Managing CS major enrollment boom with a lottery: “A lottery, by definition, is fair.”

I am excited to see that the University of California, San Diego is now managing their over-enrollment in the computer science major with a lottery — see the article here.

Instead of enrolling students holistically or based on GPA, the department selects at random — assuming they exceed the 3.3 CSE GPA threshold. With the lottery system, all students are equally considered despite differences in their experience, drive, and ability.

When asked about the implications of the new system — and possible disadvantage to high-performing students — CSE Chair Dean Tullsen explained, “a lottery, by definition, is fair.”

“I think there’s this false assumption that the students who work harder are the ones who are getting the 4.0s, that hard work directly translates to a higher grade. [The lottery system will] admit a lot of hard-working students who weren’t getting in before,” CSE Vice-Chair for Undergraduate Education Christine Alvarado added.

This is a much more fair system than simply allowing in the top GPA students. It probably doesn’t make Tech companies happier, but it’s not clear that it makes them less happy. They will still get lots of potential employees who are above the bar. Those employees will likely be more diverse than the graduates being produced from CS programs today. The students getting the top grades in the early classes are typically those with more opportunity to learn CS, more wealth, and more privilege. A lottery says that anyone who is prepared for the courses can take them.

SIGCSE 2020: Papers freely available, AP CSA over AP CSP for diversifying computing, and a tour of computing ed research in one hour

My Blog@CACM post for this month was about my first stop on my tour of SIGCSE 2020 papers (see link here). While the SIGCSE 2020 conference was cancelled, the papers are freely available now through the end of June — see all the proceedings here. I’ve started going through the proceedings myself. The obvious place to start such a tour is with the award-winning papers. My Blog@CACM post is on the paper from An Nguyen and Colleen M. Lewis of Harvey Mudd College on the negative impact of competitive enrollment policies (having students enroll to get into CS, or requiring a higher-than-just-passing GPA to get into the computing major) on students’ sense of belonging, self-efficacy, and perceptions of the department.

I said that this was the first stop on my tour, but that’s not really true. I’d already looked up the paper Does AP CS Principles Broaden Participation in Computing?: An Analysis of APCSA and APCSP Participants (see link here), because I’d heard about it from co-author Joanna Goode. I was eager to see the result. They show that AP CS Principles is effectively recruiting much more diverse students than the AP CS A course (which is mostly focused on Java programming). But, AP CS A students end up with more confidence in computing and much more interest in computing majors and tech careers. Maybe CSA students had more interest to begin with — there is likely some selection bias here. This result reminds me of the Weston et al result (see blog post here) showing that the female high school students they studied continued on to tech and computing majors and careers if they had programming classes.

I’ve been reading The Model of Domain Learning: Understanding the Development of Expertise (see Amazon link) which offers one explanation of what’s going on here. Pat Alexander’s Model of Domain Learning points out that domain knowledge is necessary to have sustained interest in a domain. You can draw students in with situational interest (having activities that are exciting and engage novices), but you only get sustained interest if they also learn enough about the domain. Maybe AP CSP has more situational interest, but doesn’t provide enough of the domain knowledge (like programming) that leads to continued success in computing.

In my SIGCSE 2020 Preview blog post (posted just two days before the conference was posted), I mentioned the cool session that Colleen Lewis was organizing where she was going to get 25 authors to present the entire 700+ page Cambridge Handbook of Computing Education Research in 75 minutes. Unfortunately, that display of organizational magic didn’t occur. However, in a demonstration of approximately the same level of organizational magic, Colleen got the authors to submit videos, and she compiled a 55 minute version (which is still shorter than reading the entire tome) — see it on YouTube here.

