상세정보
학술지
DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring
- 발행일
- 202301
- 출처
- IEEE Access, v.11, pp.8978-8987
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 22HS4800, 준지도학습형 언어지능 원천기술 및 이에 기반한 외국인 지원용 한국어 튜터링 서비스 개발, 이윤근
- 초록
- 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 제안 키워드
- English reading, Further development, High potential, Human evaluation, Learning Experience, Personalized Learning, Proposed model, Reading comprehension, Real-world, Student engagement, comprehensive analysis
본 저작물은 크리에이티브 커먼즈 저작자 표시 - 비영리 - 변경금지 (CC BY NC ND) 조건에 따라 이용할 수 있습니다.
