Science for Education Today, 2020, vol. 10, no. 3, pp. 87–107
© Slavutskaya E. V., Slavutskii L. A., Abrukov V. S., Bichurina S. U., Sadovaya V. V., 2020
UDC: 
159.9+371

Vertical system analysis of students’ psycho diagnostic data using the ‘Decision Tree’ method

Slavutskaya E. V. 1 (Cheboksary, Russian Federation), Slavutskii L. A. 2 (Cheboksary, Russian Federation), Abrukov V. S. 2 (Cheboksary, Russian Federation), Bichurina S. U. 3 (Kazan, Russian Federation), Sadovaya V. V. 3 (Kazan , Russian Federation)
1 Chuvash State Pedagogical University of I.Ya. Yakovlev
2 Chuvash State University
3 Kazan (Volga Region) Federal University, Kazan, Russian Federation
Full text (Open Access) — Downloaded 960
Abstract: 

Introduction. Choosing forms and methods of psychological and educational support for students (individual and group-based) within the modern educational paradigm requires new means of data analysis. The purpose of this research is to investigate the possibilities of the ‘Decision Tree’ method, which is a modern tool available for practical educational psychologists, for vertical system analysis of psycho diagnostic data and the choice of forms and methods of psychological and educational support for students.
Materials and Methods. Based on a systematic approach in Psychology and Education, using one of the data mining tools - the "decision tree" method, the problem of classifying the results of students’ psycho diagnostics is considered. On the example of a vertical system analysis of pre-adolescent schoolchildren tests, a hierarchical structure of connections of their multi-level psychological characteristics (inclinations, individual psychological and psychosocial characteristics) is constructed. Diagnostic tools were selected in such a way that the analyzed data conditionally present psychological characteristics of all levels. The authors used standardized, scaled methods of psycho diagnostics, which are quite widespread in psychological and educational practice. The two lower levels are represented by the type of nervous system (Ilyin's tapping test), and IQ (R. B. Cattell’s Culture Fair Intelligence Test). Personality qualities in behavior and activity were analyzed on the basis of 12-factor Cattell's Personality Factor Questionnaire. The upper level of social and psychological relations is represented by such indicators as attitude to family, peers, to school, to oneself (V. Michal’s "Unfinished Sentences" inventory). Motivational characteristics were investigated using ‘Need for Achievements’ test. The results of psychological diagnostics of 83 schoolchildren (aged 11-12, fifth grade of secondary school) were processed (19 numerical test indicators were obtained).
Results. When testing the ‘Decision Tree’ method, it was revealed that the algorithm can be used by practical educational psychologists to analyze a relatively small sample of psycho diagnostic results – starting from several dozens of respondents. It is shown that the vertical system analysis of psychological characteristics can be clearly performed using a simplified procedure: comparing the significance of input attributes when classifying by different number of subsets of the target variable. The top-level indicators (motivation and relationship system) were used as variables for classifying data. The ‘Decision Tree’ method allows analyzing and evaluating not only direct, but also latent (indirect, hidden) links of students’ psychological data. For pre-adolescent age, the analysis of relations between different-level characteristics based on the results of classification shows a direct relationship between only some characteristics of the social level with the characteristics of the basic level (inclinations) and only an indirect relationship with communicative traits. Psychological interpretation of the revealed relationships of testing data allows the authors to clarify the age specificity of certain groups of students for subsequent psychological and educational support. The possibility of using the results for the analysis of transition problems from primary to secondary schools is discussed.
Conclusions. The construction of hierarchical models of multi-level data links for students’ psycho diagnostics proves to be an efficient tool for solving a wide range of problems within the fields of Education and Psychology.

Keywords: 

Pre-adolescent schoolchildren; Multi-level psychological characteristics; Vertical system analysis; Latent links; Data mining; ‘Decision tree’.

URL WoS/RSCI: https://www.webofscience.com/wos/rsci/full-record/RSCI:43091311

Prominence Percentile SciVal: 97.701 Creative Performance | Implicit Theories | Gifted

http://apps.webofknowledge.com/full_record.do?product=RSCI&search_mode=G...

https://www.scopus.com/record/display.uri?eid=2-s2.0-85092605952&origin=...

