C.P. Snow got it right in 1961. Algorithms control our lives, and those who don’t know what algorithms are don’t know what questions to ask about them. This is a powerful argument for universal computing education. I like the below quote for highlighting that a better term for the concern is “model,” not “algorithm.”
Discussions about big data’s role in our society tends to focus on algorithms, but the algorithms for handling giant data sets are all well understood and work well. The real issue isn’t algorithms, it’s models. Models are what you get when you feed data to an algorithm and ask it to make predictions. As O’Neil puts it, “Models are opinions embedded in mathematics.”
Sepehr Vakil appointed first Associate Director of Equity and Inclusion in STEM Education at U. Texas-Austin
I just met Sepehr at an ECEP planning meeting. Exciting to meet another CS Ed faculty in an Education school! He won the Yamashita Prize at Berkeley in 2015 for his STEM activism.
Dr. Vakil’s research revolves around the intersection of equity and the teaching and learning of STEM, particularly in computer science and technology. This focus has led Dr. Vakil to conduct participatory design research projects in several contexts. These efforts include founding and directing the Oakland Science and Mathematics Outreach (OSMO) program—an after school program serving youth of color in the city of Oakland. Dr. Vakil also has experience teaching and conducting research within public schools. During graduate school, he co-taught Introductory Computer Science Courses for 3 years in the Oakland Unified and Berkeley Unified School Districts. As part of a university-research collaboration between UC Berkeley and the Oakland Unified School District, he worked with students and teachers in the Computer Science and Technology Academy at Oakland Technical High School to design an after school racial justice organization named SPOCN (Supporting People of Color Now!) Dr. Vakil’s work at the intersection of equity, STEM, and urban education has also led to publications in prestigious journals such as Cognition & Instruction, Equity and Excellence in Education, and the Journal of the Learning Sciences.
Following up on the brief that Google did last month on Blacks in CS, this month they’ve prepared a brief on the state of girls in CS.
Computer science (CS) education is critical in preparing students for the future. CS education not only gives students the skills they need to succeed in the workforce, but it also fosters critical thinking, creativity, and innovation. Women make up half the U.S. college-educated workforce, yet only 25% of computing professionals. This summary highlights the state of CS education for girls in 7th–12th grade during 2015–16. Girls are less likely than boys to be aware of and encouraged to pursue CS learning opportunities. Girls are also less likely to express interest in and confidence in learning CS.
Expanding the Pipeline: Characteristics of Male and Female Prospective Computer Science Majors – Examining Four Decades of Changes – CRN
Interesting report from CRA that offers a nuanced view about gender differences in goals for STEM education and how those interact with pursuing a degree in CS.
Another example of a variable becoming more salient over time relates to one’s scientific orientation. Students of either gender who express a stronger commitment to making a “theoretical contribution to science” are more likely to pursue a computer science major, but over time this variable has become a significantly stronger predictor for women while remaining a steady predictor for men. In other words, it is increasingly the case that computer science attracts women who see themselves as committed to scientific inquiry. While at face value that seems like positive news for the field of computer science, the fact is that women are much less likely than men to report having a strong scientific orientation upon entering college; thus, many potential female computing majors may be deterred from the field if they simply don’t “see” themselves as the scientific type.
Still, there is some positive news when it comes to attracting women to computing. The first relates to the role of mathematical self-concept. Specifically, even though women rate their math abilities lower than men do—and perceptions of one’s math ability is one of the strongest predictors of a major in computer science—the fact is that the importance of mathematical self-concept in determining who will pursue computer science has weakened over time. Thus, despite the fact that women tend to have lower math confidence than men do, this differential has become less consequential over time in determining who will major in computer science.
William G. Bowen of Princeton and of the Mellon Foundation recently died at the age of 83. His article about MOOCs in 2013 is still relevant today.
In particular is his note about “few of those studies are relevant to the teaching of undergraduates.” As I look at the OMS CS results and the empirical evidence about MOOC completers (which matches results of other MOOC experiments of which I’m aware at Georgia Tech), I see that MOOCs are leading to learning and serving a population, but that tends to be the most privileged population. Higher education is critiqued for furthering inequity and not doing enough to serve underprivileged students. MOOCs don’t help with that. It reminds me of Annie Murphy Paul’s article on lecture — they best serve the privileged students that campuses already serve well. That’s a subtle distinction: MOOCs help, but not the students who most need help.
