Reappraising Cognitive Styles in Adaptive Web Applications

1
Reappraising Cognitive Styles in Adaptive Web
Applications

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

2
Introduction

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

3
Evolution of web applications

• Shift of web sites
• Widespread use of web applications with underused
  potential for individualisation
• The power of personalisation

static information repositories
dynamic applications
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Web server
Database
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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

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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?

6
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

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WHURLE revision guide system architecture


8
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

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Student information

• Mostly 2nd/3rd year undergraduates
• Average age was 21, gender ratio of 3.6 males1
  female
• Out of 221 students who logged on at least once
• 105 were visual
• 105 were bimodal (no preference)
• 11 were verbal

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Screenshots
Verbal environment
Visual environment
No preference environment
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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

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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

13
Statistical results
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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

15
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

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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

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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?

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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?

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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

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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_at_cs.nott.ac.uk
• www.cs.nott.ac.uk/ejb