Meeting Computing Needs Across Campus Mark Guzdial, School of Interactive Computing

2 Story Why we should teach computing to everyone Making computing work for everyone at Georgia Tech. –Lesson Learned: Contextualized Computing education. –What the courses are like Side trip into second course –Results Side trip: Applying the lesson to the BS in CS. Finally, what does it buy us in CS?

3 Time Warp to Fall 1999 Fall 1999: All students at Georgia Tech must take a course in computer science. –Considered part of General Education, like mathematics, social science, humanities… Why did Georgia Tech make that decision? –Computing was a College. –ABET might start requiring CS.

4 More reasons for computing across curriculum Everyone needs to learn about process (Alan Perlis) Algorithms control our lives: The tyranny of the computationally literate (C.P. Snow) The tools of learning for computational scientists and engineers brought home. –Computers are cheaper than cyclotrons

MIT Sloan School Symposium

6 Computing for Everyone In 1961, 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

7 The Power and Fear of Algorithms The Economist (Sept., 2007) wrote on the algorithms that control us, yet we don’t understand. –Credit Scores, Adjustable Rate Mortgages, Google C.P. Snow foresaw this in –Those who don’t understand algorithms, can’t understand how the decisions are made. “A handful of people, having no relation to the will of the society, having no communication with the rest of society will be taking decisions in secret which are going to affect our lives in the deepest sense.”

8 Adopting Computing—without us At Georgia Tech and other Universities: –Biology teaches programming for mathematical and computational models. –Physics teaches VPython for labs where they solve three-body problems. Computer science provides the tools and metaphors for understanding science. Scientists and engineers use computing to model, simulate, and understand. –Why shouldn’t science and engineering students? –History repeating: Telescopes, microscopes. –Unlike other scientific instruments, computers are already cheap and plentiful. Problem: They’re doing it without us.

9 Richard Dawkins on Fresh Aire GROSS: You close your book saying, "I am thrilled to be alive at a time when humanity is pushing against the limits of understanding." How do you think that's happening in your field of evolutionary biology? Mr. DAWKINS: Well, it's the most exciting time to be a biologist…Since Watson and Crick in 1953, biology has become a sort of branch of computer science. I mean, genes are just long computer tapes, and they use a code which is just another kind of computer code. It's quaternary rather than binary, but it's read in a sequential way just like a computer tape. It's transcribed. It's copied and pasted. All the familiar metaphors from computer science fit.

10 Back to Georgia Tech in 1999 Key Point: Only one course met the requirement: CS1321 Introduction to Computing –Shackleford’s pseudocde approach in 1999 –Later Scheme: How to Design Programs

11 CS1321: Pass (A, B, or C) vs. WDF (Withdrawal, D or F) PassWDF 02 FallTotal74.01%26.74% Female62.99%36.65% Male77.00%22.90% 02 SpringTotal65.03%34.87% Female65.56%34.44% Male64.81%35.04% 01 FallTotal70.98%29.02% Female59.55%40.45% Male73.63%26.37%

12 Contextualized Computing Education Since Spring 2003, we teach 3 introductory CS courses. –Responding to research results about CS being “irrelevant” –Based on Margolis and Fisher “alternative paths” Each course introduces computing using a context (examples, homework assignments, lecture discussion) relevant to majors. Make computing relevant by teaching it in terms of what computers are good for (from the students’ perspective).

13 Our Three CS1’s Today CS1301 Introduction to Computing Traditional CS1 for our CS majors and Science majors (math, physics, psychology, etc.). CS1371 Computing for Engineers CS1 for Engineers. Same topics as CS1301, but using MATLAB with Engineering problems in homework and examples. CS1315 Introduction to Media Computation

14 Introduction to Media Computation Average 400 students/term –Overall, CS1315 has been 51% female –Required in Architecture, Management, Ivan Allen College of Liberal Arts, and Biology Focus: Learning programming and CS concepts within the context of media manipulation and creation –Converting images to grayscale and negatives, splicing and reversing sounds, writing programs to generate HTML, creating movies out of Web-accessed content. –Computing for communications, not calculation

15 Media Computation: Teaching in a Relevant Context Presenting CS topics with media projects and examples –Iteration as creating negative and grayscale images –Indexing in a range as removing redeye –Algorithms for blending both images and sounds –Linked lists as song fragments woven to make music –Information encodings as sound visualizations

