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Utilizing a Dynamic Model of Food Chains to Enhance English Learners’ Science Knowledge and Language Construction

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Abstract

A learning format gaining attention that encourages language acquisition in science is the use of dynamic models as instructional tools. This grounded theory investigates the impact of the dynamic food chain model as an alternative lesson for teaching food chains. The researchers examined the impact of a paper-based activity and a dynamic model activity on 5th grade students’ content knowledge and language development. A total of 96 English learners (ELs) and 62 native English speakers participated. Data were collected using a What I Did/What I Learned reflection and analyzed qualitatively. Results indicate that ELs exceeded native speakers in academic language development and in understanding interconnectedness of organisms. In addition, students engaging in the dynamic model activity expressed more joyful learning than students in the paper-based activity.

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References

  • Aschbacher, P., & Alonzo, A. (2006). Examining the utility of elementary science notebooks for formative assessment purposes. Educational Assessment, 11(3 & 4), 179–203.

    Article  Google Scholar 

  • Biffi, D., Hartweg, B., Stewart, M., Patterson, M., Simanek, E., & Weinburgh, M. H. (2016). Engaging students with dynamic models: Peruvian food chain Jenga. Science Scope, 39(5), 51–57.

  • Bailey, A. L. (2007). The language demands of school: Putting academic English to the test. New Haven, CT: Yale University Press.

    Google Scholar 

  • Bezemer, J., & Kress, G. (2016). Multimodality, learning and communication: A social semiotic frame. New York, NY: Routledge.

    Google Scholar 

  • Carlsson, B. (2002). Ecological understanding 1: Ways of experiencing photosynthesis. International Journal of Science Education, 24(7), 681–699.

    Article  Google Scholar 

  • Carrier, K. (2005). Supporting science learning through science literacy objectives for English language learners. Science Activities, 42(2), 5–11.

    Google Scholar 

  • Clement, J. (2000). Model based learning as a key research area for science education. International Journal of Science Education, 22(9), 1041–1053.

    Article  Google Scholar 

  • Coll, R., France, B., & Taylor, I. (2005). The role of models and analogies in science education: Implications from research. International Journal of Science Education, 27(2), 183–198.

    Article  Google Scholar 

  • Concario, M. (2016). Encouraging students to language in the science classroom. Electronic Journal of Science Education, 20(3), 126–137.

    Google Scholar 

  • Corbin, J., & Strauss, A. (2014). Basics of qualitative research: Techniques and procedures for, developing grounded theory (4th ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Creswell, J. W. (2007). Qualitative inquiry & research design: Choosing among five approaches (2nd ed.). Thousand Oaks, CA: Sage Publications, Inc..

    Google Scholar 

  • Cummins, J. (2000). Language, power and pedagogy: Bilingual children in the crossfire (1st ed.). Clevedon, England: Multilingual Matters LTD.

  • Echevarria, J., & Colburn, A. (2006). Designing lessons: Inquiry approach to science using the SIOP Model. In A. Lathman & D. Crowther (Eds.), Science for English language learners (pp. 95–108). Arlington, VA: National Science Teachers Association Press.

    Google Scholar 

  • Eilam, B. (2012). System thinking and feeding relations: Learning with a live ecosystem model. Instructional Science, 40(2), 213–239 213. https://doi.org/10.1007/s11251-011-9175-4.

    Article  Google Scholar 

  • Fulp, S. L. (2002). Status of elementary school science teaching. Retrieved on August 16, 2016 from the Horizon Research website: http://2000survery.horizon-research.com/reports/elem_science/elem_science.pdf.

  • Gallegos, L., Jerezano, M. E., & Flores, F. (1994). Preconceptions and relations used by children in the construction of food chains. Journal of Research in Science Teaching, 31(3), 259–272.

    Article  Google Scholar 

  • Galvin, E. (2015). To determine and overcome misconceptions in biology held by students and educators in the Irish schooling system (Unpublished doctoral dissertation). University of Limerick, Limerick.

  • Gee, J. P. (2001). Language in the science classroom: Academic social languages as the heart of school-based literacy. Madison, WI: University of Wisconsin.

    Google Scholar 

  • Gee, J. P. (2011). An introduction to discourse analysis theory and method. New York, NY: Routledge.

