Design of Data-Driven Learning Path Based on Knowledge Graph and Tracing Model

With the explosive growth of online courses, per-sonalized learning path recommendations have gained much attention in E-learning area. Studies aim to provide customized guidance that cater to the needs of each learner. However, generating an appropriate learning path remains a challenge due to the complex cognitive relationships among learning items and learners' different abilities. Existing methods often overlook the impacts of cognitive structure and mastery level on the outcomes of customized instructions, leading to cognitive confusion problem. To address this issue, we propose a model named Data-Driven Learning Path based on Knowledge Graph and Tracing Model (LP-KAT), which combines knowledge graph (KG) and knowledge tracing model (KT) to store the relationships among learning items and reflect learners' abilities through the abundant data. The guidance is separated into concept and practice identification; besides, through reinforcement learning, our model can suggest a sequence of practices for different learners. Specifically, we first utilize a knowledge tracing model to track learners' evolving mastery levels at each learning step. Then we design a concept selector that employs knowledge graph navigation algorithm and reinforcement learning to determine which concept to learn next. Finally, we utilize a practice selector to identify practice question based on the relationship between mastery level and difficulty of practice. We evaluate the proposed model against KNN, GRU4Rec, MCS and DQN using a real-world dataset, and the results show an improvement of more than 30 % in the effectiveness of concept mastery level and nearly 20% better than GRU4Rec across various learning path lengths.