The Bigger Part of Computing Education is outside of Engineering Education

My Blog@CACM post this month is about the differences I’ve seen between computing education and engineering education (see link here). Engineering education has a goal of producing professional engineers. I describe in the post how ASEE is about the profession of engineering, and developing an engineering identity is a critical goal of engineering education. Computing education is about producing software engineers, but that’s only part of what computing education is about. SIGCSE is about learning and teaching of computing, and as computing educators, we teach students with diverse identities. They overlap, but the part of computing education that is outside the intersection with engineering education is much bigger than the part inside.

Computing education for me is about helping people to understand computing (see the Call for Papers for the International Computing Education Research conference) — not just CS education at the undergraduate level. Preparing future software engineers is certainly part of computing education, but sometimes computing educators only see engineering education goals. Computing education has a bigger scope and range than engineering education. Here are three areas where we need to focus on the bigger part outside engineering.

1. K-12 is for everyone. Computing education in elementary and secondary school should be about more than producing software professionals. There are certainly CS teachers who disagree with me. An example is Scott Portnoff’s critique of CS curricula that does not adequately prepare students for the AP CS A exam and the CS major. I agree that we should offer CS courses at secondary school that give students adequate preparation for post-secondary CS education, if students want to go on to a CS major and become a computing professional. But K-12 has to serve everyone, and the most important goals for K-12 CS education are goals for what everyone should learn about computing. We want students:

• To see themselves as capable of doing computer science (the goal of self-efficacy),
• To see the power of the computer in transforming representations, and
• To start on the computer science learning trajectories and discover if they want to learn more. 

I am personally much more interested in K-12 teachers using computing to teach everything else better. Computational science and mathematics are powerful for helping scientists and mathematicians gain insight. We should use computing in the same way to advance student learning in STEM, social studies, and other disciplines — without turning those other classes  into CS classes. This is the difference that Shuchi Grover is talking about with her two kinds of CT: learning about CS vs using computing to learn other things.

2. Courses for non-CS Majors. I’m co-chairing a task force on computing education for the University of Michigan’s College of Literature, Sciences, & Arts (LSA) (see a blog post on this effort and our website with our NEW preliminary report). I’m learning about the ways that LSA faculty use computing and how they want their students to learn about and use computing. Their purposes are so different from what we teach in classes about computer science or data science. Sure, computational scientists analyze data like data scientists, but they also create models that turn their theories into simulations (which can then generate data). Computational artists use computing to tell engaging stories in new ways. Computational journalists investigate and discover truth with computing. LSA faculty care a great deal about their students critiquing how our computing systems and infrastructure may be unjust and inequitable. (Interesting note: The word “justice” does not appear in the new Computing Curriculum 2020, and the word “equity” appears only once.)

There are computer scientists who tell me that there is only one computer science for all students. Their argument is that better engineering practices help everyone — if those computational scientists, journalists, and artists just programmed like software engineers, the world would be a better place. Their code would be more robust, more secure, and more extensible. That is likely true, but that perspective is misunderstanding the role of code in doing science and making art. You don’t critique the poet for not writing like a journalist or a novelist. These are different activities with different goals.

We should teach non-CS majors with courses that serve their needs, speak to their identities, and support their values. We should not require all artists and scientists to think, act, and program like engineers just to take computing classes.

A CS educator in the Bay area once tried to convince me that the most important purpose for courses for non-CS majors was to identify the potential for being great programmers. He claimed that there are programmers who are two magnitudes better than their peers, and identifying them is the most important thing we can do to support and advance the software companies on which our world economy depends. He argued that we should teach non-CS majors in order to identify and promote future engineers, not for their own purposes. I see his argument, but I do not agree that scientists, journalists, and artists are less important than engineers. As I consider this pandemic, I think about the role that computing has played in medicine, logistics, and media. Of course, we have relied heavily on software engineering, but I don’t believe that it’s more important than all the other roles that computing played.

3. Supporting diverse identities. There is a disconnect between efforts to broaden participation in computing and framing CS classes as engineering education. As I mentioned in my Blog@CACM post, I taught my first EER course this last semester and read a lot of EER papers. A big focus in engineering education is developing an engineering identity, i.e., helping students to see themselves as members of the engineering community of practice and as future professional engineers.

One of my favorite papers that we read this semester was “Feminist Theory in Three Engineering Education Journals: 1995–2008” by Beddoes and Borrego. They define different branches of feminism. “Liberal feminism” is the goal for women to be treated the same as men, to get access to the same jobs at the same pay. “Standpoint feminism” points out that “liberal feminism” is too much about fitting women into the jobs and cultures of men, as opposed to asking how things would be different if created from a feminist standpoint.