16 Syllabus for Introductory Course Getting started: Defining and executing functions Pictures –Psychophysics, data structures, defining functions, loops, conditionals (red- eye removal, posterizing) –Bitmap vs. vector notations Sounds –Psychophysics, data structures, defining functions, loops, conditionals –Sampled sounds vs. synthesized, MP3 vs. MIDI Text –Converting between media, generating HTML, database, and networking –A little trees (directories) and hash tables (database) Movies Then, Computer Science topics (last 1/3 class)

17 Computer Science Topics as solutions to their problems “Why is PhotoShop so much faster?” –Compiling vs. interpreting –Machine language and how the computer works “Writing programs is hard! Are there ways to make it easier? Or at least shorter?” –Object-oriented programming –Functional programming and recursion “Movie-manipulating programs take a long time to execute. Why? How fast/slow can programs be?” –Algorithmic complexity

18 def negative(picture): for px in getPixels(picture): red=getRed(px) green=getGreen(px) blue=getBlue(px) negColor=makeColor(255-red,255-green,255-blue) setColor(px,negColor) def clearRed(picture): for pixel in getPixels(picture): setRed(pixel,0) def greyscale(picture): for p in getPixels(picture): redness=getRed(p) greenness=getGreen(p) blueness=getBlue(p) luminance=(redness+blueness+greenness)/3 setColor(p, makeColor(luminance,luminance,luminance))

19 Examples of Student Work Soup- Audio Collage Canon- LinkedList of (MIDI) Music

20 Examples of Teacher Work

21 Student voices Intro CS student (female): “I just wish I had more time to play around with that and make neat effects. But JES [IDE for class] will be on my computer forever, so… that’s the nice thing about this class is that you could go as deep into the homework as you wanted. So, I’d turn it in and then me and my roommate would do more after to see what we could do with it.” High School teacher: “This was the best (non- college credit) workshop I have ever taken.” Students in multimedia data structures: “Data structures is an important step. Use of media! It makes it fun.”

22 A Media Computation Data Structures Course Driving question: “How did the wildebeests stampede in The Lion King?”

23 Connecting to the Wildebeests It’s all about data structures

24 Rough Syllabus for CS1316 Weeks 1-4: Recreate media computation in Java (Images, sounds, turtles, MIDI). Linked lists of MIDI. –Goal: Creating flexible structures for design Linked lists and trees of images. –Scene graph as first tree. Linked lists and trees of sampled sounds. –Recursive traversals. Canon Swan Bells Fur Elise

25 Similar Assignments, but with Objects and Agents

26 Syllabus (continued) Generalized lists and trees. Graphical User Interfaces as trees –Layout managers as renderers Continuous Simulations –Predator-prey, disease propagation –UML (design notation) and reuse Mapping Simulations to Animation –Finally! The wildebeests and villagers Discrete Event Simulation –Stacks and queues are natural here gal1- rightface.jpg gal1- right2.jpg gal1- right1.jpg gal1- rightface.jpg

27 Results: CS1315 “Media Computation” PassWDF 04 FallTotal80.33%19.65% Female82.90%17.10% Male77.46%22.54% 04 SpringTotal89.87%9.37% Female91.94%7.58% Male87.50%11.41% 03 FallTotal86.47%12.54% Female88.36%10.27% Male84.71%14.65%

28 Success Rates for Specific Majors Success rates in traditional CS1 for students in various majors average Fall ’99 to Fall ’02, compared to Spring ’03 to Fall ’05 in Media Computation.

29 Results: CS1371 “Engineering” PassWDF 04 FallTotal85.03%14.87% Female85.55%14.45% Male84.92%14.96% 04 SpringTotal75.27%24.27% Female75.54%23.74% Male75.19%24.42% 03 FallTotal73.94%26.06% Female71.72%28.28% Male74.49%25.51%

30 Results of four years of evaluation MediaComp students are more motivated and engaged (retention data, interviews), and find the course social, creative, and relevant. –Replicated at several institutions now. Students in the contextualized courses program outside of class. –Immediately (engineers) and even a year later (MediaComp) Students in MediaComp classes (both) spend extra time on homework “because it’s cool.”

