Computing Education as a Foundation for 21st Century Literacy
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Computing Education as a Foundation for 21st Century Literacy
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Keynote from SIGCSE 2019. Teaching programming as a way to express ideas, communicate with others, and understand our world is one of the oldest goals for computing education. The inventor of the term ``computer science'' saw it as the third leg of STEM literacy. In this talk, I lay out the history of the idea of universal computational literacy, some of what it will take to get there, and how our field will be different when we do.
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Computing Education as a Foundation for 21st Century Literacy
- 1. Computing Education as a Foundation for 21st Century Literacy Mark Guzdial (@guzdial) #SIGCSE2019 1
- 2. 2
- 3. Our Mission: To provide a global forum for educators to discuss research and practice related to the learning and teaching of computing…at all education levels 3
- 4. Story ▪ Computer science was invented to teach everyone about everything. ▪ Computational thinking and the power of the imitation game. ▪ How computing education will change on the way. ▪ What we teach ▪ How we’ll teach 4
- 5. Definitions ▪ Computer science: The study of computers and all the phenomena associated with them. (Perlis, Newell, and Simon, 1967) ▪ Computing: CS + CE + SE + IS + IT +… Computer science facing outward (Denning). ▪ Programming: Reading and writing a notation of computation, a specification of a computer’s process. 5
- 6. George Forsythe, Inventor of “CS” (1961) The three most valuable tools in a STEM education (1968): • Language • Mathematics • Computer Science Photo Stanford News 6 Computer Science was invented to be a tool for learning everything else
- 8. Seymour Papert Thanks to Gary Stager 8
- 9. Kay & Goldberg, “Personal Dynamic Media” Thanks to Alan Kay (1977) 9
- 10. Andrea diSessa, Boxer, and Computational Literacy 10
- 13. Computation as a literacy ▪ Computing is: ▪ A causal specification of process that can be automated. ▪ The master simulator. ▪ Can be and effect the way that students learn everything else. ▪ The goal is enjoyable fluency. It’s what Computer Science was created for. 13
- 14. Our Mission: To provide a global forum for educators to discuss research and practice related to the learning and teaching of computing…at all education levels 14
- 15. Our timeline 2019 SIGCSE 50th Computational Literacy for All? 1968 Curriculum ’68 SIGCSE 1967 Forsythe: 3rd Leg of Literacy 1961 Forsythe: CS Perlis: Everyone 15
- 16. In Scotland, CS teacher numbers are declining From CAS Scotland 2016 Report 16
- 17. Computer science in high schools is growing very slowly ▪ In the UK (from Roehampton Report): ▪ 53% of schools offer CS GCSE, 12% of students take it. ▪ < 20% female ▪ 36% offer A Level CS, under 3% take it. ▪ < 10% female ▪ In US states, few students taking CS. By far, mostly male, esp. AP CS (A and CSP). 17
- 18. < 30% Indiana high schools offer CS < 0.5% of Indiana students take the most popular CS course in the state Thanks to Anne Leftwich and Miranda Parker18
- 19. 19 47% of Georgia schools offer CS in 2016. (43% have never offered CS.) < 1% of Georgia students take CS Thanks to Bryan Cox and Miranda Parker
- 20. Data and Visualization from Barbara Ericson and Willa Hua20
- 21. 21 Data and Visualization from Barbara Ericson and Willa Hua
- 22. Consider the scale ▪ AP CS A had 66K exam takers in 2018. ▪ AP CS Principles had 76K. ▪ AP English Lit has 580K ▪ AP Calculus has 305K ▪ There are 15.1 million high school students in US. Perhaps 90% of US high school students NEVER see any CS 22
- 23. Our timeline ????Computat ional Literacy for All 1968 Curriculum ’68 SIGCSE 1967 Forsythe: 3rd Leg of Literacy 1961 Forsythe: CS Perlis: Everyone 2019 SIGCSE’s 50th Anniversary 23
- 24. Developing computational literacy for all ▪ Inspiring examples: ▪ Bootstrap Algebra, Data Science, and Physics ▪ Project GUTS ▪ CT-STEM at Northwestern ▪ End-user programming is growing ▪ For every professional software developer in the US, there are 4-9 end-user programmers. 24
- 25. Computational Thinking ▪ Original definition from Seymour Papert, popularized by Jeannette Wing. ▪ Wing claimed that knowing about algorithms and computer architecture would influence everyday activities like picking a cashier line at the supermarket and packing your backpack. Alternative: Computation for Thinking about Everything 25
