# CourseKG: An Educational Knowledge Graph Based on Course Information for Precision Teaching
## 5 Conclusions and Future Work  
This paper proposes CourseKG, utilizing diverse course data, enhancing smart education by extracting teaching concepts and identifying vital educational relationships. Figure 8: The focus differentiation in CourseKG. The cursor is currently hovering over the “Sorting” knowledge point, causing it and the directly interconnected knowledge points to be highlighted, with the opacity of other knowledge points diminished, thus facilitating clear distinction among them. Figure 9: A visually augmented CourseKG, incorporating the representation of relationship associations. The scores between nodes are explicitly annotated on the edges, thereby enabling intuitive visualization and examination. We propose the BERT-BiGRU-MHSA-CRF framework for precise education entity recognition, integrating advanced mechanisms for global contextual relevance. Additionally, we present a groundbreaking relationship extraction method based on the BERT model. Experimental validation with real-world C programming course data demonstrates the scalability and superiority of our approach, surpassing state-of-the-art methods in both relation extraction and educational-entity recognition. Our research not only introduces innovative frameworks but also validates their effectiveness, emphasizing their potential impact on advancing knowledge representation in smart education. Currently, our research primarily substantiates its experiments using a dataset from C programming courses. In the future, we can explore the possibility of collecting more teaching data across diverse academic disciplines and integrate various data sources to construct a more comprehensive knowledge graph. Beyond textual data, multi-modal data such as videos and audio can also furnish substantial informational support. Hence, future endeavors could investigate how to leverage multi-modal data to enrich the content of the knowledge graph and further enhance the efficacy of personalized learning. We are fully aware that student feedback plays a pivotal role in personalized learning. Consequently, in the future, we may attempt to incorporate student feedback data and merge it with the knowledge graph to gain a deeper understanding of students' learning conditions and adaptively fine-tune instructional strategies accordingly. Moreover, natural language processing techniques can be employed to analyze student feedback, thereby extracting the most valuable insights for promoting individualized learning. In order to improve the performance of CourseKG, future research can implement domain adaptation and transfer learning, combine domain knowledge, introduce dynamic context modeling technology, and explore continuous supervised learning methods. These innovative directions are expected to enhance the accuracy, robustness, and adaptability of the model so that it can better serve the diverse and evolving needs of the education field. The proposed CourseKG could offer educators a professional and efficient solution, enabling them to provide personalized and adaptive teaching experiences. Due to its precise instructional capabilities and personalized learning paths, the proposed CourseKG could revolutionize the delivery and reception of education. Conceptualization, [PERSON] ([PERSON]) and Y.L. ([PERSON]); methodology, R.Y. ; software, J.Q. ; validation, C.Z. ; data curation, X.Z. ; writing--original draft preparation, C.Z. ; writing--review and editing, [PERSON] ([PERSON]); visualization, X.Z. ; supervision, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Project of China (No. 2021 ZD0110700) and the Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education (No. 1321008) and the National Natural Science Foundation of China (No.