An Intelligent Guide Application for English Online Education Based on Deep Learning - Zhu, Ting (Journal of advanced computational intelligence and intelligent informatics, 2025)
With the development of online education technology, the status of English online education system is also increasing. However, the current intelligent learning guide design lacks self-adaptation and cannot get the learning state of different students. In addition, it is not possible to provide personalized learning and topic push for students with different learning states. Therefore, we propose an intelligent guide design for English online education based on deep learning, which combines knowledge tracking algorithm and exercise recommendation algorithm. Our model can extract students’ knowledge state and ability level and recommend appropriate exercises. In addition, we also introduce knowledge graph technology and use knowledge graph embedding technology to preprocess the knowledge points of exercises to enhance the state representation in the model and explore the implicit relationship. Experiments are designed to prove the effectiveness and superiority of our proposed method. Experimental results of DACK model: ACC = 93.1%, AUC = 98.8%, significantly superior to traditional knowledge tracking methods. Experimental results of DRSS model: Hit-Ratio@10 = 41.6%, NDCG@10 = 70.2%, performed excellently in personalized practice recommendations.