Mining Learning Styles for Personalised eLearning
In drive of inspecting user behaviour in an adaptive eLearning system, we investigate the eye gaze movement combined with the emotional state of learners with our experiment using eye tracker and the electroencephalography (EEG) device. Not only the gaze behaviour indicates type of learning style, but also shows the set of cognitive activities and emotions that can contribute in learning process. This paper discusses the first set of results of our experiment associated with monitoring the eye gaze behaviour. Six postgraduate students participated in this study. Two key findings were identified by combining many methods including boxplots and ANOVA. First, there is no effect on the difficulty level on the visual/ verbal learner behaviour. Second, there is no difference in verbal/visual learner behavior towards their preferences in different learning materials. Also, the paper describes the implemented experiment approach in our smart lab. And finally, this paper exposes our analysis and investigation of learning styles relation with the different courses, and how the eye behaviour is affected accordingly. The results of EEG data will be analysed and correlated to our findings in the next piece of work.
Citation : Alhasan K., Chen L., Chen L. (2018) Mining Learning Styles for Personalised eLearning. In: Proceedings of the IEEE International Conference on ubiquitous intelligence and Computing, UIC2018, Guangzhou, China, June 2018.
ISBN : 9781538693803
Research Group : CIIRG
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes