AI-Powered Student Dropout Prediction and Personalized Intervention Using TC-Net in Education
TL;DR
The findings demonstrate the capability of AI models in minimising dropout risks and enhancing academic performance through prompt, data-informed assistance.
Abstract
Increasing student dropout rates, caused by social, academic, and personal obstacles, raise significant concerns for educational systems. This research introduces an AI-based method for the early detection and support of at-risk students utilizing the Student Final Grade Prediction dataset from two schools in Portugal. Following data preprocessing, which involved cleaning and feature extraction via t-distributed Stochastic Neighbor Embedding (t-SNE), a hybrid model integrating Tabular Data Network and Capsule Networks (TC-Net) was created for performance forecasting. The model obtained a Mean Squared Error (MSE) of 0.43 and an R-squared value of 60.57%, demonstrating robust predictive accuracy. Personalised strategies were then implemented, targeting 90% of at-risk students. Specifically, 80% participated in tailored learning plans, and 70% accessed tutoring support. The findings demonstrate the capability of AI models in minimising dropout risks and enhancing academic performance through prompt, data-informed assistance.