This research addresses the issues of adaptation and personalisation of the computer
interface for Web-based learning materials taking into consideration key characteristics
of learners and particularly their cognitive style.
The thesis examines main concerns driving learning towards individualisation. Different
approaches to adaptation and personalisation are analysed, as are a range of adaptive
systems. The need for further research regarding individual differences is identified; it is
argued that cognitive styles should be allowed for in designing adaptive learning
materials.
A comprehensive review of cognitive style classifications is presented, from which key
defining attributes and advantageous instructional conditions are identified and a
number of adaptive variables derived.
LEARNINT, a prototype based on these variables was developed and used in two
experimental studies. Results show a relationship between Interface Affect and learning
outcomes and also between the variables underpinning the interface style used and
variation in user reactions and performance; however, little interaction is observed
between these variables and cognitive style.
It is suggested that for most learners using Web-based learning materials performance
may improve if they experience positive affect towards the interface; also, that the
proposed variables stand as good candidates for providing adaptivity. A methodological
approach is presented that extends the functionality of LEARNINT. The generic aspects
of the research are further elaborated offering guidance on future directions for the
design of adaptive Web-based learning materials