There are lots of other great papers in the proceedings that I’m eager to get into. A couple that are high on my list:

• Dual-Modality Instruction and Learning: A Case Study in CS1 from Jeremiah Blanchard, Christina Gardner-McCune, and Lisa Anthony from University of Florida, which provides evidence that a blocks-based version of Java leads to more and deeper version on the same assessments as students learning with textual Java (see link here).
• Design Principles behind Beauty and Joy of Computing by Paul Goldenberg and others. I love design principles papers, because they explain why the authors and developers were doing what they were doing. I have been reading Paul since back in the Logo days. I’m eager to read his treatment of how BJC works (see link here).

Please share in the comments your favorite papers with links to them.

Is there a Geek Gene? Are CS grades bi-modal? Moving computing ed research forward

This month’s Communications of the ACM published Elizabeth Patitsas’s ICER paper about bimodality in CS grades (or rather, the lack thereof) as a research highlight, Evidence that Computer Science Grades are not Bimodal. It’s a big deal to have computing education in this position in the ACM flagship publication, and thanks to Shriram Krishnamurthi for his efforts in making this happen.

I wrote about Elizabeth’s paper when it was originally published at ICER at this blog post. Elizabeth wrote a guest blog post here on these topics (see here). These are important issues — Wired has just published an article talking about the Geek Gene with a great discussion of Betsy DiSalvo’s work (see post here about some of Betsy’s work).

I wrote the introductory page to the article (available here). I point out that Elizabeth’s article doesn’t end the debate, but it does move forward how we address questions about how we teach and how students learn:

This paper does not prove there is no Geek Gene. There may actually be bimodality in CS grades at some (or even many) institutions. What this paper does admirably is to use empirical methods to question some of our long-held (but possibly mistaken) beliefs about CS education. Through papers like these, we will learn to measure and improve computing education, by moving it from folk wisdom to evidence-based decision-making.

Come to the CUE.NEXT Workshop: Making computing education work for all undergraduates

I’m going to be the keynoter at the Dec. 5 workshop in DC. The workshop series is near and dear to my heart — how do we make computing education accessible to all undergraduates? Below is taken from the CRA website here.

CS Departments have seen significant enrollment increases in undergraduate computer science courses and programs. The number of non-majors in CS courses has also increased significantly, and many CS departments cannot meet the demand. One key reason for the increased demand from non-majors is the fact that computing and computer science have become relevant to undergraduate education in all disciplines. However, there is currently no consensus on how to design computing courses or how to structure curricula aimed at teaching the fundamentals of CS and computing to students who need to use computing effectively in the context of the other disciplines.

The goal of the upcoming CUE.NEXT workshops — organized by Larry Birnbaum (Northwestern), Susanne Hambrusch (Purdue), and Clayton Lewis (UC Boulder) — is to initiate a national dialog on the role of computing in undergraduate education. Computing educators and CS departments, as well as colleagues and academic units representing other stakeholder disciplines, will work together to define and address the challenges. Three NSF funded workshops are scheduled to take place in Chicago (November 18 and 19), DC (December 5 and 6) and Denver (January 2020).

Open Question around Mathematics in Undergraduate Computer Science

I’m always happy to see a new computing education blog, and I’m particularly excited by posts that identify open (research, and otherwise) questions.

At SIGCSE 2019 this past February, we organized a birds of a feather session (a one-hour discussion group) on modernizing mathematics in computer science. We expected a modest number of attendees but were surprised and delighted to host a completely filled room of discrete mathematics, algorithms, and theory of computation educators—60 attendees in total—interested in evolving how we, as a discipline, situate mathematical foundations in our curriculum!

What was even more surprising to us was how the discussion evolved over the hour. Our original intention was to focus on how we might re-shape the foundational portions of the computer curriculum in light of how computing has evolved over the last decade:

The undergraduate computer science curriculum is ever-changing but has seen particular turmoil recently. Topics such as machine learning, data science, and concurrency and parallelism have grown in importance over the last few years. As the content of our curriculum changes, so too does the mathematical foundations on which it rests. Do our current theoretical courses adequately support these foundations or must we consider new pedagogy that is more relevant to our students’ needs? In this BoF, we will discuss what a modern mathematics curriculum for computer scientists should cover and how we should go about accomplishing this in our classrooms. (https://dl.acm.org/citation.cfm?id=3293748)

