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  • Por favor, use este identificador para citar o enlazar este ítem: https://repositorio.uti.edu.ec//handle/123456789/3974
    Título : Predicting Academic Performance in Mathematics Using Machine Learning Algorithms
    Autor : Espinosa-Pinos, Carlos
    Ayala-Chauvin, Manuel
    Buele, Jorge
    Fecha de publicación : 2022
    Editorial : Communications in Computer and Information Science Volume 1658 CCIS, Pages 15 - 29. 2022. 8th International Conference on Technologies and Innovation, CITI 2022. Guayaquil. 14 November 2022 through 17 November 2022
    Resumen : Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations.
    URI : https://link.springer.com/chapter/10.1007/978-3-031-19961-5_2
    http://repositorio.uti.edu.ec//handle/123456789/3974
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