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Teaching and Learning
Computational Thinking at Scale
T.C. Pong
Senior Advisor to the Executive Vice-President & Provost
Director of Center for Engineering Education Innovation
Professor of Computer Science & Engineering
Hong Kong University of Science & Technology
9 June 2018
Outline
• What is computational thinking and
why is it important?
• How to broaden access to
computational thinking education?
• Using HKUST’s MOOCs for
delivering computational thinking at
scale
• Concluding remarks
My Lesson Learned as a
Computer Science Student
Think very carefully before
doing any actual coding!
My Lesson Learned as a
Computer Science Student
Fortran – My 1st programming language
Basic
Algol
Scheme
Prolog
Pascal
C / C++
Java
A programming language doesn’t solve
problems, it is the thought process behind
programming that formulates solution to
problems!
What is Computational Thinking?
• Computational thinking is the thought
processes involved in formulating a
problem and expressing its solution(s) in
such a way that a computer—human or
machine—can effectively carry out.
[Wikipedia, Wing 2014]
Four stages of
Computational Thinking
The four main stages of computational
thinking include:
• Problem formulation and
decomposition
• Pattern recognition and data
representation
• Problem abstraction
• Algorithm design
Changing Way of Life
Why is Computational
Thinking Important?
Changing Way of Life
Computational X
21st Century Competencies & Skills
4 + 1 C’s 21st century skills:
• Critical thinking
• Creativity
• Communication
• Collaboration
• Computational thinking
Computational Thinking in
National Curriculum in England
Computer Science For All
Policy in the USA
CT becomes Compulsory in
China’s High School Curriculum
Australian’s Digital
Technologies Curriculum
Computational Thinking in
Singapore’s Smart Nation Initiative
National Singapore University
makes CT Compulsory
CoolThink@JC in Hong Kong
How to broaden access to
computational thinking education?
Need to deliver computational thinking
education to different groups of learners:
• Students at different levels:
– K-12
– College students, including UG and PG
– Adult learners
• Teachers and educators
• Parents and others
Using MOOCs for Delivering
Computational Thinking at Scale
HKUST offers the following MOOCs for
delivering computational thinking at
scale:
- Introduction to Java Programming
- Developing Android Apps with App
Inventor
- Cornerstone Engineering Design
Project
Massive Open Online Courses
A massive open online course (MOOC) is a
type of online course aimed at large-scale
participation and open access via the Internet.
• In addition to online video lectures, learners
are involved actively in the learning process
• MOOCs go beyond just offering courses and
content. Learning analytics allow us to
understand how students learn and how
teachers can improve their teaching.
MOOC Platforms
Major MOOC platforms:
– Coursera and edX in the US
– FutureLearn in the UK
– France Université Numérique (France Digital
University) in France
– Iversity in Germany
– Open2Study in Australia
– XuetangX & CNMOOC in China
– Ewant 磨課師 in Taiwan
– JMOOC in Japan
– KMOOC in Korea
– HKMOOC in Hong Kong
MOOC completion rates
HKUST’s MOOC Experience
Over 1,000,000 learners have registered
for 40 MOOCs offered by HKUST
MOOC on edX:
Introduction to Java Programming
edX: 10 Most Popular
Courses in 2016
Top edX Courses in 2017
27
Introduction to Java Programming:
Demographic Distribution
Top Students from COMP102x
Top Students with almost perfect scores
Four stages of
Computational Thinking
The four main stages of computational
thinking include:
• Problem formulation and
decomposition
• Pattern recognition and data
representation
• Problem abstraction
• Algorithm design
Formulate a well-defined Problem:
• A well-defined problem means that
– a solution exists for the problem
– an unambiguous solution can be found in a finite
number of steps
• Examples:
– 1 x 2 + 3
– 1 + 2 x 3
Is the problem statement “Finding the best way to
travel from Hong Kong to London” a well-defined
problem?
Problem Formulation and Decomposition
1 + (2 x 3) = 7
(1 + 2) x 3 = 9
Formulate a well-defined Problem:
Is the problem statement “Finding the best way to
travel from Hong Kong to London” a well-defined
problem?
