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 eScience InstituteSECANTMatter & Interactions

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

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

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

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

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)

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

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

Clement J. (2000) Model based learning as a key research area for science education. International Journal of Science Education, 22(9), pp Colella, 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), diSessa, Andrea (2000) Changing Minds: Computers, Learning, and Literacy, MIT Press, Boston MA Foley, B. (1999), “How Visualizations Alter the Conceptual Ecology” presented at the AERA annual meeting 1999, Montreal, Canada Foley, B. & Kawasaki, J (April, 2009) “Building Models from Scratch” Paper presented at the American Educational Research Association meeting, San Diego CA Gobert, 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, US Schwarz, C. and White, B. (2005) Meta-modeling knowledge: Developing students' understanding of scientific modeling. Cognition and Instruction 23:2, pp Sherin, B., diSessa, A. & Hammer, D. (1993). Dynaturtle Revisited: Learning Physics Through Collaborative Design of a Computer Model. Interactive Learning Environments, 3 (2), Stewart, 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 Lawrence Erlbaum Associates, Mahwah, NJ White, B. and Fredericksen, J. (1998) Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction 16:1, pp