Abstract

Intelligent systems in education have proven to be highly beneficial in supporting self-learning among students, particularly in remote learning scenarios. This study proposes the necessary requirements for an intelligent educational system that serves two primary functions: querying course knowledge and evaluating learner proficiency through multiple-choice testing. Furthermore, this study builds a solution to design the knowledge base, inference engine, and tracing system based on a knowledge model that integrates ontology and knowledge graph.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call