A Web-Based Learning Support System for Rough Sets

Web-based learning is gaining popularity due to its convenience, ubiquity, personalization, and adaptation features compared with traditional learning environments. The learning subjects of Web-based learning systems are mostly for popular sciences. Little attention has been paid for learning cutting edge subjects and no such systems have been developed for rough sets. Rough set theory has obtained significant attention as a mathematical approach to data representation and analysis in recent years. To realize Web-based learning for this subject, Web-based learning support systems can provide a reasonable framework. Web-based learning support systems sustain teaching as well as learning by making use of the Web technology, and are able to offer student-centered education. This thesis presents the design principle, system architectures, and prototype implementation of a Web-based learning support system named Online Rough Sets (ORS). The system is specifically designed for learning rough sets in a studentcentered learning environment. Some special features, such as adaptation, are emphasized in the system. The ORS has the ability of adaptation to student preference and performance by modifying the size and order of learning materials delivered to each individual. Additionally, it predicts estimated learning time of each topic, which is helpful for students to schedule their learning paces. Two demonstrative examples show ORS can support students to learn rough sets rationally and efficiently.