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Representational Fluency: A Means for Students to Develop STEM Literacy

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Towards a Framework for Representational Competence in Science Education

Part of the book series: Models and Modeling in Science Education ((MMSE,volume 11))

Abstract

The problems that we face in our ever-changing, increasingly global society are multidisciplinary, and many require the integration of multiple Science, Technology, Engineering, and Mathematics (STEM) concepts to solve them. These problems are the driving force behind national calls for more and stronger students in the pipeline to enter into STEM fields (National Academy of Sciences 2006; National Center on Education and the Economy [NCEE] 2007). However, attempts to motivate students to want to enter the current pipeline into STEM fields is most likely not going to work. What is needed is a new trajectory to success that focuses on understandings and abilities that are more consistent with the new kinds of math/science/engineering thinking that are emerging to be most important in a technology-based age of information. As the problems in this technology-based age are multidisciplinary in nature, we believe that a STEM integration approach must be used to prepare students to be competitive in the twenty-first century. Therefore, research needs to be done that helps realize the most effective ways for students to learn and engage with STEM concepts in a multi-disciplinary manner, teachers to understand and implement STEM integration approaches, and curriculum to be developed to foster these new multi-disciplinary understandings in the classroom. In this paper, we focus on representational fluency within STEM integration in the K-12 system.

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References

  • Abd-El-Khalick, F., Boujaoude, S., Duschl, R., Lederman, N. G., Mamlok-Naaman, R., Hofstein, A., Niaz, M., Treagust, D., & Tuan, H. (2004). Inquiry in science education: International perspectives. Journal of Research in Science Teaching, 32, 397–419.

    Google Scholar 

  • Abell, S. K., & Lederman, N. G. (Eds.). (2007). Handbook of research on science teaching. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • ABET. (2017). General criterion 5. Curriculum. Retrieved from http://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2017-2018/#outcomes

  • American Association for the Advancement of Science. (1993). Benchmarks for science literacy. New York: Oxford University Press.

    Google Scholar 

  • Anzai, Y. (1991). Learning and use of representations for physics expertise. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 64–92). Cambridge, MA: Cambridge University Press.

    Google Scholar 

  • Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379.

    Article  Google Scholar 

  • Ball, D. L. (1993). With an eye on the mathematical horizon: Dilemmas of teaching elementary school mathematics. Elementary School Journal, 94(4), 373–397.

    Article  Google Scholar 

  • Bodner, G. M. (1991). I have found you an argument: The conceptual knowledge of beginning chemistry graduate students. Journal of Chemical Education, 68, 385–388.

    Article  Google Scholar 

  • Borko, H., & Putnam, R. T. (1996). Learning to teach. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of education psychology (pp. 673–708). New York: Macmillan.

    Google Scholar 

  • Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97, 369–387.

    Article  Google Scholar 

  • Bunce, D. M., & Gabel, D. (2002). Differential effects on the achievement of males and females of teaching the particulate nature of chemistry. Journal of Research in Science Teaching, 39, 911–927.

    Article  Google Scholar 

  • Burkam, D. T., Lee, V. E., & Smerdon, B. A. (1997). Gender and science learning early in high school: Subject matter and laboratory experiences. American Educational Research Journal, 34, 297–331.

    Article  Google Scholar 

  • Clements, D. H. (2007). Curriculum research: Toward a framework for “research-based curricula”. Journal for Research in Mathematics Education, 38, 35–70.

    Google Scholar 

  • Cohen, D., & Hill, H. (2000). Instructional policy and classroom performance: The mathematics reform in California. Teachers College Record, 102, 294–343.

    Article  Google Scholar 

  • Cramer, K. (2003). Using a translation model for curriculum development and classroom instruction. In R. Lesh & H. Doerr (Eds.), Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching (pp. 449–463). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • diSessa, A. A. (2002). Why “conceptual ecology” is a good idea. In M. Limo’n & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 29–60). Dordrecht: Kluwer.

    Google Scholar 

  • Dym, C. (1999). Learning engineering: Design, languages and experiences. Journal of Engineering Education, 88(2), 145–148.

    Article  Google Scholar 

  • Dym, C., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching and learning. Journal of Engineering Education, 94(1), 103–120.

    Article  Google Scholar 

  • Furtak, E. M. (2006). The problem with answers: An exploration of guided scientific inquiry teaching. Science Education, 90, 453–467.