31 The Other Results We don’t know if they learn the same. –The challenge of comparative studies when there is no common reality. In reality, majority of students do not find the course relevant to their degrees or professions. –Many do find it relevant to their lives. Students distinguish between “more MediaComp classes” and “more CS classes”

32 Next steps… An alternative path and a minor What happens when you have an intro to CS course for non-majors that students pass and even enjoy? Define a CS minor –About 100 students today Create new BS in Computational Media –Joint with School of Literature, Communications, and Culture –58 majors in first year, 24% female Over 200 majors today, still about ¼ female

33 How about CS? Back to CS1321 PassWDF 04 FallTotal84.34%15.26% Female89.36%10.64% Male83.17%16.34% 04 SpringTotal68.26%31.74% Female67.57%32.43% Male68.46%31.54% 03 FallTotal81.42%18.45% Female77.86%22.14% Male82.18%17.67%

34 A Context for CS1 for CS majors: Robotics Microsoft Research has funded the Institute for Personal Robotics in Education –Tucker Balch, Directing Joint between Bryn Mawr and Georgia Tech – ghttp:// g Goal is to develop a CS1 (and CS2) with robotics as the context. –Homework: Recursively follow a light Enter a pyramid and take a picture of it Film a movie and use MediaComp for special effects

35 Using Context throughout the CS Curriculum The future of computing is not in merely being a good programmer. –Those skills are now commodities that can be outsourced anywhere. When “The World is Flat” (Friedman), we become competitive by bridging areas and differentiating.

36 Microsoft wants employees who bridge “The nature of these jobs is not closing the door and coding,” (Bill) Gates said. “The great missing skill is somebody who’s good at understanding engineering and bridges that [understanding] to working with customers and marketing…We can promise these people most of what they’re doing won’t be coding.” –Gates worried over decline in US computer scientists, ComputerWorld, July 18, 2005 (by Elizabeth Montalbano)

37 The Threads™ Curriculum We have defined 8 Threads in Computing: –Computing and People –Computing and Information Internetworking –Computing and Media –Computing and Platforms –Computing and Intelligence –Computing and Foundations –Computing and Computational Modeling –Computing and Devices (was Embodiment)

38 The BS in Computer Science under Threads™ Each Thread specifies the courses needed to know that area well. –From introductory computing, through advanced courses, to beyond Computer Science (Psychology, Physics, Computer Engineering). A degree is the union of any two Threads. –Every Combination is a full Computer Science degree, but bridging disciplines and clearly different from “just programming.” –No Thread choice is necessary in first year, Can always choose different Threads during degree.

39 Next steps in Threads: Roles Threads are about conceptual focus. Within any Thread, might play different roles: –A Master Practitioner –An Entrepreneur –A Researcher –A Communicator/Teacher –A Public Policy Maker We are defining recommendations for these roles in terms of experiences and elective classes in software engineering, management, and other areas.

40 Back to Computing Across Curriculum What do we get from teaching the rest of campus? –Problems to work on. The difference between Computer Science and Computing. –Where the interesting stuff is. –A change in culture. Pedagogical methods. –Critical design in Architecture Research methods

41 Computer Scientists and Reading Alan Perlis, Norbert Weiner, J.C.R. Licklider, C.P. Snow Others included Vannevar Bush, Herbert A. Simon, Marvin L. Minsky, Jay W. Forrester, Grace M. Hopper, Claude E. Shannon, John G. Kemeny, Gene M. Amdahl

42 Summary The rest of campus needs what we have to offer. We have found that the way they need computing education is different than the way we offer it to our students. –Maybe we need to change what we offer to our own students! We have found a contextualized computing approach works (for the measures we have now). There may be benefits for our culture in making more connections to the rest of campus.

43 Thank you! Mark Guzdial For more on MediaComp approach (including papers, software, and slides): plan Media Computation Teachers’ Site: teach

44 What Georgia Tech Teaches CS1301 Intro to Programming in Python for CS majors CS1331 CS1+2 in Java CS1332 Data Structures and Algorithms in Java CS1315 (Media Computation CS1 in Python) CS1316 (Structure & Behavior— Multimedia data structures in Java) CS1371 (Computing for Engineering in MATLAB (only)) CS2110 (Low-level programming in C) CS2260: Media Device Architectures CS1372 Algorithm Design in C Institute for Computing Education Summer Workshops for High School Teachers: Media Computation CS1 in Java

45 Computing and Devices

46 Computing and Information Internetworking

47 Want a job in Information Security? Information Internetworking + Foundations –Encoding and storing information securely for organizations Information Internetworking + Platforms –Making information flow securely between large databases and small cell phones and PDAs.

48 Preparing for Jobs to Come The Future of Robotics: Devices + People

49 Preparing for Jobs to Come Platforms + Media Platforms + People

50 For More Information…