Supporting Adaptive Learning Environment through Cognitive Skill based Learner Classification

Background/Objectives: Adaptive learning has become a key accelerator to provide personalized content based on the learners’ preferences, as existing Learning management system support to manage the content and learner data analysis. Methods: To provide suitable content for the learner, Learning Style (LS) of the learner plays an important role. The limitation in existing learning environment is identifying the individual’s learning skill based on the mental processing capability of the learners. In this paper, a model is proposed to incorporate an effective learner model for adaptive learning environment through cognitive skill based learner classification. Findings: A Multiple Choice Questionaries’ was conducted to test the cognitive capability. Around 107 learners participated. The questionnaire was prepared based on the four cognitive skills such as Memory, Concentration, Perception and Logical Thinking. Each of the students is evaluated and the scores earned by them are normalized to assess them uniformly. Based on the mean score earned by each of the student under each cognitive skill category, the skill level is set as low, medium and high. The skill set categorization helps in classifying the learners into three broad categories which would facilitate the suitable learning object to be followed and hence the knowledge competency level can be improved. Most of the existing methodology permits no classification of learners and it permitted the direct approach on improving the memory based skills alone. Thus the attempted approach is observed to be helpful as the learners are classified based on their cognitive skill. Application/Improvements: The results obtained by the proposed architecture are promising in assessing the learner’s ability. The approach can be applied in any education system starting from high school to higher education.