Evaluation of the quality of practical teaching of agricultural higher vocational courses based on BP neural network
Abstract
Agriculture is the backbone of any developing or developed country that makes any living to survive. To make food available throughout the year, it is necessary to know about agriculture and the work and strategies involved. Hence, agricultural courses have to be introduced to higher education students. Additionally, agriculture-related methods are available in many higher education institutions for longer. However, students and teachers will face difficulties in real-time practical classes during certain challenging circumstances. These situations require the teacher to utilize trending technologies to improve the teaching and learning process and to make it more manageable. In this study, for this process, a novel neural network-based recognition algorithm (NN-RA) is implemented that works similarly to a backpropagation neural network (BP-NN) to provide a practical agriculture course. The proposed BP-NN is compared with the existing NN-RA, I-SC, and I-VDT algorithms based on the data transfer and signal-to-noise ratio. From the results, it can be observed that the proposed BP-NN attains a higher accuracy in data transfer of 99%.
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A new assessment method driven by AI tools (i.e., a backward propagation neural network) could automatically evaluate teaching, learning, and grading in an experiential online course in agriculture.
“A new assessment method driven by AI tools (i.e., a backward propagation neural network) could automatically evaluate teaching, learning, and grading in an experiential online course in agriculture (Kumar et al., 2023).”
From A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment (2025) · cites this paper as Kumar et al., 2023
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Huang, C., Han, Z., Li, M., Wang, X., & Zhao, W. (2021) · Australasian Journal of Educational Technology · doi:10.14742/ajet.6749 · Cited by 111 · see citing works
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Liang, J., Stephens, J. M., & Brown, G. T. L. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1522841 · Cited by 112 · see citing works
- Authors
- Kumar, M. G. V., N, V., Čepová, L., Raja, M. A. M., Balaram, A., & Elangovan, M.
- Affiliations
- Department of Computer Science Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India
- Department of ISE, BMS Institute of Technology & Management, Bengaluru 560 064, India
- Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic
- Department of Computer Science Engineering, R.M.K College of Engineering and Technology, Puduvoyal 601 206, India
- Department of Information Technology, MLR Institute of Technology, Hyderabad 500 043, India
- Department of R&D, Bond Marine Consultancy, London EC1V 2NX, UK
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2023-01-16
- Volume
- 13
- Issue
- 2
- Pages
- 1180
- ISSN
- 2076-3417
- Cited by
- 43
- References
- 1
- Language
- en
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