Thriving in Our Digital World is a year-long introductory computer science course designed cooperatively by computer science faculty and education researchers at the University of Texas at Austin. The course is designed around the NSF-funded Computer Science: Principles project, and organized into eight topical modules (Innovations, Representation, Computers, Programming, Big Data, Artificial Intelligence, Networks, and Security). The curricular resources include learning materials designed through research-based approaches to engage diverse student populations. Learning is supported with authentic uses of foundational computer science knowledge and skills in a real-world context. All course materials are online and freely accessible under a creative commons license. In this workshop, we introduced the pedagogical principles and materials that encompass the course and modeled their use.

1. Thriving in Our Digital
World
A CS Principles Course
Gregory Russell
Bradley Beth
Tara Craig
Calvin Lin
George Veletsianos
Funding provided by the
National Science Foundation
under award #1138506.

2. Icebreaker
• The Task:
–Form diverse groups for
collaboration
• Rationale:
– Introductions
– Work with new people
– College and career
readiness skills
– Diversity
– Get ambulatory!
2

3. Group Formation
• Guidelines for forming diverse groups:
– Co-ed groups of 4
– A group member with an advanced degree in
a non-CS subject matter
– At least two states represented
– Higher education and K-12 represented
3

4. Presentation
Available for download at:
http://tinyurl.com/cstatodw
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5. K-W-L
http://tinyurl.com/csta2013kwl
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6. Project Engage: Overview
• NSF Computing Education for the 21st
Century (CE21)
– Type I: Project Engage!, NSF Award #
1138506
• OnRamps (synergy)
– Texas Higher Education Coordinating Board
• Course: Thriving in our Digital World
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7. NSF CE21
• 2011 Type I solicitation was overspecified.
– BPC
• Female students: PBL, impact-oriented
• Low-SES: resource light, rural targets
• ―Middle Track‖: AP alternative vector, college readiness
– CER
• Partnership between CS and LT
• Best Practices, Implementation research
– CS10k
• Heavy PD component
• Scale to cross-certified teachers
– CS: Principles
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8. OnRamps
• Texas Legislature funded cooperative
initiative
• DE/DC courses aligned to flagship university
expectations
• Research-based best practices
– learning science
– learning technologies
– college readiness
• Aligned to TX College & Career Readiness
Standards
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9. Thriving in our Digital World
• Organized into 8 modules: Impact,
Programming, Representation, Computers,
Digital Manipulation, Big Data, Artificial
Intelligence, Innovations
• Student-centered design: PBL, open-
ended activities, discussion-oriented
• Novel Dual Enrollment design: solves the
‗chicken or egg problem‘ of college
readiness coursework
• Hybrid Learning—our roles focus on
design, PD, implementation, and support
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10. CANVAS – Easter Egg Hunt
1. Log-in to your CANVAS account
1. Check email for invitation
2. Visit: onramps.instructure.com
2. Complete the Easter Egg Hunt assignment
3. Discuss as a group
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11. Pedagogical Foundations
1. Problem-based-learning (PBL)
2. Inquiry learning
3. Student-centered learning
• Requires:
– Collaboration
– Guidance and scaffolding
– Classroom management skills
– Willingness for messiness
– Direct instruction, ‗traditional‘ assessments,
etc.
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12. ―Doing Projects‖
ACTIVITY
P
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J
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C
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LECTURE
HOME
WORK
ACTIVITY

13. PBL
ACTIVITY
P
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D
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LECTURE
HOME
WORK
C
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14. PBL
• Problem- or project-
?
• With PBL, this
course incorporates:
– Collaboration
– Critical Thinking
– Written/Oral
Communication
– Technological
Literacy
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15. Student-centered Learning
• Students dictate what they Know, Want to
Know, and Learn (KWL).
• ―Want to know‖ drives what you will Learn
in conjunction with:
– Activities
– Online content
– Videos
– Inquiry-related tasks
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16. Inquiry-based CS
• Syntax
• Sin Tax
• SYN/ACK
• Low Tedium
• Interesting Now
• Product-motivated, product-assessed
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17. The Scenario
As you approach the clearing, you see Captain Shannon coming your direction—clutching a
tattered piece of parchment.
―Ahoy thar! I did bury our treasure but a wee bit yonder,‖ he says, pointing over his shoulder with
a crooked thumb. Cracking a toothless grin, he adds, ―Bein‘ first mate, you do serve as me
backup in case me directions get lost.‖
He hands you the parchment (EXHIBIT A). Carefully straightening it, you see a list of letters
crossing the page 3 times over, nearly covering it.
―Each of the letters marks 10 paces in a card‘nal direction—them be the points on a compass
rose.‖ He holds up four fingers, stating, ―N be north, E be east, S be south, and W be west,‖
touching each in turn. ―At the end of all that pacin‘, the booty lies but 2 feet ‘neath the ground.‖
Carefully, his brow furrowed in concentration, Shannon begins ripping the lower left corner of his
page, separating a small piece of parchment from the rest. ―Hmph,‖ he grimaces.
―Piddly.‖ Apologetically, he hands you the scrap and a bulky lump of charcoal to mark your
notes. ―Err, sorry I did not bring more…‖
How can you fit all of this information in a smaller form—small enough to fit on your piddly
scrap? 17