    Google Scholar 

  • Gibbons, B. (2003). Supporting elementary science education for English learners: A constructivist evaluation instrument. The Journal of Educational Research, 96(6), 371–380.

    Article  Google Scholar 

  • Glesne, C. (2006). Becoming qualitative researchers: An introduction. Boston, MA: Pearson.

    Google Scholar 

  • Griffith, G., & Scharmann, L. (2008). Initial impacts of no child left behind on elementary science education. Journal of Elementary Science Education, 20(3), 35–48.

    Article  Google Scholar 

  • Grotzer, T. A., & Basca, B. B. (2003). How does grasping the underlying causal structures of ecosystems impact students’ understanding? Journal of Biological Education, 38(1), 16–29.

    Article  Google Scholar 

  • Harbour, K., Evanovich, L., Sweigart, C., & Hughes, L. (2015). A brief review of effective teaching practices that maximize student engagement. Preventing School Failure: Alternative Education for Children and Youth, 59(1), 5–13. https://doi.org/10.1080/1045988x.2014.919136.

    Article  Google Scholar 

  • Hogan, K. (2000). Assessing students’ systems reasoning in ecology. Journal of Biological Education, 35, 22–28.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hokayem, H., Gotwals, A.W. & Weinburgh, M. H. (2014). The methods of developing a learning progression for systemic reasoning. In Berlin, D. F. & White, A. L. (Eds.). Initiatives in mathematics and science education with global implications (pp. 63-72). Columbus, OH: International Consortium for Research in Science and Mathematics Education.

  • Hokayem, H., Ma, J., & Jin, H. (2015). A learning progression for feedback loop reasoning for feedback loop reasoning at the lower elementary level. Journal of Biological Education, 49, 246–260.

    Article  Google Scholar 

  • Hokayem, H., & Schwarz, C. (2014). Engaging fifth graders in scientific modeling to learn evaporation and condensation. International Journal of Science and Mathematics Education, 12(1), 49–72.

    Article  Google Scholar 

  • Huerta, M., & Jackson, J. (2010). Connecting literacy and science to increase achievement for English language learners. Early Childhood Education Journal, 38(3), 205–211. https://doi.org/10.1007/s10643-010-0402-4.

    Article  Google Scholar 

  • Johnson, C. C. (2007). Effective science teaching, professional development and no child left behind: Barriers, dilemmas, and reality. Journal of Science Teacher Education, 18, 133–136.

    Article  Google Scholar 

  • Jordan, R. C., Brooks, W. R., Hmelo-Silver, C., Eberbach, C., & Sinha, S. (2014). Balancing broad ideas with context: An evaluation of student accuracy in describing ecosystem processes after a system-level intervention. Journal of Biological Education, 48(2), 57–62.

    Article  Google Scholar 

  • Kenyon, L., Schwarz, C., & Hug, B. (2008). The benefits of scientific modeling. Science and Children, 46(2), 40–44.

    Google Scholar 

  • Leach, J., Driver, R., Scott, P., & Wood-Robinson, C. (1995). Children’s ideas about ecology 1: Theoretical background, design, and methodology. International Journal of Science Education, 17(6), 721–732.

    Article  Google Scholar 

  • Leach, J., Driver, R., Scott, P., & Wood-Robinson, C. (1996). Children’s ideas about ecology 2: Ideas found in children age 5-16 about the cycling of matter. International Journal of Science Education, 18(1), 19–34.

    Article  Google Scholar 

  • Lee, O., Luykx, A., Buxton, C., & Shaver, A. (2007). The challenge in altering elementary school teachers’ beliefs and practices regarding linguistic and cultural diversity in science instruction. Journal of research in Science Training, 44(9), 1269–1291.

    Google Scholar 

  • Lee, O., Quin, H., & Valdes, G. (2013). Science and language for English language learners in relation to Next Generation Science Standards and with implications for Common Core State Standards for English Language Arts and Mathematic. Educational Researcher, 42(4), 223–233. https://doi.org/10.3102/0013189x13480524.

    Article  Google Scholar 

  • Lemke, J. (2004). The literacies of science. In E. W. Saul (Ed.), Crossing borders in literacy and science instruction (pp. 33–47). Arlington, VA: NSTA Press.

