Skip to main content
Log in

A novel quality evaluation method for standardized experiment teaching

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Agostinelli F, Anderson MR, Lee H (2013) Robust image denoising with multi column deep neural networks. In: Proceedings of the neural information processing systems, Lake Tahoe, NV, USA, vol 1, pp 1493–1501

  • Bo YU, Yufeng LA (2018) Research on the current situation and factors of pre-service teachers’ teaching ability [6] training: a case study of X university. J Teacher Educ

  • Chen J (2004) Research evaluation method and empirical study. Wuhan University

  • Chung EY (2019) Facilitating learning of community-based rehabilitation through problem-based learning in higher education. BMC Med Educ 19(1):21–43

    Article  Google Scholar 

  • Dempster AP (1967) Upper and lower probabilities induced by a multi-valued mapping. Ann Math Stat 38(2):325–339

    Article  Google Scholar 

  • Dempster APA (1968) Generalization of Bayesian inference. J R Stat Soc B 30(2):205–247

    MathSciNet  MATH  Google Scholar 

  • Duan X (1993) Evidence theory and decision making. Artificial intelligence. Renmin University of China Press, Beijing, p 1993

    Google Scholar 

  • Ehrensperger G, Stabinger S, Sánchez AR (2019) Evaluating CNNs on the gestalt principle of closure. In: Artificial neural networks and machine learning—ICANN 2019: theoretical neural computation

  • Elgindy KT (2017) High-order adaptive Gegenbauer integral spectral element method for solving non-linear optimal control problems. Optimization 66(5):811–836

    Article  MathSciNet  Google Scholar 

  • Gao H, Yang W, Wang J et al (2020) Analysis of the effectiveness of air pollution control policies based on historical evaluation and deep learning forecast: a case study of Chengdu-Chongqing Region in China. Sustainability 13:206

    Article  Google Scholar 

  • Guanyu WU (2007) Evaluation of IT projects based on group AHP and fuzzy mathematics. Hefei University of Technology, Hefei

    Google Scholar 

  • Gulliksson M, Oleynik A (2017) Greedy Gauss–Newton algorithms for finding sparse solutions to nonlinear under determined systems of equations. Optimization 66(7):1201–1217

    Article  MathSciNet  Google Scholar 

  • Hinton GE, Salakhutdinov RR (2006a) Reducing the Dimensionality of Data with Neural Networks. Science 13(5548):412–457

    MathSciNet  MATH  Google Scholar 

  • Hinton GE, Salakhutdinov RR (2006b) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  Google Scholar 

  • Jamshed A, Mallick B, Kumar P (2020) Deep learning-based sequential pattern mining for progressive database. Soft Comput 131

  • Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231

    Article  Google Scholar 

  • Jiang C, Zhang H, Shen H, Zhang L (2014) Two-step sparse coding for the pan-sharpening of remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 7(5):1792–1805

    Article  Google Scholar 

  • Kuhn S, Frankenhauser S, Tolks D (2018) (2018) Digital learning and teaching in medical education: already there or still at the beginning. Bundesgesundh Gesundheitsforsch Gesundheitssch 61(2):201

    Article  Google Scholar 

  • Le Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  • Li H (2016) Remote sensing image fusion based on deep support value learning network. J Comput 39(8):14

    MathSciNet  Google Scholar 

  • Liu Z (2006) The application of group hierarchy process and variable weight theory in submarine pipeline routing. Tianjin University

  • Metham M, Benjaoran V, Sedthamanop A (2019) An evaluation of Green Road Incentive Procurement in road construction projects by using the AHP. Int J Constr Manag 1:1–13

    Google Scholar 

  • Mines R (2019) Theory, simulation, analysis and synthesis for metallic microlattice structures. Information and Communication Technologies for Ageing Well and e-Health

  • Paz JL, León-Torres JR, Lascano L, Vera CC (2017) Relaxation times and symmetries in the nonlinear optical properties of a two-level system. Opt Commun 405:238–243

    Article  Google Scholar 

  • Riyaz B, Ganapathy S (2020) (2020) A deep learning approach for effective intrusion detection in wireless networks using CNN. Soft Comput 24(22):17265–17278

    Article  Google Scholar 

  • Shahin I (2019) Emotion recognition based on third-order circular suprasegmental Hidden Markov model. In: 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT). IEEE

  • Sulis I, Porcu M, Capursi V (2019) On the use of student evaluation of teaching: a longitudinal analysis combining measurement issues and implications of the exercise. Soc Indic Res: Int Interdiscipl J Qual-of-Life Meas 2019:142

    Google Scholar 

  • Tang J, Ran Z, Mian WU (2013) Multi-feature information fusion decision diagnosis for the partial discharge pattern of gas insulated appliances. High Volt Technol 33(11):2581–2588

    Google Scholar 

  • Tao J (2013) Identification and hazard evaluation of partial discharge of composite electrical appliances. Chongqing University, Chongqing, p 2013

    Google Scholar 

  • Thenmozhi M, Saravanan M, Kumar K et al (2020) Improving the prediction rate of unusual behaviors of animal in a poultry using deep learning technique. Soft Comput 24(19):14491–14502

    Article  Google Scholar 

  • Vapnik VN (1995) The natural of statistical learning theory. Springer, New York

    Book  Google Scholar 

  • Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising auto encoders. In: Proceedings of the 25th international conference on machine learning. ACM, New York, pp 1096–1103

  • Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising auto encoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11(6):3371–3408

    MathSciNet  MATH  Google Scholar 

  • Wu T, Sun S, Wang X, Zhang H, He C, Wang J, Gu X, Liu Y (2017) Optimization of linear-wave number spectrometer for high-resolution spectral domain optical coherence tomography. Opt Commun 405(1):171–176

    Article  Google Scholar 

  • Xie J, Xu L, Chen E (2012) image denoising and inpainting with deep neural networks. In: Proceedings of the neural information processing systems, Lake Tahoe, NV, USA, vol 1, pp 350–358

  • Yang W (2018) Li L (2018) Efficiency evaluation of industrial waste gas control in China: a study based on data envelopment analysis (DEA) model. J Clean Prod 179:1–11

    Article  Google Scholar 

  • Yang S, Wang M, Jiao L (2012) Fusion of multi spectral and panchromatic images based on support value transform and adaptive principal component analysis. Inf Fus 13:177–184

    Article  Google Scholar 

  • Zhang L (2014) Study on fault diagnosis method of oil-immersed power transformer. North China Power University, Baoding

    Google Scholar 

  • Zhao X (2019) Application of deep learning algorithm in college English teaching process evaluation. Behav Inf Technol 11(1):290–311

    Google Scholar 

  • Zheng S, Shi WZ, Liu J, Tian J (2008) Remote sensing image fusion using multi scale mapped LS-SVM. IEEE Trans Geo Sci Remote Sens 46(5):1313–1322

    Article  Google Scholar 

  • Zheng S, Shi WZ, Liu J, Zhu GX, Tian JW (2017) Multi source image fusion method using support value transform. IEEE Trans Image Process 16(7):1831–1839

    Article  Google Scholar 

  • Zhu XX, Bamler RA (2013) (2013) Sparse image fusion algorithm with application to pan-sharpening. IEEE Trans Geo-Sci Remote Sens 51(5):2827–2836

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yucheng Liu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Informed consent

All the authors are aware and well informed about this submission.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06636-x

Keywords

Navigation