THE STRUCTURAL PRINCIPLE FOR MONITORING THE TEACHING OF HUMANITIES DISCIPLINES
Abstract and keywords
Abstract (English):
This paper presents a statistical analysis based on a survey of students’ attitudes towards certain humanities subjects. Purpose: to identify the structural links between different disciplines and the various types of students’ goal orientation. Methods: rank correlation coefficient-based statistical techniques were used. Results: an experimental learning model has been developed for a cycle of the university humanities disciplines, providing a qualitative analysis of pedagogical activity. The constructed model is based on probabilistic and statistical methods of graph theory. A set of programmes has been developed for the model to function in the Wolfram language. There has been a notable increase in student interest in managing their own learning activities, as well as in creating the conditions for improvement and establishing the necessary links between participants in educational activities. Practical significance: the developed model enables participants in the educational process, particularly students, to actively engage with university educational activities.

Keywords:
monitoring of learning, Spearman’s correlation coefficient, Kendall’s correlation coefficient, structural model, sample mean
Text
Text (PDF): Read Download
References

1. Zinchenko V. O. Monitoring kachestva uchebnogo processa v vuze: rezul'taty eksperimenta // Vestnik Kostromskogo gosudarstvennogo universiteta imeni N. A. Nekrasova. Seriya: Pedagogika. Psihologiya. Sociokinetika. 2016. T. 22, № 4. C. 188–192.

2. Galiahmetova A. T., Aytuganova Zh. I. Effektivnoe upravlenie kachestvom obrazovaniya v vuze na osnove integracii tradicionnyh i distancionnyh form kontrolya // Vestnik Kostromskogo gosudarstvennogo universiteta imeni N. A. Nekrasova. Seriya: Pedagogika. Psihologiya. Social'naya rabota. Yuvenologiya. Sociokinetika. 2015. T. 21, № 1. S. 92–94.

3. Volokobinskiy M. Yu., Pekarskaya O. A. Metodika prognozirovaniya itogovoy uspevaemosti obuchayuschihsya v zavisimosti ot razlichnyh faktorov // CONTINUUM. Matematika. Informatika. Obrazovanie. 2024. № 1 (33). C. 43–50. DOI:https://doi.org/10.24888/2500-1957-2024-1-43-50.

4. Gluhov A. P., Li A. S., Solomina I. G. Monitoring urovney i profiley cifrovoy gramotnosti obuchayuschihsya v regional'noy sisteme obrazovaniya: analiz cifrovyh razryvov // Perspektivy nauki i obrazovaniya. 2023. № 6 (66). S. 532–547. DOI:https://doi.org/10.32744/pse.2023.6.31.

5. Portnova A. G., Lesnikova S. L., Rusakova N. A. Ispol'zovanie matematicheskih metodov dlya monitoringa kachestva uspevaemosti studentov // Vestnik Kemerovskogo gosudarstvennogo universiteta. Seriya: Gumanitarnye i obschestvennye nauki. 2020. T. 4, № 3 (15). S. 218–226. DOI:https://doi.org/10.21603/2542-1840-2020-4-3-218-226.

6. Borovkov A. A. Matematicheskaya statistika: uchebnik. 4-e izd., ster. SPb.: Lan', 2010. 704 s.

7. Bursian E. Y., Demin A. M., Glukhov A. P. Recognition of Manuscript Tables in Computer Processing of Technical Transport Documentation // Proceedings of Models and Methods of Information Systems Research Workshop in the frame of the Betancourt International Engineering Forum (MMISR 2019), (Saint Petersburg, Russia, 04–05 December 2019). CEUR Workshop Proceedings. 2020. Vol. 2556. Pp. 10–14. DOI:https://doi.org/10.24412/1613-0073-2556-10-14.

8. Gudfellou Ya., Bendzhio I., Kurvill' A. Glubokoe obuchenie = Deep Learning / per. s angl. A. A. Slinkina. 2-e izd., ispr. M.: DMK Press, 2018, 652 s.

9. Nikolenko S., Kadurin A., Arhangel'skaya E. Glubokoe obuchenie. Pogruzhenie v mir neyronnyh setey. SPb.: Piter, 2018. 480 s.

10. Bursian E. Yu., Ushakova T. I., Shefner A. Yu. Korrelyacionnaya model' monitoringa kachestva obrazovatel'nogo processa v vysshem uchebnom zavedenii // V Betankurovskiy mezhdunarodnyy inzhenernyy forum: sbornik trudov (Sankt-Peterburg, Rossiya, 29 noyabrya — 01 dekabrya 2023 g.): v 2 t. T. 1. SPb.: PGUPS, 2023. S. 139–145.

Login or Create
* Forgot password?