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
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.3
- 발행연도
- 2023.6
- 수록면
- 243 - 251 (9page)
- DOI
- 10.5573/IEIESPC.2023.12.3.243
이용수
초록· 키워드
In the post-epidemic era, some colleges and universities in China are still semi-closed. Some courses have adopted a mixed online and offline teaching model. This study attempts to solve the problem that Chinese painting online education is challenging to integrate into emotional education. In particular, this paper proposes an emotion-oriented hybrid-teaching model(Ed note: Compound adjectives that modify a noun are typically hyphenated but not when the first word of the adjective is an adverb ending with “-ly.”) based on an improved convolutional neural network (CNN). This mode can recognize students" movements and expressions online to judge their emotional state and improve the effectiveness of online teaching. After combining the mixed teaching mode and existing research on action recognition, the mainstream long-term and short-term memory network and attention mechanism are ineffective for emotion classification. The research first reduces the dimensionality of the input image. It then introduces an emotion-oriented weight shift module and uses the content analysis method. The experimental results showed that the teaching model proposed in the study does not improve theoretical knowledge and learning attitude significantly, but the emotional development index and emotional development quality are 110.15% and 16.62% higher, respectively, than the general method. Compared to the general teaching method, the method proposed in the research has a better effect on emotional development.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- Abstract
- 1. Introduction
- 2. Related Work
- 3. Emotional Blended Teaching Mode Based on Improved Convolutional Neural Network
- 4. Training of CNN Action Recognition Classification Algorithm Based on Residual Network
- 5. Conclusion
- 6. Fundings
- Reference
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0102-2023-569-001738698
