Post on 24-Aug-2020
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0
100
200
300
400
500
600
700
800 Harvard
CS50
Introductory course enrollments are exploding
0 100 200 300 400 500 600 700 800 900 1000 University of Pennsylvania
total
CIS 110
CIS 120
0
200
400
600
800
1000
1200 MIT
total
6.01
6.00
0
100
200
300
400
500
600
700
800 Harvard
CS50
Not just at Harvard
0
500
1000
1500
2000
2500 Stanford
total
CS 106A
CS 105
CS 101
Not just at elite private insFtuFons
0
500
1000
1500
2000
2500
3000 University of Washington
CSE 142
0
200
400
600
800
1000
1200
1400 University of Michigan
EECS 280
These enrollments are blowing past previous highs
0
500
1000
1500
2000
2500
3000 University of Washington
CSE 142
CSE 143
Increasing proporFons of students are taking second courses
20%
30%
40%
50%
60%
70% University of Washington
CSE 142-‐>143 %
In at least some cases, female parFcipaFon is increasing
20%
25%
30%
35%
40% University of Washington
CSE 142 % female
0
50
100
150
200
250
300
350 Harvard
Majors
0
200
400
600
800
1000
1200 MIT
Total
6.3
6.2
0
100
200
300
400
500
600 University of Pennsylvania
Majors
Demand for the major is increasing
0
100
200
300
400
500
600
700
800 Stanford
Majors
Dot-com peak: 3,237 Dot-bust trough: 1,732 Most recent year: 6,174 Target enrollment: 135 (+ ~40 upper-division transfers)
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760 students!
CS229!Machine Learning!Autumn 2013!
CS229Machine LearningAutumn 2013
Why is this man smiling?
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But this Fme, something feels different • Possibly, students are figuring out that every 21st century ciFzen
needs to have facility with “computaFonal thinking” – problem analysis and decomposiFon (stepwise refinement), abstracFon, algorithmic thinking, algorithmic expression, stepwise fault isolaFon (debugging), modeling – Programming is the hands-‐on, inquiry-‐based way that we teach
computaFonal thinking and the principles of computer science
• Possibly, students are figuring out that fields from Anthropology to Zoology are becoming informa(on fields, and that those who can bend the power of the computer to their will – computaFonal thinking, but also computer science in greater depth – will be posiFoned for greater success than those who can’t
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• Possibly, students are figuring out that all of the STEM jobs are in computer science
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How are we, and our insFtuFons, going to respond? • 10% of Princeton’s students are computer science majors
– Far more at MIT
• 10% of Princeton’s faculty are unlikely to ever be in computer science! – Dieo, proporFonately, at MIT
• And then there is – Introductory course demand ++ – Upper-‐division non-‐major demand ++ – Graduate non-‐major demand ++
Some possibiliFes … • Restrict the size of the major
– ImplicaFons for diversity?
• Exclude non-‐majors from upper-‐division courses • Retreat to “the core” – turn over many of our courses to other
departments • Have enormous class sizes and/or enormous teaching loads • UFlize vast numbers of lecturers • Have a beer while the students use Coursera
That’s what we’ll discuss • What are you experiencing? • How are you responding today? • What do you envision in the short-‐term and long-‐term future? • What are the possible implicaFons of various direcFons for the
parFcipaFon of currently under-‐represented groups?
Our field is at a criFcal juncture!
http://lazowska.cs.washington.edu/NCWIT.pdf
Thanks for providing data! • Colorado School of Mines: Tracy Camp • Harvard Univ.: Greg Morrisee, Margo Seltzer • Harvey Mudd College: Ran Libeskind-‐Hadas • Johns Hopkins Univ.: Greg Hager • MIT: Saman Amarasinghe, Anantha Chandrakasan, Eric Grimson, Laura Moses, Victoria
Palay, Daniela Rus, Jacob White • Rochester InsFtute of Technology: Andrew Sears • Stanford Univ.: Eric Roberts, Claire Stager • Univ. Michigan: H.V. Jagadish • Univ. Pennsylvania: Sue Davidson • Univ. Rochester: Henry Kautz • Univ. Texas: Tiffany Grady, J Moore • Univ. Utah: Ross Whitaker • Univ. Washington: Raven Alexander, Crystal Eney, Ed Lazowska, Jen Pesicka • Wellesley College: Takis Metaxas