Objective: To develop and evaluate an automatic question generation system that can create high-quality examination questions from teaching materials using natural language processing and Topic Model, focusing on selecting meaningful declarative sentences and generating relevant questions.
Methods:
- Two-stage approach combining sentence selection and neural question generation
- Implementation of topic-embedding question generation (TE-QG) model
- Use of multisource teaching materials for sentence selection
- Experimental evaluation using SQuAD dataset
- Both objective metrics (BLEU, ROUGE-L) and subjective assessment (human evaluation)
- Comparison with baseline models including H&S, L2A, and NQG
Key Findings:
- The proposed Centroid_Emb method outperformed existing sentence selection methods
- TE-QG model achieved better performance than baseline models across all metrics
- Topic feature integration improved question generation quality
- Filtering parts of speech in topic words enhanced model performance
- Human evaluators rated TE-QG-generated questions higher in fluency, clarity, and usefulness
Implications: The system can help reduce teachers' workload in creating exam questions while providing students with high-quality practice materials for self-learning, contributing to more efficient and personalized educational assessment.
Limitations:
- Limited to single version/publisher of teaching materials
- Focuses only on textual content, excluding images and tables
- Restricted to factual questions
- Dependent on quality of input teaching materials
Future Directions:
- Explore innovative algorithms to improve efficiency
- Incorporate multiple versions/publishers of teaching materials
- Include image and table analysis
- Consider external resources for improved term comparison
- Develop methods to handle synonymous and proprietary terms
Title and Authors: "A Method for Generating Course Test Questions Based on Natural Language Processing and Deep Learning" by Hei-Chia Wang, Yu-Hung Chiang, and I-Fan Chen
Published on: 2024
Published by: Education and Information Technologies