A Data Mashup for Dynamic Composition of Adaptive Courses

Mohammed Al-Zoube and Baha Khasawneh
Princess Sumaya University for Technology, Jordan

Abstract: This paper presents a novel adaptive course composition system that based on mashing up learning content in a web application. The system includes three major components, static course system, data mashup, and adaptive course composer. The first system enables lecturers to build multiple lesson courses that are managed with corresponding APIs including documents stored in Google docs or videos located at YouTube. The data mashup utilizes some web and search engines APIs in which learners can search for relevant web content and display it in a grid layout. The adaptive course composer uses learning object from the data mashup to build adaptive course based on learner preferences. It uses a selected number of the IEEE LOM to sort the learning objects according to their educational role, difficulty level, or rating, and hence, eliminate the use of course domain ontology.

Received November 16, 2008; accepted December 1, 2008