Laurel Powell


2023

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Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations
Erfan Al-Hossami | Razvan Bunescu | Ryan Teehan | Laurel Powell | Khyati Mahajan | Mohsen Dorodchi
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

Socratic questioning is a teaching strategy where the student is guided towards solving a problem on their own, instead of being given the solution directly. In this paper, we introduce a dataset of Socratic conversations where an instructor helps a novice programmer fix buggy solutions to simple computational problems. The dataset is then used for benchmarking the Socratic debugging abilities of GPT-based language models. While GPT-4 is observed to perform much better than GPT-3.5, its precision, and recall still fall short of human expert abilities, motivating further work in this area.