RESEARCH PAPER
Shifting the Balance: Engaging Students in Using a Modeling Tool to Learn about Ocean Acidification
Tom Bielik 1  
,  
Dan Damelin 2
,  
 
 
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1
Michigan State University, East Lansing, Michigan, USA
2
The Concord Consortium, Concord, Massachusetts, USA
Publish date: 2018-11-01
 
EURASIA J. Math., Sci Tech. Ed 2019;15(1):em1652
KEYWORDS
ABSTRACT
Modeling is one of the core scientific and engineering practices described in A Framework for K-12 Science Education. Students are expected to construct, use, evaluate, and revise their models to make sense of phenomena or to find solutions to problems. Technology tools can support the development of students’ modeling practice when learning about environmental issues. This study investigates the incorporation of an online computational modeling tool in a middle school curricular unit focusing on ocean acidification. We present the advantages and challenges experienced by students and teachers while engaging in the unit and using the modeling tool. Our results indicate that integrating the modeling tool in the ocean acidification curricular unit facilitates students’ interest and engagement in environmental responsibility and focused students’ attention toward human involvement and impact on the environment. Students perceived the tool and the curricular unit to be relevant to their lives and important in promoting their content learning and modeling practice. However, students and teachers reported several challenges, mostly related to the complexity of using the modeling tool and working with the resulting graphs and charts. We discuss these advantages and challenges and suggest recommendations for supporting students’ modeling practice when learning about environmental issues.
 
REFERENCES (36)
1.
Abbasi, T., & Abbasi, S. A. (2011). Ocean acidification: The newest threat to the global environment. Critical Reviews in Environmental Science and Technology, 41(18), 1601-1663. https://doi.org/10.1080/106433....
 
2.
Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183-198. https://doi.org/10.1016/j.lear....
 
3.
Bielik T., Damelin D., & Krajcik J. (2018). Why do Fishermen Need Forests? Developing a Project-Based Unit with Engaging Driving Question. Science Scope, 41(6), 64-72.
 
4.
Blumenfeld, P., Fishman, B. J., Krajcik, J., Marx, R. W., & Soloway, E. (2000). Creating usable innovations in systemic reform: Scaling up technology-embedded project-based science in urban schools. Educational psychologist, 35(3), 149-164. https://doi.org/10.1207/S15326....
 
5.
Damelin, D., Krajcik, J., McIntyre, C., & Bielik, T. (2017). Students Making Systems Models: An Accessible Approach. Science Scope, 40(5), 78-82.
 
6.
Doney, S. C., Balch, W. M., Fabry, V. J., & Feely, R. A. (2009). Ocean acidification: A critical emerging problem for the ocean sciences. Oceanography, 22(4), 16-25. https://doi.org/10.5670/oceano....
 
7.
Fauville, G., Säljö, R., & Dupont, S. (2013). Impact of ocean acidification on marine ecosystems: Educational challenges and innovations. Marine Biology, 160(8), 1863-1874. https://doi.org/10.1007/s00227....
 
8.
Finzer, W., & Damelin, D. (2016). Design perspective on the Common Online Data Analysis Platform. In C. E. Konold (Chair), Student thinking, learning, and inquiry with the Common Online Data Analysis Platform. Symposium conducted at the meeting of the American Educational Research Association, Washington, D.C.
 
9.
Fishman, B., Marx, R. W., Blumenfeld, P., Krajcik, J., & Soloway, E. (2004). Creating a framework for research on systemic technology innovations. The Journal of the Learning Sciences, 13(1), 43-76. https://doi.org/10.1207/s15327....
 
10.
Fredricks, J., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763–782). New York: Springer Sciences. https://doi.org/10.1007/978-1-....
 
11.
Fretz, E. B., Wu, H. K., Zhang, B., Davis, E. A., Krajcik, J. S., & Soloway, E. (2002). An investigation of software scaffolds supporting modeling practices. Research in Science Education, 32(4), 567-589. https://doi.org/10.1023/A:1022....
 
12.
Harrison, A. G., & Treagust, D. F. (2000). A typology of school science models. International Journal of Science Education, 22(9), 1011-1026. https://doi.org/10.1080/095006....
 
13.
Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111-127. https://doi.org/10.1207/s15326....
 
