МОДЕЛИРОВАНИЕ КАК СРЕДСТВО ПРЕОДОЛЕНИЯ ДОНАУЧНЫХ АЛЬТЕРНАТИВНЫХ КОНЦЕПЦИЙ ПРИ ОСВОЕНИИ ЕСТЕСТВЕННО-НАУЧНЫХ ПОНЯТИЙ ОБУЧАЮЩИМИСЯ
Ключевые слова:
моделирование, альтернативные концепции, ментальные модели, когнитивный конфликт, учебная деятельность, педагогическая психология.
Аннотация
Постановка проблемы. Одной из фундаментальных проблем педагогической психологии является устойчивость альтернативных концепций – донаучных, интуитивных представлений учащихся, которые противоречат системе научных понятий и проявляют высокую резистентность к традиционным методам обучения. В теории учебной деятельности Д.Б. Эльконина – В.В. Давыдова ключевым средством формирования теоретического мышления выступает моделирование, однако психологический механизм, посредством которого оно преодолевает альтернативные концепции, остается недостаточно раскрытым.
Цель статьи – выявить и систематизировать психологические функции моделирования в преодолении альтернативных концепций при освоении естественно-научных понятий.
Методология. Исследование выполнено в жанре обзорной статьи с элементами систематизации. Анализ охватил публикации за период 1967–2025 гг., выявленные по базам eLIBRARY.RU, Google Scholar, Scopus и Web of Science по ключевым словам: «моделирование», «альтернативные концепции», «ментальные модели», «когнитивный конфликт», «концептуальное изменение».
Результаты. Выделены три психологические функции моделирования: создание условий для когнитивного конфликта как механизма концептуального изменения; визуализация недоступных прямому наблюдению процессов с опорой на знаково-символическое опосредование; развитие исследовательских компетенций через конструирование и преобразование моделей. Эффективность моделирования определяется не формой представления (предметная, графическая, компьютерная), а включенностью учащегося в активное преобразование модели и рефлексию границ ее применимости.
Заключение. Моделирование выполняет тройственную функцию: цель формирования модельного мышления, средство познания и форма организации учебной деятельности. Перспективы дальнейших исследований связаны с лонгитюдным изучением устойчивости сформированных понятий и разработкой валидного диагностического инструментария.
Литература
1. Воронцов А.Б., Санина С.П. Деятельностный подход к географическому образованию школьников. М.: Авторский клуб, 2015. 68 с.
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3. Давыдов В. В. Теория развивающего обучения. М.: ИНТОР, 1996. 544 с.
4. Нежнов П.Г., Горбов С.Ф., Соколова О.В. Диагностика учебно-предметных компетенций. М.: Авторский клуб, 2018. 112 с.
5. Чудинова Е.В., Зайцева В.Е., Минкин Д.И. Самостоятельность и инициативность старших школьников в учебном моделировании // Психологическая наука и образование. 2024. Т. 29, № 1. С. 61–74. DOI: http://doi.org/10.17759/pse.2024290105
6. Эльконин Б.Д., Воронцов А.Б., Чудинова Е.В. Подростковый этап школьного образования в системе Эльконина – Давыдова // Вопросы образования. 2004. № 3. URL: https://cyberleninka.ru/article/n/podrostkovyy-etap-shkolnogo-obrazovaniya-v-sisteme-elkonina-davydova (дата обращения: 12.04.2026).
7. Aidoo, B. (2024). A reflective study on adopting inquiry-based science teaching methods. *Disciplinary and Interdisciplinary Science Education Research*, 6 (29), 1–18. DOI: http://doi.org/10.1186/s43031-024-00119-3
8. Cheng, X. (2025). Conceptual change in science education: From cold to hot approaches. *Science Insights Education Frontiers*, 26 (1), 4199–4201. DOI: http://doi.org/10.15354/sief.25.co381
9. Chi, M.T.H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), *International Handbook of Research on Conceptual Change* (pp. 61–82). Routledge. DOI: http://doi.org/10.4324/9780203874813-9
10. Chung, C.-C., Huang, S.-L., Cheng, Y.-M., & Lou S.-J. (2020). Using an iSTEAM project-based learning model for technology senior high school students. *International Journal of Technology and Design Education*, 10, 905–941. DOI: http://doi.org/10.1007/s10798-020-09578-x (access date: 15.04.2026).
