A Semantic Approach to Intelligent and Personal Tutoring System

Abstract

Cyberlearning is challenged by the lack of personal and assessment-driven learning, and students are often puzzled by the lack of instructor guidance and feedback, the huge volume and diversity of the learning materials, and thelack of the ability to zoom in from the general concepts to the more specific ones, or the opposite. Intelligent tutoring systems are needed to improve the cyberlearning quality. One of the major difficulties is knowledge representation. The current industry standard is to use Web Ontology Language (OWL) for representing knowledge structure. But OWL only supports one "first-class" relation, "is-a", between the concepts, and different knowledge areas usually need different custom relations to describe the relations among the concepts. For example "part-of" and time dependency are important relations torepresent most engineering knowledge bodies. OWL has to use object properties to emulate such custom relations, leading to awkward knowledge representation hard for domain experts to code, validate and use such knowledge bases. This research uses Pace University's extension to OWL, named Knowledge Graph (KG), to support knowledge representation with custom relations. The instructors can use Pace University extended Protege IDE to declare and apply custom relations in a single document. The instructor teaching experience is also coded in the KG to better support custom learning order by students with different backgrounds. The prototype of a knowledge-driven tutoring system was designed and implemented to illustrate how the KG supports integrated assessments, using assessment results to custom student learning order or material, and let the students freely navigate in the knowledge space from general to specific or the opposite, and following various custom relations. A web technology tutorial is used to validate the design and effectiveness of this approach.