Vertical system analysis of students' psycho diagnostic data using the 'Decision Tree' method

For citation:
Slavutskaya E. V., Slavutskii L. A., Abrukov V. S., Bichurina S. U., Sadovaya V. V. Vertical system analysis of students’ psycho diagnostic data using the ‘Decision Tree’ method. Science for Education Today, 2020, vol. 10, no. 3, pp. 87–107. DOI: http://dx.doi.org/10.15293/2658-6762.2003.05
References: 
  1. Barabanchikov V. A. The system approach in the structure of psychological cognition. Methodology and History of Psychology, 2007, vol. 2, no. 1, pp. 86–99. (In Russian) URL: https://www.elibrary.ru/item.asp?id=19011785 
  2. Vorobev A. V. The review of mathematical methods application in psychological researches. Psichologicheskie issledovania,2010, no 2, pp. 8. (In Russian) URL: https://www.elibrary.ru/item.asp?id=13922162
  3. Golubeva E. A. About study of reactivity, strength and inertia of nervous system in B. M. Teplov –V. D. Nebylitsyn school. Psikhologicheskii Zhurnal, 2018, vol. 39 (2), pp. 72–78. (In Russian) DOI: https://doi.org/10.7868/80205959218020071 URL: https://www.elibrary.ru/item.asp?id=32561967
  4. Znakov V. V. Dynamic approach to the research of personality and procedural analysis in psychology of subject. Psikhologicheskii Zhurnal, 2019, vol. 40 (5), pp. 27–34. (In Russian) DOI: https://doi.org/10.31857/S020595920006073-6 URL: https://www.elibrary.ru/item.asp?id=39543754
  5. Kuznetsova V. B. Personality traits as mediator of interrelationship between upbringing methods and behavioral problems of children. Psikhologicheskii Zhurnal, 2017, vol. 38 (1), pp. 31–40. (In Russian) URL: https://elibrary.ru/item.asp?id=28358098
  6. Kolishev N. S., Slavutskaya E. V., Slavutskii L. A.Dynamics of personality trait structuring during student transition to secondary school. Integration of Education, 2019, 23 (3), pp. 390–403. (In Russian) DOI: https://doi.org/10.15507/1991-9468.096.023.201903.390-403  URL: https://www.elibrary.ru/item.asp?id=41041580
  7. Reznichenko N. S., Shilov S. N., Abdulkin V. V. Neuron network approach to the solution of the medical-psychological problems and in diagnosis process of persons with disabilities (literature review). Journal of Siberian Federal University. Humanities & Social Sciences, 2013, vol. 6 (9), pp. 1256–1264. (In Russian) URL: https://www.elibrary.ru/item.asp?id=20253460
  8. Slavutskaja E. V., Slavutskii L. A.Neural network analysis of the relationship between verbal and nonverbal intelligence in younger adolescents. Psikhologicheskii Zhurnal, 2014, vol. 35 (5), pp.  28–36. (In Russian) URL: https://www.elibrary.ru/item.asp?id=22363313
  9. Slavutskaya E. V., Abrukov V. S., Slavutskii L. A. Simple NEURO network algorithms for evaluating latent links of younger adolescent’s psychological characteristics. Experimental Psychology, 2019, vol. 12 (2), pp. 131–144. (In Russian) DOI: https://doi.org/10.17759/exppsy.2019120210 URL: https://www.elibrary.ru/item.asp?id=38587766
  10. Shadrikov V. D. To new psychological theory of abilities and giftedness. Psikhologicheskii Zhurnal, 2019, vol. 40, no. 2, pp. 15–26. (In Russian) DOI: https://doi.org/10.31857/S020595920002981-5 URL: https://www.elibrary.ru/item.asp?id=37137953
  11. Adriaens F., Lijffijt J., De Bie T. Subjectively interesting connecting trees and forests. Data Mining and Knowledge Discovery, 2019, vol. 33, pp. 1088–1124. DOI:   https://doi.org/10.1007/s10618-019-00627-1
  12. Amiel J. J., Tan Y. S. M. Using collaborative action research to resolve practical and philosophical challenges in educational neuroscience. Trends in Neuroscience and Education, 2019, vol. 16, p.  100116.  DOI:  https://doi.org/10.1016/j.tine.2019.100116
  13. Braun S. S., Davidson A. J. Gender (non)conformity in middle childhood: a mixed methods approach to understanding gender-typed behavior, friendship, and peer preference. Sex Roles, 2017, vol. 77, pp. 16–29. DOI:  https://doi.org/10.1007/s11199-016-0693-z
  14. De Bolle M., De Fruyt F., McCrae R. R., Löckenhoff C. E., Costa P. T., Jr Aguilar-Vafaie M. E., Ahn C.-k., Ahn H.-n., Alcalay L., Allik J., Avdeyeva T. V., Bratko D., Brunner-Sciarra M., Cain T. R., Chan W., Chittcharat N., Crawford J. T., Fehr R., Ficková E., … Terracciano А. The emergence of sex differences in personality traits in early adolescence: A cross-sectional, cross-cultural study. Journal of Personality and Social Psychology, 2015,vol.108 (1), pp. 171–185. DOI: https://doi.org/10.1037/a0038497
