Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 3 SI
Page No. 6139 - 6144

Learning Style Analytics for Children based on Neurophysiological Algorithmic Model of Affect

Authors : Norhaslinda Kamaruddin and Abdul Wahab Abdul Rahman

Abstract: Effective learning for children requires the matching of both teaching and learning style in such a way that both teachers and learners can enjoy and absorb the knowledge. Young aged children may not be able to understand the different learning styles, hence, disregard its adoption in learning. Such situation may slow down the children rate of understanding of the learned subject. Moreover, teachers need to manage the learning environment to suit the children learning needs to optimize absorption of information. With variety of education aids that are produced in a fast manner and in a large volume, it is assumed that one technique fits all is impossible to achieved. Such scenario results to the unfulfilled lesson plan objective because each individual has their individual learning style to adapt with the contents. Hence, learning style analytics is required to help teachers to channel their effort for better information dissemination and selection of suitable educational tools. This research focuses on investigating the different characterizations of learning style and suitable learning stimuli to ignite the brain activation function, especially, for children where they may not even understand the different learning styles. It will distinguish the impact of different learning stimuli to the different brain signals reflected by various learning style. The different learning style analytics will help teachers to be able to optimize the learning style for each group of students. The brain activation is mapped onto the affective space model because emotion influences learning motivation. The valence and arousal axes of the affective space model will be plotted and compare with learner’s style analytics. The validation of the proposed model is presented by modelling the correlation between learning style and emotion using neurophysiological input from Electroencephalogram (EEG). In the future, the proposed model can be used by learners for self-assessment to reflect continuous learning improvement and for teachers to plan supporting intervention for better teaching pedagogy. This aspiration is in line with the direction of Ministry of Education to transform Malaysia’s education in preparation for the future education needs moving forward to industrial revolution (IR4.0).

How to cite this article:

Norhaslinda Kamaruddin and Abdul Wahab Abdul Rahman, 2019. Learning Style Analytics for Children based on Neurophysiological Algorithmic Model of Affect. Journal of Engineering and Applied Sciences, 14: 6139-6144.

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