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Вачков И. В., Вачкова С. Н. Факторы образовательной среды и психологическое здоровье школьников // Вестник ПСТГУ. Серия IV: Педагогика. Психология. 2018. Вып. 50. С. 73-86. DOI: 10.15382/sturIV201850.73-86
The article presents an analysis of foreign studies dealing with the impact of various factors on the psychological health of schoolchildren and the success of their education. The most important of these factors are the pupil, the family, the school on the whole and the teacher. At the same time, the infl uence of these factors, according to the fi ndings, often turns out to be quite diff erent from what it seems to the ordinary consciousness. A study of the results of decades of research has allowed the authors to make a conclusion about the dominant role of the pupil and his or her own eff orts in the success of learning and in the state of his/her psychological health. A chronic disease of the child, contrary to the established opinion, though negatively aff ects the educational success and psychological health of the pupil, has no great infl uence on his or her performance. Much more important are the expectations of parents of the child, their emotional support and faith in him. Other important factors are the peculiarities of the school culture and the school routines. In addition to considering foreign studies, the article describes the characteristics of several of the most performant American schools, in which the factors of the educational environment are expressed with particular clarity and the study of which has been conducted by the authors themselves. The article also shows that the relationships developed with teachers strongly infl uence the success of education and psychological health of pupils. Even if other intra-school factors are not positive, a teacher who is able to take the point of pupils on certain issues may have a significant positive impact on their academic performance and well-being.
learning performance, psychological health, educational environment, metaanalysis, pupil, family, school, teacher, well-being, parental expectations
  1. Ahn S., Choi J. (2004, April). “Teachers’ Subject Matter Knowledge as a Teacher Qualifi cation. A Synthesis of the Quantitative Literature on Students’ Mathematics Achievement” in: Paper Presented at the American Educational Research Association. San Diego, CA.
  2. Allen M. (1999) “Racial Group Orientation and Social Outcomes: Summarizing Relationships Using Meta-Analysis”, in: Paper Presented at the Annual Meeting of the National Communication Association. Chicago.
  3. Allen M. (2006) “The Role of Teacher Immediacy as a Motivational Factor in Student Learning: Using Meta-Analysis to Test a Causal Model”. Communication Education, 55 (1), 21‒31.
  4. Alessi G. (1988) “Diagnosis Diagnosed: A Systemic Reaction”. Professional School Psychology, 3, 145–151.
  5. Brown L. I. (2001) A Meta-Analysis of Research on the Influence of Leadership on Student Outcomes. Unpublished Ph.D. Virginia Polytechnic Institute and State University, VA.
  6. Cornelius-White J. (2007) “Learner-Centered Teacher-Student Relationships are Eff ective: A Meta-Analysis”. Review of Education Research, 77 (1), 113‒143.
  7. Duncan G. J., Dowset C. J., Claessens A. (2007). “School Readiness and Later Achievement”. Development Psychology, 43 (6), 1428–1446.
  8. Fullan M., Stiegelbauer S. (1991) The New Meaning of Educational Change. London.
  9. Hatti John A. S. (2017) Vidimoe obuchenie: sintez rezul'tatov bolee 50 000 issledovanij s ohvatom bolee 86 millionov shkol'nikov [Visible Learning. A Synthesis of over 50 000 Studies Covering More than 86 Million Schoolchildren]. Moscow.
  10. Marzano R. J. (2000). A New Era of School Reform: Going Where the Research Takes us. Aurora, CO: Mid-Continent Research for Education and Learning.
  11. Seipp B. (1991). “Anxiety and Academic Performance: A Meta-Analysis of Findings”. Anxiety, Stress, and Coping, 4 (1), 27–41.
  12. Stekelenburg C. R. (1991). The Eff ects of Public High School Size on Student Achievement: A Meta-Analysis. Unpublished Ed. D., University of Georgia, GA.
  13. Strong W. B., Malina R. M., Blimkie C. J. R. (2005). “Evidence Based Physical Activity for School-Age Youth”. The Journal of Pediatrics, 146 (6), pp. 732–737.
  14. Witter R. A., Okun M. A., Stock W. A., Haring M. J. (1984). “Education and Subjective Well-Being: A Meta-Analysis”. Educational Evaluation and Policy Analysis. 1984, vol. 6 (2), pp. 165‒173.
Vachkov Igor
Academic Degree: Doctor of Sciences* in Psychological Sciences;
Academic Rank: Professor;
Place of work: Moscow Pedagogical State University; 6 Malyi Sukharevskii per., Moscow, 127051, Russian Federation;
Post: Professor, Department of Social Pedagogy and Psychology;
ORCID: 0000-0001-7784-7427;
Email: igorvachkov@mail.ru. *According to ISCED 2011, a post-doctoral degree called Doctor of Sciences (D.Sc.) is given to reflect second advanced research qualifications or higher doctorates.
Vachkova Svetlana
Academic Degree: Doctor of Sciences* in Education;
Academic Rank: Associate Professor;
Place of work: Moscow City University; 4/1 2-oi Sel'skokhoziaistvennyi proezd, Moscow 129226, Russian Federation;
Post: Associate Professor;
ORCID: 0000-0002-3136-3336;
Email: svachkova@mgpu.ru. *According to ISCED 2011, a post-doctoral degree called Doctor of Sciences (D.Sc.) is given to reflect second advanced research qualifications or higher doctorates.
