TY - GEN
T1 - Automatic student modelling for detecting learning style preferences in learning management systems
AU - Graf, Sabine
AU - Viola, Silvia Rita
AU - Kinshuk,
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Automatic student modelling
KW - Felder-silverman learning style model
KW - Learning management systems
KW - Learning styles
UR - http://www.scopus.com/inward/record.url?scp=67349100105&partnerID=8YFLogxK
M3 - Published Conference contribution
AN - SCOPUS:67349100105
SN - 9781627483322
T3 - IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2007
SP - 172
EP - 179
BT - IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2007
T2 - IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2007
Y2 - 7 December 2007 through 9 December 2007
ER -