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  1. 1. COM 3210, Week 6 Making sense from prior experience
  2. 2. Topics Types of reasoning that users engage in Learning theories Learning models Conclusions for interface design
  3. 3. 1. Reasoning Two types of reasoning: Based on analogies Based on metaphors
  4. 4. Analogy and Metaphor An analogy provides an explicit, isomorphic mapping between objects of two domains A metaphor is a looser connection that draws on similarities, but also includes dissimilarities.
  5. 5. Examples Killing a tumor is like a general‟s army attacking a fortress surrounded by mines Your PC‟s operating systems works like a desktop whether something is an analogy or a metaphor also depends on the scope of the comparison
  6. 6. Computing metaphors No chance for real analogies in computing computing metaphors use real world objects in a computing environment they provide an intuitive understanding of the computing object and initiate a process of active learning computer metaphors are indispensable as overarching design strategies, but choose carefully
  7. 7. The desktop metaphor Pictures of trash can Macintosh
  8. 8. The desktop metaphor  “The use of the trash can to eject a disk was present form the very beginning of the Macintosh interface. […] The original Mac had not hard disk. […] Because most users typically would switch back and forth between several diskettes during a session, it was deemed appropriate for the Mac to keep a memory image of the list of files of the various disks, regardless whether or not the diskette was actually inserted in the drive. […] Often, during the course of a session, the user would finish using a particular diskette, […] To reclaim vluable space, the now unwanted list of files represented by the grayed-out icon could be thrown away by dragging it into the trash…” Tom Erickson, Apple
  9. 9. 2. Learning Theories Major groups: behaviorist theories constructivist theories
  10. 10. Behaviorist theories Learning as changes of observable external behaviour Stimulus - response, selective reinforcement habits Prominent Behaviorist: Skinner Learning as a reactive process
  11. 11. Constructivist teories Learning as constructing meaning in one‟s mind building of conceptual structures through reflection and abstraction not directly observable requires self regulation learning as an active process Piaget, Gestalt
  12. 12. Constructivist approaches Perception Organisation Decision making Problem solving Attention Memory
  13. 13. 3. Some practical learning models concept formation learning by exploration learning by explanation learning by imitation learning by chunking proceduralisation
  14. 14. Concept formation Common response to a class of stimuli discrimination of distinctive features of objects conjunctive: Car - 4 wheels and engine disjunctive: measels - one or several of the following symptoms: relational: rectangle - four sided object with the two opposite sides of the same length
  15. 15. Concept formation Users acquire new concepts and refine them e.g. Children learn about dogs and cats first concept: animals have four legs (humans have two) refinement: birds are animals and have only two legs.
  16. 16. Concpet formation What kind of concept does a computer user need to learn? How can designers support concept formation
  17. 17. Learning by experimentation Learning as an active process exploration and experimentation: “Learning by doing” experiental learning theory (Gibbs 1988): Concrete experience Active experimentation Reflective observation Abstract conceptualization
  18. 18. Learning by experimentation How can designers facilitate this kind of learning? Restricted functionality at first training wheels feedback safety nets „undo‟
  19. 19. Explanation-based learning general ideas and supporting facts such that the learning can see the relationship between them e.g. lectures mental models What are sources of explanation for computer users? What makes a good explanation?
  20. 20. Minimalist instruction people rather learn by experimentation than by explanation explanation i.e. instruction should support that instruction should be as little as possible, but as much as necessary
  21. 21. Minimalist instruction Focus on real world activities of the task domain Choose an action oriented approach (how to do things) emphasize error recognition and recovery eliminate repetitions, summaries, reviews, and exercises
  22. 22. Learning by immitation Piaget: three types of human adaptation: Play: assimilating objects to predetermined activities regardless of the object‟s attributes, e.g. using chair as horse Simple Imitation: change behavior to be something else, e.g. using mam‟s lipstick, but also dance lessons
  23. 23. Intelligent Adaptation Assimilating aspects of the environment to the cognitive structure and accommodating cognitive structures to the environment guided by structures and resulting in changed structures e.g. apprenticeship (crafts), pilot-training, nurse training, learning to drive a car
  24. 24. Immitation and intelligent adaptation Learning to do things: skills can start as imitation and may move on to intelligent adaptation How can this be exploited in interface design? How can a designer support this type of learning?
  25. 25. Learning by chunking Forming general rules from specific instances declarative chunking: e.g. grouping digits of a phone number. Procedural chunking: grouping several actions into a new action, e.g. drag and drop
  26. 26. Proceduralisation From declarative to procedural knowledge from facts to how-to-do knowledge from knowing everything about typewriters to learning how to type from knowing everything about windows to learning how to use it Consistency is important, but can be harmful or annoying
  27. 27. Exercise: answer the following questions What is the tree that grows from an acorn? What is the black cover garment that one wraps around one self? What sound does a frog make? “knock knock” stories are a kind of … What‟s the term to say you‟ve got no money? What‟s the clear part of an egg?
  28. 28. Habit intrusion Users tend to behave in habitual ways even if it is not appropriate How can designers incorporate habitual behaviour?
  29. 29. 4. Design principles for learnability (Dix) Predictability - help users predict future actions Synthesisability - help user asses effects of past action Familiarity - help users to apply past knowledge Generalisability - help users to extend knowldge Consistency - similar bahaviour in similar situations
  30. 30. Summary week 6 Reasoning by analogy and by metaphor Models of learning: concept formation experimentation explanation imitation and intelligent adaptation chunking proceduralisation
  31. 31. Further reading  Preece, J. et al. (1994) Human Computer Interaction  Eberts, R. (1994) User Interface Design  Dix et al. (1998) Human Computer Interaction  Carroll, J. (1990) The Nurnberg Funnel MIT Press  Carroll, J. (1998) Minimalism: Beyond the Nurnberg Funnel MIT Press  Huthicns, E. (1995) Cognition in the Wild. MIT Press  Gibbs, G. (1988) Learning by Doing