Abstract
In this paper, we propose a deep model of course quality evaluation using deep neural network, which is simple and efficient to accurately evaluate the course quality. Specifically, we utilize multi-layer fully-connected (FC) layers with different activation functions to model the network. We perform a series of simulation experiments to evaluate the effect of FC layer number and activation functions. Simulation experiment results verify that the proposed model has excellent performance.
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References
Moss, J., Hendry, G.: Use of electronic surveys in course evaluation. Br. J. Educ. Technol. 33(5), 583–592 (2002)
Hoffman, K.M.: Online course evaluation and reporting in higher education. New Dir. Teach. Learn. 2003(96), 25–29 (2003)
Kogan, J.R., Shea, J.A.: Course evaluation in medical education. Teach. Teach. Educ. 23(3), 251–264 (2007)
Edstrom, K.: Doing course evaluation as if learning matters most. High. Educ. Res. Dev. 27(2), 95–106 (2008)
Wagner, Z.M.: Using student journals for course evaluation. Assess. Eval. Higher Educ. 24(3), 261–272 (1999)
Donovan, J., Mader, C., Shinsky, J.: Online vs. traditional course evaluation formats: student perceptions. J. Interact. Online Learn. 6(3), 158–180 (2007)
Kember, D., Leung, D.Y.: Establishing the validity and reliability of course evaluation questionnaires. Assess. Eval. Higher Educ. 33(4), 341–353 (2008)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)
Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, pp. 2690–2697 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, pp. 770–778 (2016)
Liu, S., Li, M., Zhang, Z., Xiao, B., Cao, X.: Multimodal ground-based cloud classification using joint fusion convolutional neural network. Remote Sens. 10(6), 822 (2018)
Zhang, Z., Huang, M., Liu, S., Xiao, B., Durrani, T.S.: Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification. IEEE Trans. Fuzzy Syst. 28(7), 1356–1368 (2019)
Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018)
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding, arXiv preprint arXiv:1906.08237 (2019)
Liu, T., Yin, S.: An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation. Multimedia Tools Appl. 76(9), 11961–11974 (2016). https://doi.org/10.1007/s11042-016-3776-5
Jiang, L., Wang, X.: Optimization of online teaching quality evaluation model based on hierarchical PSO-BP neural network. Complexity (2020)
Acknowledgements
This work was supported by College Student Innovation and Entrepreneurship Training Program under Grant No. 202010065021, Tianjin Normal University Teaching Reform Program under Grant No. JGZD01220014, Natural Science Foundation of Tianjin under Grant No. 20JCZDJC00180 and No. 19JCZDJC31500, and the Tianjin Higher Education Creative Team Funds Program.
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Xu, M., Wang, N., Gong, S., Zhang, H., Zhang, Z., Liu, S. (2022). Course Quality Evaluation Based on Deep Neural Network. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_8
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DOI: https://doi.org/10.1007/978-981-19-0390-8_8
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