Facilitating Comprehension: An NLP-Based Converter for 7th Grade Students to Transform English Sentences into Mathematical Expressions

Based on research findings, a significant obstacle for students in learning mathematics is their difficulty understanding word problems. Converting verbal explanations into mathematical equations becomes difficult, especially when several procedures are necessary. Thus, this study developed a supplemental tool for grade 7 students that is designed to assist them in effectively converting English language phrases into mathematical expressions, thereby enhancing their learning experience. This study encompasses both a web application and a translation algorithm. The web application is an educational tool that delivers content that teaches the mathematical concept of “Translating English Phrases and Sentences into Mathematical Equations and Statements”. Meanwhile, the translation algorithm, developed using Stanza, a Python Natural Language Processing (NLP) package, is tasked with transforming given English mathematical phrases into their corresponding mathematical expressions. The system was created using the Rapid Application Development (RAD) software development model to implement progressive improvements within three development iterations, and it was then assessed for functional suitability, usability, and reliability using the ISO/IEC-25010 System and software quality model. The outcomes of the survey evaluation revealed that the system proved to be fitting for learning math phrase translations. It proved dependability, convenience, and usability. Furthermore, the algorithm’s precision was evaluated, with 175 predictions yielding an accuracy rate of 81.14%. In summary, this study introduces a valuable resource for math educators and learners, enhancing their comprehension of math phrase translations.