The Development of Transactive Memory Systems in Collaborative Educational Virtual Worlds

The use of 3D virtual worlds in the form of cyber campuses has been introduced in higher education over the past decade to support and enhance stu- dents’ online learning experiences. Considering that students learn in socially constructed ways and through peer collaboration, the development of Transac- tive Memory System - the collective awareness of the group’s specialization, coordination, and credibility - is found to be beneficial for educational purposes. This paper presents the results of a study investigating the extent to which a TMS can be developed within a 3D virtual world educational setting

Intelligent Support for Knowledge Sharing in Virtual Communities

Virtual communities where people with common interests and goals communicate, share resources, and construct knowledge, are currently one of the fastest growing web environments. A common misconception is to believe that a virtual community will be effective when people and technology are present. Appropriate support for the effective functioning of online communities is paramount. In this line, personalisation and adaptation can play a crucial role, as illustrated by recent user modelling approaches that support social web-groups. However, personalisation research has mainly focused on adapting to the needs of individual members, as opposed to supporting communities to function as a whole. In this research, we argue that effective support tailored to virtual communities requires considering the wholeness of the community and facilitating the processes that influence the success of knowledge sharing and collaboration. We are focusing on closely knit communities that operate in the boundaries of organisations or in the educational sector. Following research in organisational psychology, we have identified several processes important for effective team functioning which can be applied to virtual communities and can be examined or facilitated by analysing community log data. Based on the above processes we defined a computational framework that consists of two major parts. The first deals with the extraction of a community model that represents the whole community and the second deals with the application of the model in order to identify what adaptive support is needed and when. The validation of this framework has been done using real virtual community data and the advantages of the adaptive support have been examined based on the changes happened after the interventions in the community combined with user feedback. With this thesis we contribute to the user modelling and adaptive systems research communities with: (a) a novel framework for holistic adaptive support in virtual communities, (b) a mechanism for extracting and maintaining a semantic community model based on the processes identified, and (c) deployment of the community model to identify problems and provide holistic support to a virtual community. We also contribute to the CSCW community with a novel approach in providing semantically enriched community awareness and to the area of social networks with a semantically enriched approach for modeling change patterns in a closely-knit VC

TExSS: Transparency and Explanations in Smart Systems

Smart systems that apply complex reasoning to make decisions and plan behavior, such as decision support systems and personalized recommendations, are difficult for users to understand. Algorithms allow the exploitation of rich and varied data sources, in order to support human decision-making and/or taking direct actions; however, there are increasing concerns surrounding their transparency and accountability, as these processes are typically opaque to the user. Transparency and accountability have attracted increasing interest to provide more effective system training, better reliability and improved usability. This workshop provides a venue for exploring issues that arise in designing, developing and evaluating intelligent user interfaces that provide system transparency or explanations of their behavior. In addition, we focus on approaches to mitigate algorithmic biases that can be applied by researchers, even without access to a given system’s inter-workings, such as awareness, data provenance, and validation