The use of computer-based and online education systems has made new data available that can describe the temporal and process-level progression of learning. Many scholars are interested in improving e-learning in order to provide easy access to educational materials. There is, however, the need to incorporate the ability to classify learners into these learning systems. Learner classification is used adaptively to provide relevant information for the various categories of learners. The modern ubiquity of computer use and internet access has dramatically impacted many facets of education, including engineering education. There has been a rapid rise over the last decade in the use of computer-based or online formats either to facilitate (e.g. online distribution of materials) or conduct higher-education courses, and enrollment among all students in at least one online course is now at 32%. Many universities now include online programs, and online publisher resources have also grown correspondingly. There is also a need for learning to continue, whether learners are on- or off-line. In many parts of the world, especially in the eve loping world, most people do not have reliable continuous internet connections. We tested an Adaptive e-earning Model prototype that implements an adaptive presentation of course content under conditions of intermittent internet connections This prototype was tested in February 2011 on undergraduate students studying a database systems course. This study found out that it is possible to have models that can adapt to characteristics such as the learner’s level of knowledge and that it is possible for learners to be able to study under both on- and off-line modes through adaptation.

E-learning, Internet, Adaptation, Agent-based

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