Fetching the paper…
Reading the bibliography…
Computing educators and researchers have used programming process data to understand how programs are constructed and what sorts of problems students struggle with.
Automatic graders for programming classes
Jack Hollingsworth. 1960 · 1960
Earlier work this paper cites.
Cognitive apprenticeship: Making thinking visible
Allan Collins, John Seely Brown, Ann Holum, et al · 1991
Earlier work this paper cites.
Revealing the programming process. In Proceedings of the 36th SIGCSE technical symposium on Computer science education . 186–190
Jens Bennedsen and Michael E Caspersen. 2005 · 2005
Earlier work this paper cites.
Methods and tools for exploring novice compilation behaviour. In ICER ’06: Proceedings of the second International Workshop on Computing Education Research . Association for Computing Machinery, 73–84
Matthew C Jadud. 2006 · 2006
Earlier work this paper cites.
The power of feedback
John Hattie and Helen Timperley. 2007 · 2007
Earlier work this paper cites.
Toward automatic hint generation for logic proof tutoring using historical student data. In International Conference on Intelligent Tutoring Systems , Beverley P. Woolf, Esma Aïmeur, Roger Nkambou, and Susanne Lajoie (Eds.). Springer, 373–382
Tiffany Barnes and John Stamper. 2008 · 2008
Earlier work this paper cites.
Retina: helping students and instructors based on observed programming activities. In Proceedings of the 40th ACM technical symposium on Computer Science Education , Gary Lewandowski and Steven Wolfman (Eds.). Association for Computing Machinery, 178–182
Christian Murphy, Gail Kaiser, Kristin Loveland, and Sahar Hasan. 2009 · 2009
Earlier work this paper cites.
Extreme apprenticeship method in teaching programming for beginners. In Proceedings of the 42nd ACM technical symposium on Computer science education . 93–98
Arto Vihavainen, Matti Paksula, and Matti Luukkainen. 2011 · 2011
Earlier work this paper cites.
Modeling how students learn to program. In SIGCSE ’12: Proceedings of the 43rd ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 153–160
Chris Piech, Mehran Sahami, Daphne Koller, Steve Cooper, and Paulo Blikstein. 2012 · 2012
Earlier work this paper cites.
A survey of keystroke dynamics biometrics
Pin Shen Teh, Andrew Beng Jin Teoh, Shigang Yue, et al · 2013
Earlier work this paper cites.
Scaffolding students’ learning using test my code. In ITiCSE ’13: Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 117–122
Arto Vihavainen, Thomas Vikberg, Matti Luukkainen, and Martin Pärtel. 2013 · 2013
Earlier work this paper cites.
Predicting performance in an introductory programming course by logging and analyzing student programming behavior. In 2013 IEEE 13th International Conference on Advanced Learning Technologies (ICALT) . IEEE, 319–323
Christopher Watson, Frederick WB Li, and Jamie L Godwin. 2013 · 2013
Earlier work this paper cites.
Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming
Paulo Blikstein, Marcelo Worsley, Chris Piech, Mehran Sahami, Steven Cooper, and Daphne Koller. 2014 · 2014
Earlier work this paper cites.
Blackbox: A large scale repository of novice programmers’ activity. In SIGCSE ’14: Proceedings of the 45th ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 223–228
Neil Christopher Charles Brown, Michael Kölling, Davin McCall, and Ian Utting. 2014 · 2014
Earlier work this paper cites.
Using codebrowser to seek differences between novice programmers. In Proceedings of the 45th ACM technical symposium on Computer science education . 229–234
Kenny Heinonen, Kasper Hirvikoski, Matti Luukkainen, and Arto Vihavainen. 2014 · 2014
Earlier work this paper cites.
Codewebs: scalable homework search for massive open online programming courses. In Proceedings of the 23rd international conference on World wide web . 491–502
Andy Nguyen, Christopher Piech, Jonathan Huang, and Leonidas Guibas. 2014 · 2014
Earlier work this paper cites.
Exploring machine learning methods to automatically identify students in need of assistance. In ICER ’15: Proceedings of the Eleventh Annual International Conference on International Computing Education Research , Judy Sheard and Quintin Cutts (Eds.). Association for Computing Machinery, 121–130
Alireza Ahadi, Raymond Lister, Heikki Haapala, and Arto Vihavainen. 2015 · 2015
Earlier work this paper cites.
