[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Analysis of the teaching quality using novel deep learning-based intelligent classroom teaching framework

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

Abstract

Ideological is an adjective that defines theological, political and cultural views. An ideology is a bunch of ideas, and those with an ideological stand follow the main idea. The demanding characteristics of college ideological and political education include lack of research, intelligent evaluations of effective teaching quality, and an important factor. In this paper, novel deep learning-based intelligent classroom teaching framework (NDL-ICTF) has been proposed to enhance the theoretical and realistic methods and a simulation model for the network assessment of the teaching quality system at the college. The Reform Innovative Media algorithm is integrated with NDL-ICTF to set the speed and error curve for assessment measures, defines encouraging their interest in its contents, and induces them to acquire civic competences. The simulation study is based on precision, quality and results to show the durability of the system proposed. The results are estimated in NDL-ICTF as visually communicated percentage ratio is 86.16%, brain storming activities ratio is 83.86%, real-world design performance ratio is 86.55%, model creativity performance ratio is 82.55%, and foster collaboration of students ratio is 88.85% obtained from different datasets and compared with various methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Erdem, D., Beke, A., Kumbasar, T.: A deep learning-based pipeline for teaching control theory: transforming feedback control systems on whiteboard into MATLAB. IEEE Access. 5(8), 84631–84641 (2020)

    Article  Google Scholar 

  2. Abdel-Basset, M., Manogaran, G., Gamal, A., Chang, V.: A novel intelligent medical decision support model based on soft computing and IoT. IEEE Internet Things J. 7(5), 4160–4170 (2019)

    Article  Google Scholar 

  3. Haldorai, A., Murugan, S., Ramu, A.: Evolution, challenges, and application of intelligent ICT education: An overview. Computer Appl. Eng. Edu. 29(3), 562–571 (2020)

    Article  Google Scholar 

  4. Alazab, A., Bevinakoppa, S., &Khraisat, A. Maximizing competitive advantage on E-business websites: A data mining approach. In 2018 IEEE Conference on Big Data and Analytics (ICBDA) (pp. 111–116). (2018) IEEE.

  5. Wang, J., Liu, T., Wang, X.: Human hand gesture recognition with convolutional neural networks for K-12 double-teachers instruction mode classroom. Infrared Phys. Technol. 111, 103464 (2020)

    Article  Google Scholar 

  6. Bansal, G., Hasija, V., Chamola, V., Kumar, N., &Guizani, M. Smart stock exchange market: A secure predictive decentralized model. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). (2019) IEEE.

  7. Xu, X., Han, M., Nagarajan, S.M., Anandhan, P.: Industrial Internet of Things for smart manufacturing applications using hierarchical trustful resource assignment. Comput. Commun. 160, 423–430 (2020)

    Article  Google Scholar 

  8. Tang, J., Zhou, X., Zheng, J. Design of Intelligent classroom facial recognition based on Deep Learning. In Journal of Physics: Conference Series (Vol. 1168, No. 2, p. 022043). IOP Publishing. (2019)

  9. Molano, J.I.R., Lovelle, J.M.C., Montenegro, C.E., Granados, J.J.R., Crespo, R.G.: Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. J. Ambient Intell. Human. Comput. 9(3), 709–723 (2018)

    Article  Google Scholar 

  10. Kumari, A., Behera, R. K., Sahoo, K. S., Nayyar, A., Kumar Luhach, A., &Prakash Sahoo, S. Supervised link prediction using structured‐based feature extraction in social network. Concurrency and Computation: Practice and Experience, (2020) e5839.

  11. Cope, B., Kalantzis, M., Searsmith, D.: Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educ. Philos. Theory 24, 1–7 (2020)

    Google Scholar 

  12. Kayapinar, U., Spathopoulou, F., Safieddine, F., Nakhoul, I., Kadry, S.: Tablet use in teaching: A study on developing an attitude scale for academics. Eurasian J. Educ. Res. 78, 219–234 (2018)

    Google Scholar 

  13. Verma, C., Stoffová, V., Illés, Z., Tanwar, S., Kumar, N.: Machine learning-based student’s native place identification for real-time. IEEE Access 8, 130840–130854 (2020)

    Article  Google Scholar 

  14. Benoliel, P., Berkovich I. Learning from intelligent failure: an organizational resource for school improvement. Journal of Educational Administration. (2020)

  15. Elhoseny, M., Metawa, N., Darwish, A., Hassanien, A.E.: Intelligent information system to ensure quality in higher education institutions, towards an automated-university. Int. J. Comput. Intell. Stud. 6(2–3), 115–149 (2017)

    Google Scholar 

  16. Liu C. AI blended teaching in business English based on deep learning theory. In2020 International Conference on Image, Video Processing and Artificial Intelligence (Vol. 11584, p. 1158414). International Society for Optics and Photonics. (2020)

  17. Saravanan, V. Impact of intelligence methodologies on education and training process. Journal of Intelligent & Fuzzy Systems, (Preprint), 1–2.

