Hybrid Deep Learning Approach for Education Data Mining and Improvement of Education Practices

Abstract

The exponential growth of educational data necessitates innovative approaches to mining and utilizing this information to enhance educational practices. This study proposes a hybrid deep learning framework for educational data mining (EDM) that integrates various data sources, advanced feature engineering techniques, and state-of-the-art classification algorithms to improve learning outcomes and institutional decision-making processes. The research utilizes a diverse dataset comprising EDM applications, real-time educational data, and synthetic student data to develop robust models. Feature engineering is conducted using a hybrid approach that combines TF-IDF, N-gram, bigram relational models, autoencoders, and density-based techniques, aiming to maximize data representation and reduce dimensionality. The classification phase incorporates an array of traditional and deep learning methods, including Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), AdaBoost, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and a novel hybrid RNN-SVM model. The proposed hybrid RNN-SVM classifier demonstrates superior accuracy and scalability by leveraging RNN's sequential learning capabilities and SVM's margin-based classification efficiency. Additionally, a recommendation module is designed to provide actionable insights, including class improvement strategies and industry-oriented suggestions, thus bridging the gap between academic performance and professional readiness. The hybrid deep learning framework not only enhances predictive accuracy but also facilitates informed decision-making for educators and policymakers. Experimental results validate the framework's efficacy in mining meaningful patterns from complex educational datasets and optimizing learning strategies. This research highlights the transformative potential of hybrid deep learning in advancing the field of EDM and fostering improved educational practices.