Research

 
Our website has moved.
Please visit our new site at cognitive-science.at.


Vision, Mission & Research Agenda 2014

May 2014

The primary research focus and field of expertise of the Cognitive Science Working Group at the Knowledge Management Institute at Graz University of Technology (short TUG), lies in a psycho-pedagogically profound approach to multi-adaptive educational technologies and their evaluation with rigorous scientific methodologies.

We strongly believe that a crucial aspect of applying modern technologies to teaching and learning processes, ranging from learning management and delivery systems to immersive educational computer games, requires necessarily accounting for the very unique needs, preferences, abilities, interests, prior experiences, and goals of the individual learners (and teachers). As an enormously rich body of research shows, it is not the medium, the technology alone, it is the psychological, pedagogical, and didactic approach behind, it is an appropriate and suitable learning and learning media design. In this sense, we believe in a multi-disciplinary approach to assemble solid theories, methodologies, and technologies of intelligent, adaptive tutoring (on the basis of formal frameworks such as Competence-based Knowledge Space Theory), psychological aspects (e.g., cognition, motivation, emotion), pedagogical aspects (e.g., instructional design frameworks, learning theories, didactic theories), and the appropriate deployment logistics (web platforms; games, mobile applications, widgets, etc.).
As an agenda for our research activities in the next years we want to continue and extend our achievements in the field of intelligent adaptive educational media in particular, among other topics, we are interested in

  • Multi-adaptive approaches: Most of today’s theories and solutions to “adaptivity” concentrate on knowledge and learning and perhaps on biographical information. In recent projects (e.g., ELEKTRA, 80Days, ROLE) we worked on extending this narrow approach towards a multi-adaptive approach including adaptation according to various cognitive abilities, steadily changing motivational and emotional states, and other personal characteristics (e.g., sensation seeking). In the future research steps we intend to continue this work and make recent achievements broader and more mature. The long term vision is to establish a generic multi-adaptive educational framework that is capable of identifying a broader range of individual characteristics (traits) and oscillating momentary conditions (states) and that is capable of responding to that appropriately in real-time.
  • Non-invasive assessment: The integration of aforementioned intelligent and powerful adaptive systems with seminal immersive educational technologies (e.g., educational/serious computer games or narrative learning environments) do require – necessarily, as we argued in the past – sound, valid, and reliable methodologies and technologies to gather, to ‘harvest’ information from the users and to draw smart and meaningful conclusions as basis for adaptation. Due to the immersive character, this assessment must occur unobtrusively without harming engagement, concentration, focus, and flow experiences of the learners. In previous work we have developed a promising working approach, we termed micro level assessment and adaptation (or in short “micro adaptivty”). In future work, we will concentrate on maturing and extending this approach towards (i) various learner characteristics, (ii) several media, (iii) ontological machine memory models, and (iv) computational efficiency.
  • Merging educational and AI technologies: The community of artificial intelligence (AI) research is very broad and there clearly exist a mutual unawareness of theories and solutions between those communities and the communities of intelligent adaptive educational technologies. In future work we endeavor melding and integrating various other (in the educational context useful) technologies (e.g., recommender systems). Here we do see an enormous potential to improve efficacy and, subsequently, commercial success of intelligent educational products.
  • Adaptive storytelling: Realizing strong global techniques of adaptation (e.g., adaptive curriculum sequencing) and accounting for a broad set of learner characteristics (e.g., personal gaming preferences) in immersive educational technologies such as educational games demand developing features of adjusting the global manifestation of the medium, in particular the story. This, in turn, requires smart approaches to adjust narratives, storylines, story characteristics (e.g., the pace or density of a story), and even Interaction aspects (e.g., game play or game mechanics). In recent projects (80Days, TARGET) we worked on the combination of interactive storytelling approaches and educational adaptivity. In the future we want to continue this work, enabling autonomously emerging narratives, most suitable for a specific learner in a specific situation, with a specific learning goal. This point is strongly associated with the aspects of other approaches to AI. In particular we are interested to do into the direction of “emergence” and “emergent design”.
  • Self-regulated Learning (SRL): Shifting educational responsibilities more and more from authorities to the individual has become one of the educational foundations of our learning society. In the context of life-long-learning the SRL concept integrates the benefits of learning, motivational components and individual learning characteristics. Thus, SRL finds its way into the psycho-pedagogical frameworks of computer-based learning systems (e.g. ImREAL, ROLE) and will also be part of future research. This will include investigating approaches to SRL competence assessment and SRL competence development. The aspect of self-regulation is considered also in a broader sense, including for example self-regulative and reflective processes in decision making and how to support them.
  • Evaluation methodologies: In ICT projects, it is important to use and advance evaluation techniques in order to enable a scientifically/methodologically sound and correct appraisal of the effectiveness and efficiency of new technologies. CSS aims to continue elaborating evaluation frameworks and conducting empirical user trials for adaptive and personal learning environments, digital library systems, and decision support systems. This includes further research and development on a web service supporting all stages of evaluation work and supporting the use and triangulation of different evaluation methods and data sources.

In addition to the key focus of our working group on TEL over the past years, current and future research will increasingly transfer our theoretical expertise and technical developments to other application fields; one field is:

  • Security research: CSS is involved in research activities in the context of security and related data analysis, including management of emergency situations and improving intelligence analysis. In these fields decision makers are supported to make correct decisions based on the available information. CSS investigates psychological methods to support the decision making process by applying psychological sound models on decision making, problem solving and sense-making, with a special focus on cognitive biases and their mitigation. The visualization of relevant data plays a prominent role in this research. Especially, visual analytics is emphasized, which provides useful instruments to support the analysts and knowledge workers.

As a strategy for progressing with the outlined research interests, we believe that multi-national, cross-disciplinary projects, in the EU and beyond, are inevitable. Thus, we strive for connecting with strong partner organizations and establish successful multi-disciplinary research projects. In essence, we want to drive profound research and development in adaptive, personalized technology-enhanced learning (TEL) and technology-enhanced education (TEE) and further fields of application such as knowledge working and security.

 

TO: TopCSSKTITUGraz