Computational Thinking Everywhere Eric Roberts Professor of Computer Science Stanford University The “Broad Audience for CS1” Approach Computational Thinking Workshop The National Academies February 19, 2009

Stanford’s Strategy For at least the last 30 years, Stanford has tried to implement “computational thinking everywhere” by getting as many students as possible into one of our introductory courses, which are designed to be funnels rather than filters. During the boom years of , our introductory course was the largest course at Stanford, CS was the second largest undergraduate major (behind economics), and 75 percent of all Stanford undergraduates took at least one CS course. As at everywhere else, the number of CS majors fell after the dot-com collapse, from a high of 200 graduates in 2002 (12 percent of all undergraduates) to 141 in In a trend that seems common to the larger programs, our numbers have begun to rise again. The number of students taking our introductory courses is almost back to its historical peak.

Entry Points into CS at Stanford CS 105 Introduction to Computing CS 106A Programming Methodology CS 106B Programming Abstractions CS 106X Accelerated version (A+B) general education a more serious introduction

Introductory Course Enrollment Trends projected All intro courses CS106A

Eventual Majors of CS 106A Students

Strategies for Achieving Large Enrollments Maintain a strong commitment to educating a broad audience. Assemble a team of highly effective lecturers to teach the introductory curriculum. Offer multiple entry points, both to create a greater range of opportunities and to allow students to self-select their level. Make sure the introductory courses have a reputation for being challenging but also accessible. Get undergraduates involved in teaching so that they can serve as “stepping-stone” role models. Provide a robust safety net for students who are having trouble. Draw examples from a broad range of disciplines. Encourage students to go beyond the baseline requirements, particularly in their own areas of interest.

A Recent Assignment in CS 106B There are several alignment methods for measuring the similarity of two DNA sequences (which you can think of for the purposes as strings over a four-letter alphabet: A, C, G, and T). One method to align two sequences x and y consists of inserting spaces at arbitrary locations (including at either end) so that the resulting sequences x' and y' have the same length but never have a space in the same position. You can then assign a score to each position. Position j is scored as follows: +1 if x'[j] and y'[j] are the same and neither is a space, –1 if x'[j] and y'[j] are different and neither is a space, –2 if either x'[j] or y'[j] is a space. The score for a particular alignment is just the sum of the scores over all positions. For example, given the sequences GATCGGCAT and CAATGTGAATC, one such alignment (though not necessarily the best one) is: G ATCG GCAT CAAT GTGAATC – + positive scores for matches negative scores for misses

And Peter Lee Asks... Is it possible to compose music automatically? One of my students, in the second week of CS106A, sought to answer that question by using Karel the Robot to compose new Goldberg variations: — Jennifer Mazzon, January 2009

The Dangers of a Separate Course Courses that allow students to use computers but insulate them from the process of writing programs are likely to push students away from a computer science major just when the economy needs many more people with those skills. Courses that avoid teaching programming send the implicit message that programming is just as unpleasant as its current reputation implies. Our courses, by contrast, emphasize the “passion, beauty, joy, and awe.” Teaching all types of students together makes it easier for students to switch into computer science, even if that was not their original intention. Having a large fraction of undergraduates in these classes means the students reflect the diversity of the institution. Stanford has deliberately chosen not to offer a “computational thinking” course for nonmajors to avoid the following pitfalls:

The End

Degree Production vs. Job Openings 160, , , ,000 80,000 60,000 40,000 20,000 EngineeringPhysical SciencesBiological Sciences Computer Science Ph.D. Master’s Bachelor’s Projected job openings Adapted from a presentation by John Sargent, Senior Policy Analyst, Department of Commerce, at the CRA Computing Research Summit, February 23, Original sources listed as National Science Foundation/Division of Science Resources Statistics; degree data from Department of Education/National Center for Education Statistics: Integrated Postsecondary Education Data System Completions Survey; and NSF/SRS; Survey of Earned Doctorates; and Projected Annual Average Job Openings derived from Department of Commerce (Office of Technology Policy) analysis of Bureau of Labor Statistics projections. See Sources:

160, , , ,000 80,000 60,000 40,000 20,000 EngineeringPhysical SciencesBiological SciencesComputer Science Ph.D. Master’s Bachelor’s Projected job openings Adapted from a presentation by John Sargent, Senior Policy Analyst, Department of Commerce, at the CRA Computing Research Summit, February 23, Original sources listed as National Science Foundation/Division of Science Resources Statistics; degree data from Department of Education/National Center for Education Statistics: Integrated Postsecondary Education Data System Completions Survey; and NSF/SRS; Survey of Earned Doctorates; and Projected Annual Average Job Openings derived from Department of Commerce (Office of Technology Policy) analysis of Bureau of Labor Statistics projections. See Sources: And Various Data Seem to Agree , , Network systems and data communications analysts Personal and home care aides Home health aides Computer software engineers, applications Veterinary technologists and technicians Personal financial advisors Makeup artists, theatrical and performance Medical assistants Veterinarians Substance abuse and behavioral disorder counselors Top 10 job growth categories ( ) Employment (thousands) Growth U.S. Department of Labor, Bureau of Labor Statistics, Employment Projections: , December 2007.Source: Working in the life sciences typically requires a degree in biology or some closely related field, but relatively few biology majors actually end up working in the field. 80% of workers in the life sciences have degrees in the life sciences. 14% of graduates with degrees in the life sciences work in those fields. National Science Foundation/Division of Science Resources Statistics, SESTAT (Scientists and Engineers Statistical Data System), 1999, as presented by Caroline Wardle at Snowbird Sources: 39% of workers in computing have degrees in computing. 71% of students with degrees in computing remain in the field. In computing, the pattern of degree production vs. employment is reversed.

The Microsoft Programming Personae Microsoft’s cultural lore defines three types of programmers: Mort is your most common developer, who doesn’t have a CS background, may even be a recent newcomer, and doesn’t quite understand what the computer is doing under the covers, but who writes the dinky IT programs that make businesses run. Elvis, more knowledgeable, cares about code quality, but has a life too. Einstein writes some serious-ass piece of code like device drivers, wants to get things done, needs to be able to go low level and high level, needs a language without restrictions to get his job done. Wesner Moise, “Who are you? Mort, Elvis or Einstein,” September 25, — For the most part, Microsoft (along with Google and other first-rank companies) are seeking to hire the Einsteins, which explains the low hiring ratio.