CS education research at Grok

We support CS education research through sharing of anonymised data and collaborating on education innovations.

Contact us if you would like to discuss collaborating with us.

  • Vincent Zhang, Bryn Jeffries and Irena Koprinska. 2023. Predicting Progress in a Large-scale Online Programming Course. In Proceedings of 24th International Conference on Artificial Intelligence in Education (AIED 2023), July 3 - 7, 2023, Tokyo, Japan.
    Preprint PDF
  • Jung A Lee, Irena Koprinska and Bryn Jeffries. 2022. Data Mining of Syntax Errors in a Large-Scale Online Python Course. In Proceedings of 23rd International Conference on Artificial Intelligence in Education (AIED 2022), July 27 - 31, 2022, Durham, England.
    Preprint PDF
  • Sophia Polito, Irena Koprinska and Bryn Jeffries. Exploring Student Engagement in an Online Programming Course Using Machine Learning Methods. In Proceedings of 23rd International Conference on Artificial Intelligence in Education (AIED 2022), July 27 - 31, 2022, Durham, England.
    Preprint PDF
    AIED 2022 presentation
  • Matthew Farrugia-Roberts, Bryn Jeffries, and Harald Søndergaard. 2022. Teaching Simple Constructive Proofs with Haskell Programs. In Proceedings of the eleventh workshop on Trends in Functional Programming in Education (TFPIE 2022). March 16, 2022, online.
    Preprint PDF
  • Matthew Farrugia-Roberts, Bryn Jeffries, and Harald Søndergaard. 2022. Programming to Learn: Logic and Computation from a Programming Perspective. In Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education Vol 1 (ITiCSE 2022), July 8–13, 2022, Dublin, Ireland. ACM, New York, NY, USA, 7 pages.
    https://doi.org/10.1145/3502718.3524814
    Preprint PDF
  • Bryn Jeffries, Jung A. Lee, and Irena Koprinska. 2022. 115 Ways Not to Say Hello, World!: Syntax Errors Observed in a Large-Scale Online CS0 Python Course. In Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education Vol 1 (ITiCSE 2022), July 8–13, 2022, Dublin, Ireland. ACM, New York, NY, USA, 7 pages.
    https://doi.org/10.1145/3502718.3524809 (pending)
    Preprint PDF
    Presentation Slides
  • Bryn Jeffries, Timothy Baldwin, and Marion Zalk. 2022. Online Examinations in a Large Australian CS1 Course. In Australasian Computing Education Conference (ACE ’22), February 14–18, 2022, Virtual Event, Australia. ACM, New York, NY, USA, 7 pages.
    https://doi.org/10.1145/3511861.3511864
    Preprint PDF
  • Jessica McBroom, Benjamin Paassen, Bryn Jeffries, Irena Koprinska, and Kalina Yacef. 2021. Progress Networks as a Tool for Analysing Student Programming Difficulties. In Australasian Computing Education Conference (ACE '21). Association for Computing Machinery, New York, NY, USA, 158–167.
    https://doi.org/10.1145/3441636.3442366
    Preprint PDF
  • Gabriel Raubenheimer, Bryn Jeffries, and Kalina Yacef. 2021. Toward Empirical Analysis of Pedagogical Feedback in Computer Programming Learning Environments. In Australasian Computing Education Conference (ACE '21). Association for Computing Machinery, New York, NY, USA, 189–195.
    https://doi.org/10.1145/3441636.3442321
    Preprint PDF
  • Paaßen, B., McBroom, J., Jeffries, B., Koprinska, I., & Yacef, K. Next Steps for Next-step Hints: Lessons Learned from Teacher Evaluations of Automatic Programming Hints. In Joint Proceedings of the Workshops of the 14th International Conference on Educational Data Mining (EDM 2021).
    Preprint PDF
  • Paassen, B., McBroom, J., Jeffries, B., Koprinska, I., & Yacef, K. (2021). Mapping Python Programs to Vectors using Recursive Neural Encodings. Journal of Educational Data Mining, 13(3), 1–35.
    https://doi.org/10.5281/zenodo.5634224
    EDM 2021 presentation
    Preprint PDF
  • Uwe Röhm, Lexi Brent, Tim Dawborn, and Bryn Jeffries. 2020. SQL for Data Scientists: Designing SQL Tutorials for Scalable Online Teaching. Proc. VLDB Endow. 13, 12 (August 2020), 2989–2992.
    https://doi.org/10.14778/3415478.3415526
    Preprint PDF
    VLDB 2020 presentation
  • Bryn Jeffries, Timothy Baldwin, Marion Zalk, and Ben Taylor. 2020. Online Tutoring to Support Programming Exercises. In Proceedings of the Twenty-Second Australasian Computing Education Conference (ACE'20). Association for Computing Machinery, New York, NY, USA, 56–65.
    https://doi.org/10.1145/3373165.3373172
    Preprint PDF
  • James Curran, Karsten Schulz, Amanda Hogan. 2019. Coding and computational thinking: what is the evidence? In NSW Department of Education.
    PDF
  • McBroom, J., Yacef, K., Koprinska, I., Curran, J.R. (2018). A Data-Driven Method for Helping Teachers Improve Feedback in Computer Programming Automated Tutors. In: Artificial Intelligence in Education (AIED 2018). Lecture Notes in Computer Science, vol 10947. Springer, Cham.
    https://doi.org/10.1007/978-3-319-93843-1_24
  • Sammi Chow, Kalina Yacef, Irena Koprinska, and James Curran. 2017. Automated Data-Driven Hints for Computer Programming Students. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP '17). Association for Computing Machinery, New York, NY, USA, 5–10.
    https://doi.org/10.1145/3099023.3099065