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Scratch for Science

Scratch for Science. Computational Thinking. Jeanette Wing, 2006 Core theme in CS education, more and more in other subjects Abstraction Automation eScience Institute , SECANT , Matter & Interactions. Data Collection and Analysis. Excel ( Excelets , also mathematical models)

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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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