Social Learning Environments allow learners to explore and contribute to open online spaces of learning materials. They need to support the learner in finding the right content, the right people ("right" for the context, the individual learner, purpose, learning style), and to motivate/incentivize people to learn and participate actively by discussing, helping, socializing and contributing. This talk will focus on the last issue, how to design the social learning network infrastructure so that it can motivate learners to participate, and more generally, to change their behaviours in a desirable way, beneficial for their own learning and for the community? Various motivational approaches exist. Some follow simple gamification rules. Others involve the design of rewards mechanisms and trust and reputation mechanisms. These mechanisms apply to all participants, like game rules. However, different people are motivated by different things, so it would be beneficial to personalize the incentives to every individual. Also since communities have different needs in different phases of their existence, it is necessary to model the changing needs of communities and adapt the incentive mechanisms accordingly. Therefore learner modeling and group modeling are important areas in the design of incentive mechanisms. Appropriately designed open learner models can support learners' self-efficacy and intrinsic motivation to achieve their learning goals. Social visualizations that reveal group models to the learners create awareness of the learning community as a whole and set the stage for reciprocation, group-attachment, social comparison and competition, which according to theories from the area of social psychology and behavioural economics can motivate people to participate.

Julita Vassileva is a Professor of Computer Science at the University of Saskatchewan, Canada. She obtained her PhD (Mathematics, Cybernetics and Control Theory) in 1992 from the University of Sofia, Bulgaria in the area of Intelligent Tutoring Systems. Between 1992 and 1997 she worked as a Research Associate at the Federal Armed Forces University in Munich, Germany. She has been with the University of Saskatchewan since 1997. Her research areas involve human issues in decentralized software environments: user modeling and personalization, designing incentive mechanisms for encouraging participation and facilitating trust in decentralized software applications, such as online communities, social networks, multi-agent systems and peer-to-peer systems. Professor Vassileva one of the Directors at Large of the UM Inc. Society. She is a member of the Executive Committee of the AI and Education Society, serves on the editorial board of the International Journal of User Modeling and User Adapted Interaction and of the Computational Intelligence Journal.

Learning Objects (LOs) are atomic elements of lecture materials in e-learning. Creating high quality LOs is time consuming. Automatic mechanisms to support LOs reuse must be investigated. When an author creates learning materials, in order to reuse LOs from another person, it is necessary to search for these LOs. Thus, reusability and searchability are co-related research issues. This keynote starts from an introduction of metadata, follows by a discussion of the requirements to build a distributed repository, where LOs can be stored and shared. The concept of "Reusability Tree" to represent the relationships among relevant LOs and an infrastructure of LO repository will be presented. Relevant information while users are utilizing LOs, such as citations and time period persisted as well as user feedbacks will be used as critical elements for evaluating significance degree of LOs. Through theses factors, a mechanism to weight and rank LOs is discussed. The LONET (Learning Object Network), as an extension of Reusability Tree, is addressed and constructed to clarify the vague reuse scenario in the past, and to summarize collaborative intelligence through past interactive usage experiences. As a practical contribution, an adaptive algorithm is proposed to mine the social structure in our repository. The algorithm generates adaptive routes, based on past usage experiences, by computing possible interactive input, such as search criteria and feedback from instructors, and assists them in generating specific lectures.

Dr. Shih is a Professor at the National Central University, Taiwan. He was the Dean of College of Computer Science, Asia University, Taiwan and the Department Chair of the CSIE Department at Tamkang University, Taiwan. Dr. Shih is a Fellow of the Institution of Engineering and Technology (IET). In addition, he is a senior member of ACM and a senior member of IEEE. Dr. Shih also joined the Educational Activities Board of the Computer Society. His current research interests include Multimedia Computing and Distance Learning. Dr. Shih has edited many books and published over 460 papers and book chapters, as well as participated in many international academic activities, including the organization of more than 60 international conferences. He was the founder and co-editor-in-chief of the International Journal of Distance

Education Technologies, published by the Idea Group Publishing, USA. Dr. Shih is an associate editor of the IEEE Transactions on Learning Technologies. He was an associate editor of the ACM Transactions on Internet Technology and an associate editor of the IEEE Transactions on Multimedia. Dr. Shih has received many research awards, including research awards from National Science Council of Taiwan, IIAS research award from Germany, HSSS award from Greece, Brandon Hall award from USA, and several best paper awards from international conferences. Dr. Shih has been invited to give more than 30 keynote speeches and plenary talks in international conferences, as well as tutorials in IEEE ICME 2001 and 2006, and ACM Multimedia 2002 and 2007.