Journal
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
Volume 12, Issue 1, Pages 29-43Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TLT.2018.2808187
Keywords
Deep neural networks; indexing methods; question bank management; text analysis
Funding
- Beijing Natural Science Foundation [4182031]
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In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging, an alternative, has not been studied extensively. In this paper, we propose a position-based attention model and keywords-based model to automatically tag questions with knowledge units. With regard to multiple-choice questions, the proposed models employ mechanisms to capture useful information from keywords to enhance tagging performance. Unlike traditional machine learning-based tagging methods, our models utilize deep neural networks to represent questions using contextual information. The experimental results show that our proposed models outperform some traditional classification and topic methods by a large margin on an English question bank dataset.
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