Neuroeducational software recommendational service as a tool for personalizing the educational process
https://doi.org/10.32517/0234-0453-2024-39-5-50-62
Abstract
The problem of insufficient cognitive development in schoolchildren can be solved through the organization of personalized extracurricular activities with the help of a recommendation service based on neuroscientific research and modern knowledge of brain function. The basis for compiling a personalized list of extracurricular activities is data obtained through neuroimaging technology in the process of testing the cognitive and personality characteristics of the student combined with information about their interests and preferences. Regular testing, conducted every three to six months, helps to determine the effectiveness of the chosen activities and to adjust the educational route. It is assumed that regular use of the recommendation service will stimulate the development of child’s cognitive functions, thus contributing to the improvement of his/her academic performance. The article presents the scheme and principles of the service as a whole, as well as its evaluation and recommendation modules. The possibilities of the recommendation service application in the educational process are defined. A description of the practical realization of the recommendation service is given. The proposed service can be useful for individual participants of the educational process: class teachers, methodologists, tutors, parents, and students. Teachers will be able to individualize the approach to each student using the service as an additional source of information about the structural and functional development of the child. For parents, the service will help to better understand the strengths and weaknesses of their children as well as to track the effectiveness of including a particular activity in their educational route. Students will have access to personalized recommendations for developing extracurricular activities.
Keywords
About the Authors
T. V. BukinaRussian Federation
Tatyana V. Bukina, a postgraduate student at the Higher School of Education and Psychology, ESC “Institute of Education and Humanities”; Junior Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
M. V. Khramova
Russian Federation
Marina V. Khramova, Candidate of Sciences (Education), Director of the Higher School of Education and Psychology, ESC “Institute of Education and Humanities”; Senior Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
S. A. Kurkin
Russian Federation
Semen A. Kurkin, Doctor of Sciences (Physics and Mathematics), Docent, Leading Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
D. A. Andrikov
Russian Federation
Denis A. Andrikov, Candidate of Sciences (Engineering), Docent, Associate Professor at the “Automatic Control Systems” Department, “Informatics and Control Systems” Faculty
S. S. Goman
Russian Federation
Sergei S. Goman, Director
Kaliningrad
A. E. Dedkov
Russian Federation
Alexander E. Dedkov, a postgraduate student at the Department of Mechanics and Control Processes; Academy of Engineering
A. E. Hramov
Russian Federation
Alexander E. Hramov, Doctor of Sciences (Physics and Mathematics), Professor, Chief Researcher at the Baltic Neurotechnology and Artificial Intelligence Research Centre
References
1. Filatova M. N. Individualization and personification of additional education of children as a pedagogical problem. Innovatsionnyye Proyekty i Programmy v Obrazovanii. 2015;(6):13–21. (In Russian.) EDN: WJCLYL.
2. Fedorov A. A., Kurkin S. A., Khramova M. V., Hramov A. E. Neurotechnology and artificial intelligence as key factors in the customization of the lifelong learning route. Informatics and Education. 2023;38(3):5–15. (In Russian.) EDN: SAOKSA. DOI: 10.32517/0234-0453-2023-38-3-5-15.
3. Gileva K. V., Gainutdinova Yu. Yu. Implementation of the subject-subject approach in the concept of digital education in higher education. Digital Transformations in Education (E-Digital Siberia’2021): Proc. 5th Int. Scientific and Practical Conf. Novosibirsk, Siberian Transport University; 2021:57–64. (In Russian.) EDN: HMLNLB.
4. Uvarov A. Yu., Vodopian G. M. About two indicators of the school digital renewal process. Informatics and Educa tion. 2023;38(5):5–15. (In Russian.) EDN: VBQKKW. DOI: 10.32517/0234-0453-2023-38-5-5-15.
5. Elkonin D. B. Selected psychological works. Moscow, Pedagogika; 1989. 560 p. (In Russian.)
6. Vygotsky L. S. Educational psychology. Moscow, Pedagogika; 1991. 480 p. (In Russian.)
7. Leontyev А. N. Problems of mental development. Moscow, Izdatel’stvo Moskovskogo universiteta; 1981. 584 p. (In Russian.)
8. Davydov V. V. Types of generalization in teaching: Logical and psychological problems of building academic subjects. Moscow, Pedagogika; 1972. 424 p. (In Russian.)
9. Truong H. M. Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior. 2016;55:1185– 1193. DOI: 10.1016/j.chb.2015.02.014.