18. The Tools
18
EXHIBIT A: CAPTAIN SHANNON'S NOTE

19. The Wrench
19
Does
your
solution
work?

20. The Lesson Plan
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21. Then…
• Program it! [PROCESSING]
• Connections
– Audio, Images, Network Traffic, Storage
• Canonical Algorithms
– Dictionary-based (Lempel-Ziv, Textspeak)
– Frequency-based (Huffman Coding, Morse
Code)
– Layered (Q-Codes)
• Thoughts?
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22. 22
Gimme a break!

23. AP CS:P Portfolio Tasks TioDW
Our course content and pedagogy dovetails with the portfolio
tasks
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• Your collaboratively developed artifact must include
the following:
• Overview of your investigation: a description
of the intent of the investigation and how it will
be used to gain insight and knowledge;
• The set of 3 to 5 questions that you will
answer.
• Explanation and justification of how the data
and other sources used in your investigation
(if any) are appropriate for exploring and
answering the questions.
• Information about the data set(s): a
description of each data set; the URL of the
data set; the date on which you accessed the
data; and where possible a reference to the
data set from a written work (e.g., an article,
book, or blog post).
• Description of the computational tools and
techniques used.
• Clearly presented answers to your questions
and explanations of how the answers help
gain insight and knowledge.
• Your individually written document must include the
following:
• Justification of why you chose the specific
computing tools and techniques you used to
conduct your investigation.
• An explanation of why computing is necessary
and how)computing facilitated analyzing the
data to answer the questions.
• A detailed description of how your team
processed the information in the data set to
conduct the investigation and how this
enabled you to meet your objective of gaining
insight and knowledge. This description
should be sufficiently detailed to make it clear
that you can conduct the investigation and
verify the results in answering questions and
that a reasonably skilled reader could do so
as well.
• A reflective description, explanation, and
analysis of the collaborative aspects of your
investigation. This should not be a simple
enumeration of when and how you worked
together.Portfolio Task: Data

24. AP CS:P Portfolio Tasks TioDW
• Definitions of data mining and K.D.D. are detailed, yet concise. Highlights
the role of discovery. Provides at least 3 clear examples to support
definitions.
• Applies crowdsourcing strategies insightfully. Achieves useful, useable
results. Crowdsourcing project logically relates to the talk's theme.
• Cleans unstructured data to create structured data sets insightfully.
Achieves useful, useable results. Analysis logically relates to the talk's
theme.
• Develops at least 3 visualizations that clearly demonstrate the clustering or
non-clustering of data. Analysis logically relates to the talk's theme.
• Anomaly, outlier, and change detection analysis: are accurate and insightful;
inform the audience; relate clearly to the talk's them; logically discuss the
impact of the analysis.
• Uses software to perform regression analysis accurately. Completes
regression on more than 3 data sets. Makes a logical prediction using
regression data in conjunction with other inferences from other data
sources.
• Compares and contrasts the results of classification using a linear separator
and k-means clustering. Maps newly collected data to a pre-existing
classifier. Makes insightful inferences about the classification results.
• Compares and contrasts the summarization results against one another, in
context. Provides insightful analysis of each tool for correctness of results,
in context. 24
Module Checkpoints: Big Data

25. Let‘s see the projects already!
• Round robin format (~15 min. for each)
• Explore the available modules
Use post-it notes to indicate the following:
– I like:
– I wonder:
– Tips for classroom use:
• Projects:
1. Impact (a.k.a. Conspiracy Theory)
2. Programming
3. Artificial Intelligence – Turing Test
4. Computers
5. Big Data
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26. Lessons Learned
• PREVIOUS PRESENTATION
• Revisit K-W-L chart
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27. Lessons Learned
• Programming First and Interspersed
• Teachers are not accustomed to this
pedagogical model  takes practice PD
• Smaller units/cycles
• What teachers like ≠ what students like ≠
what teachers perceive students like
• Revisit K-W-L chart
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28. Look forward to…
• Revisions to:
– Representation
– Computers
– Big Data
– AI – Maze Game
– AI – Expert Systems
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29. Look forward to…
• New content
– Digital Manipulation (with Processing)
– Innovations
– Security (course extension)
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30. Closing Thoughts
• Public course - revisions ETA: Aug. 2013
– https://onramps.instructure.com/courses/7232
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• Implementation for college credit in 2014-
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Questions? Thoughts?
• Contact us!
– Bradley Beth: bbeth@cs.utexas.edu
– Gregory Russell: grussell@utexas.edu 30