Learning style detection based on cognitive skills to support adaptive learning environment - A reinforcement approach

Presently, Learning Style Detection (LSD) has acquired a greater interest in the adaptive learning environment of any academic system. The existing methods of learning environment have facility such as content management and learner data analysis. The learning style detection based on learner's capability, assessment based on mental processing skill and knowledge improvement has not been addressed completely in these systems. Hence, this research works mainly emphasize on creating a reinforcement model for adaptive learning environment based on the Cognitive Skill (CS) of the learners. The model approaches the issues in threefolds; the first is to detect the Learning Style (LS) based on the cognitive skills of a learner dynamically. The second focus is on mapping cognitive skill, Bloom's taxonomy with the Learning Object (LO). The third focus is to create a reinforcement model to keep track and provide feedback on the knowledge competency level improvement. (C) 2016 Ain Shams University.