Research Article
Identifying Factors Influencing China Junior High Students' Cognitive Ability through Educational Data Mining: Utilizing LASSO, Random Forest, and XGBoost
@INPROCEEDINGS{10.4108/eai.8-9-2023.2340189, author={Yiming Luo}, title={Identifying Factors Influencing China Junior High Students' Cognitive Ability through Educational Data Mining: Utilizing LASSO, Random Forest, and XGBoost}, proceedings={Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8--10, 2023, Wuhan, China}, publisher={EAI}, proceedings_a={ICMEIM}, year={2023}, month={11}, keywords={educational data mining machine learning cognitive ability random forest lasoo xgboost ceps}, doi={10.4108/eai.8-9-2023.2340189} }- Yiming Luo
Year: 2023
Identifying Factors Influencing China Junior High Students' Cognitive Ability through Educational Data Mining: Utilizing LASSO, Random Forest, and XGBoost
ICMEIM
EAI
DOI: 10.4108/eai.8-9-2023.2340189
Abstract
The study innovatively applied educational data mining techniques to the China Education Panel Survey, using LASSO regression, Random Forest, and XGBoost algorithms to identify factors influencing students' cognitive ability. Experimental results indicated that the XGBoost and Random Forest algorithms significantly outperformed the baseline model in assessing Chinese junior High Students' cognitive ability. The main factors identified as influencing cognitive ability include parental and student educational expectations, reading habits and reading and math skills, school environment, and myopia. Eventually, The study concludes by emphasizing the central role of parents and schools in the development of student's cognitive ability and the importance of physical health to students.