What needs to be done in order to translate could into will? The principal barriers are the lack of hard evidence about both learning outcomes and potential cost savings; the lack of shared but customizable teaching and learning platforms (or tool kits); and the need for both new mind-sets and fresh thinking about models of decision making.
How effective has online learning been in improving (or at least maintaining) learning outcomes achieved by various populations of students in various settings? Unfortunately, no one really knows the answer to either that question or the important follow-up query about cost savings. Thousands of studies of online learning have been conducted, and my colleague Kelly Lack has continued to catalog them and summarize their findings.
It has proved to be a daunting task—and a discouraging one. Few of those studies are relevant to the teaching of undergraduates, and the few that are relevant almost always suffer from serious methodological deficiencies. The most common problems are small sample size; inability to control for ubiquitous selection effects; and, on the cost side, the lack of good estimates of likely cost savings.
In the last couple of months, I have had the opportunity to speak to groups of Engineering Education Researchers. That doesn’t happen often to me, and I feel very fortunate to get that chance.
I was asked to speak about my vision for the future of Engineering Education, from my perspective as a Computing Education Researcher. What I said wasn’t wholly unique–there are Engineering Education Researchers who are already working on some of the items I described. The response suggested that it was at least an interesting vision, so I’m telling the story here in blog form.
For readers of this blog who may not be familiar with Engineering Education Research, the Wikipedia page on EER is pretty good. The most useful paper I read is Borrego and and Bernhard’s “The Emergence of Engineering Education Research as an Internationally Connected Field of Inquiry.” I also recommend looking around the Purdue Engineering Education department website, which is the oldest Eng Ed department in the US.
Engineering has had a long relationship with computing. Engineers made computing part of their practice earlier and more pervasively than scientists or mathematicians. I love how this is described in the motion picture Hidden Figures where Octavia Spencer’s character is part of the effort to use computing as soon as possible in the American space program. Engineering educators have made computing part of the learning goals for all of today’s engineering students, again more pervasively than what I can see in science or mathematics programs.
Much of my work and my students’ work is about embedding computing education (e.g., Media Computation which embeds computing in the digital media context that students value, or Brian Dorn’s work embedding computing in a graphic design context) and tailoring computing education (e.g., high school CS teachers need something different from software developers). Computing education can be embedded in Engineering classes and tailored for Engineering students, of course. My vision is about embedding and tailoring engineering education.
There are three parts to the story below:
- Engineering Education for everyone K-16, especially for STEM learners.
- Reaching a diverse audience for engineering education.
- Recognizing the differences between Engineering Education research and teaching, and the need for more research on learning outside of the engineering classroom.
In January 2016, President Barack Obama launched the “CS for All” initiative. When he said that he wanted students to be “job-ready,” he wasn’t saying that everyone should be a software engineer. Rather, he was reflecting a modern reality. For every professional software developer, there are four-to-nine end-user-programmers (depending on the study and how you count). Most professionals will likely use some form of programming in the future. That’s an argument for “CS for All.”
We also need Engineering for All. Engineering skills like designing, planning, collaboration on diverse teams, and trouble-shooting are needed across STEM. When I look at bench science, I see the need for engineering — to design, plan, collaborate, debug, and test.
Engineering education researchers know a lot about how to teach those skills. I’d love to learn how to inculcate some engineering perspectives in my CS students. When I see Chemical Engineering students designing a plant, or Civil Engineering students designing a bridge, they predict that they made mistakes, and they look for those mistakes. There’s a humility about their process. CS students often run their program once and turn them in. If you write a hundred lines of code, odds are almost 100% that you made errors. How do we get CS students to think that way?
Engineering for All is different than what professional engineers do, in the same way that what a high school teacher needs is different than what a professional software developer needs. Both need a mental model of the notional machine. A high school teacher also needs to know how students get that wrong, and probably doesn’t need to know about Scrum or GitHub.
I believe that there is a tailored part of engineering education which should be embedded throughout K-16 STEM. The American Society of Engineering Education’s mission is focused on professional engineers, and my proposal does not diminish the importance of that goal. We need more professional engineers, and we need to educate them well. But engineering skills and practices are too important to teach only to the professionals.
Engineering should play a significant role in STEM education policy. Engineering education researchers should own that “E” in STEM. There are many research questions that we have to answer in order to achieve Engineering for All.
- What is the tailored subset of engineering that should be taught to everyone? To STEM learners?
- All technically literate US citizens should know far more about engineering than they do today. Here’s a hypothesis: If all US citizens understood what engineering is and what engineers do, we might have less crumbling infrastructure, because we citizens would know that infrastructure is critical and professional engineers design, build, and maintain infrastructure. How do we get there?
