Enhancing students’ authentic mathematical problem-solving skills and confidence through error analysis of GPT-4 solutions

Abstract

Authentic mathematical problems connect mathematics to real-life scenarios, making mathematics learning more meaningful. However, students often find it challenging to comprehend the complexity and extensive textual descriptions of authentic mathematical problems, resulting in a lack of mathematical confidence. This study aims to investigate whether error analysis learning activity of GPT-4 solutions can enhance the skill of fifth-grade students to solve authentic mathematical problems and foster their mathematical confidence. A quasi-experimental design was employed, involving 59 fifth-grade students from a primary school in northern Taiwan. The experimental group engaged in error analysis learning activity of GPT-4 solutions, while the control group received traditional instruction, with both groups using the same teaching materials. Quantitative assessments were conducted through tests on solving authentic mathematical problems and a mathematical confidence scale, complemented by qualitative data collected via semi-structured interviews. The results revealed that the experimental group showed significant improvement in solving authentic mathematical problems, both in pre- and post-test comparisons within the group and in post-test comparisons between groups. Furthermore, the low-achieving students in the experimental group showed a significant improvement in solving authentic mathematical problems compared to the control group. Additionally, the mathematical confidence of both high- and low-achieving students in the experimental group was significantly higher than that of the control group. This study confirms the effectiveness of GPT-4 in mathematics education, offering new teaching strategies and research directions for educators and researchers.