Browsing by Author "Gaviria Chavarro, Javier"
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Item Objeto virtual de aprendizaje para la enseñanza- aprendizaje de métodos estadísticos no paramétricos(Universidad Santiago de Cali, 2019) Gaviria Chavarro, Javierlevel. Despite this, they do not delve into information on statistical methods related to non-parametric statistics. This situation arises due to the large number of topics that have the inferential statistics in addition to its long extension. For this reason, a virtual learning object was created for the non-parametric statistical methods of Kruskal Wallis, Mann-Whitney U and Wilcoxon. These methods are relevant for the investigations analysis performed with small samples (less than 30) which do not comply with the statistical assumptions. The main purpose of this research is teaching and learning these three statistical tests. To achieve this, the methodology of construction of virtual learning objects proposed by Borrero and Cruz was implemented to support the training of students in the biostatistics area. The objects were evaluated by experts through the LORI instrument, showing a quality level in the medium-high interval according to the final weighting. The evaluation instrument indicated that the virtual learning object is suitable for the purpose and objectives set.Item Smartphones dependency risk analysis using machine-learning predictive models(2022-12) Giraldo Jiménez, Claudia Fernanda; Gaviria Chavarro, Javier; Sarria Paja, Milton; Bermeo Varón, Leonardo Antonio; Villarejo Mayor, John Jairo; Rodacki, André Luiz FelixRecent technological advances have changed how people interact, run businesses, learn, and use their free time. The advantages and facilities provided by electronic devices have played a major role. On the other hand, extensive use of such technology also has adverse efects on several aspects of human life (e.g., the development of societal sedentary lifestyles and new addictions). Smartphone dependency is new addiction that primarily afects the young population. The consequences may negatively impact mental and physical health (e.g., lack of attention or local pain). Health professionals rely on self-reported subjective information to assess the dependency level, requiring specialists’ opinions to diagnose such a dependency. This study proposes a data-driven prediction model for smartphone dependency based on machine learning techniques using an analytical retrospective case–control approach. Diferent classifcation methods were applied, including classical and modern machine learning models. Students from a private university in Cali—Colombia (n= 1228) were tested for (i) smartphone dependency, (ii) musculoskeletal symptoms, and (iii) the Risk Factors Questionnaire. Random forest, logistic regression, and support vector machine-based classifers exhibited the highest prediction accuracy, 76–77%, for smartphone dependency, estimated through the stratifed-k-fold cross-validation technique. Results showed that self-reported information provides insight into predicting smartphone dependency correctly. Such an approach opens doors for future research aiming to include objective measures to increase accuracy and help to reduce the negative consequences of this new addiction form.