Shamsudin, Haziqah
(2019)
Hybrid Of Optimized Random Forest
And Extreme Gradient Boosting For
Online Learning Style Classification.
Masters thesis, Universiti Sains Malaysia.
Abstract
Educational Data Mining (EDM) have raised a lot of attention among researchers since the last few decades. EDM is used to gain more insight into the behavior of learners by building models based on data collected from learning tools which result in improving
learning system to be more personalized and adaptive. Learning style of specific users in the
online learning system is determined based on their interaction and behaviour towards the
system. Felder-Silverman’s learning style model is the most common online learning theory
used in determining the learning style. Initially, in determining the users’ learning styles,
users are asked to fill in the questionnaires which is designed to learn their learning style at the
end of the learning sessions. However, this method is time consuming and the result are not
reliable due to the human factors behavior. Thus, the researchers started to study the learning
style by using an automated approach in which the activity log files are collected in order to
understand the interactivity behaviour of the users with the system.
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