Times Of Convergence Technologies Across Learning Contexts

The European Conference on Technology-Enhanced Learning (EC-TEL 2008) was the third event of a series that started in 2006. The two first editions were organized by Pro- Learn (http://www.prolearn-project.org/), a European Network of Excellence. In 2008, several members of Kaleidoscope, the other European Network of Excellence (http://www.noe-kaleidoscope.org/pub/), joined as co-chair, committee members, reviewers and authors. These two networks are no longer funded, but our aim was to turn EC-TEL into a sustainable series of high-quality events and thereby to contribute to the scientific landscape of technology-enhanced learning. A new network, named STELLAR, will be launched in 2009, with members from both existing networks as well as new members and will support the future editions of this conference. The scope of EC-TEL 2008 covered the different fields of learning technologies: e- cation, psychology, computer science. The contributions in this volume address the - sign of innovative environments, computational models and architectures, results of empirical studies on socio-cognitive processes, field studies regarding the use of te- nologies in context, collaborative processes, pedagogical scenarios, reusable learning objects and emerging objects, groups and communities, learning networks, interaction analysis, metadata, personalization, collaboration scripts, learning adaptation, collabo- tive environments, resources, tangible tools, as well as learning management systems.

The European Conference on Technology-Enhanced Learning (EC-TEL 2008) was the third event of a series that started in 2006. The two first editions were organized by Pro- Learn (http://www.prolearn-project.org/), a European Network of Excellence. In 2008, several members of Kaleidoscope, the other European Network of Excellence (http://www.noe-kaleidoscope.org/pub/), joined as co-chair, committee members, reviewers and authors. These two networks are no longer funded, but our aim was to turn EC-TEL into a sustainable series of high-quality events and thereby to contribute to the scientific landscape of technology-enhanced learning. A new network, named STELLAR, will be launched in 2009, with members from both existing networks as well as new members and will support the future editions of this conference. The scope of EC-TEL 2008 covered the different fields of learning technologies: e- cation, psychology, computer science. The contributions in this volume address the - sign of innovative environments, computational models and architectures, results of empirical studies on socio-cognitive processes, field studies regarding the use of te- nologies in context, collaborative processes, pedagogical scenarios, reusable learning objects and emerging objects, groups and communities, learning networks, interaction analysis, metadata, personalization, collaboration scripts, learning adaptation, collabo- tive environments, resources, tangible tools, as well as learning management systems.

Considerable efforts have been made in developing and assessing educational technology to support and teach argumentation. These efforts have culminated in the form of techniques which include Intelligent Tutoring Systems and Computer Supported Collaborative Learning. Many of these techniques have been shown to be effective for specific argumentation domains. At the same time, the general design problem of how to support a learner's acquisition of argumentation skills through computer aided tools has not yet been perfected. This e-book presents a collection of current approaches in educational technologies for argumentation. Technological approaches underlying successful argumentation systems are presented, along with their relation to the success of these tools.

This proceedings volume contains selected papers presented at the 2014 International Conference on Frontiers in Computer Education (ICFCE 2014), which was held December 24-25, 2014, in Wuhan, China. The objective of this conference was to provide a forum for different researchers in different fields, especially Computer Education as well as Information Technology, to exchange their various findings. The papers have been grouped under the following overarching themes: Computer Science, Computer Education, Education and Teaching Reform, and Communication and Intelligent Control.

The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.

In der E-Learning-Domäne bilden sowohl die Lernressourcen, Lehrende und Lernende als auch die stattfindenden Lernprozesse in ihrer Gesamtheit Lernökosysteme. Diese Dissertation untersucht die Modellierung von Lernökosystemen zur Unterstützung ihrer Aggregation und Wiederverwendung. Zur Erreichung dieses Ziels müssen Modelle von Lernökosystemen die Aggregierbarkeit, Austauschbarkeit, Interoperabilität und granulare Wiederverwendbarkeit ihrer Daten unterstützen. Auf Basis durchgeführter Nutzerstudien werden Konzepte digitaler Modelle von Lernökosystemen, sogenannte LOOCs (Linked Open Online Courses), entwickelt. Dabei werden insbesondere Technologien des Semantic Webs sowie Linked-Data-Konzepte betrachtet. Die entwickelten ontologischen Modelle bilden die Basis für mehrere E-Learning-Applikationen, welche die Tragfähigkeit der Konzepte sowie eine hohe Nutzerakzeptanz zeigen. Ferner wird ein formales Interpretermodell für CSCL (Computer-Supported Collaborative Learning) Scripts zur Beschreibung von Lernprozessen, welches mit Hilfe von Abstract State Machines spezifiziert wurde, vorgestellt. In the e-learning domain, the learning resources, teachers and learners and the active learning processes in their entirety construct the learning ecosystems. This thesis examines the modelling of learning ecosystems to support their aggregation and reuse. To achieve this goal, learning ecosystem models must support aggregation, compatibility, interoperability and granular re-usability of their data. Through user studies, digital model concepts of learning ecosystems, i.e. so-called LOOCs (linked open online courses), were developed. In particular, Semantic Web technologies and Linked Data concepts are considered within the context. The developed ontological models form the basis for a number of e-learning applications that show the viability of the concepts as well as a high user acceptance. Further, a formal interpreter model for CSCL (Computer-Supported Collaborative Learning) Scripts for the description of learning processes specified by using Abstract State Machines is presented.