• How do you define best? (Fastest, Cheapest)
• Any limitations? (Preferred mode of
transportation)
• Any ambiguous definitions? (London)
Problem Formulation and Decomposition
Keynote 1: Teaching and Learning Computational Thinking at Scale
Keynote 1: Teaching and Learning Computational Thinking at Scale
2D Square Apple Problem
Starting from the middle
cell, would it be possible
for the worm to finish
eating all the apples?
Rules:
 The worm can only
move into another cell
that shares a common
wall; and
 a cell that has not been
previously visited.
Problem Representation and
Pattern Recognition
Start state
Problem Representation and
Pattern Recognition
Goal state
Start state
Problem Representation and
Pattern Recognition
3D Square Apple Problem
Problem Representation and
Pattern Recognition
Problem Representation and
Pattern Recognition
Would it be possible for the worm
to finish eating through all the 27
apples?
1) Yes
2) No
3) Not sure
Question
Problem Representation and
Pattern Recognition
Problem Representation and
Pattern Recognition
Problem Representation and
Pattern Recognition
Use the new representation and discuss
with your classmate to come up with a
solution again to the following question:
Would it be possible for the worm to
finish eating through all the 27 apples?
1) Yes
2) No
3) Not Sure
Question
Importance of
Problem Representation
2D square apple:
• There are 5 red apples and 4 green
apples
• Every move will be restricted to going
between apples of different colors
• If one starts from a green apple, there
would be no more green apple after
consuming the 4th red apple
3D square apple:
• There are 14 red apples and 13 green apples
The problem shows the importance of finding
the right representation before solving a
problem!
Using the same argument as in
the 2D case, if one starts from a
green apple, there would be no
more green apple after finishing
the 13th red apple.
Importance of
Problem Representation
Can the dominos cover all the cells on the 8x8 board?
You may assume that there are enough dominos.
Pattern Recognition:
Transfer Learning
With two of the corners cutoff from the 8x8 board,
can the dominos still cover all the cells on the board?
Pattern Recognition:
Transfer Learning
There are 32 black cells and 30 white cells. Each domino
always covers one black and one white cell. After 15 dominos
are placed on the board, there would be no more white cell!
Pattern Recognition:
Transfer Learning
Problem Abstraction
Problem Abstraction
51
Algorithm Design: Selection Sort
• Selection sort performs sorting by repeatedly finding the
largest element in the unsorted portion of the array and
then placing it to the end of this unsorted portion until
the whole array is sorted.
• Algorithm
– Define the entire array as unsorted at the
beginning
– While the unsorted portion of the array has
more than one element:
• Find its largest element
• Swap with last element
• Reduce the unsorted portion of the array by 1
Algorithm Design: Selection Sort
Algorithm Design: Selection Sort
HKUST’s App Inventor MOOC
MIT App Inventor
• MIT App Inventor: a very easy-to-use Android
application development tool
– Homepage: http://appinventor.mit.edu
• It is completely started and used within a
browser. No software installation is needed
• Supported browsers: Chrome, Firefox and
Safari. Internet Explorer is not supported!
• System requirement details:
http://appinventor.mit.edu/explore/ai2/setup.html
56
MIT App Inventor
57
An Hour-of-Code Event for
Students and Parents
An Android App Game
• We will create the Super-dolphin game, where a special
dolphin is moving super fast undersea. She only appears
still in your sight for a short time (when she is tired). She
is so lovely that you want to touch her. If you manage to
touch her, she will be scared and hide herself, and your
game score will be increased by 10 points.
59
Android App Demo
To contextualize activities that allow the
application of computational thinking, a
cornerstone engineering design project course
was developed for 1st year engineering students:
• To provide students exposure to knowledge
and skills from different engineering disciplines
• To engage students in team projects that
connect engineering design with real-world
problems using computational thinking
• using a blended and experiential learning
approach
A Cornerstone Engineering
Design Project Course
A Blended Experiential Learning Course
on Cornerstone Design Project
Learning Analytics on
Student Progress
Cornerstone Engineering
Design Project Course
Cornerstone Engineering
Design Project Course
Cornerstone Engineering
Design Project Course
Cornerstone Engineering Design Project
– Design Thinking Prototyping
Cornerstone Engineering
Design Project - Competition
Cornerstone Engineering
Design Project Course
Learning Analytics on MOOCs
Prof. Huamin Qu’s VisMOOC: A visual analytics tool for MOOC
Learning Analytics on MOOCs
Learning Analytics on MOOCs
Learning Analytics on MOOCs
Keynote 1: Teaching and Learning Computational Thinking at Scale
Social Network Analysis on MOOCs
Massive Open Online Degree
(MOOD)
76
Intelligent Tutor at Georgia Tech
77
Creativity
Critical
Thinking
21st Century Competencies & Skills
Communication
Computational
Thinking
Collaboration
79
Thank you!

