Examinando por Autor "Vallejo, Paola"
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Ítem Education 4.0: a view from different digital proposals(Medellín : Editorial Eafit, 2020) Montoya-Múnera, Edwin; Aguilar, José; Monsalve-Pulido, Julián Alberto; Salazar, Camilo; Varela-Tabares, Daniela; Jiménez-Narváez, Marvin; Montoya-Jaramillo, Edwin; Tabares, Marta S.; Vallejo, Paola; Vega, Juan Sebastián; Nieto, Agustín; Córdoba, Camilo; Gil, Guillermo Alejandro; Trefftz, Helmuth; Esteban, Pedro Vicente; fonedit@eafit.edu.co; Suárez-Giraldo, Cristian; Caicedo Alarcón, ÓscarThis book is, then, a way to expose some research projects undertaken at the EAFIT University, in agreement with several technological companies, that develop three perspectives about education in a digital context, in which the informatic mediation and the use of technologies 4.0 lead the revision of the teaching and learning processes through a novel perspective. Together, this book presents how 4.0 technologies enable a highly participatory and dialogic education, where students of different levels have the possibility of learning and training in their discipline while developing soft skills and, at the same time, the teachers can follow the development and evolution of these capacities and the purposes expected in the courses. Given the variety of courses modalities that are currently offered (e-learning, b-learning, m-learning, u-learning, among others), the applications presented in each chapter, as well as the appropriation of technologies for the enrichment of the students’ cognitive and social skills, are an opportunity to explore new perspectives and approaches to Education 4.0Ítem Hacia un método de predicción de resultados de evaluación en un contexto de micro aprendizaje(Universidad EAFIT, 2020) Sánchez Castrillón, Jose David; Vallejo, Paola; Tabares Betancur, Marta Silvia; Tabares Betancur, Marta SilviaThis paper presents a method for predicting the evaluation results of learners interacting with a context-aware microlearning system. We use ASUM-DM to guide di erent data analytics tasks, including applying a genetic algorithm that selects the prediction's highest weight features. Then, we apply machine learning models like Random Forest, Gradient Boosting Tree, Decision Tree, SVM, and Neural Networks to train data and evaluate the context's e ects, either success or failure of the learner's evaluation. We are interested in nding the model of signi cant context-in uence to the learner's evaluation results. The Random Forest model provided an accuracy of 94%, which was calculated with the cross-validation technique. Thus, it is possible to conclude that the model can accurately predict the evaluation result and relate it with the learner context. The model result is a useful insight for sending noti cations to the learners to improve the learning process. We want to provide recommendations about learner behavior and context and adapt the microlearning content in the future.Ítem Proceso de ASC - EXPLORACION Y MEJORA DE VARIAMOS (UNA HERRAMIENTA PARA LA ESPECIFICACION DE MODELOS DE VARIABILIDAD)(Universidad EAFIT, 2019) Vallejo, Paola; Universidad EAFITSoftware Engineering has as its main objective the development of professional software, meaning that the software fulfills its purpose within a determined time frame and for a specific context. This area of knowledge is intended to generate positive results in any field where there is a possibility of improving a process, product, or service through automation. It is in this context that Software Engineering must be adequately applied to introduce quality to the desired software product, improve development processes, and reduce development time. Software appears as a cornerstone in organizations, even in different branches of science, to the extent that it can be asserted that software is found in all scientific research. One of the strategies for developing software is through modeling and model transformation. However, in our context, development is handled in an artisanal manner, as has already been explored in this Research Group. Many efforts aim at the industrialization of software production, and these efforts are focused on the continuous application of Software Engineering. Since 2016, the Software Engineering Research Group has been exploring questions such as: How can the application of good Software Engineering practices benefit software development in Scientific Computing? What design and architecture patterns can be applied to Scientific Computing to improve its quality? How can the teaching of Software Engineering be improved through gamification? Among others. From now on, we will focus on exploring topics related to the advantages offered by model-driven development and how these can be incorporated into industry practices, especially in Colombia, to ensure non-artisanal, easily adaptable, and high-quality developments.Ítem Sasvar website(Universidad EAFIT, 2023) Ruiz-Arenas Santiago; Rendón-Vélez Elizabeth; Vallejo, Paola; Correa Botero, Daniel; Universidad EAFITDevelopment of the Sasvar website for image scanning.