A Novel Approach: Enhancing Data Extraction from Student Handwritten Notes Using Multi-Task U-net and GPT-4

Handwritten notes by students are a critical data source reflecting their learning status. Traditionally, educators have had to review these notes individually to gauge students' comprehension and mastery of course material. However, this method is time-consuming, inefficient, and often fails to capture and quantify students' learning progress and challenges comprehensively. With advancements in technology, especially in text recognition and machine learning, new avenues have opened up for automating this review process. This allows for a more efficient and systematic analysis of students' learning situations. This study aims to explore how these technologies can be utilized to automatically identify and analyze key information in students' handwritten notes. The goal is to assess students' learning outcomes and understanding more effectively. By automating the recognition and analysis of handwritten notes, the study seeks to provide educators with a powerful tool to monitor students' learning progress more accurately, identify learning obstacles, and offer personalized feedback and support based on individual needs. This paper introduces a technology integrating Attention Multi-task U-Net and GPT-4 for extracting data from handwritten notes. The method facilitates better understanding and analysis of student notes, offering teachers precise learning data and aiding students in receiving personalized learning support. The study underscores its potential in educational technology, particularly in improving teaching quality and student learning outcomes.