Web Based Intelligent E Learning Systems

"This book offers a complete understanding of the notions, techniques, and methods related to the research and developments of web-based e-learning systems"--Provided by publisher.

"This book reviews computational models and technologies for distance education, focusing on systems, infrastructures, and frameworks for delivering quality education"--Provided by publisher.

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques.

Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems addresses information security within e-Learning based on trustworthiness assessment and prediction. Over the past decade, many learning management systems have appeared in the education market. Security in these systems is essential for protecting against unfair and dishonest conduct—most notably cheating—however, e-Learning services are often designed and implemented without considering security requirements. This book provides functional approaches of trustworthiness analysis, modeling, assessment, and prediction for stronger security and support in online learning, highlighting the security deficiencies found in most online collaborative learning systems. The book explores trustworthiness methodologies based on collective intelligence than can overcome these deficiencies. It examines trustworthiness analysis that utilizes the large amounts of data-learning activities generate. In addition, as processing this data is costly, the book offers a parallel processing paradigm that can support learning activities in real-time. The book discusses data visualization methods for managing e-Learning, providing the tools needed to analyze the data collected. Using a case-based approach, the book concludes with models and methodologies for evaluating and validating security in e-Learning systems. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Provides guidelines for anomaly detection, security analysis, and trustworthiness of data processing Incorporates state-of-the-art, multidisciplinary research on online collaborative learning, social networks, information security, learning management systems, and trustworthiness prediction Proposes a parallel processing approach that decreases the cost of expensive data processing Offers strategies for ensuring against unfair and dishonest assessments Demonstrates solutions using a real-life e-Learning context

Intelligent Collaborative e-Learning Systems and Applications is a major research theme in CSCL and CSCW research community. It comprises a variety of research topics that focus on developing systems that are more powerful and flexible and also more adaptable to the learning process and thus provide better answers to the paradigmatic principles of on-line collaborative learning and work. The chapters collected in this book provide new insights, findings and approaches both on the analysis and the development of more powerful e-collaboration settings. Researchers will find in this book the latest trends in these research topics. On the other hand, academics will find practical insights on how to use conceptual and experimental approaches in their daily tasks. Finally, developers from CSCL community can be inspired and put in practice the proposed models and evaluate them for the specific purposes of their own work and context.

This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.

This book constitutes the refereed proceedings of the 10th International Conference on Web-Based Learning, ICWL 2011, held in Hong Kong, China, in December 2011. The 27 revised full papers presented together with 9 short papers were carefully reviewed and selected from about 100 submissions. The papers report on research results or novel applications in web-based learning and address issues such as technology enhanced learning, personalized and adaptive learning, computer support for intelligent tutoring, intelligent tools for visual learning, Web-based learning for oriental languages learning, game-based learning, personal learning environments, computer supported collaborative learning, Web 2.0 and social learning environments, intelligent learner and group modeling, human factors and affective computing for learning, e-learning platforms and tools, design, model and framework of e-learning systems, deployment, organization and management of learning objects, e-learning metadata and standards, semantic Web and ontologies for e-learning, mobile, situated and blended learning, pedagogical issues, as well as practice and experience sharing.

"This book provides coverage of a wide range of factors that influence the design, use and adoption of Personalized Learning Environments"--Provided by publisher.