Examinando por Materia "Micro aprendizaje"
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Ítem 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.