- 26. Operational Definition of CT ▪ From ISTE and CSTA: 26
- 27. Computational Thinking Specification of Problems Use Automation Organizing and Analyzing Data Data representation Use of models and simulations Exploring a range of solutions Efficiency and effectiveness in problem-solving 27
- 28. Engineering Thinking “Making ‘things’ that work and making ‘things’ work better.” 28
- 29. Engineering Thinking Specification of Problems Organizing and Analyzing Data Data representation Exploring a range of solutions Efficiency and effectiveness in problem-solving Use of models and simulations 29
- 30. Scientific Thinking ▪ From Krajcik and Merritt (2012) 30
- 31. Scientific Thinking Specification of Problems Organizing and Analyzing Data Data representation Use of models and simulations Exploring a range of solutions 31
- 32. Historical Thinking ▪ Determining a significant fact, problem, or question ▪ Interpret evidence ▪ Identify cause and consequence ▪ Use multiple perspectives 32
- 33. Historical Thinking ▪ Determining a significant fact, problem, or question ▪ Interpret evidence ▪ Identify cause and consequence ▪ Use multiple perspectives Specification of Problems Organizing and Analyzing Data Data representation Exploring a range of solutions Use of models and simulations 33
- 34. Computing is a medium for all of these 34
- 35. Computing is a literacy for all of these Computing can be anything, and uniquely adds automation and causal models to help learning. Specification of Problems Use Automation Organizing and Analyzing Data Data representation Use of models and simulations Exploring a range of solutions Efficiency and effectiveness in problem-solving 21st Century Literacy. 35
- 36. “If we teach CS in other classes, can we teach enough?” ▪ How much of a foreign language do you need to communicate? To be fluent? Can’t you always learn more? ▪ We need novelists, journalists, and screenwriters. But the biggest impact of writing is the everyday use of it. ▪ Rich, Strickland, Binkowski, Moran, and Franklin (ICER 2017) asked the question: What’s the starting place for K-8 CS learners? 36
- 37. 37
- 38. Proposed: What comes first when learning programming? 1. Precision and completeness are important when writing instructions in advance. 2. Different sets of instructions can produce the same outcome. 3. Programs are made by assembling instructions from a limited set. 4. Some tasks involve repeating actions. 5. Programs use conditions to end loops. 38
- 39. Scratch fluency doesn’t need that whole list ▪ About 30 million users. ▪ Most Scratch projects are stories that use… ▪ Only Forever loops ▪ No booleans ▪ Just movement and sequence. There is expressive power in even a subset of CS. 39
- 40. Bootstrap:Algebra doesn’t use all of that list ▪ Improves learning in algebra ▪ Students do not code repetition. ▪ Functional There is learning power in even a subset of CS. 40Schanzer, Fisler, Krishnamurthi, Felleisen, 2015
- 41. EXAMPLES How would teaching for computational literacy look different? 41
- 42. Example 1: How sound works: Acoustics, the physics of sound 42
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- 46. Digitizing Sound: How do we get that into bytes? ▪ We can do the same to estimate the sound curve with samples. 46
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- 53. Reflection ▪ Prediction ▪ 7 Lines ▪ 1 Bit ▪ Learning without writing a Program Teaching CS for insight into our world, not software development. 53
- 54. Example 2: Subgoal Labeling ▪ Students are often overwhelmed when programming. “You’ve taught me so many details, I don’t know which ones to use.” (Clancy & Linn, 1990) ▪ How do we convey how to think about the purpose for the parts of the program? About why each part is there? ▪ Richard Catrambone (1994) invented a way to label the subgoals in examples provided to students. 54
- 55. Example of Written Materials With Subgoals • Define Variables from Built-in • Click on "Built-In" and "Definition" and pull out a def variable. • Click on the "variable" and replace it with "fortuneList". This creates a variable called "fortuneList". • Click on "Lists" and drag out a call make a list • Click on "Text" and drag out a text text block and drop it next to "item". Click on the rightmost "text" and replace it with your first fortune. • Handle Events from My Blocks • Click on "My Blocks" and "Button1". • Drag out a when Button1.Click. Without Subgoals • Click on "Built-In" and "Definition" and pull out a def variable. • Click on the "variable" and replace it with "fortuneList". This creates a variable called "fortuneList". • Click on "Lists" and drag out a call make a list • Click on "Text" and drag out a text text block and drop it next to "item". • Click on the rightmost "text" and replace it with your first fortune. • Click on "My Blocks" and "Button1". • Drag out a when Button1.Click. Lauren Margelieux, Mark Guzdial, and Richard Catrambone, ICER 2012 55
- 57. With Subgoals 57
- 58. Experiment with App Inventor ▪ Used subgoal labeling to teach Android App Inventor (a blocks- based programming environment) ▪ Two groups of undergraduate students: • One group was shown a video for how to use the software to build an App and given text listing the steps in the instruction. • Another group was given the video and the steps with subgoal labels. 58
- 59. Steps in Experiment Week 1: ▪ Watch the video. ▪ Take a test to demonstrate understanding. Week 2: ▪ Take a test to demonstrate retention. ▪ Watch a new video. ▪ Take a test to demonstrate understanding of second video. ▪ Take a test where students must build a new app, transferring knowledge. 59
- 62. Results: Define Variable Step in Transfer Task 0. 0.2 0.4 0.6 0.8 1. Define Variable Subgoal Conventional p < .001, f = .61 62
- 63. It works, and in other settings ▪ Small changes that result in better understanding, retention, and transfer. ▪ Effect is twice as strong for high school teachers. ▪ Works in text-based programming languages, too Work by Lauren Margelieux and Briana Morrison. Improving the efficiency and effectiveness of Computing Education 63
- 64. We can make Computing Education better ▪ Parsons Problems ▪ Worked Examples ▪ Pair Programming ▪ Media Computation Parsons Problem from Barbara Ericson; Pair programming picture from Natalie Freed We will have to make learning computing more effective and efficient to reach everyone 64
- 65. Our timeline ????Computat ional Literacy for All 1968 SIGCSE Curriculum ‘68 1967 Forsythe: 3rd Leg of Literacy 1961 Forsythe: CS Perlis: Everyone 2019 SIGCSE’s 50th Anniversary 2069? 65
- 66. Call to Action ▪ Find our allies and grow the community. ▪ Invent, mutate, evolve. 66
- 67. Our Allies ▪ Computing across the curriculum… Allies across the curriculum. ▪ Learn from everyone we can: Physics and mathematics education research, educational psychology, learning sciences. ▪ We can’t be the experts in everything. We need others to tell us how to make computing be everything. 67
- 68. Our Mission: To provide a global forum for educators to discuss research and practice related to the learning and teaching of computing…at all education levels 68
- 69. Individuals Allied Communities ▪ Computer scientists in Schools of Education ▪ Special needs educators in Computer Science ▪ Math, science, engineering, and social studies teachers who teach with programming 69
- 70. Hughes Printing Telegraph Machine 1860 70
- 71. For 30 years, this was the common keyboard ▪ We may still be waiting for our QWERTY keyboard. ▪ How much better would we all be if we had adopted something even better than QWERTY? We need to find what makes the great ideas of computing accessible. 71
- 72. Invent, evolve, mutate ▪ Invent: ▪ New tools, languages, curricula, connections. ▪ But don’t just replicate. Mutate and Evolve. ▪ Everyone knew visual programming languages had failed — until they didn’t. ▪ What we have today has been built for the few. We need to build more, try more, and involve everyone 72
- 73. Some of the Collaborators on This Work ▪ Barbara Ericson, Miranda Parker, Kathryn Cunningham, Amber Solomon, Richard Catrambone, Lauren Margulieux, Betsy DiSalvo, Tom McKlin, Rick Adrion, Renee Fall, Sarah Dunton, Brad Miller, Ria Galanos, Brian Dorn, and Briana Morrison ▪ Our Funders: US National Science Foundation ▪ http://computinged.wordpress.com ▪ http://guzdial.engin.umich.edu 73
- 74. Our Mission: To provide a global forum for educators to discuss research and practice related to the learning and teaching of computing…at all education levels 74 Thank you!
