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A Method for Generating Course Test Questions Based on Natural Language Processing and Deep Learning
- Source :
-
Education and Information Technologies . 2024 29(7):8843-8865. - Publication Year :
- 2024
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 1360-2357 and 1573-7608
- Volume :
- 29
- Issue :
- 7
- Database :
- ERIC
- Journal :
- Education and Information Technologies
- Publication Type :
- Academic Journal
- Accession number :
- EJ1424253
- Document Type :
- Journal Articles<br />Reports - Research
- Full Text :
- https://doi.org/10.1007/s10639-023-12159-9