Artificial neural network model of the results of college learning based on rubrics
In the formative evaluation of the college student, applying rubric-based techniques is significant in obtaining relevant learning achievements. So, this study addresses modeling through the use of Artificial Neural Networks (ANN) of the results rubrics, corresponding to a sample of students from the Inca Garcilaso de la Vega University. The successful prediction results make the model a useful tool for the teacher and students for predicting the outcomes and targeting learning weaknesses. Furthermore, the reason for using ANN lies in working with strongly non-linear variables, so it would be a more effective model than those developed using convergent approximation polynomials, as will explain then. Also, it is essential to have worked on the Google Collaborative platform, as it is simple and accessible for students and teachers because it does not require specialized computing resources or neural network software on the personal computer. In this sense, all processes are of the cloud computing type.
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-SinObrasDerivadas 4.0.