AUTOMATIC STUDENT MODELLING FOR DETECTING
LEARNING STYLE PREFERENCES IN LEARNING
Women’s Postgraduate College for Internet Technologies
Vienna University of Technology, Vienna, Austria
Silvia Rita Viola
Dip. Ingegneria Informatica, Gestionale e dell’ Automazione “Maurizio Panti”
Universita’ Politecnica delle Marche, Ancona, Italy
School of Computing and Information Systems
Athabasca University, Athabasca, Canada
Providing adaptivity based on learning styles can support learners and make learning easier for them. However, for
providing proper adaptivity, the learning styles of learners need to be known first. While most systems, which consider
learning styles, use questionnaires in order to identify learning styles, we propose an automatic student modelling
approach, which analyses the actual behaviour and actions of students during they are learning in an online course in
order to infer students’ learning styles. Such an automatic approach has the advantage that students do not have any
additional effort for providing information about their learning styles. Additionally, an automatic approach can be more
accurate by excluding extraordinary behaviour of students and adapting in the case that the learning styles changed over
time. In this paper, we present an automatic student modelling approach for learning management system, which aims at
identifying learning style preferences within the four dimensions of the Felder-Silverman learning style model (FSLSM).
The approach is based on patterns derived from literature and a simple rule-based method for calculating learning styles
from the students’ behaviour. The proposed approach is evaluated by a study with 75 students, comparing the results of
the learning style questionnaire with the results obtained by the proposed automatic student modelling approach. As a
result, the approach is appropriate for identifying all learning style preferences within the active/reflective dimension of
FSLSM and some learning style preferences within the sensing/intuitive and visual/verbal dimension. For the
sequential/global dimension, results of learning style preferences show only moderate precision.
Learning styles, automatic student modelling, Felder-Silverman learning style model, learning management
According to Felder and Silverman (1988), learners with a strong preference for a specific learning style
might have difficulties in learning if their learning styles are not considered by the teaching environment. On
the other hand, providing courses that fit the learning styles of learners makes learning easier for them and
* This research has been funded partly by the Austrian Federal Ministry for Education, Science, and Culture, and the European Social
Fund (ESF) under grant 31.963/46-VII/9/2002.
based on several patterns. In an experiment, they tested the effectiveness of Decision Trees and Hidden
Markov Models for detecting learning styles according to FSLSM.
In both research works, an approach strictly depending on available data is used in order to build a model
for calculating learning styles. Furthermore, the approaches are developed for specific systems, using only
those features and patterns that are incorporated in the system. Our proposed approach considers preferences
within the learning style dimensions and aims at providing a general concept for identifying learning style in
LMS, based on a simple rule-based method, similar to the one used in ILS, to calculate learning styles.
This paper introduced an automatic student modelling approach for identifying learning style preferences
according to FSLSM in learning management systems. The proposed approach is based on the idea that
students’ behaviour can give relevant hints for identifying their learning style preferences. Relevant patterns
were derived from the learning style literature (Felder and Silverman, 1988) and conclusions about the
students preferences were calculated based on a simple rule-based method, similar to the one used in the ILS
questionnaire, an instrument to identify learning style based on the FSLSM. The proposed approach was
evaluated by a study with 75 participants. The study compares the results of the proposed automatic student
modelling approach with the results of the ILS questionnaire. Results show that the approach is suitable for
identifying all preferences of the active/reflective dimension and some preferences of the sensing/intuitive
and visual/verbal dimension. For the sequential/global dimension, results show only moderated precision.
Future work will deal with extending the proposed course structure in order to find patterns that give
more accurate information about the preferences where only moderate results were achieved. Furthermore,
future work will deal with investigating the use of automatic student modelling for dynamically updating the
information in the student model by considering the behaviour of students in an online course.
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