    Google Scholar 

  • MacDonald, R., Miller, E., & Lord, S. (2017). Doing and talking science: Engaging ELs in the discourse of science and engineering practice. In A. W. Oliveira & M. H. Weinburgh (Eds.), Science teacher preparation in content-based second language acquisition (pp. 179–197). The Netherlands: Springer Publishers. https://doi.org/10.1007/978-3-319-43516-9_10.

  • Medina-Jerez, W., Clark, D., Medina, A., & Ramirez-Marin, F. (2007). Science for ELLs: Rethinking our approach. The Science Teacher, 74(3), 52–56.

    Google Scholar 

  • Miles, M. B., Huberman, A. M., & Saldana, J. (2014). Qualitative data analysis: A methods sourcebook (3rd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.

    Google Scholar 

  • NGSS Lead States. (2013). Next Generation Science Standards. Washington, DC: The National Academies Press.

    Google Scholar 

  • Oliveira, A. W., & Weinburgh, M. H. (2017). Introduction: Science teacher preparation in language and content. In A. W. Oliveira & M. H. Weinburgh (Eds.), Science teacher preparation in content-based second language acquisition (pp. 1–21). London: Springer.

  • Reiner, M., & Eilam, B. (2001). Conceptual classroom environment: A system view of learning. International Journal of Science Education, 23(6), 551–568.

    Article  Google Scholar 

  • Roessingh, H., & Kover, P. (2003). Variability of ESL learners’ acquisition of cognitive academic language proficiency: What can we learn from achievement measures? TESL Canada Journal, 21(1), 1–21.

    Article  Google Scholar 

  • Roessingh, H., Kover, P., & Watt, D. (2005). Developing cognitive academic language proficiency: The journey. TESL Canada Journal, 23(1), 1–27.

    Article  Google Scholar 

  • Schwarz, C., Reiser, B., Davis, E., Kenyon, L., Acher, 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, 6(6), 632–654.

  • Smith, E. (2009). A conceptual change model of learning science. In S. Glynn, R. Yeany, & B. Britton (Eds.), The psychology of learning science (pp. 43–64). New York, NY: Routledge.

    Google Scholar 

  • Snow, C. (2010). Academic language and the challenge of reading for learning about science. Science, 328(1), 450–452.

    Article  Google Scholar 

  • Taboada, A., & Rutherford, V. (2011). Developing reading comprehension and academic vocabulary for English language learners through science content: A formative experiment. Reading Psychology, 32(2), 113–157. https://doi.org/10.1080/02702711003604468.

    Article  Google Scholar 

  • Treagust, D. F. (1993). The evolution of an approach for using analogies in teaching and learning science. Research in Science Education, 23(1), 293–301.

    Article  Google Scholar 

  • Treagust, D. F., Chittleborough, G., & Mamilia, T. (2002). Students’ understanding of the role of scientific models in learning science. International Journal of Science Education, 24(4), 357–368.

    Article  Google Scholar 

  • U.S. Department of Education, National Center for Education Statistics (2017). The condition of education 2017 (2017–144). Retrieved on May 21, 2018 from https://nces.ed.gov/programs/coe/indicator_cgf.asp.

  • Wallis, J. (2013). A call to teachers: Don’t forget the joy. Education Week, 3(8), 27.

    Google Scholar 

  • Weinburgh, M. H., & Silva, C. (2011). Math, science, and models. Science & Children, 48(10), 38–42.

  • Wilson, S., Schweingruber, H., & Nielsen, N. (Eds.). (2015). Science teachers’ learning: Enhancing opportunities, creating supportive contexts (pp. 47–68). Washington, DC: National Academy Press. Retrieved on November 21, 2016 from https://www.nap.edu/read/21836/chapter/5#49.

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Authors and Affiliations

  1. Tarleton State University, Stephenville, TX, USA

    Erin Pearce

  2. Texas Christian University, Fort Worth, TX, USA

    Morgan Stewart, Ummuhan Malkoc, Ronald Ivy & Molly Weinburgh

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  1. Erin Pearce
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  2. Morgan Stewart
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  5. Molly Weinburgh
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Correspondence to Erin Pearce.

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Pearce, E., Stewart, M., Malkoc, U. et al. Utilizing a Dynamic Model of Food Chains to Enhance English Learners’ Science Knowledge and Language Construction. Int J of Sci and Math Educ 18, 887–901 (2020). https://doi.org/10.1007/s10763-019-10004-5

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