Predict students performance based on machine learning algorithms

Abstract

Several studies have used models for students academic performance prediction to improve teaching quality. The aim of this study is to use machine learning algorithms to forecast students performances using their daily study behaviors and the extent of parents concerns about their childrens studies to generate synchronous predictions with daily teaching activities. The data includes study attitudes, behaviors, demographic features, and parents concerns about the students. The preprocessed dataset after feature engineering was used to train the models (i.e Support Vector Machine, Decision Tree, Random Forest, And K Nearest Neighbor). Random Forest has the best performance among the algorithms applied. The impact of students daily study behavior is highly related to academic achievement, and parents impact is also an influencing factor on childrens performances. This study could encourage and motivate parents to care more about their childrens studies with their favorable actions and behaviors. Besides, this study would help students realize the importance of their daily performance and realize it is essential to their final exam grades.