A rough sets supported web-based e-assessment model

Abstract

The introduction of World Wide Web and information technology has driven the development of conventional electronic teaching and learning into a new era of web-based learning. Similarly, e-assessment as an important component of e-learning model has been researched in intensively. One of the critical challenges of e-assessment lies in the inadequacy of intelligence feature that can mimic human instructor in making decision. This is important especially in monitoring the learning path that tailored to distinct users with different behaviors. Thus, this research has proposed an e-assessment model supported by rough sets technique to provide adaptive respond to user request on test creation, result grading and providing appropriate feedback and recommendation. Rough classification modeling and association rules mining models were proposed to induce decision rule as the knowledge based in the proposed model.