The objective of the project was the development of a prototype of a mobile application that allows learning through Gamification for students of the Software Career of the University of Guayaquil, who are taking the subject of Discrete Structures. The methodology used was exploratory research, where documents such as scientific articles from indexed journals were reviewed; descriptive research was applied to describe the flow of learning processes, before and after with the proposal of the mobile application. An interview was conducted with the teacher who teaches the subject and a feasibility survey was conducted with the students to know their degree of acceptance of the development of the mobile prototype. For software development, the Cascade Methodology was followed to identify the requirements, analysis, design, implementation and testing of the application. As a result, a mobile prototype was achieved in a first phase with 8 modules: 1) Account creation, 2) Login, 3) Home, 4) Profile, 5) Content, 6) Rating, 7) Achievements and 8) Forum. As for Gamification, it consisted of the student answering a questionnaire for each level of content of the unit, giving him a score for each question, this allows him to be in a ranking position with the best scores among all the students of all the parallels of that teaching period. As a conclusion, it can be established that the App will serve as a support tool for the students' learning, besides that the App will allow a competition among them about the knowledge acquired, obtaining a scale of achievements reached and thus obtaining a rating position. Another virtue of the application is a forum space as a means of exchanging queries on a particular subject; in a next phase it is planned to conduct a more in-depth study onGamification techniques to include them in the App.
The residents of the Bastión Popular Cooperative in Ecuador have health problems due to inadequate food due to limited economic resources, poor nutrition, and lack of knowledge about healthy food in the people of this vulnerable area. On the other hand, this area has a limited number of nutritionists to serve the population, for this reason it is necessary that the professional can serve them more quickly to be able to cover the largest number of them during the working day, in this situation this project proposes the creation of a technological solution to help the specialist to minimize the time spent on manual calculations made during the consultation. The nutritionist as part of the diet prescription process must consider several variables and conditions of the patient to establish the correct BMI (body mass index) of each type of patient, so this study promotes the creation of an intelligent model that allows the doctor to obtain the BMI in a more agile way, for this case qualitative, quantitative and experimental research was applied using Machine Learning algorithms, Multinomial Logistic Regression and Dense Layer Neural Networks. As results it was obtained that the model based on Multinomial Logistic Regression obtained an efficiency level of 97.9% in test data, while the model based on Neural Networks with dense layer obtained an efficiency level of 98.95% for test data. Therefore, it was found that the Neural Networks classifier allows the nutritionist to avoid manual calculations and instead obtain the BMI with a high level of efficiency, thus saving time in the initial phase of the patient consultation, giving him/her the opportunity to use that time to attend more patients in the waiting room.
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