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LITERATURE REVIEW
Exploring the impact of modeling in science education: A systematic review
1
Kazan Federal University, Kazan, RUSSIA
2
Kabardino-Balkarian State University, Nalchik, RUSSIA
3
State Humanitarian and Technological University, Moscow, RUSSIA
Online publication date: 2023-05-13
Publication date: 2023-06-01
EURASIA J. Math., Sci Tech. Ed 2023;19(6):em2284
KEYWORDS
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.
REFERENCES (46)
1.
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/10.1080/104656....
2.
Ananishnev, V. M. (2010). Modeling in the field of education. Sistemnaya Psikhologiya i Sotsiologiya [System Psychology and Sociology], 1(2), 67-84.
3.
Aseeva, O. M. (2021). Modeling as a method of cognition of the surrounding reality. Molodoy Uchenyy [Young Researcher], 6(348), 403-404.
4.
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/euras....
5.
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....
6.
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....
7.
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/10.1080/104948....
8.
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....
9.
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....
10.
Dickes, A. C., & Sengupta, P. (2013). Learning natural selection in 4th grade with multi-agent-based computational models. Research in Science Education, 43, 921-953. https://doi.org/10.1007/s11165....
11.
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)...<806::AID-TEA5>3.0.CO;2-2.
12.
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/10.1080/095006....
13.
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....
14.
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.1....
15.
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....
16.
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.21....
17.
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.10....
18.
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....
19.
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-....
20.
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/10.1080/095006....
21.
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.
22.
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.1....
23.
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.....
24.
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....
25.
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/10.1177/136548....
26.
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/10.1080/001319....
27.
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/10.1080/003681....
28.
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.12....
29.
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.....
30.
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/10.1186/2046-4....
31.
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/10.3102/001318....
32.
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....
33.
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....
34.
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....
35.
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....
36.
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....
37.
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/10.1080/104948....
38.
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.
39.
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.21....
40.
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....
41.
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.20....
42.
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.
43.
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/10.1177/233285....
44.
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.lear....
45.
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....
46.
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.20....
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