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The advent of large language models is reshaping computing education.
Open coding
Anselm L Strauss and Juliet Corbin. 2004 · 2004
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Parson’s programming puzzles: a fun and effective learning tool for first programming courses. In Proceedings of the 8th Australasian Conference on Computing Education-Volume 52 . 157–163
Dale Parsons and Patricia Haden. 2006 · 2006
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Evaluating a new exam question: Parsons problems. In Proceedings of the fourth international workshop on computing education research . 113–124
Paul Denny, Andrew Luxton-Reilly, and Beth Simon. 2008 · 2008
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Two-Dimensional Parson’s Puzzles: The Concept, Tools, and First Observations
Petri Ihantola and Ville Karavirta. 2011a · 2011
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Two-Dimensional Parson’s Puzzles: The Concept, Tools, and First Observations
Petri Ihantola and Ville Karavirta. 2011b · 2011
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How Do Students Solve Parsons Programming Problems? An Analysis of Interaction Traces. In Proceedings of the Ninth Annual International Conference on International Computing Education Research (Auckland, New Zealand) (ICER ’12) . Association for Computing Machinery, New York, NY, USA, 119–126
Juha Helminen, Petri Ihantola, Ville Karavirta, and Lauri Malmi. 2012 · 2012
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Do racial and ethnic group differences in performance on the MCAT exam reflect test bias?
Dwight Davis, J Kevin Dorsey, Ronald D Franks, Paul R Sackett, Cynthia A Searcy, and Xiaohui Zhao. 2013 · 2013
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Increasing Adoption of Smart Learning Content for Computer Science Education
Peter Brusilovsky, Stephen Edwards, Amruth Kumar, Lauri Malmi, Luciana Benotti, Duane Buck, Petri Ihantola, Rikki Prince, Teemu Sirkiä, Sergey Sosnovsky, Jaime Urquiza-Fuentes, Arto Hellas, and Michael Wollowski. 2014 · 2014
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Structuring flipped classes with lightweight teams and gamification. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education . 392–397
Celine Latulipe, N Bruce Long, and Carlos E Seminario. 2015 · 2015
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Culturally responsive computing: A theory revisited
Kimberly A Scott, Kimberly M Sheridan, and Kevin Clark. 2015 · 2015
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Identifying Design Principles for CS Teacher Ebooks through Design-Based Research. In Proceedings of the 2016 ACM Conference on International Computing Education Research (Melbourne, VIC, Australia) (ICER ’16) . Association for Computing Machinery, New York, NY, USA, 191–200
Barbara J. Ericson, Kantwon Rogers, Miranda Parker, Briana Morrison, and Mark Guzdial. 2016 · 2016
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Distractors in Parsons Problems Decrease Learning Efficiency for Young Novice Programmers. In Proceedings of the 2016 ACM Conference on International Computing Education Research (Melbourne, VIC, Australia) (ICER ’16) . Association for Computing Machinery, 241–250
Kyle James Harms, Jason Chen, and Caitlin L. Kelleher. 2016 · 2016
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Exploring lightweight teams in a distributed learning environment. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education . 193–198
Stephen MacNeil, Celine Latulipe, Bruce Long, and Aman Yadav. 2016 · 2016
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Combining Parson’s Problems with Program Visualization in CS1 Context. In Proceedings of the 16th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling ’16) . Association for Computing Machinery, New York, NY, USA, 155–159
Teemu Sirkiä. 2016 · 2016
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Solving Parsons Problems versus Fixing and Writing Code. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling ’17) . Association for Computing Machinery, 20–29
Barbara J. Ericson, Lauren E. Margulieux, and Jochen Rick. 2017 · 2017
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The Effect of Providing Motivational Support in Parsons Puzzle Tutors. 528–531
Amruth Kumar. 2017 · 2017
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Epplets: A Tool for Solving Parsons Puzzles. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (Baltimore, Maryland, USA) (SIGCSE ’18) . Association for Computing Machinery, New York, NY, USA, 527–532
Amruth N. Kumar. 2018 · 2018
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EvoParsons: design, implementation and preliminary evaluation of evolutionary Parsons puzzle
A.T.M. Bari, Alessio Gaspar, R. Wiegand, Jennifer Albert, Anthony Bucci, and Amruth Kumar. 2019 · 2019
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Lessons Learned from Available Parsons Puzzles Software
Alessio Gaspar, Dmytro Vitel, and A.T.M. Bari. 2019 · 2019
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A Spaced, Interleaved Retrieval Practice Tool That is Motivating and Effective. In Proceedings of the 2019 ACM Conference on International Computing Education Research (Toronto ON, Canada) (ICER ’19) . Association for Computing Machinery, New York, NY, USA, 71–79
Iman YeckehZaare, Paul Resnick, and Barbara Ericson. 2019 · 2019
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Ungrading: Why rating students undermines learning (and what to do instead)
Susan Debra Blum. 2020 · 2020
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The Point-less Classroom: A Math Teacher’s Ironic Choice in not Calculating Grades
Gary Chu. 2020 · 2020
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A review of research on Parsons problems. In Proc. of the Twenty-Second Australasian Computing Education Conf. 195–202
Yuemeng Du, Andrew Luxton-Reilly, and Paul Denny. 2020 · 2020
Cited alongside, same era.
Runestone: A Platform for Free, On-Line, and Interactive Ebooks. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE ’20) . Association for Computing Machinery, New York, NY, USA, 1012–1018
Barbara J. Ericson and Bradley N. Miller. 2020 · 2020
Cited alongside, same era.
