Analysis of the web application for learning mathematics considering machine learning algorithms and non-parametric tests
DOI:
https://doi.org/10.29352/mill0231.47605Keywords:
ICT; teaching; mathematics; higher education; derivativeAbstract
Introduction: Mathematics professors can change the way of teaching and learning by incorporating technology into educational activities.
Objective: To analyze the use of the Web Application for Learning mathematics about Optimization based on the area of a rectangle through Derivatives (WALOD), considering machine learning algorithms and non-parametric tests. This technological tool shows the detailed procedure to calculate the maximum area of a rectangle using a web simulator.
Methods: The 37 participants are students of Earth Sciences who took the course of Mathematics during the 2024 school year. The sample is non-probabilistic, and the scopes used are descriptive, causal, and correlational.
Results: The Mann-Whitney test indicates that students' sex influences the use of WALOD for learning mathematics and
satisfaction. On the other hand, the Kruskal-Wallis test shows that age influences the use of WALOD for learning mathematics, satisfaction, and creation of virtual educational spaces. The results of the linear regression algorithm show that the use of WALOD for learning mathematics positively influences motivation, satisfaction, and the creation of virtual educational space. The decision
tree algorithm determined three predictive models for this application. In the field of mathematics, the practical implications about the use of WALOD include personalized learning, ease of use, autonomy during the teaching and learning process of mathematics, and ability to resolve doubts at any time.
Conclusion: In conclusion, WALOD is an innovative and interactive tool that facilitates learning mathematics about optimization through derivatives from anywhere and fosters autonomy during the education process.
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