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.

We propose and implement a novel online learning system, called CogLearn, to support learner's self-awareness and reflective thinking, which urges a proper form of knowledge representation together with individual learner's cognitive status. We thus design and employ the machine learning techniques to estimate learner's cognitive status and identify educational relations to construct the desired knowledge representation, namely cognitive graph in our system. We further demonstrate the system by presenting two practical services, i.e., learning obstacle diagnosis and learning path planning, to demonstrate how the constructed cognitive graph effectively and adaptively supports individual system user's learning process.

Knowledge tracing serves as the key technique in the computer supported education environment (e.g., intelligent tutoring systems) to model student's knowledge states. While the Bayesian knowledge tracing and deep knowledge tracing models have been developed, the sparseness of student's exercise data still limits knowledge tracing's performance and applications. In order to address this issue, we advocate for and propose to incorporate the knowledge structure information, especially the prerequisite relations between pedagogical concepts, into the knowledge tracing model. Specifically, by considering how students master pedagogical concepts and their prerequisites, we model prerequisite concept pairs as ordering pairs. With a proper mathematical formulation, this property can be utilized as constraints in designing knowledge tracing model. As a result, the obtained model can have a better performance on student concept mastery prediction. In order to evaluate this model, we test it on five different real world datasets, and the experimental results show that the proposed model achieves a significant performance improvement by comparing with three knowledge tracing models.