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  • Karnalim O. (2025). Identifying AI Generated Code with Parallel KNN Weight Outlier Detection. Advanced Technologies and the University of the Future. 10.1007/978-3-031-71530-3_29. (459-470).

    https://link.springer.com/10.1007/978-3-031-71530-3_29

  • Karnalim O, Simon and Chivers W. (2023). Reporting less coincidental similarity to educate students about programming plagiarism and collusion. Computer Science Education. 10.1080/08993408.2023.2178063. 34:3. (442-472). Online publication date: 2-Jul-2024.

    https://www.tandfonline.com/doi/full/10.1080/08993408.2023.2178063

  • Karnalim O. (2024). Work-In-Progress: Student Motivation on Gamification in Maintaining Programming Ethics. Towards a Hybrid, Flexible and Socially Engaged Higher Education. 10.1007/978-3-031-53022-7_49. (495-502).

    https://link.springer.com/10.1007/978-3-031-53022-7_49

  • Toba H, Karnalim O, Johan M, Tada T, Djajalaksana Y and Vivaldy T. (2024). Inappropriate Benefits and Identification of ChatGPT Misuse in Programming Tests: A Controlled Experiment. Towards a Hybrid, Flexible and Socially Engaged Higher Education. 10.1007/978-3-031-51979-6_54. (520-531).

    https://link.springer.com/10.1007/978-3-031-51979-6_54

  • Forden J, Gebhard A and Brylow D. Experiences with TA-Bot in CS1. Proceedings of the ACM Conference on Global Computing Education Vol 1. (57-63).

    https://doi.org/10.1145/3576882.3617930

  • Hsueh N, Lai L and Tseng W. (2023). Design of an Online Programming Platform and a Study on Learners’ Testing Ability. Electronics. 10.3390/electronics12224596. 12:22. (4596).

    https://www.mdpi.com/2079-9292/12/22/4596

  • Poitras E, Dempsey D, Crane B, Simpson S and Siegel A. (2023). The Applications of Learning Analytics to Enhance Learning and Engagement in Introductory Programming Instruction. Perspectives on Learning Analytics for Maximizing Student Outcomes. 10.4018/978-1-6684-9527-8.ch005. (89-108).

    https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-9527-8.ch005

  • Karnalim O, Simon and Chivers W. (2023). Gamification to Help Inform Students About Programming Plagiarism and Collusion. IEEE Transactions on Learning Technologies. 16:5. (708-721). Online publication date: 1-Oct-2023.

    https://doi.org/10.1109/TLT.2023.3243893

  • Karnalim O and Tan R. (2023). High School Student Perspective of Programming Plagiarism and Collusion 2023 IEEE World Engineering Education Conference (EDUNINE). 10.1109/EDUNINE57531.2023.10102902. 979-8-3503-2050-3. (1-5).

    https://ieeexplore.ieee.org/document/10102902/

  • Forden J, Gebhard A and Brylow D. Dynamic Rate Limiting with TA-Bot in CS1. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2. (1330-1330).

    https://doi.org/10.1145/3545947.3576276

  • Karnalim O, Simon and Chivers W. (2023). Non-game Incentives in Gamified Programming Education: More Marks or Prizes. Learning in the Age of Digital and Green Transition. 10.1007/978-3-031-26876-2_86. (910-920).

    https://link.springer.com/10.1007/978-3-031-26876-2_86

  • Karnalim O, Simon , Chivers W and Panca B. (2022). Educating Students about Programming Plagiarism and Collusion via Formative Feedback. ACM Transactions on Computing Education. 22:3. (1-31). Online publication date: 30-Sep-2022.

    https://doi.org/10.1145/3506717

  • Xia X, Yan Y, He X, Wu D, Xu L and Xu B. (2022). An Empirical Study on the Impact of Python Dynamic Typing on the Project Maintenance. International Journal of Software Engineering and Knowledge Engineering. 10.1142/S0218194022500243. 32:05. (745-768). Online publication date: 1-May-2022.

    https://www.worldscientific.com/doi/10.1142/S0218194022500243

  • Leinonen J, Denny P and Whalley J. A Comparison of Immediate and Scheduled Feedback in Introductory Programming Projects. Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1. (885-891).

    https://doi.org/10.1145/3478431.3499372

  • Call T, Fox E and Sprint G. (2021). Gamifying Software Engineering Tools to Motivate Computer Science Students to Start and Finish Programming Assignments Earlier. IEEE Transactions on Education. 64:4. (423-431). Online publication date: 1-Nov-2021.

    https://doi.org/10.1109/TE.2021.3069945

  • Villamor M. (2020). A review on process-oriented approaches for analyzing novice solutions to programming problems. Research and Practice in Technology Enhanced Learning. 10.1186/s41039-020-00130-y. 15:1. Online publication date: 1-Dec-2020.

    https://telrp.springeropen.com/articles/10.1186/s41039-020-00130-y

  • Scatalon L, Garcia R and Barbosa E. (2020). Teaching Practices of Software Testing in Programming Education 2020 IEEE Frontiers in Education Conference (FIE). 10.1109/FIE44824.2020.9274256. 978-1-7281-8961-1. (1-9).