…

At this point, we shifted our focus from trying to answer the original “concept” question to identifying the myriad of problems that educators wrestled with along these three dimensions. We outline the problems that people raised below:

From https://cs-foundations-ed.github.io/sigcse/2019/03/29/bof-report.html

How Machine Learning Impacts the Undergraduate Computing Curriculum

I’ve been looking forward to seeing this article in print since Ben Shapiro first talked about this, months and months ago. Ben, Rebecca Fiebrink, and Peter Norvig raise the (reasonable) argument that machine learning is now a central activity in computer science, and should be a core topic in undergraduate computing curriculum. What does that mean for what we teach and how we teach it? It’s something that we ought to be talking about.

The growing importance of machine learning creates challenging questions for computing education…

Changes to the Introductory Sequence…These same two aims can also describe introductory courses for an ML-as-core world. We do not envision that ML methods would replace symbolic programming in such courses, but they would provide alternative means for defining and debugging the behaviors of functions within students’ programs. Students will learn early on about two kinds of notional machine—that of the classical logical computer and that of the statistical model. They will learn methods for authoring, testing, and debugging programs for each kind of notional machine, and learn to combine both models within software systems.

We imagine that future introductory courses will include ML through the use of beginner-friendly program editors, libraries, and assignments that encourage students to define some functions using ML, and then to integrate those functions within programs that are authored using more traditional methods. For instance, students might take a game they created in a prior assignment using classical programming, and then use ML techniques to create a gestural interface (for example, using accelerometers from a smartphone, pose information from a webcam, or audio from a microphone) for moving the player’s character up, down, left, and right within that game. Such assignments would engage students in creating or curating training examples, measuring how well their trained models perform, and debugging models by adjusting training data or choices about learning algorithms and features.

 

Source: How Machine Learning Impacts the Undergraduate Computing Curriculum

When do we know that a programming course is not working for non-CS majors?

There’s a good discussion going on in Facebook that I wanted to make more public and raise as a question here.  The crush of undergraduates in CS today is making it difficult to offer tailored introductory CS courses to different majors.  The problem is particularly acute when designing instruction for future CS teachers.  If you put the CS teachers in the same course as the CS majors, it’s cheaper and easier — you just teach one big course, rather than multiple smaller courses. But is it as effective?

Some of my colleagues suggest that we can design undergraduate introductory computing courses that are effective for both non-CS and CS majors.  Here’s my question: How do you know when it’s not working?  At what point would you admit that the one course option isn’t working? It’s an interesting empirical question.

Here are some possible measures:

• Learning is a tricky measure.  For any discipline, the majors in that discipline are more motivated to learn more than the outside-the-discipline majors. You can’t expect the non-CS majors to learn more than the CS majors.  Then again, the non-CS majors probably come in knowing less.  If you do pre- and post-tests on CS knowledge, do non-CS majors have as large of a gain as the CS majors?  I don’t know, but in any case, it’s not a great measure for deciding if a class is succeeding for the non-CS majors.
• Taking more CS courses may be an effective measure, but only if you have more than one course that’s useful to non-CS majors.  If the rest of the classes are about software development, then non-CS majors will probably not want to go on, even if the intro course was effective and well-designed.
• Retention is a reasonable measure.  If more of the non-CS majors are dropping out from the course than the CS majors, you may not be meeting their needs.
• My favorite measure is relevance.  I argued in my blog post on Monday that programming is a practice that is relevant to many communities. Do the non-CS majors see the relevance of computing for them and their community after the introductory course?  If not, I don’t think it’s meeting their needs.
• Another tricky measure is use. Should non-CS majors be able (after their first course) to build some program that they find useful?  Certainly, if you achieve that goal, you have also achieved relevance.  How do you judge useful?  CS faculty may not be good judges of what a non-CS major would find useful, and CS faculty are most likely going to assess in terms of code quality (e.g., modularization, appropriate variable and function/module names, commenting, code style, etc.), which I consider pretty unimportant for as a measure for the non-CS students’ experience in the first course.

