Social Tools for Networked Learning: Current and Future Research Directions
Rory Sie, Adriana Berlanga, Peter Sloep, Kamakshi Rajagopal, Kees Pannekeet, Hendrik Drachsler, Soude Fazeli, Open Universiteit in the Netherlands, Heerlen, The Netherlands
Whereas the Web 1.0 was mainly driven by static content and web pages linked by hyperlinks, the social web, or Web 2.0, has opened up new ways of connecting to not just resources, but also to people. The connections that are made through the use of Social Media, contribute to a complex, but also promising network of people and resources. In an educational context, this is called a learning network, and both learning networks by themselves and the Social media by which they are constructed require that we rethink the ways we learn, our view on learners, tutors and learning, and the way we support learners and tutors. Numerous opportunities have emerged with the introduction of social media for learning, but so have numerous problems, ranging from awareness issues via (meta-)cognitive limitations and difficulties to affective and motivational problems.
This paper addresses the above issues by providing an overview of the current research we perform. The research we do is based on three themes: Peer support, Learner support and Online Learner Identity. Peer support describes the way peers may help each other through effective and efficient use of a learning network. It uses 1) natural language processing to, for instance, analyse communication between learners, 2) mine learner profiles to adapt to their individual circumstances and identity and 3) social network analysis extended by game theoretic solution concepts to recommend suitable peers for cooperative learning. Learner support focuses on how we may lead the learner through the jungle of learning resources. It uses recommender techniques to filter out unnecessary learning resources and provides concise sets of candidate resources for learning. Finally, Online Learner Identity focuses on rethinking how we construct our online identity, how to analyse such, and how to profit from the differences with offline learning. It may use multi-agent systems technology to simulate the identity of learners and their interaction in a learning network, but also semantic technology to capture the meaning of online learner identities.
The paper also describes the main techniques that we use in our research efforts to enhance networked learning. Furthermore, an overview of current projects within the themes is provided. We conclude that the results of our current research efforts will provide valuable insights to advance further on research and development of social tools for networked learning.
Social Media, learning networks, social network analysis, recommender systems, data, multi-agent simulation, e-Learning 2.0
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