ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Bukina T. V., Khramova M. В., Kurkin S. A. Modern research on primary school children brain functioning in the learning process: Review. Izvestiya VUZ. Applied Nonlinear Dynamics, 2021, vol. 29, iss. 3, pp. 449-456. DOI: 10.18500/0869-6632-2021-29-3-449-456

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Language: 
English
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Review
UDC: 
530.182
DOI: 
10.18500/0869-6632-2021-29-3-449-456

Modern research on primary school children brain functioning in the learning process: Review

Autors: 
Bukina Tatyana V., Saratov State University
Khramova Marina Викторовна, Saratov State University
Kurkin Semen Andreevich, Innopolis University
Abstract: 

The purpose of this article is to review studies related to the study of primary school children’s brain activity in the educational process. In addition, to find out how common such researches are and to define the main directions of scientists’ activities on the topic, and their chosen research approaches. Methods. Qualitative content analysis was used to select articles suitable for the topic under study: several sets of keywords, journals with a quartile of at least the second, and English language publications were identified as requirements. Results. The analysis of research papers showed an increasing interest in the subject matter among scientists. Six main groups of research in the field of neuroimaging are identified: measurement of cognitive abilities, comparison of age categories, technical and methodological nuances of researches based on neuroimaging, study of the influence of various factors on indicators, use of neuroimaging tools in the learning process, registration of brain activity in children with developmental disabilities. Works devoted to the development of memory and attention are highlighted. The features of the study of mathematical abilities and skills, as well as reading, are considered. Advantages and disadvantages of portable electroencephalography are noted. The features of the magnetic resonance imaging procedure in children with developmental disabilities are indicated. Conclusion. Despite the variety of research areas, the use of brain–computer technologies in the learning process was not found in the review. According to the authors, there is a lack of long-term research with the use of such technologies in the specifics of subject learning, which would allow tracking its progress. Conclusions on the need for further study of this issue are presented.

