Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications. Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning.

"This book aims to provide a comprehensive review of state-of-the-art practices for educational recommender systems, as well as the challenges to achieve their actual deployment"--Provided by publisher.

This book intends to bring together researchers and developers from industry, the education field, and the academic world to report on the latest scientific research, technical advances, and methodologies. The 10th International Conference in Methodologies and Intelligent Systems for Technology Enhanced Learning is hosted by the University of L’Aquila and is going to be held in L’Aquila (Italy). Initially planned on the 17th to the 19th of June 2020, it was postponed to the 7th to the 9th of October 2020, due to the COVID-19 outbreak. The 10th edition of this conference and its related workshops expand the topics of the evidence-based TEL workshops series in order to provide an open forum for discussing intelligent systems for TEL, their roots in novel learning theories, empirical methodologies for their design or evaluation, stand-alone solutions, or web-based ones. This bridge has been realized also thanks to the sponsor of this edition of MIS4TEL: the Armundia Group https://www.armundia.com, the support from national associations (AEPIA, APPIA, CINI, and EurAI), and organizers (UNIVAQ, UNIROMA1, UNIBZ, UCV, UFSC, USAL, AIR institute, UNC, and UNIBA)

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

This book focuses on the design and architecture of ubiquitous learning environments, associated technologies, various learning scenarios supported by these environments, and different contexts that arise in environments where the seamless immersion of formal and informal activities and interactions has the potential to contribute to the learning process. With particular focus on adaptivity for individual learners, the book explores the diminishing boundaries of formal and informal learning, and the potential of location-aware context-sensitive approaches that are emerging as successors to the Web 2.0 paradigm.

Strategic Role of Tertiary Education and Technologies for Sustainable Competitive Advantage explores how education enables social and economic development through the targeted training of human capital and the evaluation and dissemination of knowledge resources across generations. This book provides entrepreneurs, leaders, policy makers, and educators with the necessary tools to make the most of higher education in order to meet emerging economic and social challenges through the use of new technologies enabling effective collaboration and knowledge sharing.

"This book aims to provide a comprehensive review of state-of-the-art practices for educational recommender systems, as well as the challenges to achieve their actual deployment"--Provided by publisher.