Automated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models

TL;DR

This paper compares meta-learning models and Vision Large Language Models for automatically grading students' handwritten graphs, revealing that meta-learning excels in simpler tasks while VLLMs perform slightly better in more complex classifications.

Contribution

It introduces multimodal meta-learning models for autograding handwritten graphs and compares their performance with VLLMs, a novel approach in this context.

Findings

Meta-learning models outperform VLLMs in 2-way classification.

VLLMs slightly outperform meta-learning in 3-way classification.

VLLMs' reliability and practical use need further research.

Abstract

With the rise of online learning, the demand for efficient and consistent assessment in mathematics has significantly increased over the past decade. Machine Learning (ML), particularly Natural Language Processing (NLP), has been widely used for autograding student responses, particularly those involving text and/or mathematical expressions. However, there has been limited research on autograding responses involving students' handwritten graphs, despite their prevalence in Science, Technology, Engineering, and Mathematics (STEM) curricula. In this study, we implement multimodal meta-learning models for autograding images containing students' handwritten graphs and text. We further compare the performance of Vision Large Language Models (VLLMs) with these specially trained metalearning models. Our results, evaluated on a real-world dataset collected from our institution, show that the best-performing meta-learning models outperform VLLMs in 2-way classification tasks. In contrast, in more complex 3-way classification tasks, the best-performing VLLMs slightly outperform the meta-learning models. While VLLMs show promising results, their reliability and practical applicability remain uncertain and require further investigation.