- All K-12 students should have the opportunity to fall in love with engineering. How?
- Are there limits to what we can teach about engineering in K-16? What learning and cognitive disabilities interfere with learning engineering, and what parts of engineering? I also wonder about the kinds of bias that prevent someone from succeeding in engineering, besides race and gender. For example, here in the South, there are a lot of students who don’t believe in evolution. I’m pretty sure that belief in evolution isn’t necessary for designing a bridge or a distillation column. But someone who believes in intelligent design is going to face a lot of barriers to getting through basic science to become an engineer. Is that how it should be?
- Engineering should aim to influence K-12 STEM education nationally, in every state.
The American University (particularly the Land Grant University, developed in the late 1800’s) was supposed to blend the German University focus on research and the British focus on undergraduate education. My favorite history of that story is Larry Cuban’s How Scholars Trumped Teachers, but Michael Crow also tells the story well in his book Designing the New American University. We believed that there were synergies between research and teaching. It’s not clear that that’s true.
Research and teaching have different measures of success and don’t feed directly into one another.
Teaching should be measured in terms of student success and at what cost. Cost is always a factor in education. We know from Bloom’s two-sigma 1984 study (and all the follow-ups and replications) that the best education is an individual human tutor for each subject who works with a student to mastery. But we as a society can’t afford that. Everything else we do is a trade-off — we are trying to optimize learning for the cost that we are willing to bear.
Research should be measured in terms of impact — on outcomes, on the research community, on society.
It’s quite likely that the education research on a given campus doesn’t influence teaching practice on the same campus.
I see that in my own work.
- The best of Media Computation is no longer at Georgia Tech. Beth Simon and Leo Porter at UC San Diego have done better studies and are inventing cool interventions like MediaComp art galleries. Cynthia Lee at Stanford has created MediaComp for multiple languages. Celine Latulipe built on Beth and Leo’s work to implement lightweight teams in her MediaComp course.
- Subgoal labeling totally works (see Lauren’s dissertation or Briana’s dissertation). Coursera uses it in some of their videos. Rob Miler at MIT has picked it up. But there are very few CS classes using it.
We can see the transition for education research idea to impact in teaching practice as an adoption curve. Boyer’s “Scholarship Reconsidered” helps to explain what’s going on and how to support the adoption. There is traditional Scholarship of Discovery, the research that figures out something new. There is Scholarship of Teaching that studies the practice of teaching and learning.
Then there’s Scholarship of Application, which takes results from Discovery into something that teachers can use. We can’t expect research to influence teaching without scholars of application. Someone has to take the good ideas and carry them into practice. Someone has to figure out what practitioners want and need and match it to existing research insights. Done well, scholarship of application should also inform researchers about the open research questions, the challenges yet to be faced.
High-quality teaching for engineering education should use the most effective evidence-based teaching methods.
Good teachers balance teaching for relevance and motivation with teaching for understanding. This is hard to do well. Students want authenticity. They want project-based learning and design. I was at the University of Michigan as project-based learning for science education was first being developed, and we knew that it very often didn’t work. It’s often too complex and leads to failure, in both the project and the learning. Direct instruction is much more efficient for learning, but misses out on the components that inspire, motivate, and engage students. We have to balance these out.
We have to teach for a diverse population of students, which means teaching differently to attract women and members of under-represented groups. In our ICER 2012 paper, we found that encouragement and self-perception of ability are equally important for white and Asian males in terms of intention to persist in computing, but for women and under-represented group students, encouragement matters more than ability in terms of how satisfied they are with computing and intention to persist. This result has been replicated by others. Encouragement of individual students is critical to reach a diverse audience.
An important goal for a first year Engineering program is to explain the relevance of the classes that they’re taking. Larry Cuban tells us that a piece of the British system that got lost by the early 1920’s in the American University was having faculty advisors who would explain how all the classes fit together for a goal. The research on common first year Engineering courses (e.g., merging Physics, Calculus, Engineering in a big 12 credit hour course) shows that they worked because they explained the relevance of courses like Calculus to Engineering students. I know from my work that relevance is critical for retention and transfer.
Do students see relevance of first year Engineering programs? Most first year programs emphasize design and team problem-solving. First year Engineering students don’t know what engineers do. When they’re told “This is Engineering” in their first year, do they believe it? Do they cognitively index it as “real Engineering”? Do they remember those experiences and that learning in their 3rd and 4th years when they are in the relevant classes? I hope so, but I don’t know of evidence that shows us that they do.
