Abstract
The current experimental teaching quality evaluation methods have been difficult to meet the quantitative and accurate requirements of evaluation indicators. For that, in this paper, we propose an ICNNs-DS (improved convolution neural networks-Dempster–Shafer) integrated model for the quality evaluation of experimental teaching. We first develop the structural risk of support vector machines to replace the criteria for the minimization of empirical risk of designing ICNNs model. Then, we devise both an advanced data processing method of evaluation matrix and a reconstruction function to comprehensively calculate various values. Furthermore, we present a targeted fusion evaluation model of DS (ICNNs-DS model) to combine various results of each of the ICNNs modules. Finally, we conduct related experiments to demonstrate the performance advantage of the ICNNs-DS integrated model. Experiment results show that: (1) the operation of ICNNs can effectively solve the complex nonlinear relationship among the evaluation indexes of experimental teaching quality. (2) The fusion method of DS, which has taken into account and retained the values representing the essential characteristics of the evaluation indexes, can well assemble various independent ICNNs modules. (3) ICNNs-DS model can enhance complementary advantages in experimental teaching quality assessment. The results of performance metrics show that the proposed ICNNs-DS integrated model has the best performance of all experimental teaching quality evaluation methods in the study.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 71702068) and Social Science Foundation of Beijing (No. 20GLB028).
Funding
This research was funded by National Natural Science Foundation of China (No. 71702068); Social Science Foundation of Beijing (No. 20GLB028); Natural Science Foundation of Beijing (No. 9192005); and Capital University of Economics and Business Special Funds for Fundamental Research Funds for Beijing-affiliated Universities.
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Yang, L., Chun, Y., Liu, Y. et al. A novel quality evaluation method for standardized experiment teaching. Soft Comput 26, 6889–6906 (2022). https://doi.org/10.1007/s00500-021-06636-x
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DOI: https://doi.org/10.1007/s00500-021-06636-x