인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
To address the negative impact of information overload, an analysis is conducted on learning resource recommendation methods in teaching vertical networks. Firstly, the gating mechanism in long short-term memory networks is utilized to reflect learners’ learning and forgetting. Meanwhile, attention mechanism is combined to capture the impact of different difficulty levels on knowledge tracking. A knowledge tracking model using long short-term memory network is constructed. Then, combining model-based collaborative filtering algorithms and attention mechanisms, a collaborative filtering-based learning resource recommendation model is constructed. The proposed knowledge tracking model performed better in precision, recall, and F1 score, with scores of 92.43%, 91.37%, and 92.16%, respectively. On the Assisment2012 and RAIEd2020 datasets, the proposed model’s area under the curve was 82.37% and 81.54%, respectively. The complete model’s performance was the best. The model without attention mechanism had the smallest area under the curve, which was 72.14% and 70.46%, respectively. The proposed learning resource recommendation model performed the best for recommendation precision, recall, and F1 score. The learning resource recommendation list’s diversity was good. These research results contribute to improving learning outcomes and promoting the sustainable development of teaching vertical networks.
본문·목차
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UCI(KEPA) : I410-151-26-02-094678489
