Welcome to Francis Academic Press

International Journal of New Developments in Education, 2025, 7(3); doi: 10.25236/IJNDE.2025.070310.

Research and Practice of a Knowledge Graph-Driven Digital Multidimensional Reconstruction Strategy for Courses: A Case Study of the“Urban Railway Communication and Signal”Course

Author(s)

Fei Ye, Junyi Hu, Wen Cheng

Corresponding Author:
Fei Ye
Affiliation(s)

Zhejiang Institute of Communications, Hangzhou, China

Abstract

Aiming at the problems of knowledge systematic weakness, insufficient dynamic adaptation of resources and single evaluation dimension in the digital transformation of vocational education courses, a multi-dimensional reconstruction framework for courses driven by knowledge graph is proposed. Based on the construction practice of “Urban Railway Communication and Signal”, systematic innovation is carried out in the three dimensions of content architecture, teaching mode and evaluation system. Through the construction of domain knowledge semantic network architecture, the logical association of course knowledge modules, skill elements and human development connotation is realized, which significantly enhances the structural integration effectiveness of teaching resources; the “dynamic adaptive teaching strategy of virtual and real linkage” is designed to realize the dynamic generation of personalized learning sequences by using the path of knowledge graph, which effectively enhances the depth of interaction between teachers and students and their ability of practical migration; the composite evaluation model based on vocational ability mapping is developed to systematically construct the teaching mode covering the knowledge content and the evaluation system. Developing a composite evaluation model based on vocational competence mapping systematically builds a multi-dimensional monitoring system covering knowledge internalization, skill formation and value recognition. The research results show that the framework can strengthen the intelligent adaptation of teaching resources, the dynamic generation of teaching process and the three-dimensional diagnostic function of learning evaluation, forming a collaborative innovation paradigm of “knowledge structuring - learning personalization - evaluation value-added”, which provides a practical reference of the in-depth integration of knowledge construction and value orientation for the digital transformation of vocational education, and effectively promotes the innovation of value and optimization of path of digital transformation of education. It effectively promotes the value innovation and path optimization of education digital transformation.

Keywords

Knowledge graph; course digitization; multidimensional reconfiguration; composite evaluation; gold course construction

Cite This Paper

Fei Ye, Junyi Hu, Wen Cheng. Research and Practice of a Knowledge Graph-Driven Digital Multidimensional Reconstruction Strategy for Courses: A Case Study of the“Urban Railway Communication and Signal”Course. International Journal of New Developments in Education(2025), Vol. 7, Issue 3: 61-67. https://doi.org/10.25236/IJNDE.2025.070310.

References

[1] Liu Q, Li Y, Duan H, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582–600.

[2] Wang Y, Peng Y, Guo J. Enhancing knowledge graph embedding with structure and semantic features[J]. Applied Intelligence, 2024, 54(3): 2900–2914.

[3] Zablith F, Fernandez M, Rowe M. Production and consumption of university linked data[J]. Interactive Learning Environments, 2015, 23(1): 55–78.

[4] Ye X. A study on american knewton adaptive learning platform[D]. Southwest University, 2019.

[5] Guo Z. Construction of digital teaching resource library for urban railway operation and management specialty[J]. Guangxi Education, 2017(39): 58–59.

[6] Yu S, Xiong S. General artificial intelligence teacher architecture based on enhanced pre-trained large models[J]. Open Education Research, 2024, 30(1): 33–43.

[7] Lang Y, Su C, Wang G, et al. The construction and reasoning of c++ course knowledge graph based on neo4j[J]. Intelligent Computers and Applications, 2021, 11(7): 144–150, 155.

[8] Zhang C, Peng C, Luo M, et al. Construction of mathematics course knowledge graph and its reasoning[J]. Computer Science, 2020, 47(S2): 573–578.

[9] Huang J. The construction and application research of knowledge graph of middle school python course[D]. Central China Normal University, 2020.

[10] Zhou J. Research on the method of constructing knowledge maps for civics and political science courses[D]. Harbin Normal University, 2024.

[11] Wang X. Research on knowledge construction of digital courses based on ontology —taking e-commerce security course as an example[J]. Modern information Technology, 2023, 7(20): 194–198.

[12] Tang Y, Qi H, Sheng Y, et al. Construction of ideological and political education material database based on knowledge graph[J]. Software Guide, 2022, 21(7): 214–219.

[13] Shi J, Tang J, Wang Y, et al. The construction of curriculum resources in emerging fields based on knowledge graph[J]. Research on Higher Engineering Education, 2022(3): 15–20.

[14] Cheng P, Fan X. Research on the teaching of mpacc courses based on Knowledge graph in the context of the construction of “golden class”-- take the course of “cloud accounting and intelligent financial sharing” of chong qing polytechnic university as an example[J]. Financial Newsletter, 2019(28): 35–38.

[15] Zeng L, Li Q. Knowledge graph enabling informatization teaching innovation[J]. Modern information Technology, 2021, 5(7): 196–198.

[16] Pang L. A study on the teaching strategy of digital transformation under smart transportation——a case study of passenger transport management course in rail transit stations[J]. Auto Time, 2024(9): 77–79.

[17] Yang X, Sun L, Liu M-L, et al. Knowledge graph construction with bert-bilstm-idcnn-crf and graph algorithms for metallogenic pattern discovery: a case study of pegmatite-type lithium deposits in china[J]. Ore Geology Reviews, 2025, 179: 106514.

[18] Jacomy M, Venturini T, Heymann S, et al. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software[J]. PloS One, 2014, 9(6): e98679.