The professional identity of software engineering is male and White. That’s true from the demographics of who is in the Tech industry, but it’s also true from a historical perspective on the systemic bias in computing. Computing has become dominated by men, with many studies and books describing how women were forced out (see for example The Computer Boys Take Over and Programmed Inequality). Our tools privilege one part of the world. Every one of our mainstream programming languages is built on English keywords. That’s a barrier for 85% of the people on Earth. (Related point: I recommend Manuel Pérez Quiñones’ TED talk “Why I want My Voice Assistant to Speak Spainglish” in which he suggests that the homogenous background of American software engineers leads to few bilingual user interfaces — surprising when 60% of the human race is.)

There’s the disconnect. We want students in computing with diverse perspectives and identities. But engineering education is about developing an identity as a future professional engineer. Professional software engineering is male and White. How do we prepare diverse students to be future software engineers when that professional identity conflicts with their identities? We should teach computing, even for CS majors, in ways that go beyond the engineering education goal of developing a professional engineering identity.

We might argue that we want everyone to have the opportunity to participate in CS, but that’s taking the “liberal” perspective. Broadening participation should not be about fitting everyone into the same identity. It’s not enough to say that everyone has the chance to learn the programming languages that are based in English, that are grounded in Western epistemologies, and where the contributions of women have been marginalized. We need to find ways to accept and support the unique identities of diverse people. 

One way to support a “standpoint” perspective on computing education might be to support activity over identity in our CS curriculum. At Georgia Tech, the undergraduate computer science degree is based on Threads (see website). There are threads for Intelligence, People, Media, Devices, and Theory — eight of them in all. A BS in CS at Georgia Tech is any two threads, so there are 28 paths to a degree. This allows students to define their professional identity in terms of what they are going to DO with computing. “I’m studying People and Devices” is something a student might say who wants to create consumer computational devices like Echo or Roomba. The Threads curriculum allows students to make choices about professional identity, in terms of how they want to contribute to society.

Of course, some of our students want to become software engineers at a FAANG company. That’s great, and we should support them and prepare them for those roles. But we should not require those identities. Computing education is about more than producing software engineers who have the traditional engineering identity.

The Bigger Part of Computing Education. I claimed at the start of this post that “computing education that is outside the intersection with engineering education is much bigger than the part inside.” All the studies I have seen say that’s true. While CS undergraduate enrollment has been exploding, the number of end-user programmers is likely a magnitude larger than the number of professional software developers. K-12 is about 50 million students in the United States, and computing education is available to most of them. The number of computing education students who are NOT seeking an engineering identity or profession is much larger than those who are. That’s the more-than-engineering challenge for computing education.

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My thanks to Leo Porter, Cynthia Lee, Adrienne Decker, Briana Morrison, Ben Shapiro, Bahare Naimipour, Tamara Nelson-Fromm, and Amber Solomon who gave me comments on earlier drafts of this post.

Task-specific programming languages: People aren’t dumb. Programming is hard.

I’ve been thinking a lot about task-specific programming languages lately.  I’m inspired by the work on domain-specific programming languages (e.g., see blog post here), and have been wondering whether we can reduce the cognitive load even further by focusing on programming languages for specific tasks. I’m thinking that we should be applying HCI design techniques (e.g., user centered design) and apply them to the design of small, task-specific programming languages.

But that got me wondering: Surely this is not a new idea.  What do we know about task-specific programming languages? Do students learn generalized ideas about programming from learning a task-specific programming language? How does it change affect or cognitive load if students start with a programming language tuned especially to their task?

I did some literature searches, and found a highly relevant paper: “Task specific programming languages as a first programming language.”  And the lead author is…me.  I wrote this paper with Allison Elliott Tew and Mike McCracken, and published it in 1997.  I honestly completely forgot that I had written this paper 22 years ago. Guzdial-past knew things that Guzdial-present does not.

The paper doesn’t answer any of my questions.  It talks about some surveys and comparisons we were doing, but offers no results.  I have no idea where the data from those surveys and comparisons are today.

Abstract: This research investigates whether there is a difference in the acquisition of programming skills and knowledge as a function of a student’s first language. Our research is concerned with the comparison of task specific languages and general programming languages. In many engineering programs students are first exposed to the principles of computational solutions to problems by means of task specific languages, such as MatLab. They are then either expected to be able to use, or are specifically taught programming using more general purpose languages, such as C. Our question is whether there is a developmental preference for learning a task specific language first, or a general purpose language first. Historically, educators have emphasized fundamentals prior to application. A case could therefore be made that a student should be taught general programming skills in the context of a general purpose language before solving problems in a task specific language. More recently, contextualized educators would prefer the initial learning of task specific languages. Our research anticipates answering the question of the effectiveness of transfer of programming skills as a function of first language learning. The dimensions of this question include but are not limited to, how the languages are used, what types of problems are presented to the students, is transfer prompted between the languages, do students look for surface or structural similarities, and what are the assumptions and expectations of the faculty who teach these languages.

