MathEDU: Towards Adaptive Feedback for Student Mathematical Problem-Solving
This addresses the need for adaptive feedback in online math education, but it is incremental as it builds on existing LLM applications in education.
The paper tackled the problem of providing immediate, personalized feedback for student math problem-solving in online learning by exploring large language models (LLMs) to assess solutions and offer adaptive feedback, with results showing the fine-tuned model performs well in identifying correctness but faces challenges in generating detailed pedagogical feedback.
Online learning enhances educational accessibility, offering students the flexibility to learn anytime, anywhere. However, a key limitation is the lack of immediate, personalized feedback, particularly in helping students correct errors in math problem-solving. Several studies have investigated the applications of large language models (LLMs) in educational contexts. In this paper, we explore the capabilities of LLMs to assess students' math problem-solving processes and provide adaptive feedback. The MathEDU dataset is introduced, comprising authentic student solutions annotated with teacher feedback. We evaluate the model's ability to support personalized learning in two scenarios: one where the model has access to students' prior answer histories, and another simulating a cold-start context. Experimental results show that the fine-tuned model performs well in identifying correctness. However, the model still faces challenges in generating detailed feedback for pedagogical purposes.