- 75. SPARE SLIDES 75
- 76. Definition of Computer Science Science, 22 Sept 1967 76
- 77. First use of the term “Computer Science” Knuth, CACM 1972 77
- 78. Forsythe on “Computer Science” (1961) 78
Editor's Notes
- Let’s crowdsource the blog about my talk, please.
- My advisor, Elliot Soloway, had all of his students read this book. Snow was a science advisor to the UK during WW II. He bemoaned the split in Western Society between a science outlook (what we call today, STEM) and a humanities perspective. Elliot wanted his students to think about: Who needs what we have to offer, who might not even come into our classrooms?
- This is the start of the mission of SIGCSE. I think it’s terrific. It’s noble. Most of my talk is going to be exploring the ramifications of this mission.
- Many people have a more narrow definition of CS. I hope I can change your mind by the end of the talk. I’ll be exploring this definition of programming here — for me, programming doesn’t require curly braces or the words WHILE or ELSE. For programming is for everything.
- George Forsythe invented the term computer science in an article in the Journal of Engineering Education. The most valuable parts of a STEM education are “the general-purpose mental tools which remain serviceable for a lifetime. I rate natural language and mathematics as the most important of these tools, and computer science as a third.” (1968) Computer science is meant to be a tool for learning everything that a STEM learner needs.
- Gene Amdahl, John McCarthy, Alan Newell, Herb Simon, Grace Hopper. Greenberg’s book includes transcripts of all the lectures and all the discussants. Alan Perlis started CS at Yale and Carnegie Tech (later CMU), and won the first ACM Turing Award. “Given then the appropriate computer, the capability of developing programming systems, the proper freshman course, and possibly a good follow-up program, the computer will achieve its ultimate role as handmaiden to scholarly university activities.” Alan Perlis argued that computer science should be part of a liberal education. Explicitly, he argued that all students should learn to program. Why? Because Computer Science is the study of process. Automated execution of process changes everything Including how we think about things we already know
- Seymour Papert claimed “that children can learn to program and learning to program can affect the way that they learn everything else.” His point is not to teach computer science for its own sake, or as vocational training, but as a support for learning in any discipline. He invented the turtle as a tool to think with. Gary Stager provided me the picture of Wally Feurzeig, the inventor of Logo, with Cynthia Solomon, Seymour’s collaborator in creating the turtle and in creating Logo curriculum and classrooms.
- This was my introduction to this story. I read “Personal dynamic media” in 1982, and fell in love with the idea of the computer as the most powerful medium for thinking and learning. A meta-medium that could be all other media. This picture is from Smalltalk-76 — the first Desktop User Interface. It was meant to be a desk, and a canvas, and musical staff, and electrical diagrams. Kay wrote, “Computer literacy is a contact with the activity deep enough to make the computational equivalent of reading and writing fluent and enjoyable.” We can study reading and writing for their own sake, but for most of us, reading and writing is what enables us to express ideas, to communicate with others, and to understand our world. Literacy supports and affects how we learn.
- Andy defined the term “computational literacy.” He made Boxer as a programming environment for building literacy. He and his students have done some fundamental work on understanding computational literacy.
- The equation allows us to explore balance — know all but one of the variables, you can figure out the last. The code allows us to explore causal relations.
- First, you see no mention of X, A, V, or T. He’s not solving the equation. Then look at what he’s doing. He’s running the loop in his mind. He’s running the simulation for each value of t. He knows the causal relationships between gravity, acceleration, velocity, and position. He is tracing an algorithm. He is demonstrating computational thinking skills, unplugged. He learned the algorithm on the computer, and now he can apply it to physics.
- Imitation Game - the computer can simulate ANYTHING. Alan Kay 1991, “The computer is the greatest ‘piano’ ever invented, for is it the master carrier of representations of every kind. The heart of computing is building a dynamic model of an idea through simulation.”
- SIGCSE has taken on the charge of providing the forum for computing educators to EVERYONE.
- England now has a general qualification in CS and an A-level. Harry Potter fan may think of these as Owls and Newts. Not all schools offer CS, and very few students take it. Gender balance is awful. In US, K-12 is organized by states, so our data is by states. The data I’m showing comes from ECEP landscape surveys.
- I am only presenting data from two states, because we have so little data from states, and certainly no national data.
- This is a visualization of many of the Advanced Placement exams. Size of the dot indicates how many people took that exam. The middle line is female number of exam takers == male number of exam takers. Chemistry is pretty close to parity. Most AP exams are female dominant. See that big clump to the left? That’s where CS is. Let’s zoom in there.
- AP CS A is tied the most male dominant AP exam. AP CSP is the fifth most male, far more male than Physics 1. Now notice the axes. This is a log scale. CS is far more male-skewed.
- https://secure-media.collegeboard.org/digitalServices/pdf/research/2018/Program-Summary-Report-2018.pdf But what if Calculus used programming?
- This is what Forsyth wanted: CS as the third leg of literacy. Scaffidi, Shaw, and Myers, 2005. Brian Dorn will tell you that these end-user programmers struggle. Greg Wilson’s Software Carpentry tries to help computational scientists and engineers efficiently learn the computing they need as working professionals.
- Here’s a definition of CT from CSTA and ISTE. Let’s accept this as the definition of CT. Here’s the critical part that I want to assume: We can achieve this. We can use computing as a way to facilitate students developing these skills.
- Let’s summarize these skills. Computational thinking means developing these seven skills.
- Here’s engineering thinking. Good engineers use these “habits of mind.”
- If we can teach CT, we can also teaching engineering thinking.
- What does it mean to thinking like a scientist?
- If can teach these CT skills, we can also teach scientific thinking.
- I’m working with history professors now, to think about how we can use computing to help students develop historical thinking.
- If we can teach CT, we can teach historical thinking.
- We can use computing to teach any of these.
- This is 21st Century. Computing is the foundation for teaching all of this, because it can BE anything, and it offers automation and causal models to teach in a way different than we ever have before.
- This is a concern that I hear often from CS teachers. We need standalone CS classes to teach everything that students need. I’d like to address that. The paper by Katie Rich and colleagues from 2017 has influenced my thinking about this point.
- Katie Rich et al from ICER 2017. They used research literature and empirical data to define the concepts students needed to be able to start programming. Their focus is K-8 with the kinds of programming languages that we use today, but I think that their findings go far beyond K-8.
- This is a list of concepts that K-8 CS learners have to understand to program in most languages today. I’ll bet that this is the same for everyone. I’ll bet that few of us teach these explicitly. We figure that students will just figure them out. I’m not sure that they do. I’ll bet that most of you are looking at this list thinking, “This is the barest minimum to teach.” Well actually…
- The most popular programming language for children in the world today is Scratch. There are have been several terrific studies of what students actually do in Scratch. My favorite of these is by Yasmin Kafai and Debbie Fields. They find that the greatest use of Scratch is for story-telling. Lots of code blocks lying around unused. Yet, Scratch is a great model for what it means to be fluent in programming. You don’t even need that whole list, and you have something with massive expressive power that millions of children around the world find useful. Bootstrap
- Bootstrap Algebra doesn’t use that whole list. Students don’t write repetition. (It’s hidden.) Bootstrap adds some more CS, like functions without side effects. But look at the center column of this table. They’re not doing much CS, and they get kids to learn algebra better.
