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An education analysis of K12 students in NAEP math exam based on LightGBM

In education, quality is the focus; improving the quality of education and teaching has always been one of the goals pursued by educators. Machine learning and data mining techniques can reveal data-related laws and extract valuable information and data to solve problems in various fields. This paper proposes a model to predict the National Assessment of Educational Progress (NAEP) exam scores using LightGBM, a kind of GBDT (gradient boosting decision tree) that owns optimal performance in the industry field. It performs a comparison-based experiment using the same metrics and the same dataset. The lower the Root Mean Square Error (RMSE), the better performance that the model will gain. Accordingly, the LightGBM model has the best performance, with 0.544 and 9.344 lower than SVM and Linear Regression, respectively.