11. Clement, J. (2000). Model based learning as a key research area for science education. *International Journal of Science Education*, 22 (9), 1041–1053. DOI: http://doi.org/10.1080/095006900416901
12. Cortés-Morales, A., & Marzabal, A. (2025). The design of science teaching and learning tasks with modelling potential. *Studies in Science Education*, 1 (29). DOI: http://doi.org/10.1080/03057267.2025.2493990
13. Craik, K. (1967). *The Nature of Explanation*. Cambridge: Cambridge University Press.
14. Duit, R., & Treagust, D.F. (2003). Conceptual change: A powerful framework for improving science teaching and learning. *International Journal of Science Education*, 25 (6), 671–688. DOI: http://doi.org/10.1080/09500690305016
15. Dunleavy, M., & Dede, C. (2013). Augmented reality teaching and learning. In *Handbook of Research on Educational Communications and Technology* (pp. 735–745). Springer. DOI: http://doi.org/10.1007/978-1-4614-3185-5_59
16. Eloy, A., Fuhrmann, T., Wagh, A., de Deus Lopes, R., Wilkerson, M., & Blikstein, P. (2024). Decomposing students' design moves when programming agent-based models. In R. Lindgren, T.I. Asino, E.A. Kyza, C.K. Looi, D.T. Keifert, E. Suárez (Eds.), *Proceedings of the 18th International Conference of the Learning Sciences – ICLS 2024* (pp. 738–745). International Society of the Learning Sciences.
17. Franco-Mariscal, A.J. (Ed.). (2024). *Critical Thinking in Science Education and Teacher Training*. Cham, Springer Nature Switzerland. XXI (Contemporary Trends and Issues in Science Education). DOI: http://doi.org/10.1007/978-3-031-78578-8
18. Fuhrmann, T., Rosenbaum, L.F., Wagh, A., Eloy, A., Wolf, J., Blikstein, P., & Wilkerson, M.H. (2024). Right but wrong: How students' mechanistic reasoning and conceptual understandings shift when designing agent based models using data. *Science Education*. DOI: http://doi.org/10.1002/sce.21890
19. Ga, S.-H., Chang, C.-Y., & Martin, S. (2024). Students' acceptance of Arduino technology integration in student-led science inquiry: Insights from the Technology Acceptance Model. *IEEE Transactions on Learning Technologies*, 17, 1849–1858. DOI: http://doi.org/10.1109/TLT.2024.3406964
20. García-Carmona, A. (2023). Argumentation in science education. In R. Gunstone (Ed.), *The International Encyclopedia of Science Education*. Springer.
21. Gilbert, J.K., & Justi, R. (2016). *Modelling-based Teaching in Science Education*, Cham, Springer International Publishing (Models and Modeling in Science Education; Vol. 9). DOI: http://doi.org/10.1007/978-3-319-29039-3
22. Gobert, J.D., & Buckley, B.C. (2000). Introduction to model-based teaching and learning in science education. *International Journal of Science Education*, 22 (9), 891–894. DOI: http://doi.org/10.1080/095006900416802
23. Halloun, I.A. (2006). *Modeling Theory in Science Education*. Springer.
24. Harrison, A.G., & Treagust, D.F. (2000). A typology of school science models. *International Journal of Science Education*, 22 (9), 1011–1026. DOI: http://doi.org/10.1080/095006900416809
25. Johnson-Laird, P. (1983). *Mental Models: Towards a Cognitive Science of Language, Inference and Consciousness*, Cambridge, Harvard University Press.
