+ Computer Science Principles: A New AP Concept Marie desJardins Google CS4HS Professional Development Workshop UMBC, August 6, 2012

+ Overview What is “CS Principles?? Goals and motivation Six Computational Thinking Practices Seven Big Ideas of Computer Science Status Phase I pilot courses ( ) Phase II pilot courses ( ) Implementation/adoption timeline Resources Hands-on time! Example active learning activities from pilot courses / UMBC

+ What is CS Principles?

+ What’s Wrong with AP CS? AP CS A(B) AP CS is the only AP course whose numbers have declined in recent years The College Board dropped AP CS B after 2009 Completely counterintuitive given the job demand!! Gender balance of current AP CS A is the lowest of any exam (below 20%) Why is this the case? AP CS A(B) = Java programming Not what computer science is really all about Not appealing to many students (especially girls and minorities) Not tied to real problems Not related to bigger ideas in computer science

+ AP CS Principles: Goals and Motivation Only 9 states (not Maryland...) allow CS to fulfill a math or science requirement at the HS level (none have a CS requirement) Difficult for students to gain enough experience to make it into AP CS; students need intrinsic interest to try CS as an elective Idea: Develop an AP class focused on getting students excited about computer science Especially: appeal to female and minority students Not just a programming class Relevant to the real world Broad view of the discipline of computer science

+ Computational Thinking Practices 1. Connecting computing... to applications and problems that matter to students and society 2. Developing computational artifacts... that require creativity and problem solving 3. Abstracting... at multiple levels, from logic gates to the human genome 4. Analyzing problems and artifacts... to understand tradeoffs, strategies, and alternative solutions 5. Communicating... about problems, designs, solutions, and behaviors 6. Collaborating... to combine diverse viewpoints and skills to solve problems

+ Big Ideas of Computer Science 1. Computing is a creative activity. 2. Abstraction reduces information and detail to facilitate focus on relevant concepts. 3. Data and information facilitate the creation of knowledge. 4. Algorithms are used to develop and express solutions to computational problems. 5. Programming enables problem solving, human expression, and creation of knowledge. 6. The Internet pervades modern computing. 7. Computing has global impacts.

+ AP CS Principles: Status and Timeline

+ Phase I Pilot Courses Five pilot sites in : Metropolitan State College of Denver: Living in a Computing World UC Berkeley: The Beauty and Joy of Computing UC San Diego: Fluency with Information Technology UNC Charlotte: The Beauty and Joy of Computing University of Washington: Computer Science Principles Ten high schools paired with ten colleges in Pilot efforts included: Recruitment plan for increasing enrollment of women and underrepresented minorities Detailed, fine-grained course evaluation

+ Phase II Pilot Courses Phase II pilots include: Portfolio component for assessment (may be used as part of the AP exam) Nine(?) Phase II paired university/high school pilots: None in Maryland U Penn (South Philadelphia High School) Virginia Tech (Patrick Henry High School)

+ AP CS Principles: Topics and Resources

+ UC Berkeley Beauty and Joy of Computing Abstraction 3D graphics and computer games Algorithms Programming in Scratch Concurrency, complexity, and recursion Social implications of computing Artificial intelligence Distributed computing Limitations and future of computing

+ University of Washington Computer Science Principles Lightbot 2.0 (programming game environment) “Digitization” (binary representations) Social networking Programming in Processing Networks and digital communication Image representations CPU / instruction execution Universal computation Algorithm design and recursion The Internet/World Wide Web HTML and XML Databases

+ UMBC Intro. to Computers and Programming Grand challenges for CS Deconstructing Google Deconstructing a computer Binary representations and digital encoding Programming with Scratch Problem solving with algorithms Networks and the Internet Databases and data abstraction Computer security and privacy Artificial intelligence Computer graphics and digital entertainment Ethical and social implications of computing The future of computing

+ Resources ACM Inroads, volume 3, number 2, June 2012 (special sections on CS Principles) Curriculum, pilot sites Attesting schools: (UMBC was the second school to sign up...)

+ Active Learning Activities

+ Exercise #1: Google Computational thinking practices: Connecting computing; analyzing problems and artifacts Big ideas: Internet; data and information; global impacts Activity: Googlewhack, Googlefights, PageRank, Googlefail Create your own googlefail! Discussion: What happens when we post the googlefail to our site? How long until it shows up in google? What’s happening in between? How does PageRank work? Followup lab/project: Bonus points to the first student who spots the googlefail results; compute PageRank for a simple example network; implement PageRank on a graph

+ Exercise #2: Huffman Coding Computational practices: Abstracting; developing computational artifacts Big ideas: Abstraction; data and information Activity: Encode a passage from “Sam I Am” using a Huffman encoding (preliminary exercise: translate from Huffman code) Discussion: Why are frequency-based encodings useful? How much space could one potentially save? What are some practical applications? Is Morse code a Huffman (optimal) encoding? Does gzip use Huffman encoding? Followup lab/project: Implement a Huffman encoding and print out length statistics for non-encoded and Huffman- encoded text. Find text sources that would compress a lot, and text sources that wouldn’t compress very much.

ao SPC tlw c!pmus y re Decode the Message:

+ Green Eggs and Ham I am Sam Sam I am That Sam-I-am! I do not like that Sam-I-am! Do you like green eggs and ham? I do not like them, Sam-I-am. I do not like green eggs and ham.

+ Exercise #3: Map Coloring Computational thinking practices: Abstracting and collaborating Big ideas: Creativity; algorithms Activity: Find a way to color each map region (person) so that no neighbors have the same color Discussion: Why is this a hard problem? How many colors do you really need? What are some strategies? What are some real-world problems that might require similar strategies? Lab/project: Implement a backtracking search algorithm to color a map; optionally integrate into drawing tool to let students create their own map