Abstract. Recommender systems have been an important research topic in recent years, especially in the field of educational management. Recommender systems can be used to provide personalized recommendations to students on what to learn next, based on their past activities and performance. This can help to improve student engagement and academic performance. There are various types of recommender systems, including content-based, collaborative filtering, and hybrid recommender systems. In this paper, we aim to compare and evaluate the performance of different recommender systems in educational management. We will use real-world educational data to evaluate the accuracy, coverage, and novelty of each recommender system. The goal is to provide insights into which recommender systems are most effective for educational management and to identify areas for future research.