Castillo Salazar, David Ricardo
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Castillo Salazar, David Ricardo
Main Affiliation
Ambato
Email
davidcastillo@uti.edu.ec
Scopus Author ID
57210800584

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Application of Machine Learning for the Development of Logical-Mathematical Thinking in Basic Education Students

2025 , Diana, Peñuela , Castillo Salazar, David Ricardo , Cabrera López, Julio Rafael , Dario Castillo Salazar

The research involves upper basic education students in arguments related to Scratch software, focusing on teaching mathematics from the perspective of the theory that suggests learners construct their own knowledge through experience and reflection. Using Scratch and artificial intelligence processes to generate projects promotes active and meaningful learning. From this perspective, the goal is for students to develop logical-mathematical thinking, which is essential for analyzing, evaluating, and effectively applying information, thereby strengthening creativity and problem-solving skills centered on knowledge assessment. This approach allows for the application of theoretical and practical AI in educational software applications. The results indicate that mathematical competencies improve with the use of Scratch software. Additionally, the research identifies students’ skills and interest in the potential of AI technologies to enhance the understanding of computational mathematical concepts. The research concludes that the integration of the AI module in Scratch strengthens mathematical competencies in the field of education

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Impact of Machine Learning on the Development of Mathematical Operations Using Scratch

2025 , Cabrera López, Julio Rafael , Castillo Salazar, David Ricardo , Diana, Peñuela , Darío Castillo Salazar

This research focused on analyzing the impact of Machine Learning (ML) applied through the Scratch platform on the development of mathematical operations in secondary school students. The quantitative, descriptive, and field-based study was conducted with a sample of 30 students from public schools in educational zone three. A survey technique was used with a questionnaire validated by education experts, and the instrument’s reliability was measured with a Cronbach’s Alpha of 0.89. The results obtained from pretest and posttest, as well as the application of the t-student test and ANOVA analysis, demonstrated that the experimental group, which used Scratch with ML, showed significant improvements in their mathematical performance compared to the control group. Additionally, the impact analysis based on Cohen’s D revealed a considerable effect on academic performance, reinforcing the effectiveness of the proposal. These findings not only highlight the importance of incorporating emerging technologies in education but also suggest that the use of interactive and adaptive approaches can optimize the learning of mathematics. The research concludes that integrating ML and Scratch in mathematics education is an effective tool for enhancing students’ understanding and developing critical skills

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