Reappraising Cognitive Styles in Adaptive Web Applications Liz Brown, Tim Brailsford, Tony Fisher,...

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Transcript of Reappraising Cognitive Styles in Adaptive Web Applications Liz Brown, Tim Brailsford, Tony Fisher,...

Reappraising Cognitive Styles in

Adaptive Web Applications

Liz Brown, Tim Brailsford, Tony Fisher, Adam Moore & Helen Ashman

Introduction

• Evolution of web applications• Personalisation mechanisms• Cognitive styles for user profiling• Case study: student revision guide• Findings of study• Conclusions and discussion

• Shift of web sites:

• Widespread use of web applications with underused potential for individualisation

• The power of personalisation

Evolution of web applications

static information repositories

dynamic applications

Web server

Hello Bob! Welcomeback. Find out about our “25% off” sale

Database

Welcome to our “25% off” sale

Web server

Cognitive styles in educational web applications

• Cognitive style is a psychological construct

• Most web sites modelled on either informational or navigational concepts

• Cognitive styles can be used to inform either of these to provide personalisation for the user

Cognitive styles and learning

• Cognitive styles vs learning styles• Types of styles:

– Field dependence vs field independence– Visual/imager vs verbal– Global vs sequential– Reflector/reflective vs activist/impulsive– Convergers vs divergers– Tactile/kinaesthetic

• Which is best and how should it be used?

Experimental study

• User trials carried out with an online revision guide for a taught module

• Over 200 university students involved• Used a visual-verbal approach,

investigating 2 variables:– Visual and verbal environments– Visual-verbal learning style of students

• Feedback/evaluation via assessment data, questionnaires, interviews and log files

WHURLE revision guide: system architecture

Lesson Plan

Adaptation Filter

Display Engine

Virtual Document

Chunks

Links User Model

Skin

+ +

The TitleSome text some text some text some more text some more text. Text text text Some text some text some text some more text some more text. Text text text.

Some text some text some text some more text some more text. Text text text Some text some text some text some more text some more text. Text text text.

Learning styles in WHURLE

• Lesson plan produced for visual, verbal and no preference users

• Chunks created: mix of visual, verbal, no preference or universal

• Students filled in a learning styles questionnaire during first log-in

• Users then randomly assigned to matched group, mismatched group or neutral group

Student information

• Mostly 2nd/3rd year undergraduates• Average age was 21, gender ratio

of 3.6 males:1 female• Out of 221 students who logged on

at least once:– 105 were visual– 105 were bimodal (no preference)– 11 were verbal

Screenshots

Verbal environment

Visual environment

No preference environment

What were we investigating?

• To see if matching or mismatching would make a difference

• To see if there were any differences between students with different learning styles

• To see if there were any differences between students who used the different environments

Main findings of the study

• Matching or mismatching made no difference to student performance

• No difference between students with different learning styles

• No difference between students who used the different environments

Statistical results

Hypothesis: Statistical significance:

H1: matched students will do significantly better

F(4,210)=0.66, p=0.62, Wilks’ Lambda=0.98, partial eta squared=0.1

H2: mismatched students will do significantly worse

F(4,210)=0.66, p=0.62, Wilks’ Lambda=0.98, partial eta squared=0.1

H3: one type of learning style is more beneficial

F(2,106)=0.46, p=0.63, Wilks’ Lambda=0.99, partial eta squared=0.01

H4: one type of learning environment is more beneficial

F(4,210)=0.59, p=0.67, Wilks’ Lambda=0.98, partial eta squared=0.01

Secondary findings

• No correlation between amount of use of the system and student performance

• Qualitative data suggests students found it an enjoyable and useful resource

• All students interviewed agreed that personalisation was important

Conclusions

• Personalising for visual-verbal learning style does not seem to have much educational benefit

• However, many students studying for Computer Science degrees seem to be visual learners

• Students feel that personalisation in web-based learning is important

Discussion - 1

• Were we using suitable test subjects?

• Are learning styles static or dynamic?

… and should the system cater

for this?• Cognitive processing and dual

encoding

Discussion - 2

• What constitutes a truly "visual" representation of information?

• Are learning styles important?… or were we not using the "right one"?

• Is one learning style better than another?

Discussion - 3

• What more needs to be done with learning styles and adaptive web-based education?

• Should we be looking at other methods of personalisation for web-based education?

The next phase…

• User trials with primary school children (aged 7-10)

• Investigations into other learning styles

• More discussion needed about adaptation and user control, and matching/mismatching

Acknowledgements

• Many thanks to Dr Shaaron Ainsworth (School of Psychology) and members of the Web Technologies Lab in the School of Computer Science & IT for all their help and support

• Also to the students who participated in the study and subsequent evaluations

• This research is supported by a PhD scholarship from the University of Nottingham

ejb@cs.nott.ac.uk

www.cs.nott.ac.uk/~ejb