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1. Computational concepts, practices, and collaboration in high school students’ debugging electronic textile projects

   Jayathirtha, G. (January 2018, Conference Proceedings of International Conference on Computational Thinking Education 2018)

   Debugging, a recurrent practice while programming, can reveal significant information about student learning. Making electronic textile (e-textile) artifacts entails numerous opportunities for students to debug across circuitry, coding, crafting and designing domains. In this study, 69 high school students worked on a series of four different e-textiles projects over eight weeks as a part of their introductory computer science course. We analyzed debugging challenges and resolutions reported by students in their portfolios and interviews and found not only a wide range of computational concepts but also the development of specific computational practices such as being iterative and incremental in students’ debugging e-textiles projects. In the discussion, we address the need for more studies to recognize other computational practices such as abstraction and modularization, the potential of hybrid contexts for debugging, and the social aspects of debugging. 

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2. Computational concepts, practices, and collaboration in high school students’ debugging electronic textile projects.

   Jayathirtha, G. (January 2018, Conference Proceedings of International Conference on Computational Thinking Education 2018)

   Debugging, a recurrent practice while programming, can reveal significant information about student learning. Making electronic textile (e-textile) artifacts entails numerous opportunities for students to debug across circuitry, coding, crafting and designing domains. In this study, 69 high school students worked on a series of four different e-textiles projects over eight weeks as a part of their introductory computer science course. We analyzed debugging challenges and resolutions reported by students in their portfolios and interviews and found not only a wide range of computational concepts but also the development of specific computational practices such as being iterative and incremental in students’ debugging e-textiles projects. In the discussion, we address the need for more studies to recognize other computational practices such as abstraction and modularization, the potential of hybrid contexts for debugging, and the social aspects of debugging. 

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3. Case Studies on the Use of Storyboarding by Novice Programmers

   https://doi.org/10.1145/3502718.3524749  

   Limke, Ally; Milliken, Alexandra; Cateté, Veronica; Gransbury, Isabella; Isvik, Amy; Price, Thomas; Martens, Chris; Barnes, Tiffany (July 2022, Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education)

   Our researchers seek to support students in building block-based programming projects that are motivating and engaging as well as valuable practice in learning to code. A difficult part of the programming process is planning. In this research, we explore how novice programmers used a custom-built planning tool, PlanIT, contrasted against how they used storyboarding when planning games. In a three-part study, we engaged novices in planning and programming three games: a maze game, a break-out game, and a mashup of the two. In a set of five case studies, we show how five pairs of students approached the planning and programming of these three games, illustrating that students felt more creative when storyboarding rather than using PlanIT. We end with a discussion on the implications of this work for designing supports for novices to plan open-ended projects. 

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4. An Analysis of Middle Grade Teachers' Debugging Pedagogical Content Knowledge

   https://doi.org/10.1145/3502718.3524770  

   Tsan, Jennifer; Weintrop, David; Franklin, Diana (July 2022, The 27th ACM Conference on Innovation and Technology in Computer Science Education)

   There is an increasing need for knowledgeable K-12 computer science (CS) teachers. It is necessary to inform teachers how to debug and help their students debug programs. Research has shown that debugging is difficult for novices because the process requires different skills from creating programs and instructing students how to debug can help them acquire these skills. To this end, we developed a CS professional development for middle grade teachers (grades 5th-8th/ages 10-13) that includes lessons on debugging. The teachers completed debugging activities that involved finding bugs in Scratch programs and explaining how they would help their students in debugging. We qualitatively analyzed their responses and found that teachers successfully identified the problem but they struggled to locate it in the code. In considering how they would help students who had such a bug, the teachers often focused on helping the student find a solution for the bug rather than on identifying the problem or its source. Finally, teachers’ ability to identify bugs and the pedagogical strategies to engage students in this process differed based on CS teaching experience and prior CS knowledge. This work contributes to our understanding of teachers’ debugging abilities and advances our knowledge on how to support teachers in teaching their students how to debug their programs. 

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5. How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging

   https://doi.org/10.1007/978-3-031-64302-6_19  

   Ma, Qianou; Shen, Hua; Koedinger, Kenneth; Wu, Sherry Tongshuang (January 2024, Springer Nature Switzerland)

   Large Language Models (LLMs) now excel at generative skills and can create content at impeccable speeds. However, they are imperfect and still make various mistakes. In a Computer Science education context, as these models are widely recognized as “AI pair programmers,” it becomes increasingly important to train students on evaluating and debugging the LLM-generated code. In this work, we introduce HypoCompass, a novel system to facilitate deliberate practice on debugging, where human novices play the role of Teaching Assistants and help LLM-powered teachable agents debug code. We enable effective task delegation between students and LLMs in this learning-by-teaching environment: students focus on hypothesizing the cause of code errors, while adjacent skills like code completion are offloaded to LLM-agents. Our evaluations demonstrate that HypoCompass generates high-quality training materials (e.g., bugs and fixes), outperforming human counterparts fourfold in efficiency, and significantly improves student performance on debugging by 12% in the pre-to-post test. 

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