Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension
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.
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Recommended Citation
Tamang, L., Banjade, R., Chapagain, J., & Rus, V. (2022). Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13355 LNCS, 743-748. https://doi.org/10.1007/978-3-031-11644-5_77
