This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge. The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture - through matrix algebra - elements of learners' work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner's journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning.

Wearable technologies - such as smart glasses, smart watches, smart objects, or smart garments - are potential game-changers, breaking ground and offering new opportunities for learning. These devices are body-worn, equipped with sensors, and integrate ergonomically into everyday activities. With wearable technologies forging new human-computer relations, it is essential to look beyond the current perspective of how technologies may be used to enhance learning. This edited volume,"Perspectives on Wearable Enhanced Learning," aims to take a multidisciplinary view on wearable enhanced learning and provide a comprehensive overview of current trends, research, and practice in diverse learning contexts including school and work-based learning, higher education, professional development, vocational training, health and healthy aging programs, smart and open learning, and work. This volume features current state of the art wearable enhanced learning and explores how these technologies have begun to mark the transition from the desktop through the mobile to the age of wearable, ubiquitous technology-enhanced learning.

This book presents a key piece of the vision and strategy developed in STELLAR. It sets out a new mid-term agenda by defining Grand Challenges for research and development in technology-enhanced learning. Other than mere technology prizes, STELLAR Grand Challenges deal with problems at the interface of social and technical sciences. They pose problems that can be solved only in interdisciplinary collaboration. The descriptions of the Grand Challenge Problems were sent out to a number of stakeholders from industry, academia, and policy-making who responded with insightful, creative and critical comments bringing in their specific perspectives. This book will inspire everyone interested in TEL and its neighboring disciplines in their future projects. All of the listed problems, first hints with respect to the approach, measurable success indicators and funding sources are outlined. The challenges focus on what noted experts regard as important upcoming, pending, and innovative fields of research, the solution of which is within reach in a timeframe of a mere 2 to 15 years of work.

This book constitutes the refereed proceedings of the 6th European Conference on Technology Enhanced Learning, EC-TEL 2011, held in Palermo, Italy, in September 2010.
The 30 revised full papers presented were carefully reviewed and selected from 158 submissions. The book also includes 12 short papers, 8 poster papers, and 2 invited paper. There are many interesting papers on topics such as web 2.0 and social media, recommender systems, learning analytics, collaborative learning, interoperability of tools, etc.

This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge. The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture - through matrix algebra - elements of learners' work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner's journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning.