A Job-Oriented Knowledge Graph for Computer Networking Courses in Higher Vocational Education

Abstract

Students in higher vocational computer networking programs often learn technical topics such as TCP/IP, virtual local area networks, access control lists, firewall configuration, wireless access points, Linux services, and network monitoring without clearly understanding how these topics support real occupational roles. Teachers also need evidence-based methods to explain why a course matters and how its modules are connected with job abilities. To address this problem, this paper proposes a job-oriented knowledge graph for computer networking courses in higher vocational education. The proposed system separates raw job evidence from the student-facing teaching graph. Public and low-risk job evidence is organized in a controlled evidence layer, while the published graph presents normalized jobs, abilities, course modules, knowledge points, tools, certificates, tasks, and vendors. The student-facing explanation process connects target jobs with required abilities, course modules, and knowledge points, allowing students to understand how course learning supports career preparation. A Zhejiang pilot system was implemented using Vue 3, ECharts, Express APIs, and Neo4j. The current pilot graph contains 123 nodes and 367 relationships, including 6 job nodes, 10 ability nodes, 7 course module nodes, 34 knowledge nodes, and 98 course-to-knowledge teaching relationships. The system supports target-job course path recommendation, quiz-based ability gap diagnosis, personalized learning route generation, remedial resource recommendation, and teacher-facing curriculum improvement analysis. The pilot results show that the proposed graph can provide explainable learning paths and curriculum evidence, although larger job samples, formal teacher coding agreement, and real classroom evaluation are still required.