Exploring the impact of modeling in science education: A systematic review

Roza Valeeva; Gulnara Biktagirova; Vadim Lesev; Olga Mikhailenko; Galina Skudareva; Arturas Valentovinis

doi:10.29333/ejmste/13268

Roza Valeeva ¹ ^* , Gulnara Biktagirova ¹ , Vadim Lesev ² , Olga Mikhailenko ² , Galina Skudareva ³ , Arturas Valentovinis ³

More Detail

¹ Kazan Federal University, Kazan, RUSSIA² Kabardino-Balkarian State University, Nalchik, RUSSIA³ State Humanitarian and Technological University, Moscow, RUSSIA^* Corresponding Author

Abstract

This systematic review aimed to summarize the research results and draw conclusions related to the articles about modeling in science education between 2011-2023. A qualitative thematic review was used in this study. Initial studies pulled from the Web of Science database and examination of 31 selected articles found that using models as part of instruction has been shown to improve student understanding, particularly with regards to abstract concepts and processes. Most of these studies showed that learning models used in science education had positive impact on both cognitive, affective, social, and cultural factors. According to a detailed analysis of each of the 31 articles, the contents of the studies were coded by author name and year, sample, research design, and main results. The research reviewed has many implications for modeling in science education.

Keywords

modeling
science education
systematic review

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

EURASIA J Math Sci Tech Ed, 2023, Volume 19, Issue 6, Article No: em2284

https://doi.org/10.29333/ejmste/13268

Publication date: 01 Jun 2023

Online publication date: 13 May 2023

Article Views: 1109

Article Downloads: 1351

Open Access References How to cite this article

References

Alt, D. (2018). Science teachers’ conceptions of teaching, attitudes toward testing, and use of contemporary educational activities and assessment tasks. Journal of Science Teacher Education, 29(7), 600-619. https://doi.org/10.1080/1046560X.2018.1485398
Ananishnev, V. M. (2010). Modeling in the field of education. Sistemnaya Psikhologiya i Sotsiologiya [System Psychology and Sociology], 1(2), 67-84.
Aseeva, O. M. (2021). Modeling as a method of cognition of the surrounding reality. Molodoy Uchenyy [Young Researcher], 6(348), 403-404.
Battaglia, O. R., Di Paola, B., & Fazio, C. (2017). A quantitative analysis of educational data through the comparison between hierarchical and not-hierarchical clustering. EURASIA Journal of Mathematics, Science and Technology Education, 13(8), 4491-4512. https://doi.org/10.12973/eurasia.2017.00943a
Bo, W. V., Fulmer, G. W., Lee, C. K. E., & Chen, V. D. T. (2018). How do secondary science teachers perceive the use of interactive simulations? The affordance in Singapore context. Journal of Science Education and Technology, 27, 550-565. https://doi.org/10.1007/s10956-018-9744-2
Chorosova, O. M., Aetdinova, R. R., Solomonova, G. S., & Protodyakonova, G. Y. (2020). Conceptual approaches to the identification of teachers’ digital competence: Cognitive modeling. Education and Self Development, 15(3), 189-202. https://doi.org/10.26907/esd15.3.16
Clark, D. B., & Sengupta, P. (2020). Reconceptualizing games for integrating computational thinking and science as practice: Collaborative agent-based disciplinarily-integrated games. Interactive Learning Environments, 28(3), 328-346. https://doi.org/10.1080/10494820.2019.1636071
Danish, J. A., & Enyedy, N. (2015). Latour goes to kindergarten: Children marshaling allies in a spontaneous argument about what counts as science. Learning, Culture and Social Interaction, 5, 5-19. https://doi.org/10.1016/j.lcsi.2014.08.002
Demir, A., & Namdar, B. (2021). The effect of modeling activities on grade 5 students’ informal reasoning about a real-life issue. Research in Science Education, 51(Suppl 1), 429-442. https://doi.org/10.1007/s11165-019-09896-8
Dickes, A. C., & Sengupta, P. (2013). Learning natural selection in 4^th grade with multi-agent-based computational models. Research in Science Education, 43, 921-953. https://doi.org/10.1007/s11165-012-9293-2
Frederiksen, J. R., White, B. Y., & Gutwill, J. (1999). Dynamic mental models in learning science: The importance of constructing derivational linkages among models. Journal of Research in Science Teaching: The Official Journal of the National Association for Research in Science Teaching, 36(7), 806-836. https://doi.org/10.1002/(SICI)1098-2736(199909)36:7<806::AID-TEA5>3.0.CO;2-2
Fuchs, H. U. (2015). From stories to scientific models and back: Narrative framing in modern macroscopic physics. International Journal of Science Education, 37(5-6), 934-957. https://doi.org/10.1080/09500693.2015.1025311
Fulmer, G. W. (2015). Validating proposed learning progressions on force and motion using the force concept inventory: Findings from Singapore secondary schools. International Journal of Science and Mathematics Education, 13, 1235-1254. https://doi.org/10.1007/s10763-014-9553-x
Gilemkhanova, E. N., Khusainova, R. M., Lushpaeva, I. I., & Khairutdinova, M. R. (2022). A model of subjective well-being of a teacher in the context of the safety of educational environment. Education and Self Development, 17(4), 288-303. https://doi.org/10.26907/esd.17.4.20
Gonsalves, A. J., Seiler, G., & Salter, D. E. (2011). Rethinking resources and hybridity. Cultural Studies of Science Education, 6, 389-399. https://doi.org/10.1007/s11422-010-9295-1
Hand, B., Shelley, M. C., Laugerman, M., Fostvedt, L., & Therrien, W. (2018). Improving critical thinking growth for disadvantaged groups within elementary school science: A randomized controlled trial using the science writing heuristic approach. Science Education, 102(4), 693-710. https://doi.org/10.1002/sce.21341
Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty first century. Science Education, 88(1), 28-54. https://doi.org/10.1002/sce.10106
Huber, R. A., & Moore, C. J. (2001). A model for extending hands on science to be inquiry based. School Science and Mathematics, 101(1), 32-42. https://doi.org/10.1111/j.1949-8594.2001.tb18187.x
Ignatova, V. A., & Ignatov, S. B. (2017). Conceptual approaches to modeling the content of science education for students of social and humanitarian areas of training at the university. Vestnik Tyumenskogo Gosudarstvennogo Universiteta. Gumanitarnyye issledovaniya [Bulletin of the Tyumen State University. Humanitarian research], 3(3), 222-232. https://doi.org/10.21684/2411-197X-2017-3-3-222-232
