Real-Time Learner Classification Using Cognitive Score

Abstract

Recommending and providing suitable learning materials to the learners according to their cognitive ability is important for effective learning. Assessing the cognitive load of a learner while studying a learning material can be helpful in assessing his/her intelligence and knowledge adapting abilities. This paper presents a real-time assessment method of the intelligence of students according to their instant learning skills. The proposed system can read the brain waves of students of different age groups at the time of learning and classify their instant learning skills using the cognitive score. Based on this, the learners are suggested suitable learning materials which maintain the learner in an overall state of optimal learning. The main issues concerning this approach are constructing cognitive state estimators from a multimodal array of physiological sensors and assessing initial baseline values, as well as changes in the baseline. These issues are discussed in a data processing block-wise structure. Synchronization of different data streams and feature extraction and formation of a cognitive state metric by classification/clustering of the feature sets are done. The results demonstrate the efficiency of using cognitive score in RTLCS in the identification of instant learning abilities of learners.