Grade prediction of university student based on machine learning
Based on the historical performance data,one card consumption data,campus network log data,and library card swiping record data of undergraduate students in a certain university,a method for predicting student grades using student behavior data is proposed.Five commonly used prediction methods(logistic regression algorithm,support vector machine algorithm,decision tree algorithm,K-nearest neighbor algorithm,and naive Bayesian algorithm)for educational data mining are selected for model optimization through Stacking integration.Experimental results show that this method has higher accuracy compared to predicting grades using grades alone and classification models alone.This study has certain significance for assisting teaching management and promoting the construction of smart campuses.
education data mininggrade predictioncombination optimization
NETL
NSTL
万方数据
维普
