COM 3210, Week 6 Making sense from prior experience.
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Transcript of COM 3210, Week 6 Making sense from prior experience.
Topics
Types of reasoning that users engage in
Learning theoriesLearning modelsConclusions for interface design
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.
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
Computing metaphors
No chance for real analogies in computingcomputing metaphors use real world
objects in a computing environmentthey 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
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
Behaviorist theories
Learning as changes of observable external behavior
Stimulus - response, selective reinforcement
habitsProminent Behaviorist: SkinnerLearning as a reactive process
Constructivist theories
Learning as constructing meaning in one’s mind
building of conceptual structures through reflection and abstraction
not directly observablerequires self regulationlearning as an active processPiaget, Gestalt
3. Some practical learning models
concept formationlearning by explorationlearning by explanationlearning by imitationlearning by chunkingproceduralization
Concept formation
Common response to a class of stimulidiscrimination of distinctive features of
objectsconjunctive: Car - 4 wheels and enginedisjunctive: meazels - one or several of the
following symptoms: relational: rectangle - four sided object
with the two opposite sides of the same length
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.
Concept formation
What kind of concept does a computer user need to learn?
How can designers support concept formation
Learning by experimentation
Learning as an active processexploration and experimentation:
“Learning by doing”experiential learning theory (Gibbs
1988): Concrete experience
Reflective observation
Abstract conceptualization
Active experimentation
Learning by experimentation
How can designers facilitate this kind of learning?
Restricted functionality at firsttraining wheelsfeedbacksafety nets‘undo’
Explanation-based learning
general ideas and supporting facts such that the learning can see the relationship between them
e.g. lecturesmental modelsWhat are sources of explanation for
computer users?What makes a good explanation?
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
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
Learning by imitation
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
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
Immitation and intelligent adaptation
Learning to do things: skillscan start as imitation and may move
on to intelligent adaptationHow can this be exploited in
interface design?How can a designer support this type
of learning?
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
Proceduralization
From declarative to procedural knowledgefrom facts to how-to-do knowledgefrom knowing everything about
typewriters to learning how to typefrom knowing everything about windows
to learning how to use itConsistency is important, but can be
harmful or annoying
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?
Habit intrusion
Users tend to behave in habitual ways
even if it is not appropriateHow can designers incorporate
habitual behaviour?
4. Design principles for learnability (Dix)Predictability - help users predict future
actionsSynthesizability - help user asses effects
of past actionFamiliarity - help users to apply past
knowledgeGeneralizeability - help users to extend
knowledgeConsistency - similar behavior in similar
situations
Summary week 6
Reasoning by analogy and by metaphorModels of learning:
concept formation experimentation explanation imitation and intelligent adaptation chunking proceduralization
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