A Machine Learning-Based Framework for Predicting and Improving Student Outcomes Using Big Educational Data

Abstract

Evaluating student academic achievements through prediction tools serves as a vital resource for both teaching staff and students seeking better educational methods. Effective predictive techniques enable students to study in targeted areas that use forecast outcomes while effective analytical approaches help instructors develop proper educational materials. A deep learning-based approach uses educational data from three colleges in Assam, India to forecast student academic performance in this research. A Gated Recurrent Unit (GRU) neural network served as the proposed method for detecting temporal patterns and dependencies within student data. The proposed model outperformed traditional approaches with Artificial Neural Networks (ANN) and Decision Trees (DT) where it delivered an accuracy rate at 99.70% and precision at 98.60% and recall at 96.30% with F1-score at 97.40%. The robustness and generalization capability of the GRU model is substantiated through evaluation using confusion matrix alongside accuracy and loss curve metrics.Deep learning analytics shows great promise for educational applications because this research delivers critical information about preventive actions and academic achievement enhancements. The findings reveal that the GRU model delivers exceptional capacity for identifying at-risk students early while enabling data-driven educational interventions to create better academic results.