Abstract
Do you remember learning how to ride a bike? Or do you remember teaching someone to learn how to ride a bike? Learning to ride a bike or teaching someone to ride a bike is an iterative process where the learner wants to “experiment” too quickly and the teacher tries to impart his/her wisdom so the learner does not make the same mistakes that his/her did. In the end, the learner probably had to repeat many of the same mistakes; and most importantly, no one would have pronounced one of the early experiences as a failure because the learner was not ready to ride in the Tour de France. Learning to teach Project-Based Learning (PBL) effectively requires that an individual practice some of the patience and techniques required to teach someone to ride a bike, patience to allow the learner to take control and become more experienced in the techniques that build upon the expanding experience and knowledge base as a catalyst for accelerated learning. Just as learning to ride a bike – or learning to let the learner learn on his/her own – is not an all or nothing process, learning to learn in a PBL environment and learning to teach in a PBL environment are not all or nothing propositions.
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References
Apedoe, X., Reynolds, B., Ellefson, M., & Schunn,C. (2008). Bringing engineering design into high school science classrooms:The heating/cooling unit. Journal of Science Education and Technology, 17, 454–465.
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.
Baddeley, A. D., & Longman, D. J. (1978). The influence of length and frequency of training session on the rate of learning to type. Ergonomics, 21, 627–635.
Bettencourt, A. (1993). The construction of knowledge: A radical constructivist view. In K. Tobin (Ed.), The practice of constructivism in science education (pp. 39–50). Washington, DC: American Association for Advancement of Science.
Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.
Brunner, D. D. (1994). Inquiry and reflection: Framing narrative practice in education. New York: SUNY Press.
Bell, S. (2010). Project-based learning for the 21st century: Skills for the future. The Clearing House, 83, 39–43.
Brown, A. L. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in instructional psychology (vol. 1, pp. 77–165). Hillsdale. NJ: Erlbaum.
Brown, A. L., & Campione, J. C. (1994). Guided discovery in a community of learners. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 229–270). Cambridge, MA: MIT Press.
Bryan, J., & Slough, S. W. (2009). Converging lens simulation design and image predictions. Physics Education, 44, 264–275.
Bonnstetter, R. J. (1998). Inquiry: Learning from the past with an eye on the future. Electronic Journal of Science Education, 3(1). Retrieved from http://wolfweb.unr.edu/homepage/jcannon/ejse/bonnstetter.html.
Capraro, R. M., & Slough, S. W. (Eds.). (2008). Project-based learning: An integrated science, technology, engineering, and mathematics (STEM) approach. Rotterdam, the Netherlands: Sense.
Capraro, R. M., & Yetkiner, Z. E. (2008). Teachers’ role in developing representational fluency in middle grades. In G. Kulm (Ed.), Teacher knowledge and practice in middle grades mathematics (pp. 273–286). Rotterdam, the Netherlands: Sense.
Clement, J. J., & Steinberg, M. S. (2002). Step-wise evolution of mental models of electric circuits: A “learning-aloud” case study. The Journal of the Learning Sciences, 11(4), 389–452.
Donovan, S. M., & Bransford, J. D. (2005). How students learn: History, mathematics, and science in the classroom. Washington, DC: National Academies Press.
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34(10), 906–911.
Fortus, D., Dershimer, R. C., Krajcik, J. S., Marx, R. W., & Mamlok-Naaman, R. (2004). Design-based science and student learning. Journal of Research in Science Teaching, 41(10), 1081–1110.
Gilbert, J. K., & Boulter, C. J. (Eds.). (2000). Developing models in science education. Dordrecht, the Netherlands: Kluwer.
Goldman, S. R., Petrosino, A. J., & Cognition and Technology Group at Vanderbilt. (1999). Design principles for instruction in content domains: lessons from research on expertise and learning. In F. T. Durso, R. S. Nickerson, R. W. Schvaneveldt, S. T. Dumais, D. S. Lindsay, & M. T. H. Chi (Eds.), Handbook of applied cognition (pp. 595–627). Indianapolis, IN: John Wiley & Sons.
Horsley, D. L., & Loucks-Horsley, S. (1998). CBAM brings order to the tornado of change. Journal of Staff Development, 19(4), 17–20.
Huber, R. A., & Moore, C. J. (2001). A model for extending hands-on science to be inquiry based. School Science and Mathematics, 101, 32–42.
Johnson, D. W., & Johnson, R. (1989). Cooperation and competition: Theory and research. Edina, MN: Interaction Book Company.
Kanter, D. E. (2009). Doing project and learning the content: Designing project-based science curricula for meaningful understanding. Science Education, 94, 525–551.
Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B. B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design into practice. Journal of the Learning Sciences, 12(4), 495–547.