10. Hramov A. E., Fedorov A. A. Current trends in the development of neuroscientific research in education. Educational Studies. Moscow. 2023;(4):275–316. (In Russian.) EDN: CQUIJK. DOI: 10.17323/vo-2023-16701.
11. Feiler J. B., Stabio M. E. Three pillars of educational neuroscience from three decades of literature. Trends in Neuroscience and Education. 2018;13:17–25. DOI: 10.1016/j.tine.2018.11.001.
12. Palghat K., Horvath J. C., Lodge J. M. The hard problem of “educational neuroscience”. Trends in Neuroscience and Education. 2017;6:204–210. DOI: 10.1016/j.tine.2017.02.001.
13. Bowers J. S. The practical and principled problems with educational neuroscience. Psychological Review. 2016;123(5):600–612. DOI: 10.1037/rev0000025
14. Turner D. Which part of “two way street” did you not understand? Redressing the balance of neuroscience and education. Educational Research Review. 2011;6:223–231. DOI: 10.1016/j.edurev.2011.10.002.
15. Gabrieli J. D. E. The promise of educational neuroscience: Comment on Bowers (2016). Psychological Review. 2016;123(5):613–619. DOI: 10.1037/rev0000034.
16. Howard-Jones P. A., Varma S., Ansari D., Butterworth B., DeSmedt B., Goswami U., Laurillard D., Thomas M. S. The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological Review. 2016;123(5):620–627. DOI: 10.1037/rev0000036.
17. Zeer E. F. Neurodidactics — an innovative trend of per sonalised education. Professional’noye Obrazovaniye i Rynok Truda. 2021;(4(47)):30–38. (In Russian.) EDN: CSPPYE. DOI: 10.52944/PORT.2021.47.4.002.]
18. Mammarella I. C., Lucangeli D., Cornoldi C. Spatial working memory and arithmetic deficits in children with non-verbal learning difficulties. Journal of Learning Disabilities. 2010;43(5):455–468. DOI: 10.1177/0022219409355482.
19. Mabbott D. J., Bisanz J. Computational skills, working memory, and conceptual knowledge in older children with mathematics learning disabilities. Jour nal of Learning Disabilities. 2008;41(1):15–28. DOI: 10.1177/0022219407311003.
20. Zhou H., Geng F., Wang T., Wang C., Xie Y., Hu Y., Chen F. Training on abacus-based mental calculation en hances resting state functional connectivity of bilateral superior parietal lobules. Neuroscience. 2020;432:115–125. DOI: 10.1016/j.neuroscience.2020.02.033.
21. Lu Yu., Ma M., Chen G., Zhou X. Can abacus course eradicate developmental dyscalculia. Psychology in the Schools. 2021;58(2):235–251. DOI: 10.1002/pits.22441.
22. Alesi M., Bianco A., Padulo J., Vella F. P., Petrucci M., Paoli A., Palma A., Pepi A. Motor and cognitive development: The role of karate. Muscles, Ligaments and Tendons Journal. 2014;4(2):114–120. DOI: 10.11138/mltj/2014.4.2.114.
23. 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.
24. Dębska A., Chyl K., Dzięgiel G., Kacprzak A., Łuniewska M., Plewko J., Marchewka A., Grabowska A., Jed noróg 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.
25. Mogadam A., Keller A. E., Taylor M. J., Lerch J. P., Anagnostou E., Pang E. W. Mental flexibility: An MEG in vestigation in typically developing children. Brain and Cognition. 2018;120:58–66. DOI: 10.1016/j.bandc.2017.10.001.
26. Cycowicz Y. M. Orienting and memory to unexpected and/or unfamiliar visual events in children and adults. De velopmental Cognitive Neuroscience. 2019;36:100615. DOI: 10.1016/j.dcn.2019.100615.
27. Vostroknutov I. E., Grigoriev S. G., Surat L. I. Modern challenges of Russia and the search for a new education paradigm. Informatization of Education and Methodology of E-learning: Digital Technologies in Education. Proc. 4th Int. Sci. Conf. Krasnoyarsk, Siberian Federal University; 2020;2:396–403. (In Russian.) EDN: CNYIFZ.
28. Sysoyev P. V., Filatov E. M. Chatbots in teaching a foreign language: Advantages and controversial issues. Tambov University Review: Series Humanities. 2023;28(1):66–72. (In Russian.) EDN: PXGZTJ. DOI: 10.20310/1810-0201-2023-28-1-66-72.
