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
- 학술대회자료
- 저자정보
- 발행연도
- 2023.2
- 수록면
- 815 - 821 (7page)
이용수
초록· 키워드
As the use of online learning expands, it becomes increasingly important to predict and support at-risk students early. However, it is unclear which types of online learning behaviors (i.e., passive or active) are better able to predict at-risk students. Passive behavior (e.g., homepage access) refers to basic interaction with the system, while active behavior (e.g., foruming) refers to interactions involving other users. Using Open University Learning Analytics Dataset, we compared the predictive performance of passive and active behavior data to predict at-risk students. We used a random forest classifier to classify 3,994 students into either success or at-risk. Results showed that the predictive performance of passive behavior (accuracy: 0.78, precision: 0.79, recall: 0.91) was higher than that of active behavior (accuracy: 0.75, precision: 0.77, recall: 0.87) up to 20 weeks. These findings suggest the importance of fundamental passive behavior in online learning, such as accessing a homepage, compared to auxiliary active behavior. Through passive behavior analysis, instructors can predict at-risk students and help them successfully complete online courses.
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목차
- Abstract
- 1. Introduction
- 2. Related works
- 3. Materials and methods
- 4. Experimental results
- 5. Discussion and conclusion
- References
참고문헌
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