# CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching
## 1 Introduction  
With the development of artificial intelligence and the growing interest in the education field, smart education has attracted significant attention in recent years [1]. However, increasing digitization in the field of education has brought educators and institutions a multitude of new challenges [2, 3]. Precision teaching driven by artificial intelligence, big data analysis, and other informational technology has become a development trend and a hot research topic in the educational information field. Precision teaching strives to optimize the efficiency of the learning process by customizing the curriculum for each learner [4, 5]. The utilization of knowledge graphs (KGs) involves the reconstruction and reorganization of knowledge points derived from extensive course materials. The KG extracts and represents semantic issues of knowledge points based on the structure of nodes and edges in a KG [6]. In addition, the KG emphasizes knowledge organization, determining the optimal sequence for delivering knowledge throughout the curriculum. Using the KG, vast and fragmented knowledge can be seamlessly integrated to assist learners in intuitivelyconnecting within the knowledge system. This facilitates precision teaching, including the establishment of personalized learning paths. Although KGs have demonstrated their efficiency in various domains of artificial intelligence, their application to the field of education has been relatively unexplored. Compared to general KGs, the construction of educational KGs faces many challenges. First, the expected educational concept entity is more abstract than the real-world entity. Second, the expected relationships are more cognitive and implicit and difficult to be deduced from the literal meaning of the text, which is the case with general KGs. In addition, domain adaptation in the education field has been hindered by the lack of relevant data, making it difficult to assess students' individual cognitive abilities. Further, the automation level in the KG construction process is relatively low and often relies on expert knowledge, which can lead to cognitive discrepancies among experts on the same topic, thus affecting scientific rigor and consistency. Considering the above-mentioned challenges, this study proposes an educational KG based on course information, named CourseKG, for precision teaching. CourseKG employs advanced techniques, including deep learning and big data, for enhanced intelligence. It visually represents the points that should be taught in a lesson and their sequential relationships. Moreover, CourseKG can precisely assess a student's current learning level, determine their zone of proximal development, and identify their sweet spot. With the KG acquired, CourseKG can attain precise teaching objectives by adhering to the principle of formulating teaching based on specific learning requirements. It enables the implementation of teaching tailored to students' attitudes. The contributions of this study can be outlined as follows:  
1. A precise teaching and education KG based on course information, named CourseKG, which uses heterogeneous course data, including structured, semi-structured, and unstructured teaching and learning assessment data, is proposed to extract teaching concepts and identify important educational relationships, effectively serving smart education. 2. An education entity recognition framework called the BERT-BiGRU-MHSA-CRF, based on a pre-training BERT model, is proposed. This framework uses the BERT model for word embedding in educational text and combines the BiGRU and multi-head self-attention (MHSA) mechanisms to extract global contextual relevance from multiple perspectives and levels. In addition, a CRF layer that considers the dependency relationships and constraints between character-level labels is used for decoding. 3. A relationship extraction method based on the BERT model is developed.