Key words: 
electroencephalography
functional magnetic resonance imaging
brain–computer interface
brain research
primary school children
education
Acknowledgments: 
This work was supported by the Russian Foundation for Basic Research, grant No. 19-29-14101
Reference: 
  1. Maksimenko VA, Runnova AE, Zhuravlev MO, Protasov P, Kulanin R, Khramova MV, Pisarchik AN, Hramov AE. Human personality reflects spatio-temporal and time-frequency EEG structure. PLoS One. 2018;13(9):e0197642. DOI: 10.1371/journal.pone.0197642.
  2. Maksimenko VA, Hramov AE, Grubov VV, Nedaivozov VO, Makarov VV, Pisarchik AN. Nonlinear effect of biological feedback on brain attentional state. Nonlinear Dynamics. 2019;95(3): 1923–1939. DOI: 10.1007/s11071-018-4668-1.
  3. Makarov VV, Zhuravlev MO, Runnova AE, Protasov P, Maksimenko VA, Frolov NS, Pisarchik AN, Hramov AE. Betweenness centrality in multiplex brain network during mental task evaluation. Phys. Rev. E. 2018;98(6):062413. DOI: 10.1103/PhysRevE.98.062413.
  4. Maksimenko VA, Hramov AE, Frolov NS, Luttjohann A, Nedaivozov VO, Grubov VV, Runno- ¨ va AE, Makarov VV, Kurths J, Pisarchik AN. Increasing human performance by sharing cognitive load using brain-to-brain interface. Front. Neurosci. 2018;12:949. DOI: 10.3389/fnins.2018.00949.
  5. Papanastasiou G, Drigas A, Skianis C, Lytras M. Brain computer interface based applications for training and rehabilitation of students with neurodevelopmental disorders. A literature review. Heliyon. 2020;6(9):e04250. DOI: 10.1016/j.heliyon.2020.e04250.
  6. Diaz A, Blankenship TL, Bell MA. Episodic memory in middle childhood: Age, brain electrical activity, and self-reported attention. Cognitive Development. 2018;47:63–70. DOI: 10.1016/j.cogdev.2018.03.003.
  7. Simard F, Cadoret G. Neural correlates of active controlled retrieval development: An exploratory ERP study. Brain and Cognition. 2018;124:20–28. DOI: 10.1016/j.bandc.2018.04.005.
  8. Cycowicz YM. Orienting and memory to unexpected and/or unfamiliar visual events in children and adults. Developmental Cognitive Neuroscience. 2019;36:100615. DOI: 10.1016/j.dcn.2019.100615.
  9. Vogan VM, Morgan BR, Powell TL, Smith ML, Taylor MJ. The neurodevelopmental differences of increasing verbal working memory demand in children and adults. Developmental Cognitive Neuroscience. 2016;17:19–27. DOI:10.1016/j.dcn.2015.10.008.
  10. Pindus DM, Drollette ES, Raine LB, Kao SC Khan N, Westfall DR, Hamill M, Shorin R, Calobrisi E, John D, Kramer AF, Hillmancf CH. Moving fast, thinking fast: The relations of physical activity levels and bouts to neuroelectric indices of inhibitory control in preadolescents. Journal of Sport and Health Science. 2019;8(4):301–314. DOI: 10.1016/j.jshs.2019.02.003.
  11. Maguire MJ, Schneider JM. Socioeconomic status related differences in resting state EEG activity correspond to differences in vocabulary and working memory in grade school. Brain and Cognition. 2019;137:103619. DOI: 10.1016/j.bandc.2019.103619.
  12. De˛bska A, Chyl K, Dzie˛giel G, Kacprzak A, Luniewska M, Plewko J, Marchewka A, Grabowska A, Jednorog K. Reading and spelling skills are differentially related to phonological processing: ´ Behavioral and fMRI study. Developmental Cognitive Neuroscience. 2019;39:100683. DOI: 10.1016/j.dcn.2019.100683.
  13. Mogadam A, Keller AE, Taylor MJ, Lerch JP, Anagnostou E, Pang EW. Mental flexibility: An MEG investigation in typically developing children. Brain and Cognition. 2018;120:58–66. DOI: 10.1016/j.bandc.2017.10.001.
  14. Matejko AA, Ansari D. The neural association between arithmetic and basic numerical processing depends on arithmetic problem size and not chronological age. Developmental Cognitive Neuroscience. 2019;37:100653. DOI: 10.1016/j.dcn.2019.100653.
  15. Gonzalez-Garrido AA, G´omez-Vel´azquez FR, Salido-Ruiz RA, Espinoza-Valdez A, V´elez-P´erez H, Romo-Vazquez R, Gallardo-Moreno GB, Ruiz-Stovel VD, Mart´inez-Ramos A, Berumen G. The analysis of EEG coherence reflects middle childhood differences in mathematical achievement. Brain and Cognition. 2018;124:57–63. DOI: 10.1016/j.bandc.2018.04.006.
  16. Nemmi F, Helander E, Helenius O, Almeida R, Hassler M, Räsänen P, Klingberg T. Behavior and neuroimaging at baseline predict individual response to combined mathematical and working memory training in children. Developmental Cognitive Neuroscience. 2016;20:43–51. DOI: 10.1016/j.dcn.2016.06.004.
  17. Kersey AJ, Wakim KM, Li R, Cantlon JF. Developing, mature, and unique functions of the child’s brain in reading and mathematics. Developmental Cognitive Neuroscience. 2019;39:100684. DOI: 10.1016/j.dcn.2019.100684.
  18. Pizzie RG, Kraemer DJM. Avoiding math on a rapid timescale: Emotional responsivity and anxious attention in math anxiety. Brain and Cognition. 2017;118:100–107. DOI: 10.1016/j.bandc.2017.08.004.
  19. Semrud-Clikeman M. Research in brain function and learning [Electronic resource]. American Psychological Association. 2010, February 16. Access mode: http://www.apa.org/education/k12/brain-function.
  20. Xu J, Zhong B. Review on portable EEG technology in educational research. Computers in Human Behavior. 2018;81:340–349. DOI: 10.1016/j.chb.2017.12.037.
  21. Pua EPK, Barton S, Williams K, Craig JM, Seal ML. Individualised MRI training for paediatric neuroimaging: A child-focused approach. Developmental Cognitive Neuroscience. 2020;41:100750. DOI: 10.1016/j.dcn.2019.100750.
  22. Lau-Zhu A, Lau MPH, McLoughlin G. Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience. 2019;36:100635. DOI: 10.1016/j.dcn.2019.100635.
Received: 
21.11.2020
Accepted: 
25.01.2021
Published: 
31.05.2021
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