(ap)cs principles ce21, csprinciples16/4/2014
TRANSCRIPT
(AP)CS Principles
www.csprinciples.org
www.collegeboard.com/html/computerscience/
CE21, CSPrinciples 104/10/23
What?
A new first course in computer science
Alternative to CS1, not replacement
Designed to be an AP course: credit/placement
Collaborative:CollegeBoard, NSF, Academia (6-12/University)
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Why toward APCSprinciples?
• Why isn’t this CSPrinciples, why AP?– Entry into high schools and colleges– 2,000 audited AP(CS) teachers– National standard for curriculum/test
• Single point of national leverage– States are very, very, very different
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Who?
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Process and Content
• How are we designing and building this course?–Who is behind the development–What is the process used
• What will be in this course?– Computational Thinking Skills– Content– Pedagogy
CE21, CSPrinciples 504/10/23
Who?• Don Allen• Christine Alvarado• Owen Astrachan• Stacey Armstrong• Tiffany Barnes• Amy Briggs• Charmaine Bentley• Mark Guzdial• Rich Kick• Jody Paul• Chris Stephenson• Duane Bailey• Gail Chapman• Tom Cortina• Stephen Edwards
• Dan Garcia• Joanna Goode• Susanne Hambrusch• Michelle Hutton• Deepak Kumar• Jim Kurose• Andrea Lawrence• Richard Pattis• Katie Siek• Beth Simon• Larry Snyder• Lynn Stein• Fran Trees• Lien Diaz• Cameron Wilson
• Jan Cuny
• Kathy Haynie
CE21, CSPrinciples 604/10/23
Why is this possible now?
“AP Courses should not be designed solely to replicate introductory college courses (which are not typically exemplary models)”– Reflect knowledge of how students learn– Convey content and unifying concepts
NRC report 2002, AP Science Redesign
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Engineering a Course and Exam
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Timeline• 2009-2010– Big Ideas, Practices, Claims/Evidence
• 2010-11– Pilot I: Five colleges– College Survey– College attestation/support– Test item prototype
• 2011-12– Pilot II: 10+ colleges, 10+ high schools
CE21, CSPrinciples 904/10/23
Timeline continued
• Necessary and sufficient conditions to continue– How do we ensure “substantial” buy-in?
• 2012-2013, e.g., as part of CE21!– Curricular framework finalized?– Exam format identified
• Deploy exam and course– 201X (X >= 5)
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From Process to Product
• What will be in this course?– Pilot courses are exemplars– Seven big ideas– Six computational thinking practices– 30 claims each with 2-5 evidence
statements, total of 117
• From bits to NP to modeling to …
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Where’s the Programming?
• To that end [solving computational problems and exploring creative endeavors], the course highlights programming as one of the seven big ideas of computer science, because programming is among the creative processes that help transform ideas into reality.
Course rational, csprinciples.org
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Big Ideas
1. Computing is a creative human activity that engenders innovation and promotes exploration.
2. Abstraction reduces information and detail to focus on concepts relevant to understanding and solving problems.
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Big Ideas Continued
3. Data and information facilitate the creation of knowledge.
4. Algorithms are tools for developing and expressing solutions to computational problems.
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Big Ideas Continued
5. Programming is a creative process that produces computational artifacts.
6. Digital devices, systems, and the networks that interconnect them enable and foster computational approaches to solving problems.
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Big Ideas
7. Computing enables innovation in other fields including science, social science, humanities, arts, medicine, engineering, and business.
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Computational Thinking Practices
1. Analyzing effects of computation
2. Creating computational artifacts
3. Using abstractions and models
4. Analyzing problems and artifacts
5. Communicating processes and results
6. Work effectively in teams
csprinciples.org
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Claims and Evidence bit.ly/csprinc
• Big Idea: Abstraction reduces information and detail to focus on concepts relevant to understanding and solving problems.
– Key Concept II.A. Computational systems and problems are developed, analyzed, and solved using multiple levels of abstraction.
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Big Idea>Key Concept>Claim
• Claim 5: The student can use abstractions and models to solve computational problems and analyze systems.
–Evidence for Claim 5: Student work is characterized by:
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Evidence statements for 5
• 5a. Explanation of how data, information or knowledge are represented at different levels of abstraction.
• 5b. Use of simulation and randomness to analyze and solve problems.
• 5c. Explanation of how abstractions are used in software systems at many levels, ranging from programming languages to operating systems to the Internet.
• 5d. Explanation of the abstractions comprising the physical layers of computing hardware, including gates, chips, and components.
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Information about Pilot I(thanks to Larry Snyder)
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The pilot schools and instructors …• Metropolitan State College, Denver: Jody Paul• UC Berkeley: Dan Garcia• UC San Diego: Beth Simon• UNC at Charlotte: Tiffany Barnes• U Washington: Larry Snyder
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TitleFirst Offeri
ng
Proj Enro
ll
MSCD
Living in a Computing WorldFall (S) 2010
20
UC BThe Beauty and Joy of Computing
Fall (S) 2010
90
UC SD
Fluency with Information Technology
Fall (Q) 2010
900
UNC C
The Beauty and Joy of Computing
Spring (S)
201120
U W Computer Science PrinciplesWinter
(Q) 2011
40
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Programming Language
Lec
Lab
Dis
WksTotal Conta
ct
MSCD
Scratch, HTML/CSS
4 0 0 1560
UC B BYOB Scratch 2 4 1 14 98
UC SD
Alice, Excel 3 2 0 1050
UNC C
BYOB/Scratcy 3 0 0 1545
U W Processing 3 2 0 10 50
Future work
• Oversee pilot courses, analyze the outcomes of the pilots, prepare for next, larger pilot, … LARGER PILOT
• Gain consensus on claims and evidence
• Develop prototype exam questions • Gather support for next phase of
project, letters of attestation
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Questions
• http://www.csprinciples.org/pilots
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