A Method for Generating Course Test Questions Based on Natural Language Processing and Deep Learning

Assessment is viewed as an important means to understand learners' performance in the learning process. A good assessment method is based on high-quality examination questions. However, generating high-quality examination questions manually by teachers is a time-consuming task, and it is not ea...

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Bibliographic Details
Published in:Education and information technologies 2024-05, Vol.29 (7), p.8843-8865
Main Authors: Wang, Hei-Chia, Chiang, Yu-Hung, Chen, I-Fan
Format: Article
Language:English
Subjects:
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computers and Education
Deep learning
Education
Educational Technology
Information Systems Applications (incl.Internet)
Instructional Materials
Language Processing
Learning Processes
Natural language processing
Teaching Methods
Tests
User Interfaces and Human Computer Interaction
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Summary:Assessment is viewed as an important means to understand learners' performance in the learning process. A good assessment method is based on high-quality examination questions. However, generating high-quality examination questions manually by teachers is a time-consuming task, and it is not easy for students to obtain question banks. To solve this issue, this study proposes an automatic high-quality question generation system based on natural language processing and Topic Model. A two-stage test-question generation method (sentence selection and neural question generation) is proposed in this study. We apply multisource teaching materials to select declarative sentences, and then a neural question generation model called topic-embedding question generation (TE-QG) is employed to generate high-quality examination questions. This model is based on attention and the pointer-generator mechanism. The experimental results show that the sentence selection method can select sentences that meet the key points of the course, and the performance of the TE-QG model outperforms those of existing NQG models.
ISSN:1360-2357
1573-7608
DOI:10.1007/s10639-023-12159-9