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. 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! Welcome back. Find out about our 25% off sale Database Welcome to our 25% off sale Web server

4. 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

5. 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

7. WHURLE revision guide: system architecture Lesson Plan Adaptation Filter Display Engine Virtual Document Chunks Links User Model Skin + + 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.

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

9. Student information Mostly 2 nd /3 rd 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

10. Screenshots Verbal environment Visual environment No preference environment

11. 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

12. 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 Hypothesis:Statistical significance: H 1 : matched students will do significantly better F(4,210)=0.66, p=0.62, Wilks Lambda=0.98, partial eta squared=0.1 H 2 : mismatched students will do significantly worse F(4,210)=0.66, p=0.62, Wilks Lambda=0.98, partial eta squared=0.1 H 3 : 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 H 4 : 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

14. 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

16. 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

17. 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?

18. 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?

19. 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

20. 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