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CS1QA

Publisher GitHub
Description Repository for CS1QA: A Dataset for assisting Code-based Question Answering in an Introductory Programming Course, published at NAACL 2022 The annotated data can be found in this repository under the folder data. Due to the size of the unannotated chat and code data, we are unable to upload them to GitHub. If you want to get the data, please email the author at changyoon.lee@kaist.ac.kr. The authors have made their best efforts to anonymize the data. However, there might be some personal or sensitive information left unfound in the dataset. By using the dataset, you agree to not abuse the personal or sensitive information that might be present in the dataset, and also notify the authors at changyoon.lee@kaist.ac.kr once you come across such data.
Resource Type URL
Data Type log data
Creator Lee, Changyoon; Seonwoo, Yeon; Oh, Alice
Availability by request
Rights MIT License 2022 cyoon47
Programming Language Python
Publication Year 2022
Number of Semesters 2
Data Protection Anonymized
Measurement Type question-answer-code triples, chat logs
Population programming course
Sample Size 9237 and 17698
Country South Korea
Educational Institution KAIST, School of Computing
Learning Environment elice, https://elice.io/en
Aggregation yes
Aggregation Level question-answer pairs
Related Publication @article{lee2022cs1qa, title={CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course}, author={Lee, Changyoon and Seonwoo, Yeon and Oh, Alice}, journal={arXiv preprint arXiv:2210.14494}, year={2022} }
Research Question Develop a dataset containing questions about code
Future Work The dataset can be used as a benchmark for source code comprehension and question answering in the educational setting
Fairness Score Total score: 35% (initial) Findability: 2.5 of 7 Accessibility: 1 of 3 Interoperability: 2 of 4 Reusability: 3 of 10
ResourceOpen Dataset