A Data Mashup for Dynamic Composition of Adaptive Courses

This paper presents a novel adaptive course composition system that based on mashing up learning content in aweb application. The system includes three major components, static course system, data mashup, and adaptive coursecomposer. The first system enables lecturers to build multiple lesson courses that are managed with corresponding APIsincluding documents stored in Google docs or videos located at YouTube. The data mashup utilizes some web and searchengines APIs in which learners can search for relevant web content and display it in a grid layout. The adaptive coursecomposer uses learning object from the data mashup to build adaptive course based on learner preferences. It uses a selectednumber 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