Engineering education research, like most discipline-based education research (DBER), is focused on education. I see the study of “education” as being about implementation in a formal system. Education is a design discipline, one of Simon’s Sciences of the Artificial. Robert Glaser referred to education as psychology engineering.
We need more research on Engineering Learning. How do students learn engineering skills and practices, even outside of Engineering classes? How do those practices develop, even if it’s STEM learners and teachers using them and not professional engineers? How should we best teach engineering even if it’s not currently feasible?
That last part is much of what drives my work these days. We’re learning a lot about how great Parsons Problems are for learning CS. Very few CS classes use them. There are reasons why they don’t (e.g., they’re emphasizing the project side of the education spectrum). I’m figuring out how to teach CS well, even if it’s not feasible in current practice. CS teaching practice will eventually hit a paradigm shift, and I’ll have evidence-based practices to offer.
To focus on engineering learning requires work outside the classroom, like Multi-Institutional, Multi-National (MIMN) studies that we use in computing education research, or even laboratory studies. A focus on Engineering Learning creates new opportunities for funding, for audience, and for impact. For example, I could imagine engineering education researchers seeking science education funding to figure out how to teach high school science teachers the engineering that they ought to teach their students — not to introduce engineering, but to make their students better in science.
My vision for engineering education has three parts:
- K-16 STEM learners need Engineering for All. Engineering education has more to contribute than just for producing more professional Engineers. Engineering education ought to own the “E” in STEM education policy. Engineering skills and practices can be tailored to different audiences and embedded in STEM education.
- Reaching a diverse audience is critical for both research and teaching. For me, that diversity includes the people who need engineering education who aren’t going to become professional engineers, but also people who look different or even have different beliefs.
- Finally, research and teaching are different activities, with different measures of success. Teaching should be informed by evidence and be as efficient and effective as possible for a given cost. We need evidence for what we’re doing, and we should gather evidence if we don’t know if what we’re doing is working. Research should focus on what’s possible and on having impact, even if that impact isn’t in the on-campus classrooms. We shouldn’t expect research to impact teaching without explicit investment in adaptation to support adoption.
(Thanks to Barb Ericson, Beth Simon, Leo Porter, and Wendy Newstetter for advice on drafts of this piece.)
SIGCSE is changing how they organize ICER. Posted with Judy Sheard’s permission:
The ACM/SIGCSE International Computing Education Research conference (icer.acm.org) is the premier conference in the world focused on computer science education research, now in its 13th year. The leadership structure has recently been reorganized so that the the individual overseeing the selection of the program (the Program Chair) and the individual overseeing the running of the conference at a particular venue (the Site Chair) are to be held by different individuals.
We are currently seeking nominations for a Site Chair and a Program Chair for ICER 2019, to be held in North America.
Both appointments to Chair are for two years, called the “junior” and “senior” years, respectively. Site Chairs host the conference at their home institution during their senior year. Only one appointment for each role will be made each year, so that in any given year there is a junior and senior Site co-chair and a junior and senior Program co-chair. A nomination committee of the Program and Site chairs for the current year and the SIGCSE Board ICER liaison nominates the ICER Site chair and Program chair to start serving two years from the current year. The SIGCSE Board makes the appointments to both roles.
For both positions, the country of the home institution of each appointee will be rotated geographically by year as has been the tradition for ICER conference chairs, i.e.
- Year 1: North America
- Year 2: Europe
- Year 3: North America
- Year 4: Australasia
The criteria for appointees:
- Program co-chair:
- Prior attendance at ICER
- Prior publication at ICER
- Past service on the ICER Program Committee
- Research excellence in Computing Education
- Collaborative and organizational skills sufficient to work on the Conference Committee and to share oversight of the program selection process.
- Site chair:
- Prior attendance at ICER
- Collaborative and organizational skills sufficient to work on the Conference Committee and to oversee all of the local arrangements.
- Demonstrated interest in the computing education research community.
To nominate an individual, please include the individual’s CV and a cover letter explaining how the individual meets the criteria for the role. Self-nominations are welcomed. Please send nominations for the Site chair to the 2017 Site Chair, Donald Chinn (firstname.lastname@example.org), and nominations for the Program chair to the 2017 Program Chair, Josh Tenenberg (email@example.com). We also encourage informal expressions of interest to the individuals just mentioned.