  15. Delibalt V. V., Degtyaryov A. V., Dozortseva E. G., Chirkina R. V., Dvoryanchikov N. V., Pimonov V. A., Debolsky M. G., Malkin D. A. Evaluation of cognitive functions, personality and regulatory sphere in minors with deviant and delinquent behavior within the authority of the psychological, medical and educational committee. International Journal Of Cognitive Research In Science, Engineering And Education, 2017, vol. 5 (2), pp. 107–118. DOI: https://doi.org/10.5937/IJCRSEE1702107D
  16. Daugherty A. M., Sutton B. P., Hillman C. H., Kramer A. F., Cohen N. J., Barbey A. K. Individual differences in the neurobiology of fluid intelligence predict responsiveness to training: Evidence from a comprehensive cognitive, mindfulness meditation, and aerobic exercise intervention. Trends in Neuroscience and Education, 2020, vol. 18, p. 100123. DOI: https://doi.org/10.1016/j.tine.2019.100123
  17. Fawaz H. I., Forestier G., Weber J. Idoumghar L., Muller P.-A. Deep learning for time series classification: A review. Data Mining and Knowledge Discovery, 2019, vol. 33, pp. 917–963. DOI: https://doi.org/10.1007/s10618-019-00619-1
  18. Geary D. C. Efficiency of mitochondrial functioning as the fundamental biological mechanism of general intelligence (g). Psychological Review, 2018, no125 (6), pp. 1028–1050. DOI: https://doi.org/10.1037/rev0000124
  19. Genrikhov I. E., Djukova E. V. About methods of synthesis complete regression decision trees. Pattern Recognition and Image Analysis, 2019, vol. 29, pp. 457–470. DOI:  https://doi.org/10.1134/S1054661819030040
  20. Genrikhov I. E., Djukova E. V., Zhuravlev V. I. On full regression decision trees. Pattern Recognition and Image Analysis, 2017, vol. 27, pp. 1–7. DOI:  https://doi.org/10.1134/S1054661817010047
  21. Girn M., Mills C., Christoff K.  Linking brain network reconfiguration and intelligence: Are we there yet? Trends in Neuroscience and Education, 2019, vol. 15, pp. 62–70. DOI:  https://doi.org/10.1016/j.tine.2019.04.001
  22. Han H., Soylu F., Anchan D. M. Connecting levels of analysis in educational neuroscience: A review of multi-level structure of educational neuroscience with concrete examples. Trends in Neuroscience and Education, 2019, vol. 17, p. 100113. DOI: https://doi.org/10.1016/j.tine.2019.100113
  23. Knyazev G. G., Bazovkina D. V., Savostyanov A. N., Kuznetsova V. B., Proshina E. A. Suppression mediates the effect of 5-HTTLPR by stress interaction on depression. Scandinavian Journal of Psychology, 2017. vol. 58, no 5, pp. 373–378. DOI: https://doi.org/10.1111/sjop.12389
  24. Kosonogov V., Vorobyeva E., Kovsh E., Ermakov P. А review of neurophysiological and genetic correlates of emotional intelligence. International Journal of Cognitive Research in Science, Engineering and Education, 2019, vol. 7 (1), pp. 137–142. DOI: https://doi.org/10.5937/ijcrsee1901137K
  25. Kurdi G., Leo J., Parsia B., Sattler U., Al-Emari S. A Systematic review of automatic question generation for educational purposes. International Journal of Artificial Intelligence in Education, 2020, vol.30, pp. 121–204. DOI: https://doi.org/10.1007/s40593-019-00186-y
  26. Mei J., Lv H., Yang L., Li Y.  Clustering for heterogeneous information networks with extended star-structure. Data Mining and Knowledge Discovery, 2019, vol. 33, pp. 1059–1087. DOI: https://doi.org/10.1007/s10618-019-00626-2
  27. Pluck G., Mancero P. B., Ortíz E. P. A., Alcívar A. M. U., Gavilanez C. E. M., Chacon P. Differential associations of neurobehavioral traits and cognitive ability to academic achievement in higher education. Trends in Neuroscience and Education, 2020, vol. 18, p. 100124. DOI: https://doi.org/10.1016/j.tine.2019.100124
  28. Selimović Z., Selimović H., Opić S.Development of social skills among elementary school children. International Journal of Cognitive Research in Science, Engineering and Education, 2018, vol. 6 (1), pp. 17–30. DOI: https://doi.org/10.5937/ijcrsee1801017S  
  29. Suzin G., Ravona-Springer R, Ash E. L, Davelaar E. J., Usher M. Differences in semantic memory encoding strategies in young, healthy old and MCI patients. Frontiers in Aging Neuroscience, 2019, vol. 11, pp. 306. DOI: https://doi.org/10.3389/fnagi.2019.00306
Date of the publication 30.06.2020