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.9 No.3
- 발행연도
- 2020.6
- 수록면
- 217 - 229 (13page)
- DOI
- 10.5573/IEIESPC.2020.9.3.217
이용수
초록· 키워드
Education is crucial for the development of any country. Analysis of education datasets requires effective algorithms to extract hidden information and gain the fruitful results to improve academic performance. Multiple models were used to maximize the contribution to the education environment. In this study, we used the spot-checking algorithm to compare these methods and find the most effective method. We propose three main classes of education research tools: a statistical analysis method, machine learning algorithms, and a deep learning framework. The data were obtained from many high schools in Cambodia. We introduced feature selection techniques to figure out the informative features that affect the future performance of students in mathematics. The proposed ensemble methods of tree-based classifiers provide satisfiying results, and in that, random forest algorithm generates the highest accuracy and the lowest predictive mean squared error, thus showing potential in this prediction and classification problem. The results from this work can be used as recipe and recommendation for mining various material settings in improving high school student performance in Cambodia.
#Education data mining
#Statistical analysis technique
#Machine learning algorithms
#Deep belief network
#Predicting student performance
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목차
- Abstract
- 1. Introduction
- 2. Review of Previous Works
- 3. Research Methods
- 4. Data Collection and Preprocessing Tasks
- 5. Experimental Results and Analysis
- 6. Feature Selection
- 7. Discussion and Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2020-569-000684313
