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an experimental setup, in order to increase our
understanding on automatically attaining cognitive
styles based on specific Web interaction data of
users. Specific data metrics of CAPTCHA
mechanisms have been proposed and utilised by an
Artificial Neural Network (ANN), with the aim to
predict the users’ cognitive style.
The experimental process of the ANN yielded
very promising results for the sample examined. In
particular, the results obtained with ANNs for
predicting the cognitive styles ratio of individuals
were particularly successful in respect to their real
cognitive style ratio value. This indicates that
techniques such as ANNs are suitable for predicting
users’ cognitive typology using their interaction data
with CAPTCHA-related challenges.
The practicality and significance of this work is
that the suggested ways of capturing intrinsic
characteristics of users, like cognitive styles, and
their analysis through intelligent techniques may be
more effective and less time and effort consuming
than traditional instruments since they might
optimise the user modelling process. However, in
order to build a cohesive user model psychometric
tests are not yet to be replaced since this study is still
in its very early stages.
The meaning of the relation between cognitive
styles and interaction data needs to be further
examined to reach to a more cohesive user model
and effectively guide adaptation in interactive
systems. Future work includes further
experimentation for investigating these relations and
further employing fuzzy algorithms or other
Artificial Intelligence (AI) methods to determine the
degree of adaptation based on user profiles and the
correlations obtained by ANN models such as the
ones used in this work.
ACKNOWLEDGEMENTS
The work is co-funded by the EU project Co-
LIVING (60-61700-98-009), smarTag (University of
Cyprus) and PersonaWeb (Cyprus Research
Promotion Foundation).
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