    Article  Google Scholar 

  • Gabel, D. L. (1998). The complexity of chemistry and implications for teaching. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education (pp. 233–248). Dordrecht: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Harwell, M. R., Post, T. R., Maeda, Y., Davis, J. D., Cutler, A. L., Anderson, E., & Kahan, J. A. (2007). Standards-based mathematics curricula and secondary students performance on standardized achievement tests. Journal for Research in Mathematics Education, 38(1), 71–101.

    Google Scholar 

  • Hirsch, L. S., Carpinelli, J. D., Kimmel, H., Rockland, R., & Bloom, J. (2007). The differential effects of pre-engineering curricula on middle school students’ attitudes to and knowledge of engineering careers. Published in the proceeding of the 2007 Frontiers in Education Conference, Milwaukee, WI.

    Google Scholar 

  • Hodson, D. (1988). Experiments in science and science teaching. Educational Philosophy and Theory, 20, 53–66.

    Article  Google Scholar 

  • Hofstein, A., & Lunetta, V. N. (1982). The role of the laboratory in science teaching: Neglected aspects of research. Review of Educational Research, 52, 201–217.

    Article  Google Scholar 

  • Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88(1), 28–54.

    Article  Google Scholar 

  • International Technology Education Association. (2000). Standards for technological literacy: Content for the study of technology. Reston, VA: International Technology Association.

    Google Scholar 

  • Koszalka, T., Wu, Y., & Davidson, B. (2007). Instructional design issues in a cross-institutional collaboration within a distributed engineering educational environment. In T. Bastiaens & S. Carliner (Eds.), Proceedings of world conference on E-learning in corporate, government, healthcare, and higher education 2007 (pp. 1650–1657). Chesapeake, VA: AACE.

    Google Scholar 

  • Leinhardt, G., & Steele, M. D. (2005). Seeing the complexity of standing to the side: Instructional dialogues. Cognition and Instruction, 23(1), 87–163.

    Article  Google Scholar 

  • Lesh, R. (1999). The development of representational abilities in middle school mathematics. In I. E. Sigel (Ed.), Development of mental representation: Theories and applications (pp. 323–350). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Lesh, R., & Doerr, H. (2003). Foundations of a models and modeling perspective on mathematics teaching, learning and problem solving. In R. Lesh & H. Doerr (Eds.), Beyond constructivisim: A models and modeling perspectives on mathematics problem solving, learning and teaching (pp. 3–33). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Lesh, R., Post, T., & Behr, M. (1987). Representations and translations among representations in mathematics learning and problem solving. In C. Janvier (Ed.), Problems of representations in the teaching and learning of mathematics (pp. 33–40). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Lester, F. K., Jr. (Ed.). (2007). Second handbook of research on mathematics teaching and learning. Charlotte, NC: Information Age Publishing.

    Google Scholar 

  • Magnusson, S., Borko, H., & Krajcik, J. (1994). Teaching complex subject matter in science: Insights from an analysis of pedagogical content knowledge. Paper presented at the 1994 Annual meeting of the National Association for Research in Science Teaching, Anaheim, CA..

    Google Scholar 

  • Magnusson, S., Krajcik, J., & Borko, H. (1999). Nature, sources and development of pedagogical content knowledge for science teaching. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge (pp. 95–132). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Massachusetts Department of Education. (2009). Current curriculum frameworks: Science and technology/engineering. Retrieved January 2, 2009 from http://www.doe.mass.edu/frameworks/current.html.

  • Minnesota Department of Education. (2009). Academic standards in science: Draft two complete. Retrieved January 2, 2009 from http://education.state.mn.us/MDE/Academic_Excellence/Academic_Standards/Science/index.html

  • Moore, T. J., Tank, K. M., Glancy, A. W., & Kersten, J. A. (2015). NGSS and the landscape of engineering in K-12 state science standards. Journal of Research in Science Teaching, 52(3), 296–318. https://doi.org/10.1002/tea.21199.

    Article  Google Scholar 

  • Museum of Science Boston. (2009). Engineering is elementary engineering design process. Retrieved April 15, 2009 from http://www.mos.org/eie/engineering_design.php.

  • National Academy of Sciences, National Academy of Engineering, and Institute of Medicine of the National Academies. (2006). Rising above the gathering storm: Energizing and employing America for a brighter economic future. Washington, DC: National Academies Press.

    Google Scholar 

  • National Academy of Engineering. (2009). What is technology? Retrieved April 14, 2009 from http://www.nae.edu/nae/techlithome.nsf/weblinks /KGRG-55A3ER.

