Revolutionizing Education: Advanced Machine Learning Techniques for Precision Recommendation of Top-Quality Instructional Materials

Abstract

Abstract The integration of educational technology in the modern classroom has transformed the way students learn yet challenges in providing high-quality materials persist. To address this, we propose a novel support vector-based long short-term memory (LSTM) recommendation model. Our model combines support vector machines (SVM) and LSTM networks to enhance accuracy. The SVM analyzes material content, identifying key features for topic relevance. Meanwhile, the LSTM assesses word sequences to predict material relevance to the topic. We conducted experiments on a diverse instructional dataset, demonstrating superior performance in accuracy and relevance compared to existing models. Our model adapts to new data and continuously improves based on user feedback. Therefore, our Support Vector-based LSTM recommendation model can revolutionize instructional material recommendations. Its accuracy and relevance enhance student engagement and learning outcomes, optimizing the educational experience.