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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
      11
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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
      26
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    Topological Analysis Techniques for Improving Neural Network Performance in COVID-19 Detection Using Persistent Homology
    (2024)
    Israel Reyes
    ;
    Karen Cáceres-Benítez
    ;
    Ana Marcillo
    ;
    Andre Vera
    ;
    Carolina Cadena-Morejón
    In this study, we employ topological data analysis techniques on neural networks applied in COVID-19 detection, aiming to improve their predictive power. Leveraging the power of persistent homology, a mathematical tool for extracting topological features from intricate datasets, we turned chest X-ray images into a representation of the topological features. This representation was used to train and test the ability of neural networks to learn topological properties from images. We examine neural networks trained on chest radiographs containing both COVID-19 positive and negative cases. Our results suggest that, by identifying specific topological features correlated with COVID-19 detection, we may enhance the performance of the neural network models and analyze the underlying factors contributing to high accuracy rate of detection. The findings from this study contribute as exploratory advance in the field of medical imaging analysis and disease detection, showcasing the potential of topological analysis within neural networks.
      25
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    Machine Learning Algorithms for Filtering Data Acquired by Arduino
    (2024)
    Mateo Llerena
    ;
    Jessica López
    ;
    Cumbajín Alferez, Myriam  
    ;
    Carlos Gordón
    In this paper, we developed an artificial intelligence-based data filtering algorithm to improve the accuracy and reliability of MPU6050 sensor measurements. Using the Edge Impulse platform, we trained and optimized the machine learning model for real-time processing of accelerometer and gyroscope readings. The solution was implemented on the ESP32 with a sampling rate of 600 Hz. The experimental results validated the effectiveness of the solution, highlighting its relevance in applications requiring real-time monitoring
      10
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    Machine Learning Techniques for Academic Prediction: Comparative Analysis of Random Forest, XGBoost and Classical Techniques CaseT
    (2025)
    Edwin Alexander Aguilar Sanchez
    ;
    Marcos Chacón-Castro
    ;
    Jadán Guerrero, Janio  
    The use of machine learning techniques has transformed various sectors, including education, by allowing for more accurate prediction of students’ academic preferences. The main objective of this study is to compare the effectiveness of classical algorithms, such as Logistic Regression, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN), with advanced algorithms, such as Random Forest and XGBoost, in predicting academic program choices in students at a technical university in Ecuador. The methodology used included the use of a dataset of 1,170 student responses, which were processed and analyzed using these algorithms. The techniques were evaluated using metrics such as precision, recall, and F1-score. The results obtained indicate that the advanced algorithms, particularly XGBoost, significantly outperform the classical ones in terms of precision, reaching 89.3%. Although the classical algorithms demonstrated faster execution times, their lower precision makes them less suitable for complex prediction tasks. In conclusion, advanced algorithms are presented as more effective tools for academic planning and optimization of educational resources, providing the possibility of personalizing learning and improving decision-making in educational institutions.
      16