On the Opportunities of Large Language Models for Programming Process Data
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Abstract (arXiv)
Computing educators and researchers have used programming process data to understand how programs are constructed and what sorts of problems students struggle with. Although such data shows promise for using it for feedback, fully automated programming process feedback systems have still been an under-explored area. The recent emergence of large language models (LLMs) have yielded additional opportunities for researchers in a wide variety of fields. LLMs are efficient at transforming content from one format to another, leveraging the body of knowledge they have been trained with in the process. In this article, we discuss opportunities of using LLMs for analyzing programming process data. To complement our discussion, we outline a case study where we have leveraged LLMs for automatically summarizing the programming process and for creating formative feedback on the programming process. Overall, our discussion and findings highlight that the computing education research and practice community is again one step closer to automating formative programming process-focused feedback.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 91 references that are in the map. First posted on arXiv 1 Nov 2024.
Builds on
15 of 91 references.
References · 15
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3 more
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Citing papers per year: 2026: 1.
1 paper.