Research on the Application of Exercise Recommendation Based on Knowledge Graph and KMAKT Prediction Model

With the booming development of online education, the number of course resources has shown a geometric growth, and the surge in course resources has also brought challenges in management and personalized learning. As an important component of online course resources, question banks can provide students with a large number of practice questions to help them identify and reinforce their knowledge. However, current question bank systems often lack intelligent and personalized push mechanisms. Knowledge tracking is one of the important means to achieve smart education. This article is based on a knowledge graph and uses the knowledge tracking improvement model KMAKT. The TransR graph embedding algorithm is used to represent the course knowledge graph embedding, and the algorithm is used to obtain the degree of student knowledge point mastery. The deep neural network (DNN) attention network is used to determine the influence of precursor knowledge, and the multi-layer neural network is used to obtain the predicted output of exercise answer results. The exercise recommendation system, taking Java Web application development course as an example, has achieved accurate and personalized exercise recommendations. The experimental results have verified the effectiveness and practicality of the system.