Web recommendation system based on Artificial Intelligence for teaching linear equations to secondary school students

Abstract

High school students struggle with linear equations due to limited feedback; thus, educational institutions increasingly use technological tools to effectively support, enhance, and strengthen student learning outcomes. The research aimed to develop and implement a web-based recommendation system based on artificial intelligence designed to reinforce the learning of linear equations in secondary school students. A quantitative approach with a pre-experimental design was applied, using pre-test and post-test to measure the impact of the system on reasoning, mathematical demonstration, and problem-solving skills. The reliability of the instruments was validated using Cronbach's alpha coefficient, with values above 0.80, while the Wilcoxon test confirmed significant differences (p < 0.05) between the initial and final results. The findings showed an average increase of more than 16% in student performance, demonstrating the effectiveness of the proposed solution. From a technological standpoint, the system integrates a recommendation engine, developed using the agile Scrum methodology, which made it possible to build a tool adaptable to pedagogical needs. In conclusion, the web-based recommendation system is an innovative and effective alternative for improving mathematics learning and lays the foundation for future research related to the personalization of the educational process through artificial intelligence techniques.