29. Kireeva N. A., Rodionov A. S., Farkhutdinov R. I., Khu sainov I. R. Development of а chatbot on history for use in a technical university. Elektrotekhnicheskiye i Informatsionnyye Kompleksy i Sistemy. 2018;14(3):73–79. (In Russian.) EDN: YSAZOP.
30. Karrenbauer Ch., Brauner T., König C. M., Breit ner M. H. Design, development, and evaluation of an individual digital study assistant for higher education students. Educational Technology Research and Development. 2023;71:2047–2071. DOI: 10.1007/s11423-023-10255-8.
31. Fuller J. K., Glendening J. G. The neuroeducator: Professional of the future. Theory into Practice. 1985;24(2):135– 137. DOI: 10.1080/00405848509543161.
32. Grubov V. V., Khramova M. V., Goman S., Bada rin A. A., Kurkin S. A., Andrikov D. A., Pitsik E., Antipov V., Petushok E., Brusinskii N., Bukina T., Fedorov A. A., Hramov A. E. Open-loop neuroadaptive system for enhanc ing student’s cognitive abilities in learning. IEEE Access. 2024;12:49034–49049. EDN: RRTMXU. DOI: 10.1109/ACCESS.2024.3383847.
33. Kurkin S. A., Grubov V. V., Maksimenko V. A., Pitsik E. N., Khramova M. V., Hramov A. E. System for monitoring and adjusting the learning process of primary schoolchildren based on the EEG data analysis. Information and Control Systems. 2020;(5(108)):50–61. EDN: IYJJLE. DOI: 10.31799/1684-8853-2020-5-50-61.
34. Fuentes-Martinez V. J., Romero S., Lopez-Gordo M. A., Minguillon J., Rodríguez-Álvarez M. Low-cost EEG multi subject recording platform for the assessment of students’ attention and the estimation of academic performance in secondary school. Sensors. 2023;23(23):9361. DOI: 10.3390/s23239361.
35. Page M. J., Mckenzie J. E., Bossuyt P. M., Boutron I., Hoffmann T. C., Mulrow C. D., Shamseer L., Tetzlaff J. M., Akl E. A., Brennan S. E., Chou R., Glanville Ju., Grim shaw J. M., Hróbjartsson A., Lalu M. M., Li T., Loder E. W., Mayo-Wilson E., McDonald S., McGuinness L. A., Stew art L. A., Thomas Ja., Tricco A. C., Welch V. A., Whiting P., Moher D. The PRISMA 2020 statement: An updated guide-line for reporting systematic reviews. Systematic Reviews. 2021;10(1):1–11. EDN: LZOATG. DOI: 10.1186/s13643-021-01626-4.
36. Pochinkova P. A., Gorbatova M. A., Narkevich A. N., Grji bovskiy A. M. Updated brief recommendations on writing and presenting systematic reviews: What’s new in PRISMA-2020 guidelines? Morskaya Meditsina. 2022;8(2):88–101. (In Russian.) EDN: WHNGXL. DOI: 10.22328/2413-5747-2022-8-2-88-101.
37. Frischen U., Schwarzer G., Degé F. Music lessons enhance executive functions in 6 to 7-year-old children.Learning and Instruction. 2021;74:101442. DOI: 10.1016/j.learninstruc.2021.101442.
38. Semenova V. V., Sukharevskaya I. A. History of tutor ship as a pedagogical phenomenon. Nauchno-Metodicheskiy Elektronnyy Zhurnal “Koncept”. 2019;(V6):69–75. (In Russian.) EDN: TCEHDK.
39. Kovaleva T. M., Terov A. A., Khachatryan E. V., Grache va N. Yu., Dudchik S. V., Karastelyov V. Ye. Tutor support in modern education: From theory to practice. Moscow, Moscow City University; 2023. 212 p. (In Russian.) EDN: SXMFKK.
40. Maksakova V. I. Pedagogical anthropology: Textbook for university students studying pedagogical specialties. Moscow, Academia; 2004. 205 p. (In Russian.) EDN: QTMSIV.
41. Bim-Bad B. M. Pedagogical anthropology: Study guide. Moscow, University of the Russian Educational Academy; 1998. 576 p. (In Russian.)
Review
For citations:
Bukina T.V., Khramova M.V., Kurkin S.A., Andrikov D.A., Goman S.S., Dedkov A.E., Hramov A.E. Neuroeducational software recommendational service as a tool for personalizing the educational process. Informatics and education. 2024;39(5):50-62. (In Russ.) https://doi.org/10.32517/0234-0453-2024-39-5-50-62