关键词: 知识追踪, 决策树, 深度学习, 遗传算法, 深度项目反应理论

Abstract: With the rapid development of deep neural networks, the advantages of knowledge tracing models based on deep learning are gradually emerging. Deep-IRT combines item response theory with dynamic key-value memory networks (DKVMN), which increases the connection between students and exercises but ignores the influence of learning features. DKVMN-DT adds behavior feature preprocessing based on CART decision tree to DKVMN, but the decision tree is still a greedy algorithm. To optimize the local optimum problem caused by CART and strengthen the connection between student ability and exercise difficulty, a model combining CART based on genetic algorithm and Deep-IRT is proposed. Firstly, CART is optimized twice based on genetic algorithm, and the learning behavior characteristics of learners are preprocessed. Then, the cross characteristics are calculated and integrated into the underlying model of DKVMN. Finally, item response theory is introduced to predict the completion probability according to students' ability and exercise difficulty. The experimental results show that DKVMN-GACART-IRT model has better AUC values than the original model, and have better prediction performance. 

Key words: knowledge tracing, decision tree, deep learning, genetic algorithm, deep item response theory