Personalized,adaptive learning model developed as a service and deployed under intermittent internet connection conditions

Abstract

E-leaming has been widely used by various institutions all over the world. This has been positively embraced by many people who are eager to acquire knowledge for various reasons. This Las attracted a lot of interest by scholars who are determined to improve this learning mode and enable easy access of learning information by various categories of groups. There are a number of proposals brought forward by researchers for consideration by system developers so that they can come up with robust and scalable e-learning systems. Among the suggestions which have been put forward for consideration include but not limited to the following: System adaptability, learner profile updates and ability to provide relevant learning information suited for various categories of learners. However, there is need to incorporate into both, the existing and the proposed learning systems, the ability to classify learners as they go on with the learning process. The other issue which needs to be considered is ability of the system to allow learners to learn while they are either online or offline. In manyparts of the world, especially in the developing world, most people do not have reliable continuous internet connections. Furthermore, the cost of bandwidth is high making many people not able to afford it. A research was carried out and a model was developed that was tested in a learning institution with learners. The results of the test were analyzed which showed that 83.3% of the learners were correctly classified and 76% of them were able to learn under intermittent internet connection conditions. This study therefore found out that it was possible to have learner models that can adapt to learner characteristics andprovide relevant learning information as per the learner level of knowledge and that learners were able to learn underboth online and offline modes with correct classifications taking place.