Enhancing Educational Content Matching Using Transformer Models and InfoNCE Loss

The task of matching educational content to specific topics within curricula is crucial for enhancing learning outcomes and ensuring that students receive relevant and coherent learning experiences. This research seeks to develop a precise and efficient model, leveraging a dataset that spans various languages and numerous STEM fields. Our proposed solution integrates a Transformer model with InfoNCE Loss and model distillation techniques to effectively address noise from related content and improve matching accuracy. By incorporating advanced deep learning methods, the model is designed to handle the complexity and variability of educational datasets, outperforming existing models in terms of accuracy and efficiency. This approach provides a robust framework for educational content alignment, significantly benefiting educational practitioners by streamlining the content matching process and enhancing the quality of education delivered to students.