14.
Krajcik, J. S., & Shin, N. (2014). Project-based learning. The Cambridge Handbook of the Learning Sciences, Second Edition (pp. 275-297). Cambridge University Press.
 
15.
Krajcik, J., Codere, S., Dahsah, C., Bayer, R., & Mun, K. (2014). Planning instruction to meet the intent of the Next Generation Science Standards. Journal of Science Teacher Education, 25(2), 157-175. https://doi.org/10.1007/s10972....
 
16.
Krajcik, J., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92(1), 1-32. https://doi.org/10.1002/sce.20....
 
17.
Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. New York: Cambridge University Press. https://doi.org/10.1017/CBO978....
 
18.
Manni, A., Sporre, K., & Ottander, C. (2017). Emotions and values: A case study of meaning-making in ESE. Environmental Education Research, 23(4), 451-464. https://doi.org/10.1080/135046....
 
19.
Marx, R. W., Blumenfeld, P. C., Krajcik, J. S., & Soloway, E. (1997). Enacting project-based science. The elementary school journal, 97(4), 341-358. https://doi.org/10.1086/461870.
 
20.
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press. https://doi.org/10.17226/13165.
 
21.
Nersessian, & P. Thagard (Eds.) Model-based reasoning in scientific discovery (pp. 5-22). Boston, MA: Springer. https://doi.org/10.1007/978-1-....
 
22.
Nersessian, N. J. (1999). Model-based reasoning in conceptual change. In L. Magnani, N. J. https://doi.org/10.1007/978-1-....
 
23.
Nersessian, N. J. (2002). The cognitive basis of model-based reasoning in science. In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 133-153). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO978....
 
24.
NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press. https://doi.org/10.17226/18290.
 
25.
Passmore, C., Gouvea, J. S., & Giere, R. (2014). Models in science and in learning science: Focusing scientific practice on sense-making. In M. R. Matthews (Ed.), International handbook of research in history, philosophy and science teaching (pp. 1171-1202). Netherlands: Springer. https://doi.org/10.1007/978-94....
 
26.
Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., ... Soloway, E. (2004). A scaffolding design framework for software to support science inquiry. The journal of the learning sciences, 13(3), 337-386. https://doi.org/10.1207/s15327....
 
27.
Schneider, B., Krajcik, J., Lavonen, J., Salmela‐Aro, K., Broda, M., Spicer, J., ... Viljaranta, J. (2016). Investigating optimal learning moments in US and Finnish science classes. Journal of Research in Science Teaching, 53(3), 400-421. https://doi.org/10.1002/tea.21....
 
28.
Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., ... Krajcik, J. (2009). Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632-654. https://doi.org/10.1002/tea.20....
 
29.
Schwarz, C., Reiser, B. J., Acher, A., Kenyon, L., & Fortus, D. (2012). MoDeLS: Challenges in defining a learning progression for scientific modeling. In A. C. Alonzo & A. W. Gotwals (Eds.). Learning progressions in science: Current challenges and future directions (pp. 101-137). Rotterdam: Sense Publishers. https://doi.org/10.1007/978-94....
 
30.
Severance, S., Penuel, W. R., Sumner, T., & Leary, H. (2016). Organizing for teacher agency in curricular co-design. Journal of the Learning Sciences, 25(4), 531-564. https://doi.org/10.1080/105084....
 
31.
Shepardson, D. P., Choi, S., Niyogi, D., & Charusombat, U. (2011). Seventh grade students’ mental models of the greenhouse effect. Environmental Education Research, 17(1), 1-17. https://doi.org/10.1080/135046....
 
32.
Skinner, E. A., Marchand, G., Furrer, C., & Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic. Journal of Educational Psychology, 100(4), 765-781. https://doi.org/10.1037/a00128....
 
33.
Weizman, A., Schwartz, Y., & Fortus, D. (2008). The driving question board. The Science Teacher, 75(8), 33.
 
34.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics bulletin, 1(6), 80-83. https://doi.org/10.2307/300196....
 
35.
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. https://doi.org/10.1002/sce.20....
 
36.
Wu, H. K., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students' use of a visualization tool in the classroom. Journal of research in science teaching, 38(7), 821-842. https://doi.org/10.1002/tea.10....
 
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