Cognitive Science Section
The Cognitive Science Section (CSS) is an interdisciplinary research group consisting of psychologists, computer scientists, and mathematicians, with strong connection to the Department of Psychology at University of Graz. In general, the CSS targets research fields where cognitive science meets technology. More precisely, its research areas encompass the fields of technology-enhanced learning and related technologies, decision making and decision support systems, adaptation and personalization, cognitive modeling, human factors in secure society research, and user-centered evaluation of computer applications. CSS follows a multi-disciplinary approach to assemble solid psychological theories, methodologies, and technologies in formal frameworks that guide and integrate research and development. The group has worked and gained experience in more than thirty research projects on national and European level in the context of Technology-enhanced Learning, Secure Society, Cultural Heritage, and Smart Cities.
More information: http://cognitive-science.at/
Knowledge Discovery Group
The mission of the Knowledge Discovery group is to extract the maximum value out of data. To that end, we follow a data-driven approach, which is domain-agnostic in its application. Our research is related to the scientific fields of machine learning, natural language processing and information retrieval. Methods from these fields form the algorithmic base of data science, artificial intelligence and interactive applications.
Contact: Roman Kern (rkernnoSpam@tugraz.at)
Motivational Media Technologies Group (MMTG)
The MMT Group focuses on research and development of tools, applications and approaches to support and improve knowledge transfer, learning and training, as well as health and well-being. Research interest includes modern e-learning and e-assessment applications, game engineering, immersive environments and mobile setups as well as natural language processing, social media technologies and learner and gamer analytics. Read more »
Open and Reproducible Research Group (ORRG)
Open Science is better science. The Open and Reproducible Research Group (ORRG) uses evidence-based approaches to make research cultures more open, transparent and participatory through new practices and technologies. Combining philosophical and sociological approaches with computational and modelling methods, we research services, policies and tools to investigate and foster the uptake and evaluation of Open Science practices, including: Open Access to research publications; FAIR data and research data management services; collaborative and open research methods; reproducible research; research integrity; and responsible research evaluation (especially open peer review). Read more »
Contact: Tony Ross-Hellauer: (ross-hellauernoSpam@tugraz.at)
We research computational models and algorithms to understand and shape human activities and social phenomena in complex systems such as the Web. We work on topics like adaptive and personalized recommender systems; modeling social dynamics in online collaboration systems and exploring reasons underlying biases in algorithms and data. We are open science advocates and we research on discovery and accessibility of research artefacts and altmetrics. Via policy making on European level, we contribute to a responsible assessment of scholarly impact.
Contact: Elisabeth Lex (elisabeth.lexnoSpam@tugraz.at)
Web Science and Engineering
We work on machine learning for the analysis of big information spaces and sensor data, as well as social network theory and semantic technologies. Our aim is to use and combine these intelligent methods and as a result to create hybrid helpers that complement the cognitive abilities of humans.
Contact: Denis Helic (dhelicnoSpam@tugraz.at)
Workplace Learning and Data-Driven Business
We understand, design and evaluate socio-technical interventions in organisations, which means that we study technology together with the way of using it in an organisational context. Our research focus is on learning in organisations at all organisational levels, knowledge sharing and protection, decision making, and identifying data-driven business model opportunities.