Koschmann, T., Kelson, A. C., Feltovich, P. J., & Barrows, H. S. (1996). Computer-supported problem-based learning: A principled approach to the use of computers in collaborative learning. In T. D. Koschmann (Ed.), CSCL: Theory and practice of an emerging paradigm (pp. 83–124). Mahwah, NJ: Erlbaum.
Kozma, R. B. (1999). The use of multiple representations and the social construction of understanding in chemistry. In M. J. Jacobson & R. B. Kozma (Eds.), Innovations in science and mathematics education (pp. 11–46). Mahwah, NJ: Erlbaum.
Krajcik, J. S., Blumenfeld, P. C., Marx, R. W., Bass, K. M., Fredricks, J., & Soloway, E. (1998). Inquiry in project-based science classrooms: Initial attempts by middle school students. The Journal of the Learning Sciences, 7, 313–350.
Krajcik, J. S., Czerniak, M. C., & Berger, C. (1999). Teaching children science: A project-based approach (pp. 5–25). Boston, MA: McGraw-Hill.
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.
Lehrer, R., & Schauble, L. (2000). Modeling in mathematics and science. In R. Glaser (Ed.), Advances in instructional psychology. Mahwah, NJ: Erlbaum.
Linn, M. C., Davis, E. A., & Bell, P. B. (Eds.). (2004). Internet environments for science education. Mahwah, NJ: Lawrence Erlbaum Associates.
Linn, M. C., Davis, E. A., & Eylon, B. S. (2004). The scaffolded knowledge integration framework for instruction. In M. C. Linn, E. A. Davis, & P. Bell (Eds.), Internet environments for science education (pp. 47–72). Mahwah, NJ: Lawrence Erlbaum Associates.
Muzheve, M. T., & Capraro, R. M. (2011). An exploration of the role natural language and idiosyncratic representations in teaching how to convert among fractions, decimals, and percents. Journal of Mathematical Behavior, 31(1), 1–14. doi: 10.1016/j.jmathb.2011.08.002
Nathan, M. J., Koedinger, K. R., & Alibali, M. W. (August, 2001). Expert blind spot: When content knowledge eclipses pedagogical content knowledge. In L. Chen (Ed.), Proceedings of the Third International Conference on Cognitive Science (pp. 644–648). Beijing: University of Science and Technology of China Press.
Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching and comprehension-fostering and comprehension monitoring activities. Learning Disability Quarterly, 24, 15–32.
Parker, D., Donahue, M., Stillisano, J., Capraro, M. M., Goldsby, D., Yetkiner, Z. E., & Capraro, R. M. (2007, November). Communication and representations. Paper presented at the National Council of Teachers of Mathematics regional conference. Houston, TX.
Pea, R. D. (1987). Socializing the knowledge transfer problem. International Journal of Educational Research, 11, 639–663.
Penner, D. E., Giles, N. D., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34(2),125–143.
Pintrich, P.R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory into Practice, 41(4), 219–225.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82 (1), 33–40.
Roehrig, G. T., Moore, T. J., Wang, H.-H., & Park, M. S. (2012). Is adding the E enough? Investigating the impact of K-12 engineering standards on the implementation of STEM integration. School Science and Mathematics, 112, 31–44.
Schauble, L., Glaser, R., Duschl, R. A., Schulze, S., & John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. Journal of Learning Sciences, 4, 131–166.
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475.
Settlage, J. (2007). Demythologizing science teacher education: Conquering the false ideal of open inquiry. Journal of Science Teacher Education, 18, 461–467.
Slough, S. W., & Milam, J. (2007, October). Defending the mythology of open inquiry: A novel conceptual framework. Paper presented at the Southwest Association of Science Teacher Education Conference 2007, Ft. Worth, TX.
Schneps, M. H., & Sadler, P. M. (1987). Harvard-Smithsonian Center for Astrophysics, Science Education Department, Science Media Group. A Private Universe. Video. Washington, DC: Annenberg/CPB.
Swanson, H. L. (1990). Influence of metacognitive knowledge and aptitude on problem solving. Journal of Educational Psychology, 82(2), 306–14.
Vye, N. J., Schwartz, D. L., Bransford, J. D., Barron, B. J., Zech, L., & The Cognition and Technology Group at Vanderbilt. (1998). SMART environments that support monitoring, reflection, and revision. In D. J. Hacker, A. C. Graesser, & J. Dunlosky (Eds.), Metacognition in educational theory and practice (pp. 305–346). Mahwah, NJ: Erlbaum.
Vygotsky, L.S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
Wong, H. K., & Wong, R.T. (1998). The first days of school: How to be an effective teacher. Mountain View, CA: Wong Publications.
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Slough, S.W., Milam, J.O. (2013). Theoretical Framework for the Design of STEM Project-Based Learning. In: Capraro, R.M., Capraro, M.M., Morgan, J.R. (eds) STEM Project-Based Learning. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6209-143-6_3
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DOI: https://doi.org/10.1007/978-94-6209-143-6_3
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