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- W4288059389 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." @default.
- W4288059389 created "2022-07-28" @default.
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- W4288059389 date "2022-01-01" @default.
- W4288059389 modified "2023-10-02" @default.
- W4288059389 title "Automatic Question Generation for Scaffolding Self-explanations for Code Comprehension" @default.
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- W4288059389 doi "https://doi.org/10.1007/978-3-031-11644-5_77" @default.
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