Promoting the Practical Ability of Chinese Students by the Virtual Simulation Experiment
- 1 International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- 2 International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- 3 International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- 4 School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
Abstract
In this paper, we analyze the necessity of developing virtual simulation experiment basing on the computer technology by taking the optical cold processing as an example. For the traditional experiment teaching, the teachers mainly focus on showing how to operate experiment equipment. It is difficult to carry out innovative experiments and keep up with the step of new engineering construction and teaching reform. With the development of computer technology, the Virtual reality (VR) technology, which solves the invisibility problem, plays an important role in teaching. Our optical cold processing teaching system, which is designed on the basis of Virtual reality (VR) technology, integrates several experiments. This teaching system can fully be shared by long-range control and has more help to promote student’s practical ability. According to the teaching situation for several semesters, the teaching effect is better than that for traditional experiment teaching. The virtual simulation experiments should be popularized.
- Cai, W. L., Walter, S., Karangelis, G., & Sakas, G. (2000). Collaborative Virtual Simulation Environment for Radiotherapy Treatment Planning. Computer Graphics Forum, 19, 379-390. https://doi.org/10.1111/1467-8659.00430
- Chen, P., Yang, T., Chen, X. X., Liu, Y. J., & Chu, T. X. (2018). Intelligent Identification of Gas Anomalous Area by Electro-Magnetic Joint Exploration Technology. Journal of Engineering, 2018, 1645-1649. https://doi.org/10.1049/joe.2018.8293
- Davis, A. (2015). Virtual Reality Simulation: An Innovative Teaching Tool for Dietetics Experiential Education. The Open Nutrition Journal, 9, 65-75. https://doi.org/10.2174/1876396001509010065
- Iwagami, H., Ishihara, R., Aoyama, K., Fukuda, H., Shimamoto, Y., Kono, M., Nakahira, H., Matsuura, N., Shichijo, S., Kanesaka, T., Kanzaki, H., Ishii, T., Nakatani, Y., & Tada, T. (2021). Artificial Intelligence for the Detection of Esophageal and Esophagogastric Junctional Adenocarcinoma. Journal of Gastroenterology and Hepatology, 36, 131-136. https://doi.org/10.1111/jgh.15136
- Krathwohl, D. R. (2002). A Revision of Bloom’s Taxonomy: An Overview. Theory into Practice, 41, 212-218. https://doi.org/10.1207/s15430421tip4104_2
- Notaros, B. M., McCullough, R., Manic, S. B., & Maciejewski, A. A. (2019). Computer-Assisted Learning of Electromagnetics through MATLAB Programming of Electromagnetic Fields in the Creativity Thread of an Integrated Approach to Electrical Engineering Education. Computer Applications in Engineering Education, 27, 271-287. https://doi.org/10.1002/cae.22073
- Salto-Tellez, M., Maxwell, P., & Hamilton, P. (2019) Artificial Intelligence—The Third Revolution in Pathology. Histopathology, 74, 372-376. https://doi.org/10.1111/his.13760
- Shen, Y., Yu, P., Lu, H., Zhang, X. L., & Zeng, H. J. (2020). An AI-Based Virtual Simulation Experimental Teaching System in Space Engineering Education. Computer Applications in Engineering Education, 29, 329-338.
- Yu, Y. L., Zhang, G. L., & Zhang, H. Y. (2021). Exploration and Analysis on the Feasibility of VR Panoramic Technology Applied to Practical Training Teaching in Colleges and Universities. Advances in Applied Sociology, 11, 350-356. https://doi.org/10.4236/aasoci.2021.118032
- Zhao, C. L., & Zhao, Y. N. (2019). Study on Business English Practical Teaching from the Perspective of Economics of Language. Creative Education, 10, 726-734. https://doi.org/10.4236/ce.2019.104054
- Zheng, W., Bai, Y. T., & Che, H. X. (2018). A Computer-Assisted Instructional Method Based on Machine Learning in Software Testing Class. Computer Applications in Engineering Education, 26, 1150-1158. https://doi.org/10.1002/cae.21962