What do you think?  How would you know if your intro course was meeting non-CS students’ needs?

Study says multiple factors work together to drive women away from STEM

I wrote recently in a blog post that we don’t know enough why women aren’t going into computing, and I wrote in another blog post that CRA is finding that we lose women over the years of an undergraduate degree in CS.  Here’s an interesting study offering explanations for why we are not getting and keeping women:

The study analyzed a large, private university on the East Coast, using data from 2009-16, broken down semester-by-semester to track students’ changes in grades and majors in as close to real time as possible. While other studies have suggested that women came out of high school less prepared, or that increasing female STEM faculty could help provide women mentors, the Georgetown study didn’t support those findings.

“Women faculty don’t seem to attract more women into a field, and that was sort of sad news for us,” Kugler said. “We were hoping we could make more of a difference.”

One of the reasons women might feel undue pressure in STEM fields might actually be because of how recruiting and mentoring is framed. Many times, those efforts actually end up reinforcing the idea that STEM is for men.“Society keeps telling us that STEM fields are masculine fields, that we need to increase the participation of women in STEM fields, but that kind of sends a signal that it’s not a field for women, and it kind of works against keeping women in these fields,” Kugler said.

And while many STEM majors are male-dominated, the framing of recruitment and mentorship efforts can sometimes paint inaccurate pictures for STEM fields that aren’t male-dominated, and contribute to an inaccurate picture for STEM as a whole, the paper says:

While men may not have a natural ability advantage in STEM fields, the numerous government and other policy initiatives designed to get women interested in STEM fields may have the unintended effect of signaling to women an inherent lack of fit.

While computer science, biophysics and physics tend to be male-dominated, Kugler said, neurobiology, environmental biology and biology of global health tend to be female-dominated.

Source: Study says multiple factors work together to drive women away from STEM

The challenge of retaining women in computing: The 2016 Taulbee Survey: Supplementary Report on Course-level Enrollment

The Computing Research Association (CRA) has just released a supplement to their 2016 Taulbee Survey report.  They now are collecting individual course data, which gives them more fine-grained numbers about who is entering the major, who is retained until mid-level, and who makes it to the upper-level.  Previously, they mostly just had enrollment and graduation data.  These new data give them new insights.  For example, we are getting more women and URM in computing, but we are not retaining them all.

Except in the introductory course for non-majors, the median percentage of women in courses at each level was either fairly constant or increasing [from previous years]. The most notable increase was in the mid-level course, where the median percentage of women went from 17.4 in 2015 to 20.0 in 2016. The median percentage of women in the upper-level course also increased, from 14.1 to 15.9 percent. We see a slight drop-off from the median percentage of women in the introductory course for majors in 2015 (21.0 percent) to the median percentage of women in the mid-level course in 2016 (20.0 percent), and a somewhat larger drop-off between the median percentage of women in the mid-level course in 2015 (17.4 percent) and the median percentage of women in the upper-level course in 2016 (15.9 percent).  Because the median percentage at each level is for a single representative course, not for all students at that level, some of the differences between levels may be attributable to the specific courses on which the institutions chose to report. Overall, however, this trend of decreasing representation of women at higher course levels is congruent with other data.

Source: The 2016 Taulbee Survey: Supplementary Report on Course-level Enrollment – CRA

A Threads-using CS major joins GT Faculty: Welcome to Sauvik Das

Threads were a curriculum innovation from Georgia Tech around 2005, that we have studied in some of our research.  Today, we welcome one of the undergraduates who took Threads as faculty into our School of Interactive Computing.  (He officially starts in January, but he’s hanging out at the faculty retreat and meetings with us.) Welcome to Sauvik Das, and I’m so pleased that he wrote this reflective essay about his journey to re-join us.

