Evaluación de la percepción estudiantil en relación al uso de la plataforma Moodle desde la perspectiva del TAMEvaluation of the student perception in relation to the use of the Moodle platform from the TAM perspective RESUMEN El uso de sistemas de gestión del aprendizaje (LMS) en las instituciones educativas se ha generalizado en los últimos años, por lo que se hace necesario medir su impacto en los procesos pedagógicos. Con el objetivo de mejorar el uso de la plataforma Moodle como apoyo a los procesos de enseñanza-aprendizaje, en este trabajo se estudia la percepción estudiantil en relación a su utilización en dos asignaturas. Para ello, se utiliza el Modelo de Aceptación Tecnológica (TAM).Se hace un estudio empírico en dos semestres académicos, sobre una muestra de 101 estudiantes, a quienes se les aplicó una encuesta vía web. La información recabada sirvió para realizar análisis estadísticos univariantes y bivariantes y analizar las relaciones estructurales del modelo TAM.Los resultados obtenidos revelan una actitud moderadamente positiva hacia el uso del aula virtual, reflejan una buena valoración de su facilidad de uso y de su utilidad. Así mismo, se evidencia una buena percepción del diseño instruccional aplicado en las aulas virtuales, pues los estudiantes perciben las actividades implementadas como beneficiosas para su aprendizaje.Palabras clave: Modelo TAM, tecnología educativa, plataformas educativas, Moodle, diseño instruccional. ABSTRACTThe use of learning management systems (LMS) on educative institutions has been generalized over the last few years, and this is why it is necessary to measure its impact on the pedagogical processes. Aiming to improve the usage of the Moodle platform as a support to the teaching-learning processes, in this paper, the student's perception related to its usage in two subjects is analyzed. For this purpose, the Technological Acceptation Model (TAM) is used. An empiric study over two academic semesters

Academic performance is a topic studied not only to identify those students who could drop out of their studies, but also to classify them according to the type of academic risk they could find themselves. An application has been implemented that uses academic information provided by the university and generates classification models from three different algorithms: artificial neural networks, ID3 and C4.5. The models created use a set of variables and criteria for their construction and can be used to classify student desertion and more specifically to predict their type of academic risk. The performance of these models was compared to define the one that provided the best results and that will serve to make the classification of students. Decision tree algorithms, C4.5 and ID3, presented better measurements with respect to the artificial neural network. The tree generated using the C4.5 algorithm presented the best performance metrics with correctness, accuracy, and sensitivity equal to 0.83, 0.87, and 0.90 respectively. As a result of the classification to determine student desertion it was concluded, according to the model generated using the C4.5 algorithm, that the ratio of credits approved by a student to the credits that he should have taken is the variable more significant. The classification, depending on the type of academic risk, generated a tree model indicating that the number of abandoned subjects is the most significant variable. The admission scan modality through which the student entered the university did not turn out to be significant, as it does not appear in the generated decision tree.

No abstract

University professors face the challenge of incorporating activities that promote student engagement, discussion, conflict resolution, and teamwork. In this context, cooperative learning emerges as the pedagogical model that fosters teamwork; organizes students into groups where joint and coordinated work reinforces individual and collective learning. The proposal presented facilitates the design of cooperative activities that consider the necessary interdependence between learning, teaching, content and context. In addition to explaining how to articulate all these aspects, it also places the student as the center of the training process, for this it collects the main guidelines of cooperative learning and enriches the learning environment with the potential of management knowledge and communication provided by Information and Communication Technologies. To inform the proposal, the results obtained in four subjects of a mathematical nature are presented; results showing improvements in student learning.

Resumen-El aprendizaje cooperativo parte de la organización de los estudiantes en grupos en los que sus integrantes trabajan conjunta y coordinadamente para realizar actividades académicas de modo que el aprendizaje individual y el aprendizaje colectivo se refuerzan uno a otro. En este trabajo se describe la experiencia basada en el desarrollo de una actividad cooperativa que integra los contenidos de la