26. Joolingen, W.R., van, Jong, de, T., & Dimitrakopoulou, A. (2024). Physical and digital models in science education. In *International Handbook of Computer-Supported Collaborative Learning*, Springer.
27. Klemencic, E., Ploj, Virtič M., & Majer, Kovačič J. (2023). The role of teacher education in the science literacy development. *Journal of Elementary Education*, 16 (4), 1–22. DOI: http://doi.org/10.30958/aje.10-4-5
28. Kotsis, K.T. (2023). Alternative ideas about concepts of physics, a timelessly valuable tool for physics education. *Eurasian Journal of Science and Environmental Education*, 3 (2), 83–97. DOI: http://doi.org/10.30935/ejsee/13776
29. Lee, I.A., Sagartz, M., Meyer, P.F., & Anderson, E. (2025). Making sense of models: Connecting science and math through decoding and modifying computational models. *Science Scope*, 48(2), 39–47. DOI: http://doi.org/10.1080/08872376.2025.2456246
30. Lesh, R., & Doerr, H.M. (Eds.) (2003). *Beyond Constructivism: Models and Modeling Perspectives on Mathematics Problem Solving, Learning, and Teaching*. Lawrence Erlbaum.
31. Lin, Y.-H., Lin, H.-C.K., Wang, T.-H., & Wu, C.-H. (2023). Integrating the STEAM-6E model with virtual reality instruction. *Sustainability*, 15 (7), 6269. DOI: http://doi.org/10.3390/su15076269
32. Mamaeva, E.A., Utemov, V.V., Abramova, I.V., & Shestakova, L.G. (2024). Enhancing research skills of schoolchildren through 3D modeling. *Perspectives of Science and Education*, 68 (2), 595–613. DOI: http://doi.org/10.32744/pse.2024.2.36
33. Markula, A., & Aksela, M. (2022). The key characteristics of project-based learning: how teachers implement projects in K-12 science education. *Disciplinary and Interdisciplinary Science Education Research*, 4 (2), 1–17. DOI: http://doi.org/10.1186/s43031-021-00042-x
34. Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E. (2019). Virtual and augmented reality effects on K-12, higher and tertiary education students' twenty-first century skills. *Virtual Reality*, 23 (4), 425–436. DOI: http://doi.org/10.1007/s10055-018-0363-2 (access date: 20.05.2026).
35. Piaget, J. (1947). *La représentation du monde chez l'enfant*. Paris, France.
36. Posner, G.J., Strike, K.A., Hewson, P.W., & Gertzog, W.A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. *Science Education*, 66 (2), 211–227. DOI: http://doi.org/10.1002/sce.3730660207
37. Schwarz, C.V., Reiser, B.J., Davis, E.A., Kenyon, L., Acher, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling. *Journal of Research in Science Teaching*, 46 (6), 632–654. DOI: http://doi.org/10.1002/tea.20311
38. Schwarz, C.E. (2024). *Exploring Students' Conceptual and Epistemic Learning in Model-Centered Chemistry Courses* (Doctoral dissertation). ProQuest LLC.
39. Sessa, di, A.A., & Sherin, B.L. (1998). What changes in conceptual change? *International Journal of Science Education*, 20 (10), 1155–1191. DOI: http://doi.org/10.1080/0950069980201005
40. Tanas, J., & Fulmer, G. (2023). A content analysis of alignment messages to the Next Generation Science Standards. *Disciplinary and Interdisciplinary Science Education Research*, 5 (5), 1–18. DOI: http://doi.org/10.1186/s43031-023-00073-6
