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Building interest and proficiency in computational thinking in STEM

Meet the Team High School Lead Teachers & PD Providers: Kemi Jona Michael Horn Vicky Kalogera Laura Trouille Uri Wilensky Kai Orton Principal Investigators Graduate Students David Weintrop Elham Behesti This work is supported in part by the National Science Foundation under NSF grants CNS and is covered by IRB study STU However, any opinions, findings, conclusions, and/or recommendations are those of the investigators and do not necessarily reflect the views of the Foundation. Mark Vondracek Ami Lefevre Meagan Morscher Pilot Teachers: 11 in in XX in

CT-STEM: Goals Goals: Build teacher knowledge, interest, and confidence: developing your students’ CT-STEM skills using CT tools to improve your students’ learning of STEM concepts Connect CT-STEM to what you already do & to Illinois standards Train in discipline-specific CT-STEM lesson plans

CT-STEM: Train the Trainer Model Lead Teachers (that’s you!): 1 in each discipline at each school Attend Summer Workshop Lead 3 Academic Year Workshops for teachers at your school Teach and assess 4 CT-STEM lessons in your classroom CT-STEM Teachers: ~4 in each discipline at each school Attend 3 Academic Year Workshops Teach and assess 4 CT-STEM lessons in your classroom

CT-STEM Lesson Plans

“Big Data” is Everywhere ~ Web pages at ~300 kilobytes each = 10 Petabytes Youtube 48 hours video uploaded per minute; in 2 months in 2010, uploaded more than total NBC ABC CBS ~2.5 petabytes per year uploaded? LSST 30 TB/night LHC 15 petabytes per year Radiology 69 petabytes per year Square Kilometer Array Telescope will be 100 terabits/second Earth Observation becoming ~4 petabytes per year Earthquake Science – few terabytes total today PolarGrid – 100’s terabytes/year Exascale simulation data dumps – terabytes/second 8

McKinsey Institute on Big Data Jobs There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. 9

CT-STEM: Key Concepts Algorithmic Thinking: – create a series of ordered steps to solve a problem – allows for automation of a procedure Examples: Efficiency at a buffet table Long Division Experimental Procedure

CT-STEM: Key Concepts Abstraction: – Pulling out the important details – Identifying principles that apply to other situations Examples: Holiday dinners Construct a model of an atom Use the term ‘titration’ in an experimental design

CT-STEM: Key Concepts Computational Modeling: – Use a computational tool to develop a representation of a system (i.e., visualize an abstraction of a system) – Use a computational tool to analyze, visualize, and gain understanding of a STEM concept Examples: CAD (in engineering) Netlogo and other computational environments

CT-STEM: Key Concepts Decomposition: – Reformulating a seemingly difficult problem into one we know how to solve Examples: Road networks in a major city -> Muddy City

CT-STEM: Key Concepts Generalization: – How is this problem is similar to others? – Can we transfer the problem solving process from a solved problem to this new one? Examples: Can I apply the same strategies that I learned playing soccer to playing basketball? Gravity and flux

CT-STEM: Key Concepts Big Data: – Big Data refers to a collection of data sets so large and complex, it’s impossible to process them with the usual databases and tools. – Because of its size and associated numbers, Big Data is hard to capture, store, search, share, analyze and visualize. Examples: Sequencing the human genome The Galaxy Zoo Project of over 1 million galaxies

The Human Genome…By the Numbers 46…Chromosomes in each cell ~23,000…Genes in the human genome 2.4 million…Base pairs in the largest human gene 3.1 billion…Base pairs in each cell trillion…Cells in the human body

CT in Biology Shotgun algorithm expedites sequencing of human genome - DNA sequences are strings in a language - Protein structures can be modeled as knots - Protein kinetics can be modeled as computational processes - Cells as a self-regulatory system are like electronic circuits

CT in Astronomy Mass Determination of our Milky Way’s Black Hole – Comparing observed data to simulations

CT in Chemistry Atomistic calculations explore chemical phenomena Optimization and searching algorithms identify best chemicals for improving reaction conditions to improve yields

CT in Engineering Boeing 777 never tested in a wind tunnel, only in computer simulations Ability to calculate higher order terms implies more precision, which implies reducing weight, waste, costs in fabrication, etc.

CT in Geology Modeling the earth inner layers, using seismic waves Modeling the earth and our atmosphere to track and predict climate changes

CT in Math Discovering E8 Lie Group – took 18 mathematicians, 4 years and 77 hours of supercomputer time (200 billion numbers). – Profound implications for physics (string theory)

CT in Medicine Robotic surgery Electronic health records require privacy technologies Scientific visualization enables virtual colonoscopy

CT in Social Sciences Social networks explain phenomena like MySpace, YouTube Statistical machine learning is used for recommendation and reputation services, e.g., Netflix, affinity card

CT in the Humanities What do you do with a million books? – Nat’l Endowment for the Humanities Institute of Museum and Library Services Arts, drama, music, photography Credit: Christian Mueller

CT in Entertainment Games Music MP3 sorting/searches Movies - Dreamworks uses HP data center to renderShrek and Madagascar - Lucas Films uses 2000-node data center to make Pirates of the Caribbean.

CT-STEM Skill NGSS Standard