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Research on the Training of Outstanding Engineers in Architectural Education Based on Ant Colony Algorithm

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Application of Big Data, Blockchain, and Internet of Things for Education Informatization (BigIoT-EDU 2022)

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Abstract

By analyzing the current situation of higher education and the advantages and disadvantages of traditional student training methods, focusing on the training methods of students’ engineering application ability and innovation ability, this paper explores a new training mode of engineering and technical talents in line with the specialty of civil engineering. Combined with the quality of teachers, teaching contents and teaching methods of civil engineering specialty, this paper puts forward some reform suggestions to deal with the current situation of education, and defines the training requirements of the excellent engineer program in the construction industry. The suggestions also have important reference for promoting the actual talent training mode of local and national higher education and improving the quality of engineering and technical talent training.

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References

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Acknowledgements

The 13th Five-Year Social Science Project of Education Department of Jilin Province; Contract No.: JJKH20201290SK; Research and practice on training mode of creative engineering talents under the background of “New Engineering”.

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Correspondence to Pengcheng Yin .

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yin, P., Li, S. (2023). Research on the Training of Outstanding Engineers in Architectural Education Based on Ant Colony Algorithm. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 467. Springer, Cham. https://doi.org/10.1007/978-3-031-23944-1_30

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  • DOI: https://doi.org/10.1007/978-3-031-23944-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23943-4

  • Online ISBN: 978-3-031-23944-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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