Jones, L. K., & Hite, R. L. (2020). Who wants to be a scientist in South Korea: Assessing role model influences on Korean students’ perceptions of science and scientists. International Journal of Science Education, 42(16), 2674-2695. https://doi.org/10.1080/09500693.2020.1829158
Kamaleeva, A. R. (2010). Modeling of the system of work on the formation of science competencies of students in the humanities. Alma Mater, 4, 36-42.
Kasprzhak, A., Kobtseva, A., & Tsatrian, M. (2022). Instructional leadership models in modern schools. Education and Self Development, 17(2), 172-187. https://doi.org/10.26907/esd.17.2.02
Kolchin, I. S., Miroshnichenko, A. S., Kadeeva, O. E., & Syritsyna, V. N. (2022). 3D modeling as a tool for gamification of the process of studying science disciplines. Sovremennyye Problemy Nauki i Obrazovaniya [Modern Problems of Science and Education], 6(1), 45. https://doi.org/10.17513/spno.32256
Li, Y., Schoenfeld, A. H., diSessa, A. A., Graesser, A. C., Benson, L. C., English, L. D., & Duschl, R. A. (2019). Design and design thinking in STEM education. Journal for STEM Education Research, 2, 93-104. https://doi.org/10.1007/s41979-019-00020-z
López-Vargas, O., Ibáñez-Ibáñez, J., & Racines-Prada, O. (2017). Students’ metacognition and cognitive style and their effect on cognitive load and learning achievement. Journal of Educational Technology & Society, 20(3), 145-157. https://doi.org/10.1177/1365480217704263
Louca, L. T., & Zacharia, Z. C. (2012). Modeling-based learning in science education: Cognitive, metacognitive, social, material and epistemological contributions. Educational Review, 64(4), 471-492. https://doi.org/10.1080/00131911.2011.628748
Lucas, K. L. (2021). The use of 3-D modeling and printing to teach the central dogma of molecular biology. Science Activities, 58(2), 70-76. https://doi.org/10.1080/00368121.2021.1918048
Lucas, L. L., & Lewis, E. B. (2019). High school students’ use of representations in physics problem solving. School Science and Mathematics, 119(6), 327-339. https://doi.org/10.1111/ssm.12357
Mierdel, J., & Bogner, F. X. (2019). Is creativity, hands-on modeling and cognitive learning gender-dependent? Thinking Skills and Creativity, 31, 91-102. https://doi.org/10.1016/j.tsc.2018.11.001
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, 1. https://doi.org/10.1186/2046-4053-4-1
Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2016). Science achievement gaps begin very early, persist, and are largely explained by modifiable factors. Educational Researcher, 45(1), 18-35. https://doi.org/10.3102/0013189X16633182
Or-Bach, R., & Bredeweg, B. (2013). Support options provided and required for modeling with DynaLearn. A case study. Education and Information Technologies, 18, 621-639. https://doi.org/10.1007/s10639-012-9194-z
Pierson, A. E., Brady, C. E., & Clark, D. B. (2020). Balancing the environment: Computational models as interactive participants in a STEM classroom. Journal of Science Education and Technology, 29, 101-119. https://doi.org/10.1007/s10956-019-09797-5
Rates, C. A., Mulvey, B. K., & Feldon, D. F. (2016). Promoting conceptual change for complex systems understanding: Outcomes of an agent-based participatory simulation. Journal of Science Education and Technology, 25, 610-627. https://doi.org/10.1007/s10956-016-9616-6
Rates, C. A., Mulvey, B. K., Chiu, J. L., & Stenger, K. (2022). Examining ontological and self-monitoring scaffolding to improve complex systems thinking with a participatory simulation. Instructional Science, 50, 199-211. https://doi.org/10.1007/s11251-021-09573-2
Roth, T., Scharfenberg, F. J., Mierdel, J., & Bogner, F. X. (2020). Self-evaluative scientific modeling in an outreach gene technology laboratory. Journal of Science Education and Technology, 29(6), 725-739. https://doi.org/10.1007/s10956-020-09848-2
Saba, J., Hel-Or, H., & Levy, S. T. (2021). Much.Matter.in.Motion: Learning by modeling systems in chemistry and physics with a universal programing platform. Interactive Learning Environments. https://doi.org/10.1080/10494820.2021.1919905
Sackes, M., Trudle, K. C., & Bell, R. L. (2013). Science learning experiences in kindergarten and children’s growth in science performance in elementary grades. Eğitim ve Bilim [Education and Science], 38(167), 114-127.
Samon, S., & Levy, S. T. (2017). Micro-macro compatibility: When does a complex systems approach strongly benefit science learning? Science Education, 101(6), 985-1014. https://doi.org/10.1002/sce.21301
Schademan, A. R. (2015). Building connections between a cultural practice and modeling in science education. International Journal of Science and Mathematics Education, 13, 1425-1448. https://doi.org/10.1007/s10763-014-9554-9
Schwarz, C. V., & Gwekwerere, Y. N. (2007). Using a guided inquiry and modeling instructional framework (EIMA) to support preservice K‐8 science teaching. Science Education, 91(1), 158-186. https://doi.org/10.1002/sce.20177
Shwartz, Y., Weizman, A., Fortus, D., Krajcik, J., & Reiser, B. (2008). The IQWST experience: Using coherence as a design principle for a middle school science curriculum. The Elementary School Journal, 109(2), 199-219. https://doi.org/10.1086/590526
Southerland, S. A., Granger, E. M., Hughes, R., Enderle, P., Ke, F., Roseler, K., Saka, Y., & Tekkumru-Kisa, M. (2016). Essential aspects of science teacher professional development: Making research participation instructionally effective. AERA Open, 2(4), 2332858416674200. https://doi.org/10.1177/2332858416674200
Teig, N., Scherer, R., & Nilsen, T. (2018). More isn’t always better: The curvilinear relationship between inquiry-based teaching and student achievement in science. Learning and Instruction, 56, 20-29. https://doi.org/10.1016/j.learninstruc.2018.02.006
Wagh, A., & Wilensky, U. (2018). EvoBuild: A quickstart toolkit for programming agent-based models of evolutionary processes. Journal of Science Education and Technology, 27(2), 131-146. https://doi.org/10.1007/s10956-017-9713-1
Zitek, A., Poppe, M., Stelzhammer, M., Muhar, S., & Bredeweg, B. (2013). Learning by conceptual modeling--Changes in knowledge structure and content. IEEE Transactions on Learning Technologies, 6(3), 217-227. https://doi.org/10.1109/TLT.2013.7