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Keynote 1: Teaching and Learning Computational Thinking at Scale

  • 1. 1 Teaching and Learning Computational Thinking at Scale T.C. Pong Senior Advisor to the Executive Vice-President & Provost Director of Center for Engineering Education Innovation Professor of Computer Science & Engineering Hong Kong University of Science & Technology 9 June 2018
  • 2. Outline • What is computational thinking and why is it important? • How to broaden access to computational thinking education? • Using HKUST’s MOOCs for delivering computational thinking at scale • Concluding remarks
  • 3. My Lesson Learned as a Computer Science Student Think very carefully before doing any actual coding!
  • 4. My Lesson Learned as a Computer Science Student Fortran – My 1st programming language Basic Algol Scheme Prolog Pascal C / C++ Java A programming language doesn’t solve problems, it is the thought process behind programming that formulates solution to problems!
  • 5. What is Computational Thinking? • Computational thinking is the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out. [Wikipedia, Wing 2014]
  • 6. Four stages of Computational Thinking The four main stages of computational thinking include: • Problem formulation and decomposition • Pattern recognition and data representation • Problem abstraction • Algorithm design
  • 7. Changing Way of Life Why is Computational Thinking Important?
  • 10. 21st Century Competencies & Skills 4 + 1 C’s 21st century skills: • Critical thinking • Creativity • Communication • Collaboration • Computational thinking
  • 11. Computational Thinking in National Curriculum in England
  • 12. Computer Science For All Policy in the USA
  • 13. CT becomes Compulsory in China’s High School Curriculum
  • 15. Computational Thinking in Singapore’s Smart Nation Initiative
  • 18. How to broaden access to computational thinking education? Need to deliver computational thinking education to different groups of learners: • Students at different levels: – K-12 – College students, including UG and PG – Adult learners • Teachers and educators • Parents and others
  • 19. Using MOOCs for Delivering Computational Thinking at Scale HKUST offers the following MOOCs for delivering computational thinking at scale: - Introduction to Java Programming - Developing Android Apps with App Inventor - Cornerstone Engineering Design Project
  • 20. Massive Open Online Courses A massive open online course (MOOC) is a type of online course aimed at large-scale participation and open access via the Internet. • In addition to online video lectures, learners are involved actively in the learning process • MOOCs go beyond just offering courses and content. Learning analytics allow us to understand how students learn and how teachers can improve their teaching.