- I now want to give you two examples of teaching differently for literacy, both drawn from work that I have done with my colleagues. They speak to how we’ll teach differently, to different students, for different purposes, if we’re teaching for literacy.
- Do sound visualization work tool here.
- These 7 lines tell you about how you understand speech. How many bits per sample for understandable speech?
- Are we agreed that this is a small change? Adding headings and callouts in the video?
- Participants in the subgoal group attempted more of the subgoals necessary to solve the problem than participants in the non-subgoal group. This result suggests that these participants could identify the subgoals necessary to complete the solution whether or not they completed the solution correctly. If this conclusion is correct, subgoal participants might have formed better mental models for solving problems in AAI and need only more instruction on the features of the tool. Participants in the subgoal group also completed more subgoals correctly than participants in the non-subgoal group. This result suggests that subgoals allowed these participants to learn more effectively than the non-subgoal participants which could be a result of lower cognitive load while learning. This result could also mean that subgoals improve transfer to other problems which could be a result of better self-explanation while learning. The present study cannot disentangle these possible explanations, but future studies will examine these possibilities.
- Shorter assessment, so not enough power for the attempted subgoal effect
- Conventional participants did not define the variable and just made the list, but if one doesn’t define the variable, then one can’t use that list in the program Could be a classic example of novices focusing on incidental features of the problem and subgoals help illustrate the structure of the problem, or could just be near transfer, and the additional subgoal label helped them remember that step; can’t know based on current data
- We can get away with less efficient methods with undergraduate CS majors desperate to get into our class. Not so when we’re trying to convince students to learn something for the sake of literacy.
- American Society of Engineering Education turned 125 years old this year. The National Council of Teachers of Mathematics will be 100 years old in 2020. American Association of Physics Teachers was founded in 1950. I think we’ll still be working on achieving the vision of Forsythe and Perlis at SIGCSE’s 100th anniversary.
- Just because you can learn about anything with computer science, doesn’t mean that learning computer science makes you an expert or even a learner in everything.
- These are our words, our mission. A Global Forum. For Educators. Not computing educators.
- Aman Yadav, Beth Simon, Yasmin Kafai. I also talked about Lauren Margueliuex, Debbie Fields, and Anne Leftwich. We need people with CS expertise in Schools of Ed, to grow computing education in higher-ed. There are more of these folks in Germany and Israel, but too few. Their need for a community is part of a “Global Forum.” Maybe we get more of them here, maybe we help to grow CSTA, maybe we help create new forums like WiPSCE. We accept the responsibility for providing them a global forum. We have great CS people like Andy Stefik and Richard Ladner focusing on special needs students in CS. We need people like Maya Israel in Schools of Ed too. This is Tammy Shreiner and Bob Bain, two of the history professors I work with. They want students and teachers constructing models of historical events, and testing those models through simulation. They want programming for social studies teachers There will be a liminal period that needs our support. Back when educational technology was first moving into schools in the 1970’s, people looked for the technology to improve learning, but it didn’t. It changed learning. Computing will allow students to explore new ideas, to learn new things. The non-CS teachers who teach with computing will likely not be accepted to their disciplinary conferences, but they won’t be doing traditional CS Ed, either. We need communities and venues to support these people.
- I visited the London Science Museum and saw the device in Figure 1, a printing telegraph machine from 1860. It is an anachronism today, not just because it’s a telegraph machine. — Printing telegraph machines date from 1840, but the QWERTY keyboard was only patented in 1868. For almost 30 years, if you wanted a keyboard, a piano keyboard was the only option.
- What if we’re in this same period of computing? We’re only 50 years old. Maybe we’re still waiting for our QWERTY keyboard. Maybe we can hold out for something even better.
- Things that our community tried before, when we only aimed at CS majors, might work for others. Machine learning is programming with data. My historians sound like they want production rule, logic-based programming languages.
- I am overwhelmed to stand in front of you today. Thank you.
- These are our words, our mission. I am so very proud to receive this award from my community, from a group with such a noble and awe-inspiring mission. Thank you.