Developing Adaptive E-Learning Environment Using Cognitive and Noncognitive Parameters

Abstract

The falling learning outcome is one of the major challenges faced by most of the educational systems. Adaptive educational systems (AESs) are viewed as catalyst to reinforce learning. Several AESs have been developed considering only single aspect of learners, for example, learning styles. The impact of learning style-based AESs in terms of improving learning outcomes is still unclear. In this study, an adaptive learning system is being proposed considering combination of multiple sources of personalization such as learning styles, working memory capacity, and prior knowledge. An experiment was conducted using sample size of 184 students to assess the impact of proposed approach in comparison to traditional classroom teaching and learning. The student sample was equally divided into control and experimental groups. Both groups further consisted of subgroups formed on the basis of their cognitive and noncognitive characteristics, identified using standardized tools. The experimental subgroups learned the subject contents with proposed adaptive learning system, whereas control subgroups learned in traditional classroom environment. The results revealed that the participants of experimental subgroups exhibit significantly better learning performance than their counterparts of control subgroups.