Classification and Labeling Techniques of Educational Resources Based on BERT-CNN model

The key factor in promoting accessibility and improving the educational experience is accurate classification and labelling of educational materials. Research paper presents a novel approach to educational material classification that makes use of BERT-CNN, a hybrid model that combines and CNNs. The limitations of conventional approaches are addressed by research methodology, which seamlessly integrates a contextual understanding of BERT with CNN's extraction capabilities to address the prior difficulty. It has frequently been difficult for prior techniques to properly capture category elements and comprehend contextual complexities in educational text classification. Here, research leverage the pre-trained contextual embeddings of BERT to enable research model to capture complicated semantics in educational texts, and the CNN architecture successfully extracts categorical features from these embeddings. By combining these two potent models, research is able to capture the logical and structural characteristics of educational resources and create a thorough knowledge of them. Furthermore, research implementation in Python makes use of well-known deep learning packages like TensorFlow and PyTorch, guaranteeing repeatability and accessibility for practitioners and researchers. Research suggested model performs very well, achieving 99% classification accuracy through extensive manipulation and analysis of datasets on various educational resources. The findings demonstrate the efficiency and resilience of research approach in correctly categorizing and labelling instructional materials, underscoring its significance in the management of educational content and strategic enhancement.