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Scratch for Science

This article discusses the integration of Scratch in science education, promoting computational thinking among students. It emphasizes the power of abstraction, automation, and data collection using accessible tools like Arduino and PicoBoards. Highlighting a constructionist approach, it showcases how students can create dynamic simulations and games to understand complex scientific phenomena. The versatility of Scratch allows for tailored resources that enhance student motivation and engagement, ultimately fostering a deeper understanding of scientific concepts through model-based learning.

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Scratch for Science

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  1. Scratch for Science

  2. Computational Thinking • Jeanette Wing, 2006 • Core theme in CS education, more and more in other subjects • Abstraction • Automation • eScience Institute, SECANT, Matter & Interactions

  3. Data Collection and Analysis • Excel (Excelets, also mathematical models) • Lab probes, software • Commodity hardware (phones, Arduino) for data collection

  4. Scratch for Science • Limited need to teach the tool • Students pick it up faster than we do! • Power of a versatile programming language • Teacher-created resources • Peer-created resources • Assessments • Simulations

  5. Interactive Tutorials • Similar to HyperCard stacks of the past • More dynamic than PowerPoint • Students can tweak, contribute • Could take place of paper, poster

  6. Learning Games • Motivating for students • More likely to practice on own time • Can be tailored to your classes' needs • Students can take a part in shaping them

  7. Modeling and Simulation • "In these dynamic Turtle Microworlds, [students] come to a different kind of understanding – a feel for why the world works as it does." – Seymour Papert, 1979 • Constructionism – learning through building and testing • Explore unapproachable phenomena • Can be made into games (motivation)

  8. Students Creating Games • They want to learn realistic physics • The math can be very serious • They show their friends

  9. Potential for Data Collection, Analysis • PicoBoards • Arduino • Scratch 2.0 • Learning with Data project, Lifelong Kindergarten

  10. Clement J. (2000) Model based learning as a key research area for science education. International Journal of Science Education, 22(9), pp. 1041-1053Colella, V. S., Klopfer, E., & Resnick, M. (2001). Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo. Teachers College Press.De Jong, T., & Van Joolingen, W. R. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains. Review of Educational Research, 68(2), 179-201.diSessa, Andrea (2000) Changing Minds: Computers, Learning, and Literacy, MIT Press, Boston MAFoley, B. (1999), “How Visualizations Alter the Conceptual Ecology” presented at the AERA annual meeting 1999, Montreal, CanadaFoley, B. & Kawasaki, J (April, 2009) “Building Models from Scratch” Paper presented at the American EducationalResearch Association meeting, San Diego CAGobert, J.D. & Pallant, A. (2004) Fostering Students’ Epistemologies of Models via Authentic Model-Based Tasks Journal of Science Education and Technology, Vol. 13, No. 1,National Research Council (2011). Report of a Workshop of Pedagogical Aspects of Computational Thinking. National Academies Press.Papert, S. (1980) Mindstorms: children, computers, and powerful ideas. Basic Books, Inc. New York, NY, USSchwarz, C. and White, B. (2005) Meta-modeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction 23:2 , pp. 165-205Sherin, B., diSessa, A. & Hammer, D. (1993). Dynaturtle Revisited: Learning Physics Through Collaborative Design of a Computer Model. Interactive Learning Environments, 3 (2), 91-118Stewart, J., Passmore, C., Cartier, J., Rudolph, J. and Donovan, (2005) Modeling for understanding in science education in S. Romberg, T., Carpenter, T. and Dremock, F. (eds) Understanding mathematics and science matters pp. 159-184. Lawrence Erlbaum Associates , Mahwah, NJWhite, B. and Fredericksen, J. (1998) Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction 16:1 , pp. 3-118.

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