Implementation of e-proctoring in online teaching: A study about motivational factors
Carina S González-González, Alfonso Infante-Moro, and Juan C Infante-Moro. 2020 · 2020
Cited alongside, same era.
Problems Encountered by College Students in Online Assessment Amid COVID-19 Crisis: A Case Study
Michael B. Cahapay. 2021 · 2021
Cited alongside, same era.
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
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From ‘Ban It Till We Understand It’ to "Resistance is Futile": How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools such as ChatGPT and GitHub Copilot. In Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 (ICER ’23 V1) . ACM
Sam Lau and Philip J. Guo. 2023 · 2023
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Comparing Code Explanations Created by Students and Large Language Models
Juho Leinonen, Paul Denny, Stephen MacNeil, Sami Sarsa, Seth Bernstein, Joanne Kim, Andrew Tran, and Arto Hellas. 2023 · 2023
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023b · 2023
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Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Good Proctor or “Big Brother”? Ethics of Online Exam Supervision Technologies
Simon Coghlan, Tim Miller, and Jeannie Paterson. 2021 · 2021
Cited alongside, same era.
The Bad Test-Taker Identity
Jeffrey D Holmes. 2021 · 2021
Cited alongside, same era.
Domain Experts’ Interpretations of Assessment Bias in a Scaled, Online Computer Science Curriculum. In Proceedings of the Eighth ACM Conference on Learning@ Scale . 77–89
Benjamin Xie, Matt J Davidson, Baker Franke, Emily McLeod, Min Li, and Amy J Ko. 2021 · 2021
Cited alongside, same era.
Grounded Copilot: How Programmers Interact with Code-Generating Models
Shraddha Barke, Michael B James, and Nadia Polikarpova. 2022 · 2022
Cited alongside, same era.
Parsons problems and beyond: Systematic literature review and empirical study designs
Barbara J Ericson, Paul Denny, James Prather, Rodrigo Duran, Arto Hellas, Juho Leinonen, Craig S Miller, Briana B Morrison, Janice L Pearce, and Susan H Rodger. 2022 · 2022
Cited alongside, same era.
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Australasian Computing Education Conf. (Virtual Event, Australia) (ACE ’22) . ACM, New York, NY, USA, 10–19
James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather. 2022 · 2022
Cited alongside, same era.
Generating Diverse Code Explanations Using the GPT-3 Large Language Model. In Proc. of the 2022 ACM Conf. on Int. Computing Education Research - Volume 2 . ACM, 37–39
Stephen MacNeil, Andrew Tran, Dan Mogil, Seth Bernstein, Erin Ross, and Ziheng Huang. 2022 · 2022
Cited alongside, same era.
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On the hidden mystery of ocr in large multimodal models
Yuliang Liu, Zhang Li, Hongliang Li, Wenwen Yu, Mingxin Huang, Dezhi Peng, Mingyu Liu, Mingrui Chen, Chunyuan Li, Lianwen Jin, et al · 2023
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The Implications of Large Language Models for CS Teachers and Students. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (SIGCSE 2023) . Association for Computing Machinery, 1255
Stephen MacNeil, Joanne Kim, Juho Leinonen, Paul Denny, Seth Bernstein, Brett A. Becker, Michel Wermelinger, Arto Hellas, Andrew Tran, Sami Sarsa, James Prather, and Viraj Kumar. 2023a · 2023
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Transformed by Transformers: Navigating the AI Coding Revolution for Computing Education: An ITiCSE Working Group Conducted by Humans. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2 . 561–562
James Prather, Paul Denny, Juho Leinonen, Brett A Becker, Ibrahim Albluwi, Michael E Caspersen, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, et al · 2023
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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
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Evaluating the performance of code generation models for solving Parsons problems with small prompt variations. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 . 299–305
Brent Reeves, Sami Sarsa, James Prather, Paul Denny, Brett A Becker, Arto Hellas, Bailey Kimmel, Garrett Powell, and Juho Leinonen. 2023 · 2023
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ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
Jürgen Rudolph, Samson Tan, and Shannon Tan. 2023 · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses
Jaromir Savelka, Arav Agarwal, Marshall An, Chris Bogart, and Majd Sakr. 2023a · 2023
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Large language models (gpt) struggle to answer multiple-choice questions about code
Jaromir Savelka, Arav Agarwal, Christopher Bogart, and Majd Sakr. 2023b · 2023
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Jaromir Savelka, Arav Agarwal, Christopher Bogart, Yifan Song, and Majd Sakr. 2023d · 2023
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Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models
Shawn Shan, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y Zhao. 2023 · 2023
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Improving Student Motivation by Ungrading. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1
Scott Spurlock. 2023 · 2023
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Generating Multiple Choice Questions for Computing Courses using Large Language Models
Andrew Tran, Kenneth Angelikas, Egi Rama, Chiku Okechukwu, David H Smith IV, and Stephen MacNeil. 2023 · 2023
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Review of large vision models and visual prompt engineering
Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, et al · 2023
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Using GitHub Copilot to Solve Simple Programming Problems
Michel Wermelinger. 2023 · 2023
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Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–21
JD Zamfirescu-Pereira, Richmond Y Wong, Bjoern Hartmann, and Qian Yang. 2023 · 2023
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Generative AI in Computing Education: Perspectives of Students and Instructors
Cynthia Zastudil, Magdalena Rogalska, Christine Kapp, Jennifer Vaughn, and Stephen MacNeil. 2023 · 2023
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