Abstract
In order to improve the teaching quality of applied logistics management course, a model of teaching effect evaluation based on deep learning is designed. The evaluation system of teaching effect of applied logistics management course is constructed, and the evaluation model of teaching effect is established by using the method of deep learning according to the evaluation system, and the evaluation level analysis is carried out on the evaluation value. The experimental results show that the design evaluation model is more accurate and the error value is smaller than the traditional model. Therefore, the evaluation model of teaching effect based on deep learning is more in line with the teaching effect requirements of applied logistics management course.
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References
Chen, J.,Wang, Z.,Chen, J. , et al.: Design and research on intelligent teaching system based on deep learning.Comput. Sci. 46(S1), 550–554+576 (2019)
Liu, C., Lin, L., Yu, C., et al.: Research on peanut hyperspectral image classification method based on deep learning. Comput. Simul. 37(03), 189–192+283 (2020)
Luo, H., Jiang, W., Fan, X., et al.: A survey on deep learning based person re-identification. Acta Autom. Sinica 45(11), 2032–2049 (2019)
Wang, R., Zhang, W.: implicit evaluation object recognition method based on deep learning. Comput. Eng. 45(11), 2032–2049 (2019)
Wang, X., Zhang, H., Zhang, R., et al.: Big data pipeline network risk assessment method based on deep learning. Fire Sci. Technol. 38(06), 902–905 (2019)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
Liu, S., Li, Z., Zhang, Y., et al.: Introduction of key problems in long-distance learning and training. Mobile Networks Appl. 24(1), 1–4 (2019)
Liu, S., Glowatz, M., Zappatore, M., et al. (Eds.). E-Learning, e-Education, and Online Training, pp. 1–374. Springer International Publishing, Berlin, Heidelberg (2018). https://doi.org/10.1007/978-3-030-63952-5
Qi, Y.U.E., Xin, W.E.N.: Application of improved GA-BP neural network in teaching quality evaluation. J. Natural Sci. Heilongjiang Univ. 36(03), 353–358 (2019)
Xu, F., Chen, Y., Huang, Z., et al.: An empirical study on teaching quality evaluation of pharmaceutical administration. J. Shenyang Pharm. Univ. 36(12), 1119–1126 (2019)
Liang, H.: Role of artificial intelligence algorithm for taekwondo teaching effect evaluation model. J. Intell. Fuzzy Syst. 40(2), 3239–3250 (2021)
Luyten, H., Bazo, M.: Transformational leadership, professional learning communities, teacher learning and learner centred teaching practices; evidence on their interrelations in Mozambican primary education. Stud. Educ. Eval. 60, 14–31 (2019)
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He, L., Wu, Qx., Jiang, Q. (2021). Evaluation Method of Teaching Effect of Applied Logistics Management Course Based on Deep Learning. In: Fu, W., Liu, S., Dai, J. (eds) e-Learning, e-Education, and Online Training. eLEOT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 390. Springer, Cham. https://doi.org/10.1007/978-3-030-84386-1_32
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DOI: https://doi.org/10.1007/978-3-030-84386-1_32
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