Technology enhanced learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of both individuals and organisations. It is an application domain that generally addresses all types of technology research & development aiming to support teaching and learning activities. 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. There are plenty of resources available on the Web, both in terms of digital learning content and people resources (e.g. other learners, experts, tutors) that can be used to facilitate teaching and learning tasks. The challenge 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. Its main goal was to bring together researchers and practitioners who are working on topics related to the design, development and testing of recommender systems in educational settings as well as present the current status of research in this area and create cross-disciplinary liaisons between the RecSys and EC-TEL communities. Overall, its contributions outline the rich potential of TEL as an application area for recommender systems and identify the challenges of developing such systems in a TEL context.

This article is reproduced with permission from Koper, R. & Tattersall, C. (2005) (Eds.) Learning Design: A handbook on modelling and delivering networked education and training. Berlin: Springer.

This article argues that there is a need for Personal Recommender Systems (PRSs) in Learning Networks (LNs) in order to provide learners advice on the suitable learning activities to follow. LNs target lifelong learners in any learning situation, at all educational levels and in all national contexts. They are community-driven because every member is able to contribute to the learning material. Existing Recommender Systems (RS) and recommendation techniques used for consumer products and other contexts are assessed on their suitability for providing navigational support in an LN. The similarities and differences are translated into specific requirements for learning and specific requirements for recommendation techniques. The article focuses on the use of memory-based recommendation techniques, which calculate recommendations based on the current data set. We propose a combination of memory-based recommendation techniques that appear suitable to realise personalised recommendation on learning activities in the context of e-learning. An initial model for the design of such systems in LNs and a roadmap for their further development are presented.

Smart learning environments (SLEs) are defined in this paper as physical environments that are enriched with digital, context-aware and adaptive devices, to promote better and faster learning. In order to identify the requirements for 'better and faster learning', the idea of Human Learning Interfaces (HLI) is presented, i.e. the set of learning related interaction mechanisms that humans expose to the outside world that can be used to control, stimulate and facilitate their learning processes. It is assumed that humans have and use these HLIs for all types of learning, and that others, such as parents, teachers, friends, and digital devices can interact with the interface to help a person to learn something. Three basic HLIs are identified that represent three distinct types of learning: learning to deal with new situations (identification), learning to behave in a social group (socialization) and learning by creating something (creation). These three HLIs involve a change in cognitive representations and behavior. Performance can be increased using the practice HLI, and meta-cognitive development is supported by the reflection HLI. This analysis of HLIs is used to identify the conditions for the development of effective smart learning environments and a research agenda for SLEs.

Several collaboration problems in virtual project teams can be attributed to a hindered process of interpersonal trust formation. In order to design solutions to solve these trust formation problems, it is important to understand how interpersonal trust is formed in face-to-face project teams and how this differs in a virtual setting. Synthesising literature from various disciplines, we propose a cognitive-(factor) and process-oriented model for the formation of interpersonal trust between faceto-face project team members. Taking this kernel theory as a starting point, we analyse how virtual settings alter or even obstruct the process of trust formation. We propose that one method to improve the formation of interpersonal trust is to facilitate virtual project team members with the estimation of each others trustworthiness. This can be done by making information available about individual team members that is based on the antecedents of trustworthiness. We extent current research by taking a designer's perspective to the proposed cognitive schema of trustworthiness and apply it for the design of artefacts, such as personal identity profiles to support estimation of trustworthiness in virtual project teams.