Threads are specializations in different application areas of Computer Science: for example, embedded systems (e.g., computing embedded in physical systems), media (e.g., computer graphics, games), machine intelligence, etc. The thread that truly made me think was “people”: “where computing meets its users”. Everything I wanted to do with computing, I reflected, was not actually about computing. It was about using computing to create new, better and engaging experiences for the people that used the systems I made.

Source: Beginnings: Old and New – Sauvik Das – Medium

Why Professors Resist Inclusive Teaching by Annie Murphy Paul: Especially important in CS

Annie Murphy Paul is talking about inclusive teaching here, but she could just as well be talking about active learning.  The stages are similar (recall the responses to my proposal to build active learning methods into hiring, promotion, and tenure packages). These are particularly critical for computing where we have so little diversity and CS teachers are typically poor at teaching for diverse audiences.

Stages of Inclusive Teaching Acceptance

Denial: “I treat all my students the same.  I don’t see race/ethnicity/gender/sexual orientation/nationality/disability. They are just people.”

Anger: “This is all just social science nonsense! Why won’t everyone just get over this PC stuff? When I went to grad school, we never worried about diversity.”

Bargaining: “If I make one change in my syllabus, will you leave me alone?”

Depression: “Maybe I’m not cut out to teach undergraduates. They’re so different now. Maybe I just don’t understand.”

Overwhelmed: “There is so much I didn’t know about teaching, learning, and diversity. How can I possibly accommodate for every kind of student?”

Acceptance: “I realize that who my students are and who I am influences how we interact with STEM. I can make changes that will help students learn better and make them want to be part of our community.”

Source: Why Professors Resist Inclusive Teaching « Annie Murphy Paul

 

A goal for higher ed: “There is magic in our program. Our program changes lives.”

My daughter is enrolled in Georgia’s “Governor’s Honor Program” which started this week.  The program is highly competitive — my daughter filled out multiple applications, wrote essays, and went through two rounds of interviews.  Over 700 high school students from across Georgia attend for four weeks of residential classes on a university campus for free.

At the parent’s orientation, we heard from two former GHP students, the Dean of Student Life, the Dean of Residence Halls, the GHP Program Manager, and the Dean of Instruction.  It’s that last one who really got me.

“You heard from these students, and many other students.  GHP changes lives.  There is magic in our program.“

The program sounds remarkable.  No grades, no tests.  The Dean of Instruction said she told the teachers to “give these students learning opportunities beyond what’s in any high school classroom.” Students are only there to learn for learning’s sake.

I was thrilled for my daughter, that she was going to have this experience. I was also thrilled as a teacher.

I want to teach in a program whose leadership says, “There is magic in our program. Our program changes lives.”  Last week, I took my daughter to tour three universities.  Our daughter is the youngest of three, so I’ve attended other prospective student tours at other universities.  I’ve never heard anybody at any of these universities make that kind of claim.

I don’t mean to critique my leadership at Georgia Tech in particular.  When I was the Undergraduate Program Director, I never said anything like that to my teachers or to prospective parents.  I am critical of higher education more broadly. Higher education in America sets goals like preparing students for careers, giving them experiences abroad and in research, giving them options so that they can tailor their program to meet their particular desires, and surrounding them with great fellow students — I’ve heard all of those claims many times on many tours.  I’ve never heard anyone say, “We change lives.”

Rich DeMillo argued in his book Apple to Abelard that higher education institutions need to differentiate from one another.  Offering the same thing in the same way makes it hard to compete with the on-line and for-profit options.  At Georgia Tech, the faculty are frequently told, “We get amazingly smart students.”  We’re told to think about how to tune our education for these super-smart students.  I’ve never been told, “Give these students experiences beyond what they will get in any other program. Create magic. Change their lives.”

What I gained at GHP is a new definition for what higher education should be about. We need to step up our game.

