SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929553996251136 |
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| author | Tang, Xiaohang Wong, Sam Huynh, Marcus He, Zicheng Yang, Yalong Chen, Yan |
| author_facet | Tang, Xiaohang Wong, Sam Huynh, Marcus He, Zicheng Yang, Yalong Chen, Yan |
| contents | Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenges for instructors. This paper introduces SPHERE, an interactive system that leverages Large Language Models (LLMs) and structured LLM output review to scale personalized feedback for in-class coding activities. SPHERE employs two key components: an Issue Recommendation Component that identifies critical patterns in students' code and discussion, and a Feedback Review Component that uses a ``strategy-detail-verify'' approach for efficient feedback creation and verification. An in-lab, between-subject study demonstrates SPHERE's effectiveness in improving feedback quality and the overall feedback review process compared to a baseline system using off-the-shelf LLM outputs. This work contributes a novel approach to scaling personalized feedback in programming education, addressing the challenges of real-time response, issue prioritization, and large-scale personalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs Tang, Xiaohang Wong, Sam Huynh, Marcus He, Zicheng Yang, Yalong Chen, Yan Human-Computer Interaction Effective personalized feedback is crucial for learning programming. However, providing personalized, real-time feedback in large programming classrooms poses significant challenges for instructors. This paper introduces SPHERE, an interactive system that leverages Large Language Models (LLMs) and structured LLM output review to scale personalized feedback for in-class coding activities. SPHERE employs two key components: an Issue Recommendation Component that identifies critical patterns in students' code and discussion, and a Feedback Review Component that uses a ``strategy-detail-verify'' approach for efficient feedback creation and verification. An in-lab, between-subject study demonstrates SPHERE's effectiveness in improving feedback quality and the overall feedback review process compared to a baseline system using off-the-shelf LLM outputs. This work contributes a novel approach to scaling personalized feedback in programming education, addressing the challenges of real-time response, issue prioritization, and large-scale personalization. |
| title | SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.16513 |