Abstract
This work presents two systems, Machine Noun Question Generation (QG) and Machine Verb QG, developed to generate short questions and gap-fill questions, which Intelligent Tutoring Systems then use to guide students’ self-explanations during code comprehension. We evaluate our system by comparing the quality of questions generated by the system against human expert-generated questions. Our result shows that these systems performed similarly to humans in most criteria. Among the machines, we find that Machine Noun QG performed better.
This work is supported by the National Science Foundation under grant number 1822816 and 1934745. All findings and opinions expressed are solely the authors’.
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Tamang, L.J., Banjade, R., Chapagain, J., Rus, V. (2022). Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_77
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