After conducting a historical review and establishing the state of the art of the various approaches regarding the design and implementation of adaptive e-learning systems-taking into consideration the characteristics of the user, in particular their learning styles and preferences in order to focus on the possibilities for personalizing the ways of utilizing learning materials and objects in a manner distinct from what e-learning systems have traditionally been, which is to say designed for the generic user, irrespective of individual knowledge and learning styles-the authors propose a system model for the classification of user interactions within an adaptive e-learning platform, and its analysis through a mechanism based on backpropagation neural networks and fuzzy logic, which allow for automatic, online identification of the learning styles of the users in a manner which is transparent for them and which can also be of great utility as a component of the architecture of adaptive e-learning systems and knowledge-management systems. Finally, conclusions and recommendations for future work are established.

ITS (Intelligent Tutoring Systems) are integrated and complex systems, designed and developed using approaches and methods of artificial intelligence (AI), for the resolution of problems and requirements of the teaching/learning activities in the field of education and training of students and the workforce based in computers an web based emerging resources. These systems can establish the level of student knowledge and the learning strategies used to improve the level of knowledge to support the detection and correction of student misconceptions. Their purpose is to contribute to the process of teaching and learning in a given area of knowledge, respecting the individuality of the student. In this paper, a review of intelligent tutorial systems (ITS) is presented, from the perspective of their application and usability in modern learning concepts. The methodology used was that of bibliographical review of classic works of the printed and digital literature in relation to ITS and e-Learning systems, as well as searches in diverse databases, of theses and works in universities and digital repositories. The main weakness of the research lies in the fact that the search was limited to documents published in the English, Spanish and Portuguese.

In the present work, we present a Fuzzy Neural System Model for online identification of Learning Styles which gives support for contents personalization. The model was developed to serve as a component for an Adaptive Hybrid E-Learning System Architecture, which focus on a high degree of customization and content adaptation. We proposal a Hybrid System model, in which techniques of Neural Networks, Fuzzy Logic and Case Based Reasoning are incorporated into the multiagent system. Finally, the authors present the architecture of the Fuzzy Neural System model, the results of the analysis of the model validation tests establishing conclusions and recommendations.

The importance of the e-technologies available to support teaching and learning in e-learning systems is becoming increasingly evident to educators and system developers. In this chapter, the authors review some of the e-technologies and e-learning that are used to support the individual requirements of teachers, allowing them to provide the best opportunities to students, considering that the current situation, in which educational systems have new immediate claims, derived in part from the COVID-19 pandemic, motivated face-to-face educational practices to give way to remote activities mediated by technological resources. The new contemporary trends in e-learning and e-technologies development and applications utilize a wide range of available technologies, which are framed in web and virtual reality environments among other emerging technologies; therefore, the decision to use a particular technology must be based on solid research and evidence. This chapter reviews many of these e-technologies and provides information on their use, opportunities, and trends in development and applications.

Abstract-In the present work, we present a Fuzzy Neural System Model for online identification of Learning Styles which gives support for contents personalization. The model was developed to serve as a component for an Adaptive Hybrid ELearning System Architecture, which focus on a high degree of customization and content adaptation. We proposal a Hybrid System model, in which techniques of Neural Networks, Fuzzy Logic and Case Based Reasoning are incorporated into the multiagent system. Finally, the authors present the architecture of the Fuzzy Neural System model, the results of the analysis of the model validation tests establishing conclusions and recommendations.