SPAT: An intelligent educational system for student performance prediction using learning analytics and visualization

Abstract

The aim of personalized education is to tailor the learning experience in accordance with individual students’ academic needs and maximize their academic performance through these customized approaches. However, the unavailability of real-time insights and personalized guidance poses a major challenge in the prediction of students’ grades and the development of customized study plans. To overcome these limitations, our research proposes the Student Performance Analysis Tool (SPAT), a machine learning-based analytical platform implemented in Python. SPAT was built using a dataset of 145 university students from U.S. university data. The tool focuses on predicting student performance based on academic and non-academic features which is novel in execution. Further, the tool investigates on seven machine learning models such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, and AdaBoost. Also, SPAT combines real-time data visualization and grade prediction with an interactive interface, allowing teachers and students to make data-driven, informed decisions for academic enhancement. Results represented by SPAT indicates high accuracy for predicting the student rate for algorithms Gradient Boost and XGBoost as 79%. Through the use of AI-powered analytics, the tool proposes adaptive learning methods, showing the power of machine learning in developing data-driven with tailored education.