  18. Nieto, Y., García-Díaz, V., Montenegro, C., Crespo, R.G.: Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft. Comput. 23(12), 4145–4153 (2019)

    Article  Google Scholar 

  19. Ye D. Artificial Intelligence and Deep Learning Application in Evaluating the Descendants of TuboMgarStongBtsan and Social Development. InData Processing Techniques and Applications for Cyber-Physical Systems DPTA 2019 (pp 1869–1876). Springer, Singapore (2020)

  20. Niet, Y. V., Díaz, V. G., & Montenegro, C. E. (2016). Academic decision making model for higher education institutions using learning analytics. In 2016 4th International Symposium on Computational and Business Intelligence (ISCBI) (pp. 27–32). IEEE.

  21. Sigurðardóttir, M.S., Heijstra, T.M.: Mixed approaches to learning in the flipped classroom: how students approach the learning environment. Canadian J. Scholar. Teaching Learn. 11(1), 1 (2020)

    Google Scholar 

  22. Sun, Z., Anbarasan, M., Praveen Kumar, D. Design of online intelligent English teaching platform based on artificial intelligence techniques. Computational Intelligence. 2020

  23. Ullah, F., Wang, J., Farhan, M., Jabbar, S., Wu, Z., Khalid, S.: Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology. Multimedia Tools Appl. 79(13), 8581–8598 (2020)

  24. Di, W., Danxia, X., Chun, L.: The effects of learner factors on higher-order thinking in the smart classroom environment. J Comput Educ. 6(4), 483–498 (2019)

  25. Sahla, K.S., Kumar, T.S.: Classroom teaching assessment based on student emotions. In: The International Symposium on Intelligent Systems Technologies and Applications, pp. 475–486. Springer, Cham (2016)

  26. Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3713–3722. (2016)

  27. Namitha, C.: Modern methods of teaching. J Appl Adv Res. 3(S1), 39–41 (2018)

  28. Li, J., Shi, D., Tumnark, P., Xu, H.: A system for real-time intervention in negative emotional contagion in a smart classroom deployed under edge computing service infrastructure. Peer-to-Peer Networking Appl. 13(5):1706–1719 (2020)

  29. Gupta, S.K., Ashwin, T.S., Guddeti, R.M.: Students’ affective content analysis in smart classroom environment using deep learning techniques. Multimedia Tools Appl. 78(18):25321–25348 (2019)

  30. Huang, L.S., Su, J.Y., Pao, T.L.: A context aware smart classroom architecture for smart campuses. Appl Sci. 9(9), 1837 (2019)

  31. Han, X., Liu, Y., Li, H., Fan, Z., Luo, H.: Augmenting the makerspace: designing collaborative inquiry through augmented reality. In: International Conference on Blended Learning, (pp. 148–159). Springer, Cham (2020)

  32. Han, Z., Xu, A.: Ecological evolution path of smart education platform based on deep learning and image detection. Microprocess. Microsyst. 80, 103343 (2021)

  33. Kassymova, G., Akhmetova, A., Baibekova, M., Kalniyazova, A., Mazhinov, B., Mussina, S.: E-learning environments and problem-based learning. Int J Adv Sci Technol. 29, 346–356 (2020)

Download references

Author information

Authors and Affiliations

  1. Yellow River Conservancy Technical Institute, Kaifeng, 475004, Henan, China

    Feng Geng

  2. Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, India

    Alfred Daniel John

  3. Department of Computer Science, Vel Tech Rangarajan Dr.Sagunhatla R&D Institute of Science and Technology, Chennai, India

    Chandru Vignesh Chinnappan

Authors
  1. Feng Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfred Daniel John
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chandru Vignesh Chinnappan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Geng.

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

Geng, F., John, A.D. & Chinnappan, C.V. Analysis of the teaching quality using novel deep learning-based intelligent classroom teaching framework. Prog Artif Intell 12, 147–162 (2023). https://doi.org/10.1007/s13748-021-00256-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-021-00256-0

Keywords

Search

Navigation