MNIST-Fraction: Enhancing Math Education with AI-Driven Fraction Detection and Analysis

TL;DR

This paper introduces MNIST-Fraction, a specialized dataset for recognizing handwritten fractions, and demonstrates how deep learning models can effectively detect and analyze fractions to support math education.

Contribution

The creation of MNIST-Fraction dataset and its validation for AI-driven recognition of handwritten fractions in educational contexts.

Findings

Deep learning models accurately recognize handwritten fractions.

MNIST-Fraction outperforms original MNIST in classification tasks.

The dataset effectively supports educational tools for math learning.

Abstract

Mathematics education, a crucial and basic field, significantly influences students' learning in related subjects and their future careers. Utilizing artificial intelligence to interpret and comprehend math problems in education is not yet fully explored. This is due to the scarcity of quality datasets and the intricacies of processing handwritten information. In this paper, we present a novel contribution to the field of mathematics education through the development of MNIST-Fraction, a dataset inspired by the renowned MNIST, specifically tailored for the recognition and understanding of handwritten math fractions. Our approach is the utilization of deep learning, specifically Convolutional Neural Networks (CNNs), for the recognition and understanding of handwritten math fractions to effectively detect and analyze fractions, along with their numerators and denominators. This capability is pivotal in calculating the value of fractions, a fundamental aspect of math learning. The MNIST-Fraction dataset is designed to closely mimic real-world scenarios, providing a reliable and relevant resource for AI-driven educational tools. Furthermore, we conduct a comprehensive comparison of our dataset with the original MNIST dataset using various classifiers, demonstrating the effectiveness and versatility of MNIST-Fraction in both detection and classification tasks. This comparative analysis not only validates the practical utility of our dataset but also offers insights into its potential applications in math education. To foster collaboration and further research within the computational and educational communities. Our work aims to bridge the gap in high-quality educational resources for math learning, offering a valuable tool for both educators and researchers in the field.