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Journal Article DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring
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Authors
Jin-Xia Huang, Yohan Lee, Oh-Woog Kwon
Issue Date
2023-01
Citation
IEEE Access, v.11, pp.8978-8987
ISSN
2169-3536
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/ACCESS.2022.3233224
Project Code
22HS4800, Development of semi-supervised learning language intelligence technology and Korean tutoring service for foreigners, Lee Yunkeun
Abstract
A major challenge in education is to provide students with a personalized learning experience. This study aims to address this by developing a dialogue-based intelligent tutoring system (ITS) that imitates human expert tutors. The ITS asks questions, assesses student answers, provides hints, and even chats to encourage student engagement. We constructed the Dialogue-based Reading Comprehension Tutoring (DIRECT) dataset to simulate real-world pedagogical scenarios with the assessment labels and key sentences to support tutoring. The DIRECT dataset is based on RACE, which is a large-scale English reading comprehension dataset. In addition, we propose a neural pipeline approach to model the tutoring tasks and conduct a comprehensive analysis on the results, including a human evaluation. The results show that our model performs well in generating questions, assessing answers, and chatting, showing high potential although some challenges remain. The proposed model provides a good basis for further development of dialogue-based ITSs.
KSP Keywords
English reading, Further development, High potential, Human evaluation, Learning Experience, Personalized Learning, Proposed model, Reading comprehension, Real-world, Student engagement, comprehensive analysis
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
CC BY NC ND