  • 21. MOOC Platforms Major MOOC platforms: – Coursera and edX in the US – FutureLearn in the UK – France Université Numérique (France Digital University) in France – Iversity in Germany – Open2Study in Australia – XuetangX & CNMOOC in China – Ewant 磨課師 in Taiwan – JMOOC in Japan – KMOOC in Korea – HKMOOC in Hong Kong
  • 22. MOOC completion rates HKUST’s MOOC Experience Over 1,000,000 learners have registered for 40 MOOCs offered by HKUST
  • 23. MOOC on edX: Introduction to Java Programming
  • 24. edX: 10 Most Popular Courses in 2016
  • 25. Top edX Courses in 2017
  • 26. 27 Introduction to Java Programming: Demographic Distribution
  • 27. Top Students from COMP102x Top Students with almost perfect scores
  • 28. Four stages of Computational Thinking The four main stages of computational thinking include: • Problem formulation and decomposition • Pattern recognition and data representation • Problem abstraction • Algorithm design
  • 29. Formulate a well-defined Problem: • A well-defined problem means that – a solution exists for the problem – an unambiguous solution can be found in a finite number of steps • Examples: – 1 x 2 + 3 – 1 + 2 x 3 Is the problem statement “Finding the best way to travel from Hong Kong to London” a well-defined problem? Problem Formulation and Decomposition 1 + (2 x 3) = 7 (1 + 2) x 3 = 9
  • 30. Formulate a well-defined Problem: Is the problem statement “Finding the best way to travel from Hong Kong to London” a well-defined problem? • How do you define best? (Fastest, Cheapest) • Any limitations? (Preferred mode of transportation) • Any ambiguous definitions? (London) Problem Formulation and Decomposition
  • 33. 2D Square Apple Problem Starting from the middle cell, would it be possible for the worm to finish eating all the apples? Rules:  The worm can only move into another cell that shares a common wall; and  a cell that has not been previously visited. Problem Representation and Pattern Recognition
  • 34. Start state Problem Representation and Pattern Recognition
  • 35. Goal state Start state Problem Representation and Pattern Recognition
  • 36. 3D Square Apple Problem Problem Representation and Pattern Recognition
  • 38. Would it be possible for the worm to finish eating through all the 27 apples? 1) Yes 2) No 3) Not sure Question
  • 42. Use the new representation and discuss with your classmate to come up with a solution again to the following question: Would it be possible for the worm to finish eating through all the 27 apples? 1) Yes 2) No 3) Not Sure Question
  • 43. Importance of Problem Representation 2D square apple: • There are 5 red apples and 4 green apples • Every move will be restricted to going between apples of different colors • If one starts from a green apple, there would be no more green apple after consuming the 4th red apple
  • 44. 3D square apple: • There are 14 red apples and 13 green apples The problem shows the importance of finding the right representation before solving a problem! Using the same argument as in the 2D case, if one starts from a green apple, there would be no more green apple after finishing the 13th red apple. Importance of Problem Representation
  • 45. Can the dominos cover all the cells on the 8x8 board? You may assume that there are enough dominos. Pattern Recognition: Transfer Learning
  • 46. With two of the corners cutoff from the 8x8 board, can the dominos still cover all the cells on the board? Pattern Recognition: Transfer Learning
  • 47. There are 32 black cells and 30 white cells. Each domino always covers one black and one white cell. After 15 dominos are placed on the board, there would be no more white cell! Pattern Recognition: Transfer Learning
  • 50. 51
  • 51. Algorithm Design: Selection Sort • Selection sort performs sorting by repeatedly finding the largest element in the unsorted portion of the array and then placing it to the end of this unsorted portion until the whole array is sorted. • Algorithm – Define the entire array as unsorted at the beginning – While the unsorted portion of the array has more than one element: • Find its largest element • Swap with last element • Reduce the unsorted portion of the array by 1
  • 55. MIT App Inventor • MIT App Inventor: a very easy-to-use Android application development tool – Homepage: http://appinventor.mit.edu • It is completely started and used within a browser. No software installation is needed • Supported browsers: Chrome, Firefox and Safari. Internet Explorer is not supported! • System requirement details: http://appinventor.mit.edu/explore/ai2/setup.html 56
  • 57. An Hour-of-Code Event for Students and Parents
  • 58. An Android App Game • We will create the Super-dolphin game, where a special dolphin is moving super fast undersea. She only appears still in your sight for a short time (when she is tired). She is so lovely that you want to touch her. If you manage to touch her, she will be scared and hide herself, and your game score will be increased by 10 points. 59
  • 60. To contextualize activities that allow the application of computational thinking, a cornerstone engineering design project course was developed for 1st year engineering students: • To provide students exposure to knowledge and skills from different engineering disciplines • To engage students in team projects that connect engineering design with real-world problems using computational thinking • using a blended and experiential learning approach A Cornerstone Engineering Design Project Course
  • 61. A Blended Experiential Learning Course on Cornerstone Design Project
  • 66. Cornerstone Engineering Design Project – Design Thinking Prototyping
  • 69. Learning Analytics on MOOCs Prof. Huamin Qu’s VisMOOC: A visual analytics tool for MOOC
  • 75. Massive Open Online Degree (MOOD) 76
  • 76. Intelligent Tutor at Georgia Tech 77
  • 77. Creativity Critical Thinking 21st Century Competencies & Skills Communication Computational Thinking Collaboration