EEG Based Learner’s Learning Style and Preference Prediction for E-learning

Abstract

The prominence of diagnosing a learner’s Learning Style is necessary has to demonstrate success in a teaching and learning process. At the same time, the learner’s preferences on MultiMedia Content (MMC) are also being keenly examined in consistent attempts to understand learner in a more adept way. For the above competence in emergence of Electroencephalography (EEG) technology, learner’s brain characteristics should be observed directly and the consequence may well support the learning style and preferences. In this study, Learners are categorized by David Merrill’s First Principles of Instruction (FPI) which contains four phases namely Activation, Demonstration, Application and Integration. Also it is assessing learning preference by identifying the type of multimedia content the learner prefers. The proposed approach proposes to use participant’s commitment level measured with an EEG system, the neurosky mind wave instrument. The main advantage of proposed system is that it enables continuous assessment of various learners with MMC for predicting learning style and preferences.