The knowledge points setting of artificial intelligence curriculum has shortcomings in connection between theory and practices. To overcome the problem, this study designs an open knowledge point design model based on knowledge graph. Fist, to promote the construction of the knowledge graph (KG) of curriculums, associated teaching research was analyzed visually. Then the order and hierarchical structure of the knowledge points were defined, and the ontology structure of curriculum knowledge and the relationship between knowledge points and posts were designed as well. Moreover, an overall logic structure for the construction of the open KG of curriculums was proposed. Results demonstrated that high attention should be paid to the construction and concern of teaching teams for artificial intelligence algorithms and the KG of curriculum construction. Additionally, the opening model can strengthen the openness of the KG of curriculums to reinforce the close connections between classroom knowledge and practices. Research conclusions are conducive to understand the existing problems in the KG of curriculums and provide beneficial references to the integration of information technology and education.
Abstract-By analyzing the current situation of experimental platforms based on the Internet of Things (IOT) and the cultivation of talents, we established the talents' cultivating orientation and an experiment platform for innovation. In accordance with the requirements of a practical and creative curriculum, a method of modularization was adopted to design a platform. With this, the platform can basically satisfy the needs of varying teaching experiments, which can increase the opportunities for students' comprehensive application. The platform has been widely used in experimental and practice teaching in the past three years, such as in synthetic experiments, graduate programs, and practice projects at Zhongkai University of Agriculture and Engineering, Guilin University of Technology, etc. Results show that the innovative experimental platform has broad application prospects and effectiveness to meet the requirements of students' in-depth learning and research of IOT technologies. The platform expanded the students' basic understanding of IOT and improved their innovation.
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