  • National Academy of Engineering, & National Research Council. (2010). Standards for K-12 engineering education? Washington, DC: National Academies Press.

    Google Scholar 

  • National Center on Education and the Economy. (2007). The report of the new commission on the skills of the American workforce. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • National Governors Association Center for Best Practices, & Council of Chief State School Officers. (2010). Common core state standards for English language arts and mathematics. Washington, DC: Author.

    Google Scholar 

  • National Research Council. (1996). National science education standards. Washington DC: National Academy Press.

    Google Scholar 

  • National Research Council. (2000). Inquiry and the national science education standards: A guide for teaching and learning. Washington DC: National Academy Press.

    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: For states, By states. Washington, DC: National Academic Press. Retrieved from http://www.nextgenscience.org/.

  • Nurrenbern, S. C., & Pickering, N. (1987). Concept learning versus problem solving: Is there a difference. Journal of Chemical Education, 64(6), 508–510.

    Article  Google Scholar 

  • Oregon Department of Education. (2009). Oregon science K-HS content standards. Retrieved April 27, 2009 from http://www.ode.state.or.us/teachlearn/subjects/science/curriculum/2009feb-adopted-k-h-science-standards.pdf.

  • Post, T. R., Harwell, M. R., Davis, J. D., Maeda, Y., Cutler, A., Anderson, E., Kahan, J. A., & Norman, K. W. (2008). Standards-based mathematics curricula and middle-grades students performance on standardized achievement tests. Journal for Research in Mathematics Education, 39(2), 184–212. .

    Google Scholar 

  • Powell, J., & Anderson, R. D. (2002). Changing teachers’ practice: Curriculum materials and science education reform in the USA. Studies in Science Education, 37, 107–135.

    Article  Google Scholar 

  • Schoenfeld, A. S. (1998). Toward a theory of teaching-in-context. Issues in Education, 4(1), 1–95.

    Article  Google Scholar 

  • Sherin, M. G. (2002). When teaching becomes learning. Cognition and Instruction, 20(2), 119–150.

    Article  Google Scholar 

  • Sheppard, S. D., Macantangay, K., Colby, A., & Sullivan, W. M. (2009). Educating engineers: Designing for the future of the field. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching (pp. 4–14). February: Educational Researcher.

    Google Scholar 

  • Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Reviews, 57, 1–22.

    Article  Google Scholar 

  • Stein, M. K., Engle, R. A., Smith, M. S., & Hughes, E. K. (2008). Orchestrating productive mathematical discussions: Five practices for helping teachers move beyond show and tell. Mathematical Thinking and Learning, 10, 313–340.

    Article  Google Scholar 

  • Texas Education Agency. (2009). Curriculum: Texas Essential Knowledge and Skills: Science TEKS. Retrieved January 2, 2009 from http://www.tea.state.tx.us/teks/scienceTEKS.html.

  • von Aufschnaiter, C., Erduran, S., Osborne, J., & Simon, S. (2008). Arguing to learn and learning to argue: Case studies of how students’ argumentation relates to their scientific knowledge. Journal of Research in Science Teaching, 45, 101–131.

    Article  Google Scholar 

  • Von Secker, C. (2002). Effects of inquiry-based teacher practices on science excellence and equity. Journal of Educational Research, 95, 151–160.

    Article  Google Scholar 

  • Zembal-Saul, Z., Blumenfeld, P., & Krajcik, J. (2000). Influence of guided cycles of planning, teaching, and reflection on prospective elementary teachers science content representations. Journal of Research in Science Teaching, 37(4), 318–339.

    Article  Google Scholar 

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Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Tamara J. Moore & S. Selcen Guzey

  2. University of Minnesota, Minneapolis, MN, USA

    Gillian H. Roehrig

  3. Indiana University, Bloomington, IN, USA

    Richard A. Lesh

Authors
  1. Tamara J. Moore
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  2. S. Selcen Guzey
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  3. Gillian H. Roehrig
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  4. Richard A. Lesh
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Correspondence to Tamara J. Moore .

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Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Kristy L. Daniel

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Moore, T.J., Selcen Guzey, S., Roehrig, G.H., Lesh, R.A. (2018). Representational Fluency: A Means for Students to Develop STEM Literacy. In: Daniel, K. (eds) Towards a Framework for Representational Competence in Science Education. Models and Modeling in Science Education, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-89945-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-89945-9_2

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