41. Vosniadou, S. (Ed.) (2013). *International Handbook of Research on Conceptual Change* (2nd ed.). Routledge.
42. Wagh, A., Rosenbaum, L.F., Fuhrmann, T., Eloy, A., Blikstein, P., & Wilkerson, M. (2025). Toward ontological alignment: Coordinating student ideas with the representational system of a computational modeling unit for science learning. *Cognition and Instruction*, 43 (1–2), 1–32. DOI: http://doi.org/10.1080/07370008.2024.2427400
43. Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. *Science Education*, 92 (5), 941–967. DOI: http://doi.org/10.1002/sce.20263
44. Zadorozhnii, V.M. (2024). Using Arduino to develop research competencies of secondary and high school students. *CTE Workshop Proceedings*, 11, 427–441. DOI: http://doi.org/10.55056/cte.663
45. Zhang, W., Guan, Y., & Hu, Z. (2024). The efficacy of project-based learning in enhancing computational thinking among students: A meta-analysis of 31 experiments and quasi-experiments. *Education and Information Technologies*, 29, 14513–14545. DOI: http://doi.org/10.1007/s10639-023-12392-2
2. Выготский Л.С. Собрание сочинений: в 6 т. / под ред. А.М. Матюшкина. М.: книга по требованию, 2021. Т. 3: Проблемы развития психики. 369 c.
3. Давыдов В. В. Теория развивающего обучения. М.: ИНТОР, 1996. 544 с.
4. Нежнов П.Г., Горбов С.Ф., Соколова О.В. Диагностика учебно-предметных компетенций. М.: Авторский клуб, 2018. 112 с.
5. Чудинова Е.В., Зайцева В.Е., Минкин Д.И. Самостоятельность и инициативность старших школьников в учебном моделировании // Психологическая наука и образование. 2024. Т. 29, № 1. С. 61–74. DOI: http://doi.org/10.17759/pse.2024290105
6. Эльконин Б.Д., Воронцов А.Б., Чудинова Е.В. Подростковый этап школьного образования в системе Эльконина – Давыдова // Вопросы образования. 2004. № 3. URL: https://cyberleninka.ru/article/n/podrostkovyy-etap-shkolnogo-obrazovaniya-v-sisteme-elkonina-davydova (дата обращения: 12.04.2026).
7. Aidoo, B. (2024). A reflective study on adopting inquiry-based science teaching methods. *Disciplinary and Interdisciplinary Science Education Research*, 6 (29), 1–18. DOI: http://doi.org/10.1186/s43031-024-00119-3
8. Cheng, X. (2025). Conceptual change in science education: From cold to hot approaches. *Science Insights Education Frontiers*, 26 (1), 4199–4201. DOI: http://doi.org/10.15354/sief.25.co381
9. Chi, M.T.H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), *International Handbook of Research on Conceptual Change* (pp. 61–82). Routledge. DOI: http://doi.org/10.4324/9780203874813-9
10. Chung, C.-C., Huang, S.-L., Cheng, Y.-M., & Lou S.-J. (2020). Using an iSTEAM project-based learning model for technology senior high school students. *International Journal of Technology and Design Education*, 10, 905–941. DOI: http://doi.org/10.1007/s10798-020-09578-x (access date: 15.04.2026).
11. Clement, J. (2000). Model based learning as a key research area for science education. *International Journal of Science Education*, 22 (9), 1041–1053. DOI: http://doi.org/10.1080/095006900416901
12. Cortés-Morales, A., & Marzabal, A. (2025). The design of science teaching and learning tasks with modelling potential. *Studies in Science Education*, 1 (29). DOI: http://doi.org/10.1080/03057267.2025.2493990
13. Craik, K. (1967). *The Nature of Explanation*. Cambridge: Cambridge University Press.
14. Duit, R., & Treagust, D.F. (2003). Conceptual change: A powerful framework for improving science teaching and learning. *International Journal of Science Education*, 25 (6), 671–688. DOI: http://doi.org/10.1080/09500690305016
15. Dunleavy, M., & Dede, C. (2013). Augmented reality teaching and learning. In *Handbook of Research on Educational Communications and Technology* (pp. 735–745). Springer. DOI: http://doi.org/10.1007/978-1-4614-3185-5_59
16. Eloy, A., Fuhrmann, T., Wagh, A., de Deus Lopes, R., Wilkerson, M., & Blikstein, P. (2024). Decomposing students' design moves when programming agent-based models. In R. Lindgren, T.I. Asino, E.A. Kyza, C.K. Looi, D.T. Keifert, E. Suárez (Eds.), *Proceedings of the 18th International Conference of the Learning Sciences – ICLS 2024* (pp. 738–745). International Society of the Learning Sciences.