Here’s my favorite paragraph in the paper. Yup, still have all those same questions.

We have developed a comparison of task specific languages and general purpose languages to allow us to investigate ontological boundaries between languages and their impact on transfer. For example, MatLab essentially has no typing. It uses built in types. Whereas, general purpose languages have various types, including enumerated types, and support the construction of complex data structures around those types. Does this difference cause a boundary to transfer? If a student learns MatLab first, will data types be more difficult to learn? If a student learns a general purpose language first, will they be able to transfer their skills to a language that prevents them from constructing many of the structures they have previously used?

I’m still catching up on podcasts that I missed during my move.  One of those was a rebroadcast of an interview with Richard Thaler, one of the founders of behavioral economics and a recent Nobel prize winner in Economics.  He explains the central idea of behavioral economics: “People aren’t dumb. The world is hard.”

So, we don’t think people are dumb. We think the world is hard. I mean, figuring out how much to save for retirement is a really hard cognitive problem that very few economists have solved for themselves. And it’s not only cognitively hard, it involves delay of gratification, which people find hard. It’s just like navigating in a strange city is hard. So, why not try to help? When I first was working with the U.K. Behavioral Insight Team, the first “Nudge unit,” the phrase I kept saying in every meeting with some minister was, “If you want to get people to do something, make it easy. Remove the barriers.” That’s what we’re about.

If we want people to program, make it easy. Remove the barriers. That’s what we’re about. People aren’t dumb. Programming languages are hard.  If we can fix that, we should. That’s what I see task-specific programming as being about.

 

Come to my workshop on CS Education at ASEE June 16!

I am attending my first American Society for Engineering Education (ASEE) Conference this year — see the website here: https://www.asee.org/conferences-and-events/conferences/annual-conference/2019.

I’m still figuring out Engineering Education Research, so I’ll be offering a workshop based on our work at Georgia Tech: Techniques for Improved Engagement and Learning of Programming. The workshop is Sunday, June 16, 2019 from 9:00 am to noon. Please come, and please pass this on to others you know who are attending ASEE and might be interested.

Computing education research at Georgia Tech over the last 15 years has led to techniques for teaching programming which improve student learning. Learning is enhanced through greater engagement and reduced cognitive load.

These techniques are:

• Media computation: Teaching programming through manipulation of digital media which improves students’ sense of utility and relevance leading to greater engagement;
• Worked examples: Using worked examples in peer instruction and for prompting for predictions that improve learning;
• Subgoal labeling: Structuring and labeling worked examples to improve immediate learning, retention over time, and transfer to new problems.

The learning objectives for this workshop are for participants to experience these techniques so that they might be able to judge which are most useful for their own practice. Participants will:

• Manipulate digital media with programs that they write during the workshop (laptops required).
• Participate in peer instruction questions using worked examples.
• Compare worked examples with and without subgoal labeling.

 

In last five years, little progress in increasing the fraction of American CS BS degree recipients who are African Americans

Keith Bowman published a series of blog posts this summer on African American undergraduate degrees in engineering.  In July, he wrote one on computer science – linked here. It’s interesting, careful, and depressing. I’m quoting the conclusion below, but I highly recommend clicking on the link and seeing the whole report. What’s most interesting is the greater context — Bowman is comparing across different engineering programs, so he has a strong and data-driven sense of what’s average and what’s below average.

There has been little progress in increasing the fraction of American CS BS degree recipients who are African Americans. Progress will likely only take place through a concerted effort by industry, professional societies, academia and government to foster change, including stronger efforts in graduate degrees. CS undergraduate programs fare poorly compared to many other engineering disciplines in the context of gender diversity and slightly worse than engineering overall in the fraction of African Americans earning undergraduate degrees. Many of the largest CS programs in the US are strikingly behind the national averages for CS BS degrees earned by African Americans.

 

Increasing the Roles and Significance of Teachers in Policymaking for K-12 Engineering Education

National Academies have released a report that relates to the idea of Engineering for All.

Engineering is a small but growing part of K–12 education. Curricula that use the principles and practices of engineering are providing opportunities for elementary, middle, and high school students to design solutions to problems of immediate practical and societal importance. Professional development programs are showing teachers how to use engineering to engage students, to improve their learning of science, technology, engineering, and mathematics (STEM), and to spark their interest in engineering careers. However, many of the policies and practices that shape K–12 engineering education have not been fully or, in some cases, even marginally informed by the knowledge of teacher leaders.

