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We introduce CS1QA, a dataset for code-based question answering in theprogramming education domain. CS1QA consists of 9,237 question-answer pairsgathered from chat logs in an introductory programming class using Python, and17,698 unannotated chat data with code. Each question is accompanied with thestudent's code, and the portion of the code relevant to answering the question.We carefully design the annotation process to construct CS1QA, and analyze thecollected dataset in detail. The tasks for CS1QA are to predict the questiontype, the relevant code snippet given the question and the code and retrievingan answer from the annotated corpus. Results for the experiments on severalbaseline models are reported and thoroughly analyzed. The tasks for CS1QAchallenge models to understand both the code and natural language. This uniquedataset can be used as a benchmark for source code comprehension and questionanswering in the educational setting.

CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course