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Articles

From classroom lessons to exploratory learning progressions: mathematics + computational thinking

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Pages 362-382 | Received 28 Nov 2017, Accepted 27 Aug 2019, Published online: 21 Oct 2019

References

  • Begel, A. (1996). Logoblocks: A graphical programming language for interacting with the world (pp. 62–64). Boston, MA: Electrical Engineering and Computer Science Department, MIT.
  • Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.
  • Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, BC (Vol. 1, p. 25).
  • Bryan, L. A., Moore, T. J., Johnson, C. C., & Roehrig, G. H. (2015). Integrated STEM education. In C. C. Johnson, E. E. Peters-Burton, & T. J. Moore (Eds.), STEM road map (pp. 23–37). New York, NY: Routledge.
  • Burns, M. (1994). The greedy triangle. New York, NY: Scholastic.
  • Clements, D. H., & Sarama, J. (2004). Mathematical thinking and learning trajectories in mathematics education learning trajectories in mathematics education. Mathematical Thinking and Learning, 6(2), 81–89.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46.
  • Computer Science Teachers Association. (2017). CSTA K-12 computer science standards, revised 2017. Retrieved from https://www.csteachers.org/page/standards
  • Confrey, J., Maloney, A. P., & Corley, A. K. (2014, October). Learning trajectories: A framework for connecting standards with curriculum. ZDM Mathematics Education, 46(5), 719–733. doi: 10.1007/s11858-014-0598-7
  • Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research. Los Angeles, CA: SAGE Publications.
  • Cuoco, A., Goldenberg, E. P., & Mark, J. (1996). Habits of mind: An organizing principle for mathematics curricula. The Journal of Mathematical Behavior, 15(4), 375–402.
  • Daro, P., Mosher, F. A., & Corcoran, T. B. (2011). Learning trajectories in mathematics: A foundation for standards, curriculum, assessment, and instruction (CPRE Research Reports). Retrieved from https://repository.upenn.edu/cgi/viewcontent.cgi?article=1019&context=cpre_researchreports
  • Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). (2007). Taking science to school: Learning and teaching science in grades K-8 (Vol. 49, No. 2, pp. 163–166). Washington, DC: National Academies Press.
  • English, L. D. (2017). Advancing elementary and middle school stem education. International Journal of Science and Mathematics Education, 15(1), 5–24.
  • Ferrini-Mundy, J., & Martin, W. G. (2000). Principles and standards for school mathematics. Reston, VA: National Council of Teachers of Mathematics (NCTM).
  • Glaser, B. G., & Strauss, A. L. (1967). Grounded theory: The discovery of grounded theory. Sociology, the Journal of the British Sociological Society, 12, 27–49.
  • Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.
  • Harden, R. M. (1999). What is a spiral curriculum? Medical Teacher, 21(2), 141–143.
  • Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive Learning Environments, 1(1), 1–32.
  • Jona, K., Wilensky, U., Trouille, L., Horn, M., Orton, K., Weintrop, D., & Beheshti, E. (2014). Embedding computational thinking in science, technology, engineering, and math (CT-STEM). Future directions in computer science education summit meeting, Orlando, FL.
  • Kiray, S. A. (2012). A new model for the integration of science and mathematics: The balance model. Energy Education Science and Technology Part B: Social and Educational Studies, 4(3), 1181–1196.
  • Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K-8 curriculum. ACM Inroads, 5(4), 64–71.
  • Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., … Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37.
  • Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education, 10(4), 1–15. doi: 10.1145/1868358.1868363
  • Masters, G. (2016). Policy insights: Five challenges in Australian school education. Melbourne: Australian Council for Educational Research. Retrieved from https://research.acer.edu.au/cgi/viewcontent.cgi?article=1004&context=policyinsights
  • Mateas, V. (2016). Debunking myths about the standards for mathematical practice. Mathematics Teaching in the Middle School, 22(2), 92–99.
  • Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1 (2), Art. 20. Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/1089/2385#gcit.
  • National Research Council. (2005). On evaluating curricular effectiveness: Judging the quality of K–12 mathematics evaluations. Washington, DC: National Academy Press.
  • Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social & behavioral research (pp. 397–430). Thousand Oaks, CA: Sage Publications.
  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books.
  • Papert, S. (2006). Afterword: After How comes what. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 581–586). Cambridge: Cambridge University Press.
  • Pei, C., Weintrop, D., & Wilensky, U. (2018). Cultivating computational thinking practices and mathematical habits of mind in lattice land. Mathematical Thinking and Learning, 20(1), 75–89.
  • Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K.,  …  Kafai, Y. B. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60–67.
  • Rich, K. M., Strickland, C., Binkowski, T. A., Moran, C., & Franklin, D. (2017, August 18–20). K-8 learning trajectories derived from research literature: Sequence, repetition, conditionals. Proceedings of the 2017 ACM conference on International Computing Education Research, Tacoma, WA (pp. 182–190). New York, NY: ACM.
  • Schanzer, E., Fisler, K., Krishnamurthi, S., & Felleisen, M. (2015, March 4–7). Transferring skills at solving word problems from computing to Algebra through bootstrap. Proceedings of the 46th ACM technical symposium on Computer Science Education, Kansas City, MO (pp. 616–621). New York, NY: ACM.
  • Sneider, C., Stephenson, C., Schafer, B., & Flick, L. (2014). Teacher's toolkit: Exploring the science framework and NGSS: Computational thinking in the science classroom. Science Scope, 38(3), 10.
  • Standards for Mathematical Practice. Common Core State Standards Initiative. (2019). Retrieved October 11, 2019, from Corestandards.org website: http://www.corestandards.org/Math/Practice/
  • Vasquez, J. A. (2015). STEM—Beyond the Acronym. Educational Leadership, 72(4), 10–15.
  • Vasquez, J. A., Comer, M., & Sneider, C. (2013). STEM lesson essentials, grades 3–8: Integrating science, technology, engineering, and mathematics. Portsmouth, NH: Heineman.
  • Waterman, K., Goldsmith, L., Pasquale, M., Goldenberg, E. P., Malyn-Smith, J., DeMallie, A., & Lee, I. A. (2018). Integrating computational thinking into elementary mathematics and science curriculum materials and instruction. Pixel (Ed.), Conference proceedings: The Future of Education 2018. Florence: Libreria Universitaria Edizioni.
  • 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.
  • Weintrop, D., & Holbert, N. (2017, March 8–11). From blocks to text and back: Programming patterns in a dual-modality environment. Proceedings of the 2017 ACM technical symposium on Computer Science Education, Seattle, WA (pp. 633–638). New York, NY: ACM.

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