Skip to main content

Modeling for Dynamic Mathematics

Toward Technology-Integrated Aesthetic Experiences in School Mathematics

  • Chapter
  • First Online:
Emerging Technologies for STEAM Education

Abstract

Dynamic mathematics learning technologies support a model-centered approach to mathematics teaching and learning, which enhances students’ and teachers’ mathematical experience in the STEM disciplines toward mathematical understanding and aesthetic feelings. This chapter first reviews the theoretical foundation of model-centered learning and instruction and then elaborates a model-centered prospective on the teaching and learning of middle and high school mathematics, integrating emerging dynamic and interactive mathematical learning technologies (e.g., GeoGebra) and drawing illustrative cases from recent mathematics teacher development projects and classroom experiments. Specific mathematical topics are discussed to showcase the transformative nature of dynamic mathematics in supporting sense-making and enriching classroom discussions. Using mathematical modeling and didactical modeling as a primary theme, this chapter illustrates the generative power of emerging digital technologies in addressing new standards for mathematical practices. Modeling not only provides a pathway toward mathematical understanding and self-assessment, but also allows students to experience the aesthetic dimension of mathematical inquiry in the broad context of STEAM education.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. Current Directions in Psychological Science, 11(5), 181–185.

    Article  Google Scholar 

  • Battista, M. T. (2008). Development of the shape makers geometry microworld: Design principles and research. In G. W. Blume & M. K. Heid (Eds.), Research on technology and the teaching and learning of mathematics: Cases and perspectives (Vol. 2, pp. 131–156). Charlotte: Information Age Publishing.

    Google Scholar 

  • Booch, G. (1994). Object-oriented analysis and design with applications (2nd ed.). Menlo Park: Addison-Wesley.

    Google Scholar 

  • Borwein, J., & Devlin, K. (2009). The computer as crucible: An introduction to experimental mathematics. Wellesley: A K Peters.

    Google Scholar 

  • Bruner, J. (1991). The narrative construction of reality. Critical Inquiry, 18, 1–21.

    Article  Google Scholar 

  • Bu, L., & Schoen, R. (Eds.). (2011). Model-centered learning: Pathways to mathematical understanding using geogebra. Rotterdam: Sense Publishers.

    Book  Google Scholar 

  • Bu, L., Mumba, F., Henson, H., & Wright, M. (2013). Geogebra in professional development: The experience of rural inservice elementary (k-8) teachers. Mevlana International Journal of Education, 3(3), 64–76.

    Article  Google Scholar 

  • Council of Chief State School Officers. (2010). Common core state standards for mathematics. Washington, D. C.: National Governors Association Center for Best Practices, Council of Chief State School Officers. http://www.corestandards.org/math.

    Google Scholar 

  • Darling-Hammond, L., Barron, B., Pearson, P. D., Schoenfeld, A. H., Stage, E. K., Zimmerman, T. D., et al. (2008). Powerful learning: What we know about teaching for understanding. San Francisco: Jossey-Bass.

    Google Scholar 

  • de Jong, T., & van Joolingen, W. R. (2008). Model-facilitated learning. In J. M. Spector, M. D. Merrill, J. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 457–468). New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • diSessa, A. A. (2007). Systemics of learning for a revised pedagogical agenda. In R. A. Lesh, E. Hamilton, & J. J. Kaput (Eds.), Foundations for the future in mathematics education (pp. 245–261). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Doerr, H. M., & Pratt, D. (2008). The learning of mathematics and mathematical modeling. In M. K. Heid & G. W. Blume (Eds.), Research on technology and the teaching and learning of mathematics: Research syntheses (Vol. 1, pp. 259–285). Charlotte: Information Age Publishing.

    Google Scholar 

  • Dreyfus, T., & Eisenberg, T. (1986). On the aesthetics of mathematical thought. For the Learning of Mathematics, 6(1), 2–10.

    Google Scholar 

  • Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Educational Studies in Mathematics, 61, 103–131.

    Article  Google Scholar 

  • Eisner, E. W. (2002). The arts and the creation of mind. New Haven: Yale University Press.

