A Prediction Mechanism of Adaptive Learning Content in the Scalable E-Learning Environment

Abstract

In e-learning environments, adaptive learning is a critical requirement to enhance the teaching quality of the e-learning. Adaptive learning feature provides content specific to a student's learning style. Hence, the first step of adaptive learning is to identify the student's learning style and then to determine the appropriate learning content that corresponds to the individual students learning style. This paper proposes a mechanism to predict the adaptive learning content for each student. To prove the usability and availability of the proposed mechanism, this paper implements the proposed mechanism in a scalable e-learning environment. In the scalable e-learning environment, every student can share diverse learning contents distributed in different learning management systems through peer to peer technology. By means of the prediction mechanism, the adaptive learning content can be acquired at the student site in advance of its use. Hence, the waiting time for downloading learning content can be reduced and thus the learning performance is enhanced. Furthermore, the complexity of storage space is decreased since the student only needs to acquire the learning content corresponding to her/his learning style. In addition, this paper also uses the IRIS dataset and real student data to verify the accuracy of the prediction mechanism.