Classification Accuracy in Cognitive Load for Users Preference in Web based Learning

With increasing popularity of web based learning, it is required to design the web layout to reduce cognitive load. Cognitive load theory is widely used to predict the effectiveness of the web based and multimedia learning. The cognitive load induced by instructional and multimedia modes are measured by indirect or subjective methods. Questionnaires are one common form of measuring cognitive load indirectly. In this paper, a questionnaire is prepared to identify the cognitive load of the student and his website preferences in a web learning environment. The cognitive attributes are used as the training input for the Naïve Bayes, Classification Regression Tree(CART), Random Forest and Random Tree for classification. Based on the response of the user, areas for improvement in layout of the web learning system are identified.