Abstract
The course planning for the Precision Measurements from the Incorporation of Precision Machinery and Artificial Intelligence course is a major work in vocational education. This study applied the DISCOVER model in course planning and divided the students into two groups: teacher and non-teacher trainees. This study further revised and optimized teaching units and strategies by implementing courses and tests based on the prepared teaching strategies. It applied DISCOVER paired t-tests for analyzing differences in learning effects. There were significant differences in pre- and post-test learning effects on chapters 1, 2, 5, 6, and 7 for the whole class. This represents the effective teaching of these chapters. The effects were not significant in both groups in chapters 3 and 4. Combining precision measurement, AI, and precision machinery enables mechanical engineering undergraduates to consolidate their professional competence. After integrating AI into professional knowledge and building on the base of mechanical knowledge, students will be more interested in mechanical engineering. In this way, in an environment where teaching benefits teachers and students, a win-win core value will be jointly created by teachers and students. This study emphasizes course spirit, teaching objectives, teaching implementation, teaching evaluation, and multi-dimensional evaluation. It encourages students to develop diverse capabilities and improves the teaching effectiveness of future teacher trainees. This will improve the overall educational level and accelerate the educational development process.
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Acknowledgments
I am grateful to the National Science and Technology Council, Taiwan, for its support and funding for this research with Project Number MOST 110-2511-H-018-004.
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Chen, DC., Chen, YT., Wen, KC., Deng, WL., Lu, SW., Chen, XW. (2023). The Course of Precision Measurements from the Incorporation of Precision Machinery and Artificial Intelligence and the Learning Effects of Its Learning Materials. In: Huang, YM., Rocha, T. (eds) Innovative Technologies and Learning. ICITL 2023. Lecture Notes in Computer Science, vol 14099. Springer, Cham. https://doi.org/10.1007/978-3-031-40113-8_3
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