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Mental Models and Network Pedagogy

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  1. Mental Models and Network Pedagogy Philip Barker ENABLE99 Presentation Espoo, Finland 2nd June, 1999 Human-Computer Interaction Laboratory University of Teesside Cleveland, UK

  2. Overview • Introduction • What are Mental Models? • Why are they Important? • How are the Studied? • Network Pedagogy • Concluding Remarks

  3. 1. Introduction knowledge and skills innate limitations augmented performance performance support skilled behaviour

  4. Knowledge Skills The Importance of Knowledge and Skills Task Domain Goal fulfilment via Task Execution

  5. Knowledge Defined • a representation of previous experience of a domain • reflects ‘what we know’ about a topic • usually developed through observation, exploration, experiment and inference • collectively referred to as ‘learning processes’

  6. Skills Defined • skills are affective processes that we use in order to solve problems and achieve goals • they may be ‘innate’ or ‘acquired’ • usually improve with practice • there is a limit to improvement

  7. Performance N Innate Limitations We are all subject to the limitations of our natural abilities. Many skills are subject to the Power Law of Practice How can we improve performance beyond our natural abilities?

  8. Human Task Human Human Task Human Augmented Performance We must consider tools and techniques (called performance aids) that will enable us to augment our own individual abilities. Group

  9. Technology Task Human Technology Task Group Using Technological Support

  10. Performance Support • EPSS • DPSS • MPSS

  11. Performance Support • EPSS • DPSS • MPSS Further details: presentation.ppt and paper.htm

  12. Skilled Behaviour • Can we build learning systems that will enable users to develop ‘skilled behaviour’ in a minimal time span at minimal cost? • We need to study the relationship between knowledge and expert/skilled behaviour. • An important steping stone in achieving this is an understanding of mental models.

  13. 2. What are Mental Models? What is Knowledge? How do we Know Things? The Role of Memory Types of Knowledge Cognitive Structures Mental Models Defined

  14. What is Knowledge? put stimuli Memory fetch behaviour Knowledge is what we have ‘in our heads’ and which controls higher order behaviour.

  15. Knowing Things • Verbatim Knowledge • poem • song • speech • multiplication tables • Procedures • generic (eg long multiplication) • specific (eg key recognition - largest off small ring) • Mental Images • house • car • person • Mental Models • rich structures based on a variety of representational techniques

  16. The Role of Memory Stimuli Transient Memory Stimuli Working Memory (Short Term Memory Long Term Memory

  17. Types of Knowledge • tacit and explicit • private and public • local and global • declarative and procedural

  18. Some Definitions Declarative Knowledge - facts and figures - relationships Procedural Knowledge - how to do things NB The ‘recall’ versus the ‘rule’ debate (eg multiplication)

  19. Cognitive Structures • simple associations • lists • plans • schemata • scripts • simple models • complex models

  20. Mental Models Defined According to Rogers et al: mental models are representations ‘in the head’ of experiences gained through the process of living

  21. Models and Model Building rules properties Generic Class Associated Object referent e.g. Jim MENTAL MODELS Associated Object Generic Class referent e.g. house Generic Class specific properties specific rules

  22. 3. Why are they Important? general points dialogue and knowledge transfer human-computer interaction teaching and learning the mental model hypothesis

  23. General Points • reduce memory overheads • reduce complexity • allow derivation of information • support cognitive processing • dynamic character

  24. Mental Models Mental Models Dialogue and Knowledge Transfer experiments experiments knowledge transfer books books dialogue environments and experiences environments and experiences Computer Mediation dialogue dialogue

  25. Mental Models SYSTEM USER Interface Mental Models in HCI

  26. The Role of Interfaces communicate system image to user teach user about system help user to develop skills help user to achieve goals map ‘intent’ onto ‘results’ enable tasks to be performed provide ‘handles’ onto system functionality

  27. text icons drawings widgets images SYSTEM video sound Interface tactile sensations Interface Agents

  28. Environments generate produce Stimuli Experiences initiate Learning Activities activate generate involve Cognitive Structures control Mental BEHAVIOUR Models Designing Learning Environments

  29. A Research Problem Task Domain Skilled Behaviour Performance Knowledge Skills

  30. Mental Model Hypothesis The quality of a person’s mental models determines the quality of task performance in a given problem domain.

  31. 4. How are they Studied? Representational Spaces Basic Techniques Experimental Design Case Study Findings

  32. Study Spaces

  33. Basic Techniques • diagramming • concept maps • hierarchy diagrams • rating • sorting • laddering • teach-back • think aloud • acton sequences

  34. Applicability of Methods Different techniques can capture: (a) different aspects of mental models, and (b) the same aspects in different ways

  35. Experimental Design • identify domain to be studied (eg Web browsing) • select participants (eg 1st Year Students) • identify measurement techniques to be used (eg concept mapping, laddering, teachback) • design scenario involving these techniques • rate solutions against an expert’s answer • additionally, rate task performance (using metrics such as time on task, error counts, quality of solution. and so on)

  36. Case Study - Word for Windows • applied these techniques to measuring mental models students had of Word for Windows • Experiment 1 (richness of mental models) (1) concept elicitation (2) sorting (3) laddering (4) teachback • Experiment 2 (performance on task) (1) Task using Word (Prepare an Invoice) (2) Solution’s compared with an Expert’s • Statistical Analysis

  37. Findings • strong correlations found between those who performed well in Experiment 1 and the quality of solution observed in Experiment 2 • results confirm mental model hypothesis • two basic types of model (1) generic - applicable across all systems (2) specific - relevant to a particular system • represented as hierarchical tree structures • object hierarchies • command hierarchies • states of system • transitions and transformations between states

  38. 4. Network Pedagogy definition principle techniques examples results mental models

  39. Definition Network pedagogy refers to the use of computer network systems for the support of and/or delivery of teaching and learning

  40. Definition Network pedagogy refers to the use of computer network systems for the support of and/or delivery of teaching and learning What are the implications of this for the development of mental models?

  41. Mental Models E1 E2 E4 E3 E5 Principle Time Network interactions provide a powerful mechanism for stimulating the growth and adaptation of mental models.

  42. Techniques • delivery • browsing • monitoring • email • chat • conferencing

  43. Examples • using networks to provide access to teaching material • development of new teaching strategies • using networks to support self-study • lifelong learning applications

  44. Expected Language Spelling Maths Reading Results Gain

  45. Implications for Studying Mental Models • results suggest richer mental models are being performed • however, this needs to be confirmed • how can mental models in network environments be studied? • appropriate techniques need to be developed

  46. 5. Concluding Remarks Our Own Position on Mental Models van Merriënboer’s view (1997) Seel’s opinion (1995) Final Questions

  47. Our Own View Research into the development of mental models is a fundamental requirement if we are to gain a complete understanding of teaching and learning activities

  48. van Merriënboer’s view ‘from an instructional point of view, it may be worthwhile to think in terms of mental models because they provide a higher level of reasoning about the knowledge underlying skilled performance’

  49. Seel suggests that ‘learning a complex cognitive skill can be regarded as the development of increasingly complex mental models that describe both the procedural and the declarative knowledge that is required for effectively solving problems at each stage of acquiring the skill’

  50. Final Questions • For any given skill, what mental models are needed? • How do we design learning experiences to facilitate the development of these models? • How do we create effective learning environments to generate the necessary learning experiences?