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Using neural network decision support systems in the formation of pedagogical communication skills

https://doi.org/10.32517/0234-0453-2024-39-5-78-91

Abstract

The article presents the results of a study of the potential of using neural network decision support systems (DSS) to develop pedagogical communication skills among research teachers. Based on the analysis of scientific literature, the key principles and methodological approaches to the development and implementation of neural network DSS in the context of modern educational paradigms are identified. A conceptual model of neural network DSS functioning is proposed, taking into account the specifics of teachers’ communicative competencies and the multimodal nature of the data. An empirical study involving 120 teachers from various subject areas demonstrated the effectiveness of using neural network DSS for analyzing and improving pedagogical communication skills. The developed system of assessment metrics, including quantitative (student engagement level, reaction time) and qualitative (expert assessment) indicators, allowed us to validate the results of using neural network DSS in real educational conditions. The article discusses ethical and socio-cultural aspects of responsible implementation of neural network technologies in the practice of developing the communicative competencies of research teachers. The prospects of integrating neural network DSS with adaptive learning systems and virtual reality technologies to create complex solutions in the field of improving pedagogical communication skills are outlined.

About the Authors

T. M. Voiteleva
Federal State University of Education
Russian Federation

Tatyana M. Voiteleva, Doctor of Sciences (Pedagogy), Professor, Professor at the Department of Russian Language and Literature Teaching Methods, Faculty of Russian Philology



N. V. Loginova
Federal State University of Education
Russian Federation

Natalya V. Loginova, a postgraduate student at the Department of Slavic Studies, General Linguistics, and Communication Culture, Faculty of Russian Philology



References

1. Kuzmin N. N., Glazunova I. N., Chistyakova N. A., Bay timerova L. S. Artificial intelligence and its role in building an individual development trajectory for university students. Education Management Review. 2024;(3-1):113–121. (In Russian.) EDN: QPGZLF. DOI: 10.25726/f3942-2092-6900-m.

2. Albrecht J. R., Karabenick S. A. Relevance for learning and motivation in education. The Journal of Experimental Education. 2018;86(1):1–10. DOI: 10.1080/00220973.2017.1380593.

3. Bower M. Technology-mediated learning theory. Bri tish Journal of Educational Technology. 2019;50(3):1035– 1048. DOI: 10.1111/bjet.12771.

4. Chen L., Chen P., Lin Z. Artificial intelligence in edu cation: A review. IEEE Access. 2020;8:75264–75278. DOI: 10.1109/ACCESS.2020.2988510

5. Chetty K., Qigui L., Gcora N., Josie J., Wenwei L., Fang C. Bridging the digital divide: Measuring digital litera cy. Economics: The Open-Access, Open-Assessment E-Journal. 2018;12(2018–23):1–20. DOI: 10.5018/economics-ejournal.ja.2018-23.

6. Makarova E. N., Pirozhkova I. S. Digital transformation of higher education: EFL teaching and learning experiences. Proc. 2nd Int. Scientific and Practical Conf. on Digital Economy (ISCDE 2020). Dordrecht, Netherlands, Atlantis Press; 2020:34–39. DOI: 10.2991/aebmr.k.201205.006.

7. Gruzdev M. V., Kuznetsova I. V., Tarkhanova I. Y., Kazakova E. I. University graduates’ soft skills: The employers’ opinion. European Journal of Contemporary Education. 2018;7(4):690–698. DOI: 10.13187/ejced.2018.4.690.

8. Jang H. Teachers’ intrinsic vs. extrinsic instructional goals predict their classroom motivating styles. Learning and Instruction. 2019;60:286–300. DOI: 10.1016/j.learnin-struc.2017.11.001.

9. Karakozov S. D., Ryzhova N. I. Information and education systems in the context of digitalization of education. Journal of Siberian Federal University. Humanities & Social Sciences. 2019;12(9):1635–1647. EDN: BUDCDL. DOI: 10.17516/1997-1370-0485.

10. König J., Jäger-Biela D. J., Glutsch N. Adapting to on-line teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020;43(4):608–622. DOI: 10.1080/02619768.2020.1809650.

11. Kuzminov Y., Sorokin P., Froumin I. Generic and specific skills as components of human capital: New challenges for education theory and practice. Foresight and STI Governance. 2019;13(2):19–41. EDN: QZXFED. DOI: 10.17323/2500-2597.2019.2.19.41.

12. Kyriakides L., Christoforou C., Charalambous C. Y. What matters for student learning outcomes: A meta-analysis of studies exploring factors of effective teaching. Teaching and Teacher Education. 2013;36:143–152. DOI: 10.1016/j.tate.2013.07.010.

13. Liu Z., Miao Z., Zhan X., Wang J., Gong B., Yu S. X. Large-scale long-tailed recognition in an open world. Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach, CA, USA, Institute of Electrical and Electronics Engineers Inc.; 2021:2537–2546. DOI: 10.1109/CVPR.2019.00264.

14. McKenney S., Visscher A. Technology for teacher learn ing and performance. Technology, Pedagogy and Education. 2019;28(2):129–132. DOI: 10.1080/1475939X.2019.1600859.

15. Papavlasopoulou S., Giannakos M. N., Jaccheri L. Empirical studies on the Maker Movement, a promising approach to learning: A literature review. Entertainment Computing. 2017;18:57–78. DOI: 10.1016/j.entcom.2016.09.002.

16. Pellas N., Kazanidis I., Konstantinou N., Georgiou G. Exploring the educational potential of three-dimensional multi-user virtual worlds for STEM education: A mixed method systematic literature review. Education and Infor mation Technologies. 2017;22(5):2235–2279. DOI: 10.1007/s10639-016-9537-2.

17. Remesh A. Microteaching, an efficient technique for learning effective teaching. Journal of Research in Medi cal Sciences: The Official Journal of Isfahan University of Medical Sciences. 2013;18(2):158–163. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC3724377/pdf/JRMS-18-158.pdf

18. Glotova M. Yu., Samokhvalova E. A., Mukhlynina O. A. Development of skills in the field of neural network tech-nologies for future teachers: Opportunities and advantages. Nauka i Shkola. 2023;(5):162–172. (In Russian.) EDN: NHOTUQ. DOI: 10.31862/1819-463Х-2023-5-162-172.

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For citations:


Voiteleva T.M., Loginova N.V. Using neural network decision support systems in the formation of pedagogical communication skills. Informatics and education. 2024;39(5):78-91. (In Russ.) https://doi.org/10.32517/0234-0453-2024-39-5-78-91

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ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)