A Practical Model for Educators to Predict Student Performance in K-12 Education using Machine Learning

Abstract

Predicting classifiers can be used to analyze data in K-12 education. Creating a classification model to accurately identify factors affecting student performance can be challenging. Much research has been conducted to predict student performance in higher education, but there is limited research in using data science to predict student performance in K-12 education. Predictive models are developed and examined in this review to analyze a K-12 education dataset. Three classifiers are used to develop these predictive models, including linear regression, decision tree, and Naive Bayes techniques. The Naive Bayes techniques showed the highest accuracy when predicting SAT Math scores for high school students. The results from this review of current research and the models presented in this paper can be used by stakeholders of K-12 education to make predictions of student performance and be able to implement intervention strategies for students in a timely manner.