Earticle

초록

영어

A common approach to the problem of predicting student test scores is based on the student's previous educational history. In this study, high school transcripts of about two thousand candidates, who took the High-school Student Assessment (HSA) were collected. The data were estimated through building a regression model - Random Forest and optimizing the model's parameters based on Genetic Algorithm (GA) to predict the HSA scores. The RMSE (Root Mean Square Error) measure of the predictive models was used to evaluate the model’s performance.

목차

Abstract
1. Introduction
2. Machine learning framework to predicts HSA score
3. Experiments and results
3.1. Dataset
3.2. Experimental results and discussions
4. Conclusion
References

키워드

저자

• Nguyen Ba Tiến [ Center for Educational Testing Vietnam National University Hanoi, Vietnam ]
• Hoai-Nam Nguyen [ VNU University of Education, Vietnam National University in Hanoi, Vietnam, ICT Department, Ministry of Education and Training, Vietnam ]
• Hoang-Ha Le [ VNU University of Education, Vietnam National University in Hanoi, Vietnam ]
• Tran Thu Trang [ Faculty of Information Technology, Dainam university, Hanoi, Vietnam ]
• Chau Van Dinh [ Faculty of Information Technology, Electric Power University, Hanoi Vietnam ]
• Ha-Nam Nguyen [ Faculty of Information Technology, Electric Power University, Hanoi Vietnam ]
• Gyoo Seok Choi [ Department of Computer Science, Chungwoon University, Incheon, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380