Intelligent Querying for Adaptive Course Preparation and Delivery in E-Learning

Today many open sources of information are available on the Internet that provide sharing and reusing of learning materials to reduce the cost of designing new courses, save the time, and avoid effort duplication. In this research, mechanisms that support instructors and e-tutors in selecting the most appropriate learning materials for more effective learning outcomes are investigated. On one hand, instructors need to prepare course materials that meet specific goals such as course objectives and syllabus. On the other hand, students need to have studying materials that match their learning styles and that are built based on their background knowledge. Therefore, the objective of the research is to build a model and an architecture for a Smart e-Learning Assistant (SeLA) that provides instructors and e-tutors with smart assistance in selecting the most appropriate Learning Objects (LOs) for both Adaptive Course Preparation and Delivery from a higher level perspective. SeLA employs two main theories in building its model: the Revised Bloom’s Taxonomy of instructional design (RBT) and Felder-Silverman Learning Style Model (FSLSM). Under this research, a prototype in .NET environment has been developed and evaluated.