Towards eLearning 2.0 University

The editorial introduction to the special issue: Towards eLearning 2.0 University. Interactive Learning Environment

Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Social networks existing among employees, customers or users of various IT systems have become one of the research areas of growing importance. A social network consists of nodes - social entities and edges linking pairs of nodes. In regular, one-layered social networks, two nodes - i.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Nowadays data about people and their interactions, which exists in all social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but also gain understanding about semantic of human relations. Are they direct or not? Do people tend to sustain single or multiple relations with a given person? What types of communication is the most important for them? Answers to these and more questions enable us to draw conclusions about semantic of human interactions. Unfortunately, most of the methods used for social network analysis (SNA) may be applied only to one-layered social networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper, in particular: cross-layer clustering coefficient, cross-layer degree centrality and various versions of multi-layered degree centralities. Authors also investigated the dynamics of multi-layered neighbourhood for five different layers within the social network. The evaluation of the presented concepts on the real-world dataset is presented. The measures proposed in the paper may directly be used to various methods for collective classification, in which nodes are assigned to labels according to their structural input features.Comment: 16 pages, International Journal of Computational Intelligence System

Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities

Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed

Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities

Hummel, H. G. K., Van den Berg, E. J., Berlanga, A. J., Drachsler, H., Janssen, J., Nadolski, R.J., & Koper, E.J.R. (2007). Combining social- and information-based approaches for personalised recommendation on sequencing learning activities. International Journal of Learning Technology, 3(2), 152-168.Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed.This work has been sponsored by the EU project TENCompetenc

Educational Innovation with Learning Networks: Tools and Developments

Professional Development is ill served by traditional ways of learning. It can profit from a Learning Networks approach, which emphasizes logistic, content and didactic flexibility. Learning Networks are online, social networks that have been de- signed and tooled to foster informal learning. Three European projects are discussed – idSpace, LTfLL, Handover - which have developed tools befitting networked learning. Each in its own way, the projects illustrate the benefits of a networked learning ap- proach. This goes for all three flexibilities but in particular for the need to be didactical- ly flexible. Finally, it is argued that formal education could profit from the tools dis- cussed

Enhancing the Social Capital of Learning Communities by Using an Ad Hoc Transient Communities Service

Fetter, S., Berlanga, A. J., & Sloep, P. B. (2009). Enhancing the Social Capital of Learning Communities by Using an Ad Hoc Transient Communities Service. In M. Spaniol, Q. Li, R. Klamma & R. W. H. Lau (Eds.), Proceedings of the 8th International Conference Advances in Web-based Learning - ICWL 2009 (pp. 150-157). August, 19-21, 2009, Aachen, Germany. Lecture Notes in Computer Science 5686; Berlin, Heidelberg: Springer-Verlag.In online learning, communities can help to enhance learning. However, because of the dynamic nature of communities, attaining and sustaining these communities can be difficult. One aspect that has an influence on, and is influenced by these dynamics is the social capital of a community. Features of social capital are the social network structure, the sense of belonging and, the support received and provided. It is hypothesized that these features can be improved by using Ad Hoc Transient Communities (AHTCs). Through an AHTC learners are brought together for a specific, learning-related goal (‘ad hoc’) and for only a limited amount of time (‘transience’). To test whether the use of AHTCs has a positive influence on the social capital, a learner support service which enables the use of AHTCs is proposed. Furthermore, requirements, pre-requisites, and future research are discussed.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org