Investigating Preservice Elementary Science Teachers’ Understanding of Climate Change from a Computational Thinking Systems Perspective
More details
Hide details
University of Maryland, College Park, 2226 Benjamin Building, College Park, MD 20742, USA
Online publish date: 2019-01-31
Publish date: 2019-01-31
EURASIA J. Math., Sci Tech. Ed 2019;15(6):em1696
There is a need to approach environmental education (EE) topics, such as climate change, with a framework that productively reflects its inherent complexity. This study investigates how computational thinking (CT), specifically systems thinking (ST), may prepare educators to teach climate change. As scientists increasingly rely on computational techniques in their studies of complex EE topics, it is incumbent on science education to provide learners with computational thinking opportunities. We investigated how elementary preservice teachers (PSTs) in a science methods course (N=35) adapted a curricular resource on the climate change topic of sea level rise to integrate the CT practice of ST. Changes in their thinking were analyzed. Findings suggest that PSTs prior to instruction held a limited understanding of climate systems, often conflating weather and climate. Post instruction, their thinking expanded to consider the relationships between carbon dioxide, global warming, ice melt, and sea level rise. Further, many were able to describe these systems in a future EE teaching activity for young learners. A major implication was the need to develop a continuum of CT practices for elementary educators, with an emphasis on ST, for complex environmental education topics, that could frame their pedagogical thinking for climate change education.
Assaraf, O. B.-Z., & Orion, N. (2010). System thinking skills at the elementary school level. Journal of Research in Science Teaching, 47(5), 540–563.
Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670.
Banilower, E. R., Smith, P. S., Weiss, I. R., Malzahn, K. A., Campbell, K. M., & Weis, A. M. (2013). Report of the 2012 National Survey of Science and Mathematics Education. Horizon Research, Inc. (NJ1).
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is Involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48-54.
Berland, M., & Wilensky, U. (2015). Comparing virtual and physical robotics environments for supporting complex systems and computational thinking. Journal of Science Education and Technology, 24(5), 628-647.
Breslyn, W., Drewes, A., McGinnis, J. R., Hestness, E., & Mouza, C. (2017). Development of an Empirically-Based Conditional Learning Progression for Climate Change. Science Education International, 28(3), 214–223.
Breslyn, W., McGinnis, J. R., McDonald, R. C., & Hestness, E. (2016). Developing a learning progression for sea level rise, a major impact of climate change. Journal of Research in Science Teaching, 53(10), 1471–1499.
Climate Central (2018). [Interactive Map of Sea Level Rise Projection Based on Geographic Area]. Surging Seas: Mapping Choices. Retrieved from http://sealevel.climatecentral....
Cook, J., Oreskes, N., Doran, P. T., Anderegg, W. R., Verheggen, B., Maibach, E. W., … Green, S. A. (2016). Consensus on consensus: A synthesis of consensus estimates on human-caused global warming. Environmental Research Letters, 11(4), 048002.
DiSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. MIT Press.
Drewes, A., Breslyn, W., McGinnis, R., Mouza, C., Hestness, E., & Henderson, J. (2017). Designing and Validating a Climate Change Knowledge Instrument. San Antonio, TX: Proposal Presented at American Educational Research Association Annual Conference.
Evagorou, M., Korfiatis, K., Nicolaou, C., & Constantinou, C. (2009). An investigation of the potential of interactive simulations for developing system thinking skills in elementary school: a case study with fifth‐graders and sixth‐graders. International Journal of Science Education, 31(5), 655–674.
Hestness, E. McGinnis, J. R., Riedinger, K., & Marbach-Ad, G. (2011). A study of teacher candidates’ experiences investigating global climate change education within an elementary science methods course. Journal of Science Teacher Education, 22, 351-369.
Hill, D., & Redden, M. (1985). An investigation of the system concept. School Science and Mathematics, 85, 233–239.
Hokayem, H., & Gotwals, A. W. (2016). Early elementary students’ understanding of complex ecosystems: A learning progression approach. Journal of Research in Science Teaching, 53(10), 1524–1545.
Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into practice, 41(4), 212-218.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
Loughran, J. (2014). Developing understandings of practice: Science teacher learning. Handbook of Research on Science Education, 2, 811–829.
McGinnis, J. R. & McDonald, C. (2011). Controversial or sensitive topics in science education: A Literature Review. University of Maryland MADE CLEAR Climate Change Learning Sciences Research Team. Retrieved on September 22, 2018 from http://www.climateedresearch.o....
McGinnis, J. R. (2018, March). Climate change education: What do we know and how might that assist the science classroom teacher? A keynote presented at Paleoscience Day, Smithsonian National Museum of Natural History, Washington, DC. Retrieved on September 6, 2018 from
McGinnis, J. R., Hestness, E., & Riedinger, K. (2011). Changing science teacher education in a changing global climate: Telling a new story. In Jing Ling and R. Oxford (Eds.), Transformative Eco-Education for Human Survival: Environmental Education in A New Era (pp. 117-133). Charlotte, North Carolina: Information Age Publishing.
McGinnis, J. R., McDonald, C., Breslyn, W. & Hestness, E. (March, 2017). Supporting the inclusion of climate change in U.S. science education curricula by use of learning progressions. In D. Shephardson, A. Roychoudury & A. Hirsch (Eds), Teaching and Learning about Climate Change: A Framework for Educators, pp. 135-151. Routledge, New York.
National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, DC: National Academies Press.
National Research Council. (2011). Report of a Workshop on the Pedagogical Aspects of Computational Thinking. National Academies Press.
Next Generation Science Standards Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.
Parchmann, I., Gräsel, C., Baer, A., Nentwig, P., Demuth, R., & Ralle, B. (2006). “Chemie im Kontext”: A symbiotic implementation of a context‐based teaching and learning approach. International Journal of Science Education, 28(9), 1041–1062.
Plutzer, E., McCaffrey, M., Hannah, A. L., Rosenau, J., Berbeco, M., & Reid, A. H. (2016). Climate confusion among US teachers. Science, 351(6274), 664–665.
Roychoudhury, A., Shepardson, D. P., Hirsch, A., Niyogi, D., Mehta, J., & Top, S. (2017). The need to introduce system thinking in teaching climate change. Science Educator, 25(2), 73.
Sharma, A. (2012). Global climate change: What has science education got to do with it? Science & Education, 21(1), 33–53.
Shepardson, D. P., Niyogi, D., Choi, S., & Charusombat, U. (2009). Seventh grade students’ conceptions of global warming and climate change. Environmental Education Research, 15(5), 549–570.
Shepardson, D. P., Niyogi, D., Roychoudhury, A., & Hirsch, A. (2012). Conceptualizing climate change in the context of a climate system: Implications for climate and environmental education. Environmental Education Research, 18(3), 323–352.
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review.
Stephenson, C., & Barr, V (2012). Defining Computational Thinking for K-12. In Computer Science Teachers Association (CSTA) and Association for Computing Machinery (ACM): Computer Science K-8: Building a Strong Foundation. CSTA & ACM: New York.
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147.
Wing, J. (2011). Research notebook: Computational thinking—What and why? The Link Newsletter, 6, 1–32. Retrieved from
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.