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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.
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Thriving in Our Digital WorldA CS Principles Course
Gregory RussellBradley Beth
Tara CraigCalvin 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!
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
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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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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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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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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
10
CANVAS – Easter Egg Hunt
1. Log-in to your CANVAS account1. Check email for invitation2. Visit: onramps.instructure.com
2. Complete the Easter Egg Hunt assignment
3. Discuss as a group
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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.
“Doing Projects”
PROJECT
PBL
PRODUCT
CHECKPOINTS
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PBL
• Problem- or project-?
• With PBL, this course incorporates:– Collaboration– Critical Thinking–Written/Oral
Communication– Technological
Literacy
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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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Inquiry-based CS
• Syntax• Sin Tax• SYN/ACK• Low Tedium• Interesting Now • Product-motivated, product-assessed
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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?
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The Tools
EXHIBIT A: CAPTAIN SHANNON'S NOTE
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The Wrench
Doesyour
solution work?
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The Lesson Plan
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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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Gimme a break!
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AP CS:P Portfolio Tasks TioDW
Our course content and pedagogy dovetails with the portfolio tasks
• 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
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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.
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. Programming3. Artificial Intelligence – Turing Test4. Computers5. Big Data
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
28
Look forward to…
• Revisions to:– Representation– Computers– Big Data– AI – Maze Game– AI – Expert Systems
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Look forward to…
• New content– Digital Manipulation (with Processing)– Innovations– Security (course extension)
30
Closing Thoughts
• Public course - revisions ETA: Aug. 2013– https://onramps.instructure.com/courses/723
227
• Implementation for college credit in 2014-15
Questions? Thoughts?
• Contact us!– Bradley Beth: bbeth@cs.utexas.edu– Gregory Russell: grussell@utexas.edu
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