Is There a Crisis in Computer-Science Education? Decrease in graduation rates in CS

We’ve talked about this problem before — that it looks like we’re graduating fewer CS undergraduates, despite rising enrollment.  Interesting analysis in The Chronicle:

Aside from looking remarkably like the Cisco logo itself a representation of San Francisco’s iconic Golden Gate Bridge, the chart clearly shows fluctuation in interest among undergraduates and graduates in computer science.The reason for that fluctuation isn’t clear from the graph, but we have a couple of theories:

1. The pipeline was primed: In the 1970s and 1980s, many elementary, middle, and high schools taught computer programming to students, according to Joanna Goode. As an associate professor of education studies at the University of Oregon, Ms. Goode has researched access for women and students of color in computer science.“But, as the PC revolution took place, the introduction to the CD-ROMS and other prepackaged software, and then the Internet, changed the typical school curriculum from a programming approach to a ‘computer literacy’ skill-building course about ‘how to use the computer,’”…
2. The job market: Fluctuations in college-degree attainment are often connected to fluctuations in the job market in certain industries.

via Is There a Crisis in Computer-Science Education? – Data Points – Blogs – The Chronicle of Higher Education.

NOW is the time to apply for NSF Computing Education funding

Last month, I wrote about the new NSF program Improving Undergraduate Stem Education (see NSF page on IUSE here). I talked to Jane Prey about this program a couple weeks ago, and she was concerned. She said that lots of people are expressing doubt about applying for a program that only has a single page description–not the standard multi-page solicitation.

That’s exactly why this is the time to apply! IUSE doesn’t have a solicitation this year, but most likely will in future years. That means that anything goes this year! If you have any idea that you want to get funded, THIS is the year to apply.

The program description is wonderfully broad:

• Want to work on broadening participation in computing? It’s there: “broadening participation of individuals and institutions in STEM fields.”
• Want to work on after school programs, service learning, new ways of structuring your department, formal education research, new ways of measuring learning? It’s all there: “experiential learning, assessment/metrics of learning and practice, scholarships, foundational education research, professional development/institutional change, formal and informal learning environments.”

• Want to work on teacher professional development, or even adult learners? It’s there: “educating a STEM-literate populace, improving K-12 STEM education, encouraging life-long learning, and building capacity in higher education.”

In short, the lack of a formal solicitation means that there are few barriers. You should go for it.

From here on, this is my advice based on talking with NSF program managers and having written (rejected mostly, but a bunch accepted) proposals. This is not coming from NSF:

• You need to demonstrate that your proposal has intellectual merit and broader impacts. That’s part of any NSF proposal.
• No, there’s nothing there that says you must have evaluation, but if you read phrases like “empirically validated teaching practices,” you have to believe that funded proposals will have good evaluation. You can probably be competitive without an external evaluator if you come up with a good evaluation plan in the proposal body itself. If you don’t know how to do this, bring in an external evaluator.
• The really tough part of applying to a program without a solicitation is deciding how much to budget. Here’s me just gazing into a crystal ball: Smaller but realistic budgets have the greatest chance of getting funded. If you can do your project in $100-200K/year for two to three years, you increase your odds of getting funded. I think there’s a psychological barrier for review committees at a $1M proposal, so stay below that or make your really proposal great.

The big message is: Apply on February 4, 2014. Take this rare opportunity to get your wildest and most exciting ideas on the table at NSF.

How to design service courses | Gas station without pumps

I appreciate “Gas station without pumps” recent blog post on how to design service courses.  I strongly agree with the emphasis on giving students skills to do useful things now.  The greatest need for computing education is in the courses for non-CS majors.

It is never enough, even in a course for majors, to design the course around “they’ll need this later”.  It is far better to make them want to know it now, for things that they can do now.  For the Applied Circuits course, I concentrated ton the students doing design and construction in the labs, with just enough theory to do the design.  This is a big contrast to the traditional circuits course, which is all theory and math which EE students will use “later”—totally useless if the students then never take another EE course.

via Service courses | Gas station without pumps.