Emerging Research Topics in Social Learning - Klamma, Ralf (Proceedings of the International Conference on Networked Learning, 2010)
Despite the fact, that an uncountable crowd of technology affine learners has entered the Internet with fast connections lines, little support exists for scalable learning support technologies. In this paper, we explore research topics which address the new needs of long tail learning and technologies which may help to overcome the current situation. Especially, we investigated the concepts of connectivism. Key theorists state that (a) learning is a process of connecting entities, (b) nurturing and maintaining connections is needed to continual learning, (c) learners need the ability to see connections between fields, ideas, and concepts, (d) the capacity to know more is more critical than what is currently known, and (e) decision-making is itself a learning process. Drawing on these concepts, we have combined social network analysis (SNA), visualization and recommendation technologies supporting social navigation, positioning and reflection in learning networks. We demonstrate first results from recent research in case studies. In the PROLEARN Academy we studied the co-authorship relations of PhD students having attended the summer schools and found some impact. In the eTwinning database we applied SNA on a large dataset about collaboration among European schools and demonstrated the need for gaining competences in reading network data. In a digital database we identified research communities based on co-authorship relations and recommended events for young scientists. Longitudinal SNA studies with small but self-created network datasets help to facilitate the understanding of dynamic aspects of learning networks on a very coarse level. However, creating and maintaining network datasets in extremely expensive and needs financial support, e .g. from a network of excellence. Analyzing existing datasets from digital libraries or learning networks can be done very effectively and efficiently by experts using a handful of good analysis and visualization tools. However, when non-experts and stakeholders (like school teachers for example) need support, we have to design special environments for doing so. If not only understanding of historical data is aimed at but also forecasting future behaviour of learning networks some general laws about complex networks can be applied. Still, if the domain knowledge can be modeled into game theoretic payoff functions, multi agent simulation is a way to predict at least trends in the future. In the future we will add game theoretic multi agent simulation. So, there is a big need for supporting the learners in their communities and domains and not only expert data analysts.