Transcript Reappraising Cognitive Styles in Adaptive Web Applications

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
Evolution of web applications
• Shift of web sites:
static information
repositories
Web
server
Welcome
to our
“25% off”
sale
dynamic
applications
Web
server
Hello Bob!
Welcome
back. Find
out about our
“25% off” sale
Database
• Widespread use of web applications with
underused potential for individualisation
• The power of personalisation
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
Chunks
+
+
Lesson Plan
User Model
Links
Adaptation Filter
Skin
Display Engine
The Title
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 Some text
some text some text some
more text some more text. Text
text text.
Virtual Document
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
Visual
environment
No preference
environment
Verbal
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 webbased 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
[email protected]
www.cs.nott.ac.uk/~ejb