    Google Scholar 

  • Eseryel, D., Ge, X., Ifenthaler, D., & Law, V. (2011). Dyanmic modeling as a cognitive regulation scaffold for developing complex problem solving skills in an educational massively multiplayer online game environment. Journal of Educational Computing Research, 45(3), 265–286.

    Article  Google Scholar 

  • Fey, J. T. (2006). Connecting technology and school mathematics: A review of the didactical challenge of symbolic calculators: Turning a computational device into a mathematical instrument. Journal for Research in Mathematics Education, 36, 348–352.

    Google Scholar 

  • Freudenthal, H. (1973). Mathematics as an educational task. Dordrecht: D. Reidel Publishing.

    Google Scholar 

  • Freudenthal, H. (1978). Weeding and sowing: Preface to a science of mathematics education. Dordrecht: D. Reidel Publishing.

    Google Scholar 

  • Freudenthal, H. (1983). Didactical phenomenology of mathematical structures. New York: Kluwer Academic Publishers.

    Google Scholar 

  • Ge, X., & Land, S. M. (2004). A conceptual framework for scaffolding ill-structured problem-solving processes using questions prompts and peer interactios. Educational Technology Research and Development, 52(2), 5–22.

    Article  Google Scholar 

  • Gojak, L. M. (2014). A reflection on 25 years in mathematics education. http://www.nctm.org/about/content.aspx?id=41883.

  • Goldin, G. (2007). Aspects of affect and mathematical modeling processes. In R. A. Lesh, E. Hamilton, & J. J. Kaput (Eds.), Foundations for the future in mathematics education (pp. 281–299). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gravemeijer, K. (1994). Educational development and developmental research in mathematics education. Journal for Research in Mathematics Education, 25, 443–471.

    Article  Google Scholar 

  • Gravemeijer, K. (2008). Learning mathematics: The problem of learning abstract knowledge. In J. M. Spector, M. D. Merrill, J. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 545–549). New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gravemeijer, K., & van Galen, F. (2003). Facts and algorithms as products of students’ own mathematical activity. In J. Kilpatrick, W. G. Martin, & D. Schifter (Eds.), A research companion to principles and standards for school mathematics (pp. 114–122). Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Gravemeijer, K., Cobb, P., Bowers, J., & Whitenack, J. (2000). Symbolizing, modeling, and instructional design. In P. Cobb, E. Yackel, & K. McClain (Eds.), Symbolizing and communicating in mathematics classrooms: Perspectives on discourse, tools, and instructional design (pp. 225–273). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gresham, G. (2007). A study of mathematics anxiety in pre-service teachers. Early Childhood Education Journal, 35(2), 181–188.

    Article  Google Scholar 

  • Groff, J. (2013). Expanding our “frames” of mind for education and the arts. Harvard Educational Review, 83(1), 15–39.

    Article  Google Scholar 

  • Hardy, G. H. (1940/1967). A mathematician’s apology. London: Cambridge University Press. (Original work published 1940).

    Google Scholar 

  • Hegedus, S. J., & Moreno-Armella, L. (2009). Introduction: The transformative nature of “Dynamic” Educational technology. Zentralblatt für Didaktik der Mathematik, 41, 397–398.

    Article  Google Scholar 

  • Hohenwarter, M., & Preiner, J. (2007). Dynamic mathematics with geogebra. Journal of Online Mathematics and Its Applications, 7. http://mathdl.maa.org/mathDL/4/?pa=content&sa=viewDocument&nodeId=1448.

  • Ifenthaler, D., & Seel, N. M. (2010). From model-based to schema-based reasoning: Looking ofr the magic number x. Paper presented at the 2010 American Educational Research Association.

    Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2011). A longitudinal perspective on inductive reasoning tasks. Illuminating the probability of change. Learning and Instruction, 21(4), 538–549.

    Article  Google Scholar 

  • Johnson, M. (2007). The meaning of the body: Aesthetics of human understanding. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Johnson-Laird, P. N. (1983). Mental models: Toward a cognitive science of language, inference, and consciousness. Cambridge: Harvard University Press.

    Google Scholar 

  • Jonassen, D. H. (1996). Computers in the classroom: Mindtools for critical thinking. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Jonassen, D. H. (2006). Modeling with technology: Mindtools for conceptual change (3rd ed.). Upper Saddle River: Pearson.

