RadarMath: An Intelligent Tutoring System for Math Education
Yu Lu, Yang Pian, Penghe Chen, Qinggang Meng, Yunbo Cao
Abstract
We propose and implement a novel intelligent tutoring system, called RadarMath, to support intelligent and personalized learning for math education. The system provides the services including automatic grading and personalized learning guidance. Specifically, two automatic grading models are designed to accomplish the tasks for scoring the text-answer and formula-answer questions respectively. An education-oriented knowledge graph with the individual learner’s knowledge state is used as the key tool for guiding the personalized learning process. The system demonstrates how the relevant AI techniques could be applied in today's intelligent tutoring systems.
Topics & Concepts
Intelligent tutoring systemGrading (engineering)Computer sciencePersonalized learningKey (lock)Process (computing)GraphArtificial intelligenceIntelligent decision support systemMultimediaHuman–computer interactionMathematics educationTeaching methodTheoretical computer scienceEngineeringCooperative learningMathematicsProgramming languageCivil engineeringComputer securityOpen learningIntelligent Tutoring Systems and Adaptive LearningTopic ModelingText Readability and Simplification