    https://ieeexplore.ieee.org/document/9274256/

  • Pereira F, Oliveira E, Oliveira D, Cristea A, Carvalho L, Fonseca S, Toda A and Isotani S. (2020). Using learning analytics in the Amazonas: understanding students’ behaviour in introductory programming. British Journal of Educational Technology. 10.1111/bjet.12953. 51:4. (955-972). Online publication date: 1-Jul-2020.

    https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.12953

  • Fossati D and Hashemi Tonekaboni N. Practice Exams and Student Performance in Introductory Programming. Proceedings of the 51st ACM Technical Symposium on Computer Science Education. (1362-1362).

    https://doi.org/10.1145/3328778.3372676

  • Albluwi I. (2019). Plagiarism in Programming Assessments. ACM Transactions on Computing Education. 20:1. (1-28). Online publication date: 5-Feb-2020.

    https://doi.org/10.1145/3371156

  • Carter A, Hundhausen C and Olivares D. (2019). Leveraging the Integrated Development Environment for Learning Analytics. The Cambridge Handbook of Computing Education Research. 10.1017/9781108654555.024. (679-706).

    https://www.cambridge.org/core/product/identifier/9781108654555%23CN-bp-23/type/book_part

  • Fincher S and Robins A. (2019). The Cambridge Handbook of Computing Education Research

    https://www.cambridge.org/core/product/identifier/9781108654555/type/book

  • Birch G, Fischer B and Poppleton M. (2019). Fast test suite-driven model-based fault localisation with application to pinpointing defects in student programs. Software and Systems Modeling (SoSyM). 18:1. (445-471). Online publication date: 1-Feb-2019.

    https://doi.org/10.1007/s10270-017-0612-y

  • Szabo C, Falkner N, Knutas A and Dorodchi M. Understanding the Effects of Lecturer Intervention on Computer Science Student Behaviour. Proceedings of the 2017 ITiCSE Conference on Working Group Reports. (105-124).

    https://doi.org/10.1145/3174781.3174787

  • Hundhausen C, Olivares D and Carter A. (2017). IDE-Based Learning Analytics for Computing Education. ACM Transactions on Computing Education. 17:3. (1-26). Online publication date: 29-Aug-2017.

    https://doi.org/10.1145/3105759

  • Brown N and Altadmri A. (2017). Novice Java Programming Mistakes. ACM Transactions on Computing Education. 17:2. (1-21). Online publication date: 8-Jun-2017.

    https://doi.org/10.1145/2994154

  • Hui B and Farvolden S. How Can Learning Analytics Improve a Course?. Proceedings of the 22nd Western Canadian Conference on Computing Education. (1-6).

    https://doi.org/10.1145/3085585.3085586

  • Diana N, Eagle M, Stamper J, Grover S, Bienkowski M and Basu S. An instructor dashboard for real-time analytics in interactive programming assignments. Proceedings of the Seventh International Learning Analytics & Knowledge Conference. (272-279).

    https://doi.org/10.1145/3027385.3027441

  • Birch G, Fischer B and Poppleton M. Using Fast Model-Based Fault Localisation to Aid Students in Self-Guided Program Repair and to Improve Assessment. Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. (168-173).

    https://doi.org/10.1145/2899415.2899433

  • Ihantola P, Vihavainen A, Ahadi A, Butler M, Börstler J, Edwards S, Isohanni E, Korhonen A, Petersen A, Rivers K, Rubio M, Sheard J, Skupas B, Spacco J, Szabo C and Toll D. Educational Data Mining and Learning Analytics in Programming. Proceedings of the 2015 ITiCSE on Working Group Reports. (41-63).

    https://doi.org/10.1145/2858796.2858798

  • Auvinen T. Harmful Study Habits in Online Learning Environments with Automatic Assessment. Proceedings of the 2015 International Conference on Learning and Teaching in Computing and Engineering. (50-57).

    https://doi.org/10.1109/LaTiCE.2015.31

  • Spacco J, Denny P, Richards B, Babcock D, Hovemeyer D, Moscola J and Duvall R. Analyzing Student Work Patterns Using Programming Exercise Data. Proceedings of the 46th ACM Technical Symposium on Computer Science Education. (18-23).

    https://doi.org/10.1145/2676723.2677297

  • Bradshaw M. Ante Up. Proceedings of the 46th ACM Technical Symposium on Computer Science Education. (488-493).

    https://doi.org/10.1145/2676723.2677247

  • Brusilovsky P, Edwards S, Kumar A, Malmi L, Benotti L, Buck D, Ihantola P, Prince R, Sirkiä T, Sosnovsky S, Urquiza J, Vihavainen A and Wollowski M. Increasing Adoption of Smart Learning Content for Computer Science Education. Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference. (31-57).

    https://doi.org/10.1145/2713609.2713611

  • Helminen J, Ihantola P and Karavirta V. Recording and analyzing in-browser programming sessions. Proceedings of the 13th Koli Calling International Conference on Computing Education Research. (13-22).

    https://doi.org/10.1145/2526968.2526970