Abstract
To address the problems of poor performance evaluation and performance management of college curriculum reform, the performance evaluation method of college curriculum reform using artificial neural networks is proposed. First, the performance evaluation index system of college curriculum reform using artificial neural network technology is constructed. Second, the performance evaluation algorithm of college curriculum reform is improved, and the performance evaluation process of college curriculum reform is simplified. The experiment proves that the performance evaluation method of college curriculum reform using artificial neural networks has higher practicality than the traditional method and fully meets the research requirements.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Liu, F.: Language database construction method based on big data and deep learning. Alexandria Eng. J. 61(12), 9437–9446 (2022)
Park, J., Kim, J., Lee, S., Choi, J.K.: Machine learning based photovoltaic energy prediction scheme by augmentation of on-site IoT data. Fut. Gener. Comput. Syst. 134, 1–12 (2022)
Baashar, Y., et al.: Evaluation of postgraduate academic performance using artificial intelligence models. Alexandria Eng. J. 61(12), 9867–9878 (2022)
Wipfli, H., Withers, M.: Engaging youth in global health and social justice: a decade of experience teaching a high school summer course. Glob. Health Action 15(1), 1987045–1987045 (2022)
Wang, C., Li, B., Cheng, B., Yang, J., Zhou, L.: Research on learning initiative Based on behavior quantization and potential value clustering. Alexandria Eng. J. 61(7), 5621–5627 (2022)
Wu, D., Wang, S., Liu, Q., Abualigah, L., Jia, H., Razmjooy, N.: An improved teaching-learning-based optimization algorithm with reinforcement learning strategy for solving optimization problems. Comput. Intell. Neurosci. 2022, 1535957–1535957 (2022)
Gill, H.S., Khehra, B.S.: Apple image segmentation using teacher learner based optimization based minimum cross entropy thresholding. Multimedia Tools Appl. 81(8), 11005–11026 (2022)
Lu, W., Vivekananda, G.N., Shanthini, A.: Supervision system of English online teaching based on machine learning. Prog. Artif. Intell., 1–12 (2022). https://doi.org/10.1007/s13748-021-00274-y
Mashwani, W.K., Shah, H., Kaur, M., Bakar, M.A., Miftahuddin, M.: Large-scale bound constrained optimization based on hybrid teaching learning optimization algorithm. Alexandria Eng. J. 60(6), 6013–6033 (2021)
Sokoli, D., Širca, N.T., Koren, A.: Quality of teaching in Kosovo’s higher education institutions: viewpoints of institutional leaders and lecturers1. Hum. Syst. Manage. 40(5), 685–700 (2021)
Ma, H., Yang, S., Feng, D., Jiao, L., Zhang, L.: Progressive mimic learning: a new perspective to train lightweight CNN models. Neurocomputing 456, 220–231 (2021)
Yang, N.-C., Liu, S.-W.: Multi-objective teaching–learning-based optimization with pareto front for optimal design of passive power filters. Energies 14(19), 6408 (2021)
Pratama, M., Za’in, C., Lughofer, E., Pardede, E., Rahayu, D.A.P.: Scalable teacher forcing network for semisupervised large scale data streams. Inf. Sci. 576, 407–431 (2021)
Mathur, G., Chauhan, S.A.: Teacher evaluation of institutional performance: managing cultural knowledge infrastructure in knowledge organizations. Int. J. Knowl. Manage. 17(4), 93–108 (2021)
Hua, L., Liu, G.: Development of basketball tactics basic cooperation teaching system based on CNN and BP neural network. Comput. Intell. Neurosci. 2021, 1–11 (2021). https://doi.org/10.1155/2021/9497388
Tsai, F.H., Hsiao, H.S., Yu, K.C., Lin, K.Y.: Development and effectiveness evaluation of a STEM-based game-design project for preservice primary teacher education. Int. J. Technol. Des. Educ. 3, 1–22 (2021)
Tian, Y., Zhang, L., Sun, J., Yin, G., Dong, Y.: Consistency regularization teacher–student semisupervised learning method for target recognition in SAR images. Vis. Comput., 1–14 (2021). https://doi.org/10.1007/s00371-021-02287-z
Zhang, B., Velmayil, V., Sivakumar, V.: A deep learning model for innovative evaluation of ideological and political learning. Prog. Artif. Intell., 1–13 (2021). https://doi.org/10.1007/s13748-021-00253-3
Tamai, T., Okamoto, K., Iuchi, K., Kawada, K.: Development of teaching material to design a vehicle on data science in junior high school technology education. IEEJ Trans. Electr. Electron. Eng. 16(10), 1407–1413 (2021)
Dietrich, J., Greiner, F., Weber-Liel, D., Berweger, B., Kämpfe, N., Kracke, B.: Does an individualized learning design improve university student online learning? A randomized field experiment. Comput. Hum. Behav. 122, 106819 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, J., Zhi, S. (2022). Performance Evaluation for College Curriculum Teaching Reform Using Artificial Neural Network. In: Wang, Y., Zhu, G., Han, Q., Zhang, L., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1629. Springer, Singapore. https://doi.org/10.1007/978-981-19-5209-8_25
Download citation
DOI: https://doi.org/10.1007/978-981-19-5209-8_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5208-1
Online ISBN: 978-981-19-5209-8
eBook Packages: Computer ScienceComputer Science (R0)