Education, Science, Technology, Innovation and Life
Open Access
Sign In

Utilization of Artificial Intelligence Technology in Higher Education Management: Teaching Theory and Practical Skills of Landscape Architecture Construction Technology

Download as PDF

DOI: 10.23977/jaip.2023.060804 | Downloads: 11 | Views: 283

Author(s)

Guodong Sang 1

Affiliation(s)

1 School of Education, Philippine Women's University, Ermita Manila, Metro Manila, Philippine

Corresponding Author

Guodong Sang

ABSTRACT

In traditional university education management, landscape construction technology helps students comprehensively master landscape construction technology by teaching theoretical knowledge such as the basic principles of landscape construction and setting up practical bases for landscape construction. However, this approach has some limitations, such as delayed information transmission, which limits the flexibility and effectiveness of learning. Therefore, artificial intelligence technology can be applied to the teaching theory and practice of landscape construction technology in university education management. The word bag model and SVM (Support Vector Machine) algorithm were used as a case analysis tool for landscape construction technology to analyze construction problems and solutions in real cases, and then virtual reality (VR) and augmented reality (AR) technologies were used to enable students to practice landscape construction in a virtual environment. Finally, a Convolutional Neural Network (CNN) model was used to provide specific learning resources and operational recommendations. This article applied artificial intelligence technology to the theory and practice of landscape construction technology in university education management. The average score of students in the test has increased by 8 points, and over 90% of students can independently complete the experiment. With the help of artificial intelligence technology, university education management can break the limitations of time and space, improve the flexibility and convenience of students' learning, and provide more timely feedback for education managers.

KEYWORDS

Artificial Intelligence Technology, University Education Management, Garden Landscape Construction, Teaching Theory, Practical Skill

CITE THIS PAPER

Guodong Sang, Utilization of Artificial Intelligence Technology in Higher Education Management: Teaching Theory and Practical Skills of Landscape Architecture Construction Technology. Journal of Artificial Intelligence Practice (2023) Vol. 6: 18-25. DOI: http://dx.doi.org/10.23977/jaip.2023.060804.

REFERENCES

[1] Jeanette Sjöberg, Patrik Lilja. University teachers' ambivalence about the digital transformation of higher education. International Journal of Learning, Teaching and Educational Research, 2019, 18(13): 133-149.
[2] Jennifer Catharine Evans, Hennie Yip, Kannass Chan, Christine Armatas, Ada Tse. Blended learning in higher education: professional development in a Hong Kong university. Higher Education Research & Development, 2020, 39(4): 643-656.
[3] Elena R. Vershitskaya, Anna V. Mikhaylova, Suriya I. Gilmanshina, Evgeniy M. Dorozhkin, Vladimir V. Epaneshnikov. Present-day management of universities in Russia: Prospects and challenges of e-learning. Education and Information Technologies, 2020, 25: 611-621.
[4] Abdulaziz Aldiab, Harun Chowdhury, Alex Kootsookos, Firoz Alam, Hamed Allhibi. Utilization of Learning Management Systems (LMSs) in higher education system: A case review for Saudi Arabia. Energy Procedia, 2019, 160: 731-737.
[5] Yoav Mintz, Ronit Brodie. Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 2019, 28(2): 73-81.
[6] Shuai Zhao, Frede Blaabjerg, Huai Wang. An overview of artificial intelligence applications for power electronics. IEEE Transactions on Power Electronics, 2020, 36(4): 4633-4658.
[7] Rusul Abduljabbar, Hussein Dia, Sohani Liyanage, Saeed Asadi Bagloee. Applications of artificial intelligence in transport: An overview. Sustainability, 2019, 11(1): 189.
[8] Stamatios-Aggelos N. Alexandropoulos, Sotiris B. Kotsiantis, Michael N. Vrahatis. Data preprocessing in predictive data mining. The Knowledge Engineering Review, 2019, 34: 1.
[9] Yan Yaya. Comparative Study of Word Bag Model and TF-IDF in Text Classification. Computer Knowledge and Technology, 2021, 17 (28): 138-140.
[10] Savita Ahlawat, Amit Choudhary. Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 2020, 167: 2554-2560.
[11] Alankrita Aggarwal, Mamta Mittal, Gopi Battineni. Generative adversarial network: An overview of theory and applications. International Journal of Information Management Data Insights, 2021, 1(1): 100004. 
[12] Truemone Simse. Characteristics of the Environment and Landscape Planning of the Historic and Cultural District of the First Automobile Factory. Academic Journal of Environmental Biology (2022), Vol. 3, Issue 2: 35-47.

Downloads: 6060
Visits: 182496

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.