17. Franco-Mariscal, A.J. (Ed.). (2024). *Critical Thinking in Science Education and Teacher Training*. Cham, Springer Nature Switzerland. XXI (Contemporary Trends and Issues in Science Education). DOI: http://doi.org/10.1007/978-3-031-78578-8
18. Fuhrmann, T., Rosenbaum, L.F., Wagh, A., Eloy, A., Wolf, J., Blikstein, P., & Wilkerson, M.H. (2024). Right but wrong: How students' mechanistic reasoning and conceptual understandings shift when designing agent based models using data. *Science Education*. DOI: http://doi.org/10.1002/sce.21890
19. Ga, S.-H., Chang, C.-Y., & Martin, S. (2024). Students' acceptance of Arduino technology integration in student-led science inquiry: Insights from the Technology Acceptance Model. *IEEE Transactions on Learning Technologies*, 17, 1849–1858. DOI: http://doi.org/10.1109/TLT.2024.3406964
20. García-Carmona, A. (2023). Argumentation in science education. In R. Gunstone (Ed.), *The International Encyclopedia of Science Education*. Springer.
21. Gilbert, J.K., & Justi, R. (2016). *Modelling-based Teaching in Science Education*, Cham, Springer International Publishing (Models and Modeling in Science Education; Vol. 9). DOI: http://doi.org/10.1007/978-3-319-29039-3
22. Gobert, J.D., & Buckley, B.C. (2000). Introduction to model-based teaching and learning in science education. *International Journal of Science Education*, 22 (9), 891–894. DOI: http://doi.org/10.1080/095006900416802
23. Halloun, I.A. (2006). *Modeling Theory in Science Education*. Springer.
24. Harrison, A.G., & Treagust, D.F. (2000). A typology of school science models. *International Journal of Science Education*, 22 (9), 1011–1026. DOI: http://doi.org/10.1080/095006900416809
25. Johnson-Laird, P. (1983). *Mental Models: Towards a Cognitive Science of Language, Inference and Consciousness*, Cambridge, Harvard University Press.
26. Joolingen, W.R., van, Jong, de, T., & Dimitrakopoulou, A. (2024). Physical and digital models in science education. In *International Handbook of Computer-Supported Collaborative Learning*, Springer.
27. Klemencic, E., Ploj, Virtič M., & Majer, Kovačič J. (2023). The role of teacher education in the science literacy development. *Journal of Elementary Education*, 16 (4), 1–22. DOI: http://doi.org/10.30958/aje.10-4-5
28. Kotsis, K.T. (2023). Alternative ideas about concepts of physics, a timelessly valuable tool for physics education. *Eurasian Journal of Science and Environmental Education*, 3 (2), 83–97. DOI: http://doi.org/10.30935/ejsee/13776
29. Lee, I.A., Sagartz, M., Meyer, P.F., & Anderson, E. (2025). Making sense of models: Connecting science and math through decoding and modifying computational models. *Science Scope*, 48(2), 39–47. DOI: http://doi.org/10.1080/08872376.2025.2456246
30. Lesh, R., & Doerr, H.M. (Eds.) (2003). *Beyond Constructivism: Models and Modeling Perspectives on Mathematics Problem Solving, Learning, and Teaching*. Lawrence Erlbaum.
