PSYCHOPHYSIOLOGICAL ADAPTATION POTENTIAL TO STUDYING AT THE PEDAGOGICAL UNIVERSITY IN RUSSIAN AND KAZAKH STUDENTS

Maltsev V.; Suyundikova Z.

doi:10.14529/jpps190307

V. P. Maltsev Surgut State Pedagogical University, Surgut, Russia http://orcid.org/0000-0002-2453-6585 mal585@mail.ru
Z. T. Suyundikova Kostanay State Pedagogical University (Kostanay, Kazakhstan) http://orcid.org/0000-0003-2188-7985 janar-1412@mail.ru

DOI: https://doi.org/10.14529/jpps190307

Keywords: psychophysiological adaptation potential, neurodynamic indicators, chronoreflexometry, functional status of the central nervous system, ethnos

Abstract

Aim. The Federal State Educational Standard of Higher Education prescribes providing the educational process in accordance with the individual and typological features of students. The nature of adaptation shifts in students depends on ethnic, ecological, and morphological indicators determining the functional status of the body. Its functional status and functional abilities conditioned by academic and professional activities are determined by the cerebral process, which influences the efficiency of adaptation and cognitive performance in general. The article deals with studying the features of psychophysiological potential in Russian and Kazakh first-year female students during their adaptation to studying at the pedagogical university. Materials and methods. We conducted the diagnostics of the functional status of the central nervous system and cognitive performance in first-year female students at pedagogical universities. The average age of participants was 17.9 years. The total sample (n = 120) consisted of the Russian and Kazakh students living in Kostanay (Kazakhstan) and Chelyabinsk (Russia). A simple eye motor response (SEMR) and a complex eye motor response (CEMR) to a light stimulus were measured with the NS-Psychotest equipment. The calculated data are the following: the functional status of the system; response stability; level of functional abilities. These data were used as a basis for the assessment of cognitive performance in the participants of the study. Results. The results obtained characterize the neuro-dynamic basis of first-year students’ adaptation expressed in the average level of CNS activity, mobility of nervous processes, and optimal functional abilities. In the conditions of the average intragroup expression of the functional criteria of the central nervous system better cognitive processes for the processing of sensory information were revealed in students from Chelyabinsk compared to Kostanay. Conclusion. The analysis of the data obtained revealed the regional specifics of the average values of chronoreflexometry in first-year students, namely a significantly higher level of neural interaction and sensorimotor processing in first-year students from Chelyabinsk.

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Author Biographies

V. P. Maltsev , Surgut State Pedagogical University, Surgut, Russia

Candidate of Biology, associate professor, associate professor of medicobiological disciplines and health and safety

Z. T. Suyundikova , Kostanay State Pedagogical University (Kostanay, Kazakhstan)

Candidate of Biological Sciences, Senior Lecturer, Department of Biology and Geography

References

21. Alison, M.H. Temporal dynamics of sensorimotor networks in effort-based cost-benefit valuation: early emergence and late net value integration / M.H. Alison, L. Seung-Lark // Journal of Neuroscience. – 2016. – Vol. 36. – Р. 7167–7183. DOI: https://doi.org/10.1523/JNEUROSCI.4016-15.2016.
22. Bailey, M. The changing important of factors influencing students’ choice of study mode / M. Bailey, D. Ifenthaler, M. Gosper, M. Kretzschmar, C. Ware // Technology, Knowledge and Learning. – 2015. – Vol. 20, Is. 2. – P. 169–184. DOI: https://doi.org/10.1007/s10758-015-9253-9.
23. Gregory, M.S.-J. Academic workload: The silent barrier to the implementation of technology-enhanced learning strategies in higher education / M.S.-J. Gregory, J.M. Lodge // Distance Education. – 2015. – Vol. 36 (2). – P. 210–230. DOI: https://doi.org/10.1080/01587919.2015.1055056.
24. Moritz, К. Evaluating an instrument to measure mental load and mental effort considering different sources of validity evidence / К. Moritz // Cogent Education. – 2017. – no. 4. – P. 1–10. DOI: https://doi.org/10.1080/2331186X.2017.1280256.
25. Ramos, M. Perceptions of quantitative methods in higher education: mapping student profiles // Higher Education. – 2011. – Vol. 61, Is. 6. – P. 629–647. DOI: https://doi.org/10.1007/s10734-010-9353-3.
26. Van Bragt, C. Looking for students’ personal characteristics predicting study outcome / C. Van Bragt, A. Bakx, T. Bergen, M. Croon [et al.] // Higher Education. – 2011. – Vol. 61, Is. 1. – P. 59–75. DOI: 10.1007/s10734-010-9325-7.
27. Weisburst, E. Innovative pathways through developmental education and postsecondary success: an examination of developmental math interventions across Texas / E. Weisburst, L. Daugherty, T. Miller, P. Martorell, J. Cossairt // The journal of higher education. – 2017. – Vol. 88, Is. 2. – P. 183–209. DOI: 10.1080/00221546.2016.1243956

References on translit

21. Alison M.H., Seung-Lark L. Temporal dynamics of sensorimotor networks in effort-based cost-benefit valuation: early emergence and late net value integration. Journal of Neuroscience, 2016, vol. 36, рр. 7167–7183. DOI: https://doi.org/10.1523/JNEUROSCI.4016-15.2016.
22. Bailey M., Ifenthaler D., Gosper M., Kretzschmar M., Ware C. The changing important of factors influencing students’ choice of study mode. Technology, Knowledge and Learning, 2015, vol. 20, Is. 2, рр. 169–184. DOI: https://doi.org/10.1007/s10758-015-9253-9.
23. Gregory M.S.-J., Lodge J.M. Academic workload: The silent barrier to the implementation of technology-enhanced learning strategies in higher education. Distance Education, 2015, vol. 36(2), рр 210–230. DOI: https://doi.org/10.1080/01587919.2015.1055056.
24. Moritz K. Evaluating an instrument to measure mental load and mental effort considering different sources of validity evidence. Cogent Education, 2017, no. 4, рр. 1–10. DOI: https://doi.org/10.1080/2331186X.2017.1280256.
25. Ramos M., Carvalho H. Perceptions of quantitative methods in higher education: mapping student profiles. Higher Education, 2011, vol. 61, is. 6, рр. 629–647. DOI: https://doi.org/10.1007/s10734-010-9353-3.
26. Van Bragt C., Bakx A., Bergen T., Croon M. et al. Looking for students’ personal characteristics predicting study outcome. Higher Education, 2011, vol. 61, is. 1, рр. 59–75. DOI: 10.1007/s10734-010-9325-7.
27. Weisburst E., Daugherty L., Miller T., Martorell P., Cossairt J. Innovative pathways through developmental education and postsecondary success: an examination of developmental math interventions across Texas. The journal of higher education, 2017, vol. 88, is. 2., рр. 183–209. DOI: 10.1080/00221546.2016.1243956