Abstract
The traditional allocation method of teaching resources is unbalanced, which leads to the low utilization value of teaching resources in talent training courses. In order to make the allocation of teaching resources of talent training course balanced and facilitate the management and application of teachers before, during and after teaching, a method of allocating teaching resources of talent training course based on BP neural network is proposed. Firstly, the BP neural network model is constructed according to the framework of teaching information resource allocation of talent training courses, and the information characteristics of teaching resources of talent training courses are extracted. According to the characteristics, the target object feature tag is managed to complete the information allocation of teaching resources of talent training courses. The experimental results show that the proposed method has good performance in practical application and can promote the utilization value of teaching resources in talent training courses.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dilworth, M.E.: Historically black colleges and universities in teacher education reform. J. Negro Educ. 81(2), 121–135 (2022)
Shi, Y.A.N.G.: Research on the cultivation and development of creative talents in colleges and universities. Sci. J. Hum. Soc. Sci. 4(1), 49–52 (2022)
Segura, J.: The teaching of usability in software development: case study in the computer engineering career at the university of matanzas. Int. Assoc. Online Eng. (IAOE) 12(1), 1–13 (2021)
Saito, T., Watanobe, Y.: Learning path recommendation system for programming education based on neural networks. Int. J. Distance Educ. Technol. 18(1), 36–64 (2020)
Aishan, Y., Li, G., Yangyang, D.: Practical research on the construction of professional curriculum teaching resources under the informationization of education. Jiangsu Bus. Rev. 02, 124–126 (2023)
Morales, A., Gonzalez, J., Alquerque, K., et al.: Recommendation system with graph-oriented databases for repository of open educational resources. IOP Conf. Ser.: Mater. Sci. Eng. 1154(1), 21–29 (2021)
Wang, X., Mu, C.: Reform of the classification and evaluation system for scientific and technological innovation talents in the intelligent age. E3S Web Conf. 251, 02024 (2021). https://doi.org/10.1051/e3sconf/202125102024
Shrifan, N., Akbar, M.F., Isa, N.: An adaptive outlier removal aided K-means clustering algorithm. J King Saud Univ. – Comput. Info. Sci. 34(4), 6365–6376 (2021)
Taheri, S.I., Salles, M., Nassif, A.B.: Distributed energy resource placement considering hosting capacity by combining teaching-learning-based and honey-bee-mating optimisation algorithms. Appl. Soft Comput. 113(45), 107953–107966 (2021)
Liu, S., Li, Y., Fu, W.: Human-centered attention-aware networks for action recognition. Int. J. Intell. Syst. 37(12), 10968–10987 (2022)
Wang, Y.Y., Luo, S., Wang, Z.J.: Photovoltaic power prediction combined with popular learning and improved BP Neural network. Comput. Simul. 39(11), 153–157 (2022)
Tucker, B.V., Kelley, M.C., Redmon, C.: A place to share teaching resources: speech and language resource bank. J. Acoust. Soc. America 149(4), 147 (2021)
Yao, S., Li, D., Yohannes, A., et al.: Exploration for network distance teaching and resource sharing system for higher education in epidemic situation of COVID-19 – Science Direct. Procedia Comput. Sci. 183, 807–813 (2021)
Hu, S., Liu, Y., Wang, S.: Teaching exploration of case-based data modeling optimization for database system. Open J. Soc. Sci. 08(3), 514–521 (2020)
Yan, S., **gsheng, Z.: 2021 Design of informationbased teaching resources sharing system based on multimedia technology Mod. Electron. Tech. 44 20 32 36
**, L.: Design of MOOC idea based teaching resource sharing system for digital film and television production. Mod. Electron. Tech. 43(16), 115–118 (2020)
Wang, Z., Muthu, B.A., Kadry, S.N.: Research on the design of analytical communication and information model for teaching resources with cloud: haring platform. Comput. Appl. Eng. Educ. 29(2), 359–369 (2021)
Li, H., Zhong, Z., Shi, J., Li, H., Zhang, Y.: Multi-Objective optimization-based recommendation for massive online learning resources. IEEE Sens. J. 21(22), 25274–25281 (2021)
Ying, F., Meng, L.: Design and research of online education course resource sharing based on cloud platform. Mod. Electron. Tech. 43(1), 175–178 (2020)
Zou, F., Chen, D., Xu, Q., Jiang, Z., Kang, J.: A two-stage personalized recommendation based on multi-objective teaching–learning-based optimization with decomposition. Neurocomputing 452(6), 716–727 (2021)
Liu, T., Yang, Z.: Personalized recommendation method of sports online video teaching resources based on multiuser characteristics. Math. Probl. Eng. 2022(2), 1–8 (2022)
Chen, L., Bai, W., Yao, Z.: A secure and privacy‐preserving watermark based medical image sharing method. Chin. J. Electron. 29(5), 819–825 (2020). https://doi.org/10.1049/cje.2020.07.003
Tian, X., Ahmad, M T.: Method of Ideological and political teaching resources in universities based on school-enterprise cooperation mode. Math. Probl. Eng. 2022, 10(1):1098–1121 (2022)
Zhang, Z.: A Method of recommending physical education network course resources based on collaborative filtering technology. Sci. Program. 2021(10), 1–9 (2021)
Liang, X., Yin, J.: Recommendation algorithm for equilibrium of teaching resources in physical education network based on trust relationship. J. Internet Technol. 23(1), 133–141 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Liu, Y., Wang, S., Sui, H. (2024). Allocation Method of Teaching Resources of Talent Training Course Based on BP Neural Network. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-031-50543-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-50543-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50542-3
Online ISBN: 978-3-031-50543-0
eBook Packages: Computer ScienceComputer Science (R0)