Knowledge Visualization - Representational Epistemology

Representational Systems Lab, Creative Technology Group, Department of Informatics, University of Sussex
Peter Cheng, Rossano Barone, Ronald Grau, Noora Fetais

The Representational Systems Lab conducts research on the nature and use of representational system from the perspective of Cognitive Science.  This area of the Lab's research studies how symbolic systems encode knowledge.   Representational Systems fundamentally and dramatically shape forms of higher cognition, including complex problem solving, conceptual learning and discovery.  We have coined the term Representational Epistemology for the study of the nature of effective knowledge representations.  Some of the questions that lead our research include:

• How can novel knowledge visualizations be designed that will improve performance in comprehension, problem solving and learning by at least a factor of two?
  
• What are principles for the re-codification in effective representational systems?
• How do representations impact the ease of learning, what is learned, the way knowledge is structured in memory, and the problem-solving skills acquired?
  
• How should effective graphical user-interfaces and computer-based learning environments be designed?
  
• How can automated system be humanized – made understandable and guidable – using knowledge visualization?
  

Much of the work has focused on the design and evaluation of Law Encoding Diagrams (LEDs) for conceptually demanding educational topics and information-intensive problem solving.  Some of these LEDs have been implemented as computer-based learning environments or as decision support systems.  We have run studies to evaluate LEDs in comparison to convention representations, using eye-movement recording, task analyses, verbal protocol analyses and computational models (see key papers).  By generalizing over the evaluations of diverse representations in many knowledge domains we have developed Representational Epistemic principles for knowledge visualization.  The domains in which we have created novel graphical representations include:    

• Electric circuit theory
  
• Dynamics (particle collisions)
• Kinematics (Galilean)
• Set theory
• Probability theory
• Statistical distributions
  
• High-school algebra
• Propositional and Boolean logic
• Syllogistic logic
• Timetabling
  
• Personnel rostering
• Production planning and scheduling
  
• Transit maps (London Underground Tube)
  
• Formulation engineering (analysis and design of recipes)

Key papers:

Cheng, P. C.-H. (2011). Probably good diagrams for learning: Representational epistemic re-codification of probability theory Topics in Cognitive Science 3(3), 475-498. doi: 10.1111/j.1756-8765.2009.01065.x
Cheng, P. C.-H., & Barone, R. (2007). Representing complex problems: A representational epistemic approach. In D. H. Jonassen (Ed.), Learning to solve complex scientific problems. (pp. 97-130). Mahmah, N.J.: Lawrence Erlbaum Associates.
Peebles, D. J., & Cheng, P. C.-H.  (2003). Modelling the effect of task and graphical representations on response latencies in a graph-reading task. Human factors 45(1).
Cheng, P. C.-H. (2002). Electrifying diagrams for learning: principles for effective representational systems. Cognitive Science, 26(6), 685-736.
Cheng, P. C.-H., & Simon, H. A. (1995). Scientific discovery and creative reasoning with diagrams. In S. Smith, T. Ward & R. Finke (Eds.), The Creative Cognition Approach (pp. 205-228). Cambridge, MA: MIT Press.