The normalized programming state model: Predicting student performance in computing courses based on programming behavior. In ICER’15: Proceedings of the Eleventh Annual International Conference on International Computing Education Research . Association for Computing Machinery, 141–150
Adam S Carter, Christopher D Hundhausen, and Olusola Adesope. 2015 · 2015
Earlier work this paper cites.
OverCode: Visualizing variation in student solutions to programming problems at scale
Elena L Glassman, Jeremy Scott, Rishabh Singh, Philip J Guo, and Robert C Miller. 2015 · 2015
Earlier work this paper cites.
Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies. In Proceedings of the 2015 ITiCSE on Working Group Reports (Vilnius, Lithuania) (ITICSE-WGR ’15) . Association for Computing Machinery, New York, NY, USA, 41–63
Petri Ihantola, Arto Vihavainen, Alireza Ahadi, Matthew Butler, Jürgen Börstler, Stephen H. Edwards, Essi Isohanni, Ari Korhonen, Andrew Petersen, Kelly Rivers, Miguel Ángel Rubio, Judy Sheard, Bronius Skupas, Jaime Spacco, Claudia Szabo, and Daniel Toll. 2015 · 2015
Earlier work this paper cites.
Identification of programmers from typing patterns. In Proceedings of the 15th Koli Calling Conference on Computing Education Research (Koli, Finland) (Koli Calling ’15) . Association for Computing Machinery, New York, NY, USA, 60–67
Krista Longi, Juho Leinonen, Henrik Nygren, Joni Salmi, Arto Klami, and Arto Vihavainen. 2015 · 2015
Earlier work this paper cites.
Keystroke biometrics for student authentication: A case study. In ITiCSE ’15: Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 337–337
Aythami Morales and Julian Fierrez. 2015 · 2015
Earlier work this paper cites.
Analyzing student work patterns using programming exercise data. In SIGCSE ’15: Proceedings of the 46th ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 18–23
Jaime Spacco, Paul Denny, Brad Richards, David Babcock, David Hovemeyer, James Moscola, and Robert Duvall. 2015 · 2015
Earlier work this paper cites.
On the number of attempts students made on some online programming exercises during semester and their subsequent performance on final exam questions. In ITiCSE ’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 218–223
Alireza Ahadi, Raymond Lister, and Arto Vihavainen. 2016 · 2016
Earlier work this paper cites.
A new metric to quantify repeated compiler errors for novice programmers. In ITiCSE ’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 296–301
Brett A Becker. 2016 · 2016
Earlier work this paper cites.
Can interaction patterns with supplemental study tools predict outcomes in CS1?. In ITiCSE ’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 236–241
Anthony Estey and Yvonne Coady. 2016 · 2016
Earlier work this paper cites.
Typing patterns and authentication in practical programming exams. In ITiCSE ’16: Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 160–165
Juho Leinonen, Krista Longi, Arto Klami, Alireza Ahadi, and Arto Vihavainen. 2016a · 2016
Earlier work this paper cites.
Translating principles of effective feedback for students into the CS1 context
Claudia Ott, Anthony Robins, and Kerry Shephard. 2016 · 2016
Earlier work this paper cites.
Evaluation of a frame-based programming editor. In Proceedings of the 2016 ACM Conference on International computing education research . Association for Computing Machinery, 33–42
Thomas W Price, Neil CC Brown, Dragan Lipovac, Tiffany Barnes, and Michael Kölling. 2016 · 2016
Cited alongside, same era.
Keystroke biometric systems for user authentication
Md Liakat Ali, John V Monaco, Charles C Tappert, and Meikang Qiu. 2017 · 2017
Cited alongside, same era.
Using programming process data to detect differences in students’ patterns of programming. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education . Association for Computing Machinery, 105–110
Adam Scott Carter and Christopher David Hundhausen. 2017 · 2017
Cited alongside, same era.
Evaluating neural networks as a method for identifying students in need of assistance. In SIGCSE’17: Proceedings of the 2017 ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 111–116
Karo Castro-Wunsch, Alireza Ahadi, and Andrew Petersen. 2017 · 2017
Cited alongside, same era.
