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64302-6_8
  4. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2023). Machine Learning-Based Automated Grading and Feedback Tools for Programming: A Meta-Analysis. Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1, 491–497. https://doi.org/10.1145/3587102.3588822
  5. Messer, M. (2022). Grading Programming Assignments with an Automated Grading and Feedback Assistant. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (pp. 35–40). Springer International Publishing.
  6. Messer, M. (2022). Detecting When a Learner Requires Assistance with Programming and Delivering a Useful Hint. In A. Mitrovic & N. Bosch (Eds.), Proceedings of the 15th International Conference on Educational Data Mining (pp. 778–781). International Educational Data Mining Society. https://doi.org/10.5281/zenodo.6852958
  7. Messer, M. (2022). Automated Grading and Feedback of Programming Assignments. Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 2, 638–639. https://doi.org/10.1145/3502717.3532113