Knowledge Graph–Driven Modular Redesign: A Study of the Development Path for a High-Quality “Machine Vision Technology” Course in Higher Vocational Education

DOI: https://doi.org/10.62517/jnse.202617506

Author(s)

Chuanjing Guo*

Affiliation(s)

Shandong polytechnic university, Weifang, Shandong, China *Corresponding Author

Abstract

As the "intelligent eyes" of smart manufacturing, machine vision technology plays a vital role in cultivating highly skilled professionals in visual inspection. In response to challenges in the current Machine Vision Technology course in higher vocational education, including superficial school–enterprise collaboration, a weak alignment between job requirements and course competencies, a lack of progressive course projects, fragmented integration of ideological and political education, and limited use of digital teaching methods, this study proposes a five-step approach guided by coordinated reform of key teaching elements. Centered on “knowledge graph–driven modular redesign,” the approach comprises building collaborative mechanisms, mapping competencies, restructuring the curriculum, integrating ideological and political education, and innovating teaching methods. Specifically, school–enterprise collaboration provides institutional support; a course competency graph links five key elements—programs, courses, textbooks, teachers, and practical training; progressively structured project modules are redesigned around work processes; professional ethics and technical skills are integrated into the curriculum; and AI agents and digital twin technology support a teaching model in which teachers, technology, and students interact and develop together. This study offers a measurable and transferable pathway for developing high-quality core courses in smart manufacturing programs at higher vocational institutions.

Keywords

Machine Vision Technology; High-Quality Course Development; Coordinated Reform of Teaching Elements; Course Competency Graph; Modular Redesign

References

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