Козин С. В., Каган Э. М., Вачкова С. Н. Большие данные для педагогических исследований: возможности, проблемы, ограничения // Вестник ПСТГУ. Серия IV: Педагогика. Психология. 2021. Вып. 63. С. 28-39. DOI: 10.15382/sturIV202163.28-39
Modern educational information systems collect and store a huge amount of data on the learning tracks of schoolchildren and students during the academic year, as well as the actions of the teaching staff. This data makes it possible to analyze the digital footprints of users, study various learning paths, investigate educational materials and their impact on the educational environment, identify information gaps in education, compare the results of knowledge control with preconditions for obtaining them, etc. Nevertheless, the collection and analysis of such data is associated with a large number of difficulties: inconsistent data, data retrieval in source databases, the volume of transfer data, problems associated with data gateways for external consumers, infrastructure performance, visualization and interpretation of such data. This article discusses several cases of using big data analysis for pedagogical research: identification and analysis of the most popular didactic materials created by teachers in the environment of the Moscow Electronic School (MES), analysis of the filling of the tree of didactic units of the thematic framework of the MES library with educational materials, analytics dynamics of the composition of homework by type, identification of teachers and students, the most active users of the MES and analysis of the relationship of this activity with other parameters. In addition, the author describes the problems encountered by the authors at the stage of data transfer and analysis and how to solve them; The main results are generalization of experience with big data MES, identification of opportunities, problems and limitations of big data for the implementation of pedagogical research.Opportunities — analysis of digital methods of users, studying the results of researching digital teaching methods, researching educational materials and their impact on the educational environment, identifying information gaps in education, comparing the results of knowledge control with the conditions for obtaining them, etc. Problems — data search in original undocumented databases, data inconsistency, data visualization, large amount of data, data interpretation. Limitations — data gateways, infrastructure performance, personal data.
big data, data analysis, Moscow Electronic School, MES, digital footprint, data visualization, pedagogical research, Educational Data Mining, homeworks, digital activity
  1. Barannikov K., Lesin S. (2020) “Metodologiia analiza bol′shikh dannykh v obrazovanii. Sistemnometodologicheskii podkhod, osnovannyi na analize obrazovatel’nykh dannykh, poiske strategii priniatiia upravlencheskikh i organizatsionno-pedagogicheskikh issledovanii” [Metho dology of big data analysis in education. A systematic and methodological approach based on the analysis of educational data, the search for a strategy for making managerial and organisationalpedagogical research]. Narodnoe obrazovanie, 2020, vol. 2, pp. 81–89 (in Russian).
  2. Вarannikov K., Lesin S., Vachkova S., Suleimanov R., Kupriianov R. (2020) “Application of educational data analysis methods in the evaluation of lesson scenarios in Moscow Electronic School”. Revista inclusions, 2020, 7, vol. 3/3, pp. 1–8.
  3. Levin I. (2016) “Cyber-physical Systems as a Cultural Phenomenon”. International Journal of Design Sciences and Technology, 2016, 22 (1), available at https://www.tau.ac.il/~ilia1/levin_i_cyber-physical_syst.pdf (16.06.2021).
  4. Patarakin E., Vachkova S. (2019) “Setevoi analiz kollektivnykh deistvii nad tsifrovymi obrazovatel′nymi ob»ektami” [Network analysis of collective actions on digital educational objects]. Vestnik Moskovskogo gorodskogo pedagogicheskogo universiteta. Seriia: Pedagogika i psikhologiia, 2019, vol. 4 (50), pp. 101–112 (in Russian).
  5. Petriaeva E., Vachkova S. (2020) “Tsifrovoi profi l’ avtora stsenariev urokov MESh” [Digital profile of the author of the MES lesson scenarios], in Bo’′shie dannye v obrazovanii. Sbornik statei po itogam mezhdunarodnoi konferentsii [Big data in education. Conference papers], 2020, pp. 79–94 (in Russian).
  6. Vachkova S., Obydenkova V., Zaslavskii A., Kats S. (2020) “O prichinakh vostrebovannosti stsenariev urokov «Moskovskoi elektronnoi shkoly»” [On the reasons for the demand for scenarios of lessons of Moscow Electronic School]. Vestnik Moskovskogo gorodskogo pedagogicheskogo universiteta. Seriia: Pedagogika i psikhologiia, 2020, vol. 1 (51), pp. 8–24 (in Russian).
  7. Vachkova S., Patarakin E., Petriaeva E. (2020) “Content Quality of Lesson Scenarios in Moscow E-School”. SHS Web of Conferences Theory and Practice of Project Management in Education: Horizons and Risks, 2020, vol. 79.
  8. Vachkova S., Petriaeva E., Kupriianov R., Suleymanov R. (2021) “School in Digital Age: How Big Data Help to Transform the Curriculum”. Information, 2021, vol. 12 (1), 33, available at https://www.mdpi.com/2078-2489/12/1/33 (17.01.2021).
Kozin Svyatoslav
Place of work: Moscow City University; 14 Panferova ul., Moscow 119261, Russian Federation;
Post: Researcher at the Research Institute of Urban Science and Global Education;
ORCID: 0000-0002-7936-5795;
Email: kozyyy@yandex.ru.
Kagan Edward
Place of work: Moscow City University; 14 Panferova ul., Moscow 119261, Russian Federation;
Post: Researcher at the Research Institute of Urban Science and Global Education;
ORCID: 0000-0002-4317-2123;
Email: kaganem@mgpu.ru.
Vachkova Svetlana
Academic Degree: Doctor of Sciences* in Education;
Academic Rank: Associate Professor;
Place of work: Moscow City University; 14 Panferova ul., Moscow 119261, Russian Federation;
Post: Director of the Research Institute of Urban Science and Global Education;
ORCID: 0000-0002-3136-3336;
Email: svachkova@gmail.com. *According to ISCED 2011, a post-doctoral degree called Doctor of Sciences (D.Sc.) is given to reflect second advanced research qualifications or higher doctorates.