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RECONOCIMIENTO DE PATRONES DE APRENDIZAJE PARA ELEVAR LA CALIDAD EDUCATIVA EN LA EDUCACIÓN SUPERIOR MEDIDO EN UNA ESCALA NEUTROSÓFICA INDETERMINADA DE LIKERT

Abstract:

The use of technologies in the development of classroom activities contributes to improved educational quality in Higher Education, allowing raising learning results. The scientific problem lies in how to contribute to the identification of learning patterns generated through the Moodle platform. The analytical-synthetic, inductive-deductive, historical-logical, and systematization methods made it possible to identify techniques that allow us to recognize patterns stored in databases. The objective of this study is based on the analysis of the data obtained in the Moodle platform and its processing in Orange tool, using the CRIP-DM methodology. Neural networks were used to pbkp_redict the final evaluation and the SHAP index to explain the model. It can be seen that the three most important attributes are related to the knowledge acquired and the level of effort. Questionnaires were applied to students, which answered using an indeterminate Likert scale that consists in the assessment of every question with five components instead of only one of them, so, responses are more indeterminate and accurate.