cmt 3210, week 6 making sense from prior experience
TRANSCRIPT
CMT 3210, Week 6
Making sense from prior experience
Topics
Recap MetaphorsLearning theoriesLearning modelsConclusions for interface design
Analogy and Metaphor
An analogy provides an explicit, completely identical mapping between objects of two domains
A metaphor provides a partial mapping that draws on similarities, but also includes dissimilarities.
Examples
He hops like a rabbit.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 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
Computing Metaphors
computer metaphors are important as overarching design strategies,
but choose carefully, because……they have to match with the user’s
mental model
The trash can 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. […]
The trash can metaphor
Often, during the course of a session, the user would finish using a particular diskette, […]
To reclaim valuable 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
2. Learning Theories
Major groups:behaviorist theories constructivist theories
Behaviorist theories
Learning as changes of observable external behaviour
Stimulus - response, selective reinforcement
Habitual behaviourProminent Behaviorist: SkinnerLearning as a reactive process
Constructivist teories
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
3. Some HCI learning models
Learning by concept formationlearning by explorationlearning by explanationlearning by imitationlearning by chunkingproceduralisation
Concept formation
Common response to a class of stimulidiscrimination of distinctive features of
objects conjunctive: Car - 4 wheels and engine disjunctive: measels - one or several of
the following symptoms: xyz relational: rectangle - four sided object
with four right angles
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
Which kinds of concepts do computer users need to learn? Click, double click, drag and drop with mouse Open, close, save file or document Copy, cut, paste parts of a document
How can designers support concept formation? Novice interface with few simple features Allowing increased complexity as user moves on Online help, role over text Designing for human error: make errors impossible
and support recovery from errors
Learning by experimentation
Learning as an active processexploration and experimentation:
“Learning by doing”experiental 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 first training wheels feedback safety nets ‘undo’
Explanation-based learning
Provision of general ideas and supporting facts such that the learning can see the relationship between them e.g. lectures Based on mental models Manuals and extended online help
What 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 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
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
Proceduralisation
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?
Recap: Design principles for learnability (Dix) Predictability - help users predict future actions
“If I click on the document icon the document will open.”
Synthesisability - help user asses effects of past action “The document was opened because I clicked on this icon.”
Familiarity - help users to apply past knowledge “This icon looks like a pair of scissors. It will probably cut.”
Generalisability - help users to extend knowledge “’Copy, cut and paste’ in Excel works similar to ‘copy cut
and past’ in Word, but there are also special ‘pastes’ such as ‘paste formula only’ or ‘paste value only’.”
Consistency - similar bahaviour in similar situations “The printer icon always prints out the current document, in
Word, PowerPoint and all other applications.”
Summary week 6
Analogy and metaphorModels of learning:
concept formation experimentation explanation imitation and intelligent adaptation chunking proceduralisation
Further reading
Preece, J. et al. (1994) Human Computer Interaction
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 Hutchins, E. (1995) Cognition in the Wild. MIT
Press Gibbs, G. (1988) Learning by Doing