    Google Scholar 

  • Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving environments. New York: Routledge.

    Google Scholar 

  • Kaput, J. (1992). Technology and mathematics education. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 515–556). New York: Macmillan.

    Google Scholar 

  • Kaput, J., & Schorr, R. (2008). Changing representational infrastructures changes most everything: The case of simcalc, algebra, and calculus. In G. W. Blume & M. K. Heid (Eds.), Research on technology and the teaching and learning of mathematics: Cases and perspectives (Vol. 2, pp. 211–253). Charlotte: Information Age Publishing.

    Google Scholar 

  • Kaput, J., & Thompson, P. W. (1994). Technology in mathematics education research: The first 25 years in the jrme. Journal for Research in Mathematics Education, 25, 676–684.

    Article  Google Scholar 

  • Kaput, J., Noss, R., & Hoyles, C. (2002). Developing new notations for a learnable mathematics in the computational era. In L. D. English (Ed.), Handbook of international research in mathematics education (pp. 51–75). Mahwah: Lawrence and Erlbaum Associates.

    Google Scholar 

  • Kaput, J., Hegedus, S., & Lesh, R. (2007). Technology becoming infrastructural in mathematics education. In R. A. Lesh, E. Hamilton, & J. Kaput (Eds.), Foundations for the future in mathematics education (pp. 173–191). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Kelly, B. (2003). The emergence of technology in mathematics education. In G. M. A. Stanic & J. Kilpatrick (Eds.), A history of school mathematics (Vol. 2, pp. 1037–1081). Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Lesh, R., & Doerr, H. M. (Eds.). (2003). Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching. Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Martinovic, D., & Karadag, Z. (2012). Dynamic and interactive mathematics learning environments: The case of teaching the limit concept. Teaching Mathematics and its Applications, 31(1), 41–48.

    Article  Google Scholar 

  • Merrill, M. D. (2000). Knowledge objects and mental models. The instructional use of learning objects: Online version.

    Google Scholar 

  • Milrad, M., Spector, J. M., & Davidsen, P. I. (2003). Model facilitated learning. In S. Naidu (Ed.), Learning and teaching with technology: Principles and practices (pp. 13–27). London: Kogan Page.

    Google Scholar 

  • Minsky, M. (2006). The emotion machine: Commonsense thinking, artificial intelligence, and the future of the human mind. New York: Simon & Schuster.

    Google Scholar 

  • Mooney, D. D., & Swift, R. J. (1999). A course in mathematical modeling. Washington, D. C.: Mathemaitcal Association of America.

    Google Scholar 

  • Moreno-Armella, L., & Hegedus, S. (2009). Co-action with digital technologies. ZDM, 41, 505–519.

    Article  Google Scholar 

  • Moreno-Armella, L., Hegedus, S. J., & Kaput, J. J. (2008). From static to dynamic mathematics: Historical and representational perspectives. Educational Studies in Mathematics, 68, 99–111.

    Article  Google Scholar 

  • National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston: Author.

    Google Scholar 

  • National Research Council. (1989). Everybody counts: A report to the nation on the future of mathematics education. Washington, D. C.: National Academy of Sciences.

    Google Scholar 

  • National Research Council. (2000). How people learn: Brain, mind, experience, and school (expanded edition): Committee on Developments in the Science of Learning. Washington, D. C.: National Academies Press.

    Google Scholar 

  • National Research Council. (2001). Adding it up: Helping children learn mathematics. In J. Kilpatrick, J. Swafford, & B. Findell (Eds.), Mathematics learning study committee, center for education, division of behavioral and social sciences and education. Washington, D. C.: National Academy Press.

    Google Scholar 

  • National Research Council. (2005). How students learn: Mathematics in the classroom. Washington, D. C.: National Academies Press. (Committee on how people learn, a targeted report for teachers, M.S. Donovan and J.D. Bransford, Editors. Division of behavioral and social sciences and education).

    Google Scholar 

  • National Research Council. (2010). Preparing teachers: Building evidence for sound policy. Committee on the study of teacher preparation programs in the United States, Center for Education. Division of behavioral and social sciences and education. Washington, D. C.: National Academies Press.