Towards giving timely formative feedback and hints to novice programmers
Johan Jeuring, Hieke Keuning, Samiha Marwan, Dennis Bouvier, Cruz Izu, Natalie Kiesler, Teemu Lehtinen, Dominic Lohr, Andrew Peterson, and Sami Sarsa. 2022 · 2022
Later among the works it cites.
Evaluating CodeClusters for Effectively Providing Feedback on Code Submissions. In 2022 IEEE Frontiers in Education Conference (FIE) . IEEE, 1–9
Teemu Koivisto and Arto Hellas. 2022 · 2022
Later among the works it cites.
Methodological Considerations for Predicting At-risk Students. In Australasian Computing Education Conference . Association for Computing Machinery, 105–113
Charles Koutcheme, Sami Sarsa, Arto Hellas, Lassi Haaranen, and Juho Leinonen. 2022 · 2022
Later among the works it cites.
Time-on-Task Metrics for Predicting Performance. In SIGCSE ’22: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 871–877
Juho Leinonen, Francisco Enrique Vicente Castro, and Arto Hellas. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Automatically classifying students in need of support by detecting changes in programming behaviour. In SIGCSE’17: Proceedings of the 2017 ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 189–194
Anthony Estey, Hieke Keuning, and Yvonne Coady. 2017 · 2017
Cited alongside, same era.
Plagiarism in take-home exams: help-seeking, collaboration, and systematic cheating. In ITiCSE ’17: Proceedings of the 2017 ACM conference on innovation and technology in computer science education . Association for Computing Machinery, 238–243
Arto Hellas, Juho Leinonen, and Petri Ihantola. 2017 · 2017
Cited alongside, same era.
DevEventTracker: Tracking development events to assess incremental development and procrastination. In ITiCSE ’17: Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education . Association for Computing Machinery, 104–109
Ayaan M Kazerouni, Stephen H Edwards, T Simin Hall, and Clifford A Shaffer. 2017b · 2017
Cited alongside, same era.
Quantifying incremental development practices and their relationship to procrastination. In Proceedings of the 2017 ACM Conference on International Computing Education Research , Josh Tenenberg and Lauri Malmi (Eds.). Association for Computing Machinery, 191–199
Ayaan M Kazerouni, Stephen H Edwards, and Clifford A Shaffer. 2017a · 2017
Cited alongside, same era.
Preventing Keystroke Based Identification in Open Data Sets. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (Cambridge, Massachusetts, USA) (L@S ’17) . Association for Computing Machinery, New York, NY, USA, 101–109
Juho Leinonen, Petri Ihantola, and Arto Hellas. 2017a · 2017
Cited alongside, same era.
Comparison of time metrics in programming. In ICER ’17: Proceedings of the 2017 ACM Conference on International Computing Education Research . Association for Computing Machinery, 200–208
Juho Leinonen, Leo Leppänen, Petri Ihantola, and Arto Hellas. 2017b · 2017
Cited alongside, same era.
Evaluation of a Data-Driven Feedback Algorithm for Open-Ended Programming.. In Proceedings of The 10th International Conference on Educational Data Mining (EDM 2017) , X. Hu, T. Barnes, A. Hershkovitz, and L. Paquette (Eds.). International Educational Data Mining Society, 192–197
Thomas Price, Rui Zhi, and Tiffany Barnes. 2017b · 2017
Cited alongside, same era.
iSnap: towards intelligent tutoring in novice programming environments. In SIGCSE’17: Proceedings of the 2017 ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 483–488
Thomas W Price, Yihuan Dong, and Dragan Lipovac. 2017a · 2017
Cited alongside, same era.
Generating diverse code explanations using the gpt-3 large language model. In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 2 . 37–39
Stephen MacNeil, Andrew Tran, Dan Mogil, Seth Bernstein, Erin Ross, and Ziheng Huang. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
Later among the works it cites.
Automated Assessment in Computer Science Education: A State-of-the-Art Review
José Carlos Paiva, José Paulo Leal, and Álvaro Figueira. 2022 · 2022
Later among the works it cites.
Social simulacra: Creating populated prototypes for social computing systems. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology . 1–18
Joon Sung Park, Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2022 · 2022
Later among the works it cites.
Tracking Large Class Projects in Real-Time Using Fine-Grained Source Control. In SIGCSE ’22: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education . Association for Computing Machinery, 565–570
Gustavo Rodriguez-Rivera, Jeff Turkstra, Jordan Buckmaster, Killian LeClainche, Shawn Montgomery, William Reed, Ryan Sullivan, and Jarett Lee. 2022 · 2022
Later among the works it cites.
