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The Relationship between Learning Styles and Cognitive Traits - Getting Additional Information for Improving Student Modelling

Introduction
Student models (for example, see Brusilovsky, 1994) are essential to any adaptive virtual learning
environments. These models contain information about learners such as personal data, domain competence,
learning style and cognitive traits, and use this information to adapt to the learners’ needs. An important task
for such adaptive environments is to build a robust student model in order to be able to provide adaptivity in
an appropriate way, but filling the student model with proper information about the learner is quite
challenging. 
The simplest approach to construct a student model is to ask a student for relevant data. However, this
approach is not suitable for identifying accurate information for a number of components of a student model,
such as cognitive traits, domain competence, and preferred learning styles. For example, the estimation of
domain competence is subjective. To determine cognitive  traits and learning styles, comprehensive tests or
questionnaire-based surveys are the ordinary means used but these are time consuming and hardly definitive.
An alternative approach to collect the information pertinent to a student model is to track the student’s
behaviour and responses and then make inferences  about general domain competence, cognitive traits, and
learning styles. The challenge of this approach is  to identify and collect sufficient information to make
reliable and useful inferences. To support the detection process of required information, it is beneficial to find
mechanisms that use whatever information about the learner is already available to obtain as much reliable
information as possible to build a more robust student model.
The aim of this paper is to demonstrate the relationship between the learning style and the cognitive traits
of a learner. The identified relationship provides additional information which can be used to improve the
detection process of both, the learning style and the cognitive traits, in an adaptive virtual learning
environment.
To exemplify this relationship, we investigate the interaction of working memory capacity, one cognitive
trait included in the Cognitive Trait Model (Lin, Kinshuk, and Patel, 2003), with Felder-Silverman learning
style model (Felder and Silverman, 1988). Both models as well as their possible implementation in adaptive
virtual learning environments are described in the next section in more detail. In Section 3, we present the
mapping between the Felder-Silverman learning style model and working memory capacity. This mapping is


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