Applications


Our research results have been successfully applied in EU Funded projects AFEL and Learning Layers and in COMET projects in the field of automotive industry.

Collaboration

If you are interested in setting up a research project, please contact the head of the social computing team
Ass. Prof. Elisabeth Lex
elex@know-center.at
Elisabeth Lex

If you are interested in a business project please contact our business developer Matthias Traub
mtraub@know-center.at
Matthias Traub

Services for Engineering Consensus Building in Networked Learning Environments

Services for engineering consensus building have been applied within the EU funded project Learning Layers to facilitate learners on negotiation processes taking place in terms of scaffolding in networked online environments. Typically, learners turn to networked online environments with the aim to seek for online help. In this context, negotiation processes include learners’ interactions while understanding and defining certain problems, exchanging opinions, identifying the experts in the field, analysing possible solutions and building a consensus on a best provided solution. We focus on addressing the scaffolding phenomenon in online settings by studying the role of learners’ social status on negotiation processes. With services for engineering consensus building we model the optimal extent of the influence of learners’ social status that speeds up the process of consensus building in collaboration networks.
Our research in terms of the Layers project has shown that the presence of high social status learners, known as hubs, in a networked online environment is crucial for negotiation processes, because such learners can distribute a single common opinion to a large number of other learners. Additionally, the consensus building process is indeed affected by the learners’ social status. But, there is a specific setting for each collaboration network, in which social status speeds up the consensus building process.

Demand Forecasting

Social Learning Analytics

Social learning analytics make use of data generated by learners online activities in order to identify behaviors and patterns within learning environments. In this regard, we aim at identifying, extracting and enriching the features that characterize learning activities within online contexts across multiple platforms. Examples for such feature extraction approaches are computing the complexity of a resource, determining the semantic stability of a resource, or accessing the influencing factors in consensus building processes in online collaboration scenarios.
Within the EU Horizon 2020 funded Project AFEL (Analytics for everyday learning), together with consortium partners (GNOSS, Knowledge Media Research Center Tübingen, Leibniz Universitaet Hannover, National University of Ireland Galway and The Open University), we address both the theoretical and technological challenges arising from applying learning analytics in the context of online, social learning.

Information flow in and across research communities

Social Semantic Server

The Social Semantic Server (SSS) is an extensible, open-source application server environment that equips client-side tools and back-end applications with services that support social collaboration and interactions with actors and artefacts. Based on these interactions, a social semantic network is created that links actors, artefacts and metadata. This so-called "semantically-enriched Artifact-Actor Network (AAN)" combines ideas from social and artifact network approaches to describe the relationships among actors and artifacts in different contexts. Together with evolving diverse structures and contents, this emerging (social) information concerning entities and their relationships can be exploited to design functionality such as e.g. context-aware recommender services, social learning analytics, or community building.
The SSS has been part of the technological infrastructure in the EU funded project Learning Layers.