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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.08396 |
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| _version_ | 1866909135529836544 |
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| author | Cipriano, Bruno Pereira Alves, Pedro Denny, Paul |
| author_facet | Cipriano, Bruno Pereira Alves, Pedro Denny, Paul |
| contents | Much research has highlighted the impressive capabilities of large language models (LLMs), like GPT and Bard, for solving introductory programming exercises. Recent work has shown that LLMs can effectively solve a range of more complex object-oriented programming (OOP) exercises with text-based specifications. This raises concerns about academic integrity, as students might use these models to complete assignments unethically, neglecting the development of important skills such as program design, problem-solving, and computational thinking. To address this, we propose an innovative approach to formulating OOP tasks using diagrams and videos, as a way to foster problem-solving and deter students from a copy-and-prompt approach in OOP courses. We introduce a novel notation system for specifying OOP assignments, encompassing structural and behavioral requirements, and assess its use in a classroom setting over a semester. Student perceptions of this approach are explored through a survey (n=56). Generally, students responded positively to diagrams and videos, with video-based projects being better received than diagram-based exercises. This notation appears to have several benefits, with students investing more effort in understanding the diagrams and feeling more motivated to engage with the video-based projects. Furthermore, students reported being less inclined to rely on LLM-based code generation tools for these diagram and video-based exercises. Experiments with GPT-4 and Bard's vision abilities revealed that they currently fall short in interpreting these diagrams to generate accurate code solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08396 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Picture Is Worth a Thousand Words: Exploring Diagram and Video-Based OOP Exercises to Counter LLM Over-Reliance Cipriano, Bruno Pereira Alves, Pedro Denny, Paul Software Engineering Human-Computer Interaction Much research has highlighted the impressive capabilities of large language models (LLMs), like GPT and Bard, for solving introductory programming exercises. Recent work has shown that LLMs can effectively solve a range of more complex object-oriented programming (OOP) exercises with text-based specifications. This raises concerns about academic integrity, as students might use these models to complete assignments unethically, neglecting the development of important skills such as program design, problem-solving, and computational thinking. To address this, we propose an innovative approach to formulating OOP tasks using diagrams and videos, as a way to foster problem-solving and deter students from a copy-and-prompt approach in OOP courses. We introduce a novel notation system for specifying OOP assignments, encompassing structural and behavioral requirements, and assess its use in a classroom setting over a semester. Student perceptions of this approach are explored through a survey (n=56). Generally, students responded positively to diagrams and videos, with video-based projects being better received than diagram-based exercises. This notation appears to have several benefits, with students investing more effort in understanding the diagrams and feeling more motivated to engage with the video-based projects. Furthermore, students reported being less inclined to rely on LLM-based code generation tools for these diagram and video-based exercises. Experiments with GPT-4 and Bard's vision abilities revealed that they currently fall short in interpreting these diagrams to generate accurate code solutions. |
| title | A Picture Is Worth a Thousand Words: Exploring Diagram and Video-Based OOP Exercises to Counter LLM Over-Reliance |
| topic | Software Engineering Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.08396 |