Educational Text Classification

Educational text classification aims to automatically categorize educational materials, such as student questions or dialogue transcripts, to gain insights into learning processes and improve educational practices. Current research focuses on leveraging large language models (LLMs) like GPT and Llama, often employing techniques like transfer learning and data augmentation to address challenges such as limited labeled data and the complexity of multi-label classification. These advancements hold significant potential for automating tasks in learning analytics, enabling more efficient and effective personalized learning experiences and providing educators with valuable data-driven feedback.