Promoting computational thinking through project-based learning

Disciplinary and Interdisciplinary Science Education Research - Tập 3 Số 1 - 2021
Namsoo Shin1, Jonathan Bowers1, Joseph Krajcik1, Daniel Damelin2
1CREATE for STEM Institute, Michigan State University, 620 Farm Lane, Suite 115, East Lansing, MI, 48824, USA
2The Concord Consortium, 25 Love Lane, Concord, MA, 01742, USA

Tóm tắt

AbstractThis paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT.

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Tài liệu tham khảo

Bielik, T., Damelin, D., & Krajcik, J. (2019). Shifting the balance: Engaging students in using a modeling tool to learn about ocean acidification. EURASIA Journal of Mathematics, Science and Technology Education, 15(1). https://doi.org/10.29333/ejmste/99512.

Blumenfeld, P. C., Marx, R. W., Krajcik, J. S., & Soloway, E. (1996). Learning with peers: From small group cooperation to collaborative communities. Educational Researcher, 24(8), 37–40.

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). MIT Press.

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition of learning. Educational Researcher, 18(1), 32–42. https://doi.org/10.3102/0013189X018001032.

Chiu, M. H., Chou, C. C., Chen, Y. H., Hung, T. M., Tang, W. T., Hsu, J. W., … Tsai, M. K. (2018). Model-based learning about structures and properties of chemical elements and compounds via the use of augmented realities. Chemistry Teacher International, 1(1). https://doi.org/10.1515/cti-2018-0002.

Damelin, D., Stephens, L., & Shin, N. (2019). Engaging in computational thinking through system modeling. @Concord, 24(2), 4–6.

Eiden, E., Bielik, T., Touitou, I. Bowers, J., McIntyre, C., & Damelin, D. (2020, June 21-23). Characterizing advantages and challenges for students engaging in computational thinking and systems thinking through model construction. ICLS. https://repository.isls.org//handle/1/6460 (conference canceled, online).

Grover, S., & Pea, R. (2017). Computational thinking: A competency whose time has come. Computer Science Education: Perspectives on Teaching and Learning in School, (December), 19–39.

Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive Learning Environments, 1(1), 1–32. https://doi.org/10.1080/1049482900010102.

Hasni, A., Bousadra, F., Belletête, V., Benabdallah, A., Nicole, M., & Dumais, N. (2016). Trends in research on project-based science and technology teaching and learning at K–12 levels: A systematic review. Studies in Science Education, 52(2), 199–231. https://doi.org/10.1080/03057267.2016.1226573.

Krajcik, J. S., & Czerniak, C. M. (2018). Teaching science in elementary and middle school classrooms: A project-based approach, (5th ed., ). Taylor and Francis. https://doi.org/10.4324/9781315205014.

Krajcik, J., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92(1), 1–32.

Krajcik, J. S., & Mun, K. (2014). Promises and challenges of using learning technologies to promote student learning of science. In N. G. Lederman, & S. K. Abell (Eds.), The Handbook of Research on Science Education, (pp. 337–360). Routledge.

Krajcik, J. S., & Shin, N. (in press). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences, (3rd ed., ). Cambridge University Press.

Meadows, D. (2008). Thinking in systems: A primer. White River Junction, Vermont: Chelsea Green Publishing.

Miller, E. C., & Krajcik, J. S. (2019). Promoting deep learning through project-based learning: A design problem. Disciplinary and Interdisciplinary Science Education Research, 1(1), 7. https://doi.org/10.1186/s43031-019-0009-6.

National Academies of Sciences, Engineering, and Medicine (2018). How people learn II: Learners, contexts, and cultures. The National Academies Press. https://doi.org/10.17226/24783.

National Research Council (2007). Taking science to school: Learning and teaching science in grades K-8. National Academies Press.

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

NGSS Lead States (2013). Next generation science standards: For states, by states. National Academies Press.

Papert, S. (2005). Teaching children thinking. Contemporary Issues in Technology and Teacher Education (CITE Journal), 5(3–4), 353–365.

Schwarz, C., Passmore, C., & Reiser, B. J. (2017). Helping students make sense of the world using next generation science and engineering practices. National Science Teachers Association.

Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. (2006). Implications of research on children’s learning for standards and assessment: A proposed learning progression for matter and the atomic molecular theory. Measurement: Interdisciplinary Research and Perspectives, 14(1 and 2), 1–98.

Tinker, R. (1997). Thinking about science. Concord consortium http://www.concord.org/library/papers.html.

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. https://doi.org/10.1007/s10956-015-9581-5.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215.

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Disciplinary and Interdisciplinary Science Education Research

Tập 3 Số 1

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