Modeling in High School: Rate of Change Model for Predicting the Number of Cases and Deaths from Covid-19
Laboratory of Educational Data Analysis and Applied Statistics, Department of Physics and Mathematics, Federal Institute of Ceará. Fortaleza, Ceará, Brazil.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1032–1043
Publication history:
Received on 25 June 2024; revised on 04 August 2024; accepted on 06 August 2024
Abstract:
This study presents an innovative approach to mathematical modeling in the context of high school education, introducing a new paradigm aimed at simplifying the understanding and application of this discipline for students in this age group. The core of this approach is the creation of the MTV model (Rate of Variation Model). This creation consistently demonstrated superior performance compared to traditional models such as the Gompertz model.
The novelty brought by MTV stands out by incorporating machine learning algorithms, which dynamically adapt the model parameters based on available data. This results in more accurate predictions and remarkable ability to adjust to variations in observed data.
Experiments conducted with different datasets reinforce the effectiveness of this new paradigm, demonstrating consistently superior performance compared to the Gompertz model. The flexibility shown in dealing with a variety of growth patterns and the ability to adapt to less structured data consolidate its robustness. Moreover, by allowing students to obtain results closer to reality, the approach promotes a deeper understanding of the underlying mathematical principles.
Consequently, the creation of the MTV Model, with its foundation in machine learning principles and dynamic adaptation, may lead to a new generation of high school students better prepared to tackle quantitative challenges in an ever-changing landscape.
Keywords:
Mathematics; Statistics; Probability; Machine Learning; Gompertz.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0