    Google Scholar 

  • Nickerson, R. S. (1985). Understanding understanding. American Journal of Education, 93, 201–239.

    Article  Google Scholar 

  • Norman, D. A. (1983). Some observations on mental models. In D. Gentner & A. L. Stevens (Eds.), Mental models (pp. 7–14). Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Perkins, D. N. (1986). Knowledge as design. Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Perkins, D. N. (1993). Teaching for understanding. American Educator, 17(3), 8, 28–35.

    Google Scholar 

  • Perkins, D. N. (2009). Making learning whole: How seven principles of teaching can transform education. San Francisco: Jossey-Bass.

    Google Scholar 

  • Pimm, D., & Sinclair, N. (2006). Aesthetics and the ‘mathematical mind’. In N. Sinclair, D. Pimm, & W. Higginson (Eds.), Mathematics and the aesthetic: New approaches to an ancient affinity (pp. 223–254). New York: Springer.

    Google Scholar 

  • Pollak, H. O. (2003). A history of the teaching of modeling. In G. M. A. Stanic & J. Kilpatrick (Eds.), A history of school mathematics (Vol. 1, pp. 647–671). Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Polya, G. (1945/2004). How to solve it: A new aspect of mathematical method (2nd ed.). Princeton: Princeton University Press.

    Google Scholar 

  • Polya, G. (1981). Mathematical discovery: On understanding, learning, and teaching problem solving (Combined ed.). New York: Wiley.

    Google Scholar 

  • Resnick, L. B., & Ford, W. W. (1981). The psychology of mathematics for instruction. Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Ryan, J., & Williams, J. (2007). Children’s mathematics 4–15: Learning from errors and misconceptions. New York: Open University Press.

    Google Scholar 

  • Salomon, G., Perkins, D. N., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 20(3), 2–9.

    Article  Google Scholar 

  • Schoenfeld, A. H. (1985). Mathematical problem solving. Orlando: Academic Press.

    Google Scholar 

  • Schwartz, J. L. (2007). Models, simulations, and exploratory environments: A tentative taxonomy. In R. A. Lesh, E. Hamilton, & J. J. Kaput (Eds.), Foundations for the future in mathematics education (pp. 161–171). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Seel, N. M. (2003). Model-centered learning and instruction. Technology, Instruction, Cognition and Learning, 1, 59–85.

    Google Scholar 

  • Seel, N. M. (2005). Designing model-centered learning environments: Hocus-pocus or the focus must strictly be on locus. In J. M. Spector, C. Ohrazda, A. van Schaack, & D. A. Wiley (Eds.), Innovations in instructional technology: Essays in honor of m. David Merrill (pp. 65–90). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Seel, N. M. (2008). Empirical perspectives on memory and motivation. In J. M. Spector, M. D. Merrill, J. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 39–54). New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • Seel, N. M. (2014). Model-based learning and performance. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 465–484). New York: Springer.

    Chapter  Google Scholar 

  • Seel, N. M., & Blumschein, P. (2009). Modeling and simulation in learning and instruction: A theoretical perspective. In P. Blumschein, W. Hung, D. Jonassen, & J. Strobel (Eds.), Model-based approaches to learning: Using systems models and simulations to improve understanding and problem solving in complex domains (pp. 3–15). Rotterdam: Sense Publishers.

    Google Scholar 

  • Sfard, A. (1991). On the dual nature of mathematical conceptions: Reflections on processes and objects as different sides of the same coin. Educational Studies in Mathematics, 22, 1–36.

    Article  Google Scholar 

  • Silver, E. A. (1986). Using conceptual and procedural knowledge: A focus on relationships. In J. Hilbert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 181–198). Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Silver, E. A., Mesa, V. M., Morris, K. A., Star, J. R., & Benken, B. M. (2009). Teaching mathematics for understanding: An analysis of lessons submitted by teachers seeking nbpts certification. American Educational Research Journal, 46, 501–531.

    Article  Google Scholar 

  • Sinclair, N. (2006). Mathematics and beauty: Aesthetic approahces to teaching children. New York: Teachers College Press.

    Google Scholar 

  • Sinclair, N., Pimm, D., & Higginson, W. (Eds.). (2006). Mathematics and the aesthetic: New approaches to an ancient affinity. New York: Springer.

