Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations

Erfan Al-Hossami, Razvan Bunescu, Ryan Teehan, Laurel Powell, Khyati Mahajan, Mohsen Dorodchi


Abstract
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.
Anthology ID:
2023.bea-1.57
Volume:
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
709–726
Language:
URL:
https://aclanthology.org/2023.bea-1.57
DOI:
10.18653/v1/2023.bea-1.57
Bibkey:
Cite (ACL):
Erfan Al-Hossami, Razvan Bunescu, Ryan Teehan, Laurel Powell, Khyati Mahajan, and Mohsen Dorodchi. 2023. Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 709–726, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations (Al-Hossami et al., BEA 2023)
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