Automatic generation of programming exercises and code explanations using large language models. In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1 . 27–43
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen. 2022 · 2022
Later among the works it cites.
CodeProcess Charts: Visualizing the Process of Writing Code. In Proceedings of the 24th Australasian Computing Education Conference (Virtual Event, Australia) (ACE ’22) . Association for Computing Machinery, New York, NY, USA, 46–55
Raj Shrestha, Juho Leinonen, Arto Hellas, Petri Ihantola, and John Edwards. 2022 · 2022
Later among the works it cites.
Review of CSEDM Data and Introduction of Two Public CS1 Keystroke Datasets
John Edwards, Kaden Hart, and Raj Shrestha. 2023 · 2023
Later among the works it cites.
Prompt engineering for ChatGPT: a quick guide to techniques, tips, and best practices
Sabit Ekin. 2023 · 2023
Later among the works it cites.
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study. In Proceedings of the Conference on Human Factors in Computing Systems (CHI ’23)
Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023 · 2023
Later among the works it cites.
Accurate Estimation of Time-on-Task While Programming. In ACM Technical Symposium on Computing Science Education (SIGCSE)
Kaden Hart, Christopher Warren, and John Edwards. 2023 · 2023
Later among the works it cites.
Exploring the responses of large language models to beginner programmers’ help requests. In Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 . 93–105
Arto Hellas, Juho Leinonen, Sami Sarsa, Charles Koutcheme, Lilja Kujanpää, and Juha Sorva. 2023 · 2023
Later among the works it cites.
Automated program repair using generative models for code infilling. In International Conference on Artificial Intelligence in Education . Springer, 798–803
Charles Koutcheme, Sami Sarsa, Juho Leinonen, Arto Hellas, and Paul Denny. 2023 · 2023
Later among the works it cites.
Using large language models to enhance programming error messages. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 . 563–569
Juho Leinonen, Arto Hellas, Sami Sarsa, Brent Reeves, Paul Denny, James Prather, and Brett A Becker. 2023 · 2023
Later among the works it cites.
Experiences from using code explanations generated by large language models in a web software development e-book. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 . 931–937
Stephen MacNeil, Andrew Tran, Arto Hellas, Joanne Kim, Sami Sarsa, Paul Denny, Seth Bernstein, and Juho Leinonen. 2023 · 2023
Later among the works it cites.
Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology . 1–22
Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023 · 2023
Later among the works it cites.
The robots are here: Navigating the generative ai revolution in computing education
James Prather, Paul Denny, Juho Leinonen, Brett A Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, et al · 2023
Later among the works it cites.
Always provide context: The effects of code context on programming error message enhancement. In Proceedings of the ACM Conference on Global Computing Education Vol 1 . 147–153
Eddie Antonio Santos, Prajish Prasad, and Brett A Becker. 2023 · 2023
Later among the works it cites.
Developing Novice Programmers’ Self-Regulation Skills with Code Replays. In Proceedings of the 2023 ACM Conference on International Computing Education Research-Volume 1 . 298–313
Benjamin Xie, Jared Ordona Lim, Paul KD Pham, Min Li, and Amy J Ko. 2023 · 2023
Later among the works it cites.
Computing education in the era of generative AI
Paul Denny, James Prather, Brett A Becker, James Finnie-Ansley, Arto Hellas, Juho Leinonen, Andrew Luxton-Reilly, Brent N Reeves, Eddie Antonio Santos, and Sami Sarsa. 2024 · 2024
Closest in time.
Charles Koutcheme, Nicola Dainese, Sami Sarsa, Arto Hellas, Juho Leinonen, and Paul Denny. 2024 · 2024
Closest in time.
Evaluating Contextually Personalized Programming Exercises Created with Generative AI
Evanfiya Logacheva, Arto Hellas, James Prather, Sami Sarsa, and Juho Leinonen. 2024 · 2024
Closest in time.
InsProg: Supporting Teaching Through Visual Analysis of Students’ Programming Processes. In Proceedings of the 2024 International Conference on Advanced Visual Interfaces . 1–5
Hongyan Zhong, Jun Niu, and Junjie Li. 2024 · 2024
Closest in time.