Computational Intelligence for Technology Enhanced Learning [electronic resource] / edited by Fatos Xhafa, Santi Caballé, Ajith Abraham, Thanasis Daradoumis, Angel Alejandro Juan Perez.

Intelligent Techniques in Personalization of Learning in e-Learning Systems -- Fuzzy ECA Rules for Pervasive Decision-Centric Personalised Mobile Learning -- Developing an Adaptive Learning Based Tourism Information System Using Ant Colony Metaphor -- Intelligent and Interactive Web-Based Tutoring System in Engineering Education: Reviews, Perspectives and Development -- Granular Mining of Student’s Learning Behavior in Learning Management System Using Rough Set Technique -- T-Learning 2.0: A Personalised Hybrid Approach Based on Ontologies and Folksonomies -- Computational Intelligence Infrastructure in Support for Complex e-Learning Systems -- SISINE: A Negotiation Training Dedicated Multi-Player Role-Playing Platform Using Artificial Intelligence Skills -- Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities -- Advanced Learning Technology Systems in Mathematics Education.

E-Learning has become one of the most wide spread ways of distance teaching and learning. Technologies such as Web, Grid, and Mobile and Wireless networks are pushing teaching and learning communities to find new and intelligent ways of using these technologies to enhance teaching and learning activities. Indeed, these new technologies can play an important role in increasing the support to teachers and learners, to shorten the time to learning and teaching; yet, it is necessary to use intelligent techniques to take advantage of these new technologies to achieve the desired support to teachers and learners and enhance learners’ performance in distributed learning environments. The chapters of this volume bring advances in using intelligent techniques for technology enhanced learning as well as development of e-Learning applications based on such techniques and supported by technology. Such intelligent techniques include clustering and classification for personalization of learning, intelligent context-aware techniques, adaptive learning, data mining techniques and ontologies in e-Learning systems, among others. Academics, scientists, software developers, teachers and tutors and students interested in e-Learning will find this book useful for their academic, research and practice activity.