31. Lin, Y.-H., Lin, H.-C.K., Wang, T.-H., & Wu, C.-H. (2023). Integrating the STEAM-6E model with virtual reality instruction. *Sustainability*, 15 (7), 6269. DOI: http://doi.org/10.3390/su15076269
32. Mamaeva, E.A., Utemov, V.V., Abramova, I.V., & Shestakova, L.G. (2024). Enhancing research skills of schoolchildren through 3D modeling. *Perspectives of Science and Education*, 68 (2), 595–613. DOI: http://doi.org/10.32744/pse.2024.2.36
33. Markula, A., & Aksela, M. (2022). The key characteristics of project-based learning: how teachers implement projects in K-12 science education. *Disciplinary and Interdisciplinary Science Education Research*, 4 (2), 1–17. DOI: http://doi.org/10.1186/s43031-021-00042-x
34. Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E. (2019). Virtual and augmented reality effects on K-12, higher and tertiary education students' twenty-first century skills. *Virtual Reality*, 23 (4), 425–436. DOI: http://doi.org/10.1007/s10055-018-0363-2 (access date: 20.05.2026).
35. Piaget, J. (1947). *La représentation du monde chez l'enfant*. Paris, France.
36. Posner, G.J., Strike, K.A., Hewson, P.W., & Gertzog, W.A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. *Science Education*, 66 (2), 211–227. DOI: http://doi.org/10.1002/sce.3730660207
37. Schwarz, C.V., Reiser, B.J., Davis, E.A., Kenyon, L., Acher, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientific modeling. *Journal of Research in Science Teaching*, 46 (6), 632–654. DOI: http://doi.org/10.1002/tea.20311
38. Schwarz, C.E. (2024). *Exploring Students' Conceptual and Epistemic Learning in Model-Centered Chemistry Courses* (Doctoral dissertation). ProQuest LLC.
39. Sessa, di, A.A., & Sherin, B.L. (1998). What changes in conceptual change? *International Journal of Science Education*, 20 (10), 1155–1191. DOI: http://doi.org/10.1080/0950069980201005
40. Tanas, J., & Fulmer, G. (2023). A content analysis of alignment messages to the Next Generation Science Standards. *Disciplinary and Interdisciplinary Science Education Research*, 5 (5), 1–18. DOI: http://doi.org/10.1186/s43031-023-00073-6
41. Vosniadou, S. (Ed.) (2013). *International Handbook of Research on Conceptual Change* (2nd ed.). Routledge.
42. Wagh, A., Rosenbaum, L.F., Fuhrmann, T., Eloy, A., Blikstein, P., & Wilkerson, M. (2025). Toward ontological alignment: Coordinating student ideas with the representational system of a computational modeling unit for science learning. *Cognition and Instruction*, 43 (1–2), 1–32. DOI: http://doi.org/10.1080/07370008.2024.2427400
43. Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the scientific method: Model-based inquiry as a new paradigm of preference for school science investigations. *Science Education*, 92 (5), 941–967. DOI: http://doi.org/10.1002/sce.20263
44. Zadorozhnii, V.M. (2024). Using Arduino to develop research competencies of secondary and high school students. *CTE Workshop Proceedings*, 11, 427–441. DOI: http://doi.org/10.55056/cte.663
45. Zhang, W., Guan, Y., & Hu, Z. (2024). The efficacy of project-based learning in enhancing computational thinking among students: A meta-analysis of 31 experiments and quasi-experiments. *Education and Information Technologies*, 29, 14513–14545. DOI: http://doi.org/10.1007/s10639-023-12392-2
Опубликован
2026-06-30
Как цитировать
G., U. (2026). МОДЕЛИРОВАНИЕ КАК СРЕДСТВО ПРЕОДОЛЕНИЯ ДОНАУЧНЫХ АЛЬТЕРНАТИВНЫХ КОНЦЕПЦИЙ ПРИ ОСВОЕНИИ ЕСТЕСТВЕННО-НАУЧНЫХ ПОНЯТИЙ ОБУЧАЮЩИМИСЯ. Вестник Красноярского государственного педагогического университета им. В.П. Астафьева, 76(2), 112-125. извлечено от https://vestnik.kspu.ru/index.php/vestnik/article/view/687
Раздел
Психология