    Google Scholar 

  • Skemp, R. R. (1978). Relational understanding and instrumental understanding. Arithmetic Teacher, 26(3), 9–15.

    Google Scholar 

  • Skemp, R. R. (2006). Relational understanding and instrumental understanding. Mathematics Teaching in the Middle School, 12(2), 88–96. (Original work published in 1976).

    Google Scholar 

  • Smith, M. S., & Stein, M. K. (2011). Five practices for orchestrating productive mathematics discussions. Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Somervell, E. L. (1906/1975). A rhythmic approach to mathematics. Reston: National Council of Teachers of Mathematics. (ERIC Document Reproduction Service No. ED108964). Retrieved, June 22, 2009. from ERIC.

    Google Scholar 

  • Spector, J. M. (2000). Building theory into practice in learning and instruction. In J. M. Spector & T. M. Anderson (Eds.), Integrated and holistic perspectives on learning, instruction and technology: Understanding complexity (pp. 79–90). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Spector, J. M. (2008). Cognition and learning in the digital age: Promising research and practice. Computers in Human Behavior, 24(2), 249–262.

    Article  Google Scholar 

  • Spector, J. M. (2009). Foreword. In P. Blumschein, W. Hung, D. Jonassen, & J. Strobel (Eds.), Model-based approaches to learning: Using systems models and simulations to improve understanding and problem solving in complex domains (pp. ix–x). Rotterdam: Sense Publishers.

    Google Scholar 

  • Streefland, L. (Ed.). (1991). Fractions in realistic mathematics education: A paradigm of developmental research. Dordrecht: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Streefland, L., & van den Heuvel-Panhuizen, M. (1998). Uncertainty, a metaphor for mathematics education? The Journal of Mathematical Behavior, 17, 393–397.

    Article  Google Scholar 

  • Stroup, W. M., Ares, N. M., & Hurford, A. C. (2005). A dialectic analysis of generativity: Issues of network-supported design in mathematics and science. Mathematical Thinking and Learning, 7, 181–206.

    Article  Google Scholar 

  • Stroup, W. M., Ares, N., Hurford, A. C., & Lesh, R. (2007). Diversity-by-design: The what, why, and how of generativity in next-generation classroom networks. In R. A. Lesh, E. Hamilton, & J. J. Kaput (Eds.), Foundations for the future in mathematics education (pp. 367–393). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Tobias, S. (1993). Overcoming math anxiety (revised and expanded). New York: W. W. Norton & Company.

    Google Scholar 

  • Treffers, A. (1987). Three dimensions: A model of goal and theory description in mathematics instruction–the wiskobas project. Dordrecht: D. Reidel Publishing Company.

    Book  Google Scholar 

  • Van den Heuvel-Panhuizen, M. (2003). The didactical use of models in realistic mathematics education: An example from a longitudinal trajectory on percentage. Educational Studies in Mathematics, 54, 9–35.

    Article  Google Scholar 

  • Vygotsky, L. S. (1997). Educational psychology (R. Silverman, Trans.). Boca Raton: St. Lucie Press. (Original work published 1926).

    Google Scholar 

  • Yackel, E., & Cobb, P. (1996). Sociomathematical norms, argumentation, and autonomy in mathematics. Journal for Research in Mathematics Education, 27, 458–477.

    Article  Google Scholar 

  • Zbiek, R., & Conner, A. (2006). Beyond motivation: Exploring mathematical modeling as a context for deepening students’ understandings of curricular mathematics. Educational Studies in Mathematics, 63, 89–112.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and editors for their constructive comments and suggestions. The article makes references to findings of a project supported by the Illinois School Board of Education through an MSP Grant (#4963-80-30-039-5400-51). The analyses and views expressed in the article belong to the authors and do not necessarily reflect those of the funding agency.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lingguo Bu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Bu, L., Hohenwarter, M. (2015). Modeling for Dynamic Mathematics. In: Ge, X., Ifenthaler, D., Spector, J. (eds) Emerging Technologies for STEAM Education. Educational Communications and Technology: Issues and Innovations. Springer, Cham. https://doi.org/10.1007/978-3-319-02573-5_19

Download citation

Publish with us

Policies and ethics