Human-AI Co-Creation of Worked Examples for Programming Classes (2402.16235v2)
Abstract: Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary.We also present a study that assesses the quality of explanations created with this approach
- M. C. Linn, M. J. Clancy, The case for case studies of programming problems, Commun. ACM 35 (1992) 121–132.
- Problem solving examples as first class objects in educational digital libraries: Three obstacles to overcome, Journal of Educational Multimedia and Hypermedia 18 (2009) 267–288.
- Codecast: An innovative technology to facilitate teaching and learning computer programming in a c language online course, Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (2017).
- K. Khandwala, P. J. Guo, Codemotion: expanding the design space of learner interactions with computer programming tutorial videos, Proceedings of the Fifth Annual ACM Conference on Learning at Scale (2018).
- Elicast: embedding interactive exercises in instructional programming screencasts, Proceedings of the Fifth Annual ACM Conference on Learning at Scale (2018).
- Improving engagement in program construction examples for learning python programming, International Journal of Artificial Intelligence in Education 30 (2020) 299–336.
- I.-H. Hsiao, P. Brusilovsky, The role of community feedback in the student example authoring process: an evaluation of annotex, British Journal of Educational Technology 42 (2011) 482–499.
- Learning benefits of structural example-based adaptive tutoring systems, IEEE Trans. Educ. 46 (2003) 241–251.
- Subgoals help students solve parsons problems, Proceedings of the 47th ACM Technical Symposium on Computing Science Education (2016).
- Analysis of interactive features designed to enhance learning in an ebook, Proceedings of the eleventh annual International Conference on International Computing Education Research (2015).
- Translating the icap theory of cognitive engagement into practice, Cognitive Science 42 (2018) 1777–1832.
- 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, CHI ’23, Association for Computing Machinery, New York, NY, USA, 2023.
- 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, SIGCSE 2023, Association for Computing Machinery, New York, NY, USA, 2023, p. 931–937.
- Gptutor: A chatgpt-powered programming tool for code explanation, in: N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, O. C. Santos (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky, Springer Nature Switzerland, Cham, 2023, pp. 321–327.
- 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, ICER ’22, Association for Computing Machinery, New York, NY, USA, 2022, p. 27–43.
- Explaining code examples in introductory programming courses: Llm vs humans, in: Workshop on AI for Education - Bridging Innovation and Responsibility at AAAI 2024,, 2024.
- Mohammad Hassany (5 papers)
- Peter Brusilovsky (15 papers)
- Jiaze Ke (3 papers)
- Kamil Akhuseyinoglu (2 papers)
- Arun Balajiee Lekshmi Narayanan (7 papers)