A professional course design approach for the integrated Vocational-Bachelor cultivation mode based on knowledge graphs within the STEM framework
The integrated vocational-bachelor cultivation mode is an effective means of achieving mass higher education, under which, students complete associate education at one vocational college, then transfer to another university after passing a transition examination to continue with bachelor’s degree courses, ultimately obtaining a bachelor’s degree. Due to the different focuses of vocational education and undergraduate education, the coherence between the two educational stages has a significant impact on the implementation effectiveness of this cultivation mode. To enhance the coherence between these two educational stages, this paper proposes a method for designing professional courses based on knowledge graphs within the STEM framework. The principles of STEM education serve as the guiding thread for designing professional courses at both educational stages. To ensure that STEM education concepts are implemented in professional course objectives, content, teaching activities, and course assessments, so as to enhance students’ interdisciplinary problem-solving abilities, a dynamic mechanism for constructing, updating, and mapping knowledge and ability graphs is constructed. Both the knowledge and ability graphs are tagged with S, T, E, and M labels. These labels would be used by teachers to assess whether the courses effectively incorporate STEM teaching principles. After completing their course tasks, students receive corresponding ability tags, allowing them to adjust their learning focus based on their tag distribution. To stimulate students’ enthusiasm for interdisciplinary learning, a reward system is designed to encourage feedback on knowledge elements within the knowledge graph during the teaching process, continuously improving both graphs. This research could provide a reference for integrated education.
Authors
- Weichong Choo
- Xiaodong Li
- Yifeng Cui
- Dongping Chen
- Ruixiao Yu
- Yuyun Kang
Publication Details
- Journal
- Springer Link (Chiba Institute of Technology)
- Published
- 2025-10-02
- DOI
- https://doi.org/10.1051/shsconf/202522301002/pdf
- Primary Topic
- Advanced Graph Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00