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