How to cite this article

APA

Valeeva, R., Biktagirova, G., Lesev, V., Mikhailenko, O., Skudareva, G., & Valentovinis, A. (2023). Exploring the impact of modeling in science education: A systematic review. Eurasia Journal of Mathematics, Science and Technology Education, 19(6), em2284. https://doi.org/10.29333/ejmste/13268

Vancouver

Valeeva R, Biktagirova G, Lesev V, Mikhailenko O, Skudareva G, Valentovinis A. Exploring the impact of modeling in science education: A systematic review. EURASIA J Math Sci Tech Ed. 2023;19(6):em2284. https://doi.org/10.29333/ejmste/13268

AMA

Valeeva R, Biktagirova G, Lesev V, Mikhailenko O, Skudareva G, Valentovinis A. Exploring the impact of modeling in science education: A systematic review. EURASIA J Math Sci Tech Ed. 2023;19(6), em2284. https://doi.org/10.29333/ejmste/13268

Chicago

Valeeva, Roza, Gulnara Biktagirova, Vadim Lesev, Olga Mikhailenko, Galina Skudareva, and Arturas Valentovinis. "Exploring the impact of modeling in science education: A systematic review". Eurasia Journal of Mathematics, Science and Technology Education 2023 19 no. 6 (2023): em2284. https://doi.org/10.29333/ejmste/13268

Harvard

Valeeva, R., Biktagirova, G., Lesev, V., Mikhailenko, O., Skudareva, G., and Valentovinis, A. (2023). Exploring the impact of modeling in science education: A systematic review. Eurasia Journal of Mathematics, Science and Technology Education, 19(6), em2284. https://doi.org/10.29333/ejmste/13268

MLA

Valeeva, Roza et al. "Exploring the impact of modeling in science education: A systematic review". Eurasia Journal of Mathematics, Science and Technology Education, vol. 19, no. 6, 2023, em2284. https://doi.org/10.29333/ejmste/13268