Browsing by Autor "Lorenzo Cevallos-Torres"

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BYTE: Integration of Narrative and Gamification for Teaching Programming in Educational Environments
(2025) Dayron Rumbaut-Rangel; Franklin Parrales-Bravo; Peggy Ricaurte-Ulloa; Ángel Yasmil Echeverría Guzmán; Lorenzo Cevallos-Torres; Rene Cardona-Tipan
In the current educational context, the teaching of programming faces challenges related to low motivation and difficulty of understanding by students without previous experience. This study addresses this problem by evaluating the educational video game BYTE, developed by the Bolivarian University of Ecuador, which integrates interactive narrative and gamification elements. The objective was to analyze its impact on motivation and learning of basic programming concepts. A mixed exploratory-descriptive methodology was used with 145 participants, applying a 27-question questionnaire and qualitative analysis of comments. The results show a high acceptance of the game, with no significant differences between teachers and students. Strong correlations were found between overall satisfaction and aspects such as educational usefulness and narrative design. In conclusion, BYTE is validated as an effective tool that promotes active learning, motivation and conceptual understanding, being applicable in mixed educational environments and adaptable to other disciplines.
Multidimensional Bayesian Classifier for Predicting the Multi-stage Patient's Response to the BoNT-A Treatment for Migraine
(2024) Franklin Parrales–Bravo; Víctor Gustavo Gómez Rodríguez; Lorenzo Cevallos-Torres
Since other treatments often do not work for treating migraine headaches, Onabotulinumtoxin-A, or BoNT-A, has gained a lot of popularity, especially in chronic migraines. The treatment consists of multiple sessions of drug injections. At the moment, it's unclear why BoNT-A therapy produces a beneficial reaction. In order to address this issue, the present work explores the use of Multidimensional Bayesian Classifiers (MBC) for training a multi-stage prediction model. It is carried out in a realistic setting by considering retrospective data from migraine patients receiving treatment with BoNT-A. As far as we are aware, there are no known studies using MBC in this domain. The model has achieved an average accuracy of 79.45%, 82.57%, and 77.35 % when predicting responses to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1^{st}, 2^{nd}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3^{rd}$</tex> stages of treatment, respectively. It has also achieved similar values of sensitivity and specificity, enabling medical professionals to get a panoramic prediction of a patient's reaction to therapy and base their decisions accordingly. When looking at the prediction models, some clinical features have been identified as important, such as the number of days with a headache, the anesthetic blockade of the greater occipital nerve (GON), and others. Doctors have also described these features as significant aspects.