To address the lack of teacher leadership in engineering education policymaking and how it might be mitigated as engineering education becomes more widespread in K–12 education in the United States, the National Academies of Sciences, Engineering, and Medicine held a convocation on September 30–October 1, 2016. Participants explored how strategic connections both within and outside classrooms and schools might catalyze new avenues of teacher preparation and professional development, integrated curriculum development, and more comprehensive assessment of knowledge, skills, and attitudes about engineering in the K–12 curriculum. This publication summarizes the presentations and discussions from the event.

Source: Increasing the Roles and Significance of Teachers in Policymaking for K-12 Engineering Education: Proceedings of a Convocation | The National Academies Press

Embedding and Tailoring Engineering Learning: A Vision for the Future of Engineering Education

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:

1. 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.
2. 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.
3. 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.)

Why ‘U.S. News’ should rank colleges and universities according to diversity: Essay from Dean Gary May #CSforAll

Georgia Tech’s Dean of Engineering Gary May was one of the advisors on “Georgia Computes!”  He makes a terrific point in his essay linked below.  Want broadened participation in computing (BPC)? CS for All?  Make diversity count — and rankings are what “counts” in higher education today.

U.S. News & World Report, that heavyweight of the college rankings game, recently hosted a conference focused partially on diversity in higher education. I did an interview for the publication prior to the forum and spoke on a panel at the event.I was happy to do it. As dean of one of the country’s most diverse engineering schools, I am particularly invested in these issues. My panel focused on how to help women and underrepresented minority students succeed in STEM fields, and I’m grateful to U.S. News for leading the discussion.But the publication, for all its noble intentions, could do more to follow through where it counts. Diversity is currently given no weight in the magazine’s primary university and disciplinary rankings, and it’s time for that to change. As U.S. News goes, so goes higher education.

Source: Why ‘U.S. News’ should rank colleges and universities according to diversity (essay)

One reason we have so much engineering and so little computer science taught at US high schools. | ACM Inroads

Joe Kmoch wrote an interesting follow-on to my blog post about why we have so little CS ed in the US.  Why is that engineering is succeeding so much more than CS in high schools in the US?  He suggests that (in part) it’s because engineering is getting the PD right.

I think the reason is that groups like Project Lead the Way (PLTW) offer an “off the shelf” high quality program, vetted by engineers.  The attractiveness of this is that the school and students get access to a number of well-written up-to-date courses and they also get access to intensive professional development for teachers who want to teach a particular PLTW course.  Teachers must not only take but also pass the two-week intensive summer course before being allowed to teach a particular course.  There is regular monitoring of schools in terms of offering a minimal 3-course sequence of engineering courses and evaluating how well these courses are being taught.

In computer science we have really never had such a program available.  The AP is not such a program.  If a school wants to teach a computer science course, they have to find a teacher who is willing to put together a course syllabus, and then teach that course.  (For AP, the course must be audited for fidelity).  There really isn’t any professional development required to teach any kind of computer science course in most states.

via One reason we have so much engineering and so little computer science taught at US high schools. | ACM Inroads.

All-female engineering class and programming academy

In one week, I found two articles about all female programming academy (first quote and link below) and engineering high school class (second link and quote below). Both of them talk about issues of sexism and intimidation that they hope an all-female cohort will help to avoid.

Why just for women? Because some hiring managers, in response to these statistics, are particularly interested in hiring women, Worthy says. “The need is very top-of-mind,” she says. In addition, there are other training options for men, though they aren’t free like the Ada Developers Academy is, she admits. Moreover, some women and girls have encountered sexism in school and training programs themselves; an all-female class may forestall that problem.

The Ada Developers Academy isn’t the only such effort to challenge this trend. A number of other parallel training opportunities for women are also springing up, some for students and some for working women, to help fill jobs and address the growing gender gap in programming.

via Washington State Group Announces One-Year Programming Academy for Women.

Seventeen female students are enrolled in Wisconsin’s first high school class aimed at women in engineering.

…

Women comprise more than 20% of engineering school graduates but only 11% of practicing engineers, according to the National Science Foundation. Only about 30% of the 14 million Americans who work in manufacturing are women, a study from the National Women’s Law Center noted.

“If we are going to have any hope of replacing all of the retiring baby boomers, we have to get women involved,” Moerchen said.

“It’s a pretty wide gender gap,” Moerchen said, adding that only about three of 35 students in computer-aided machining courses are female.

“The data show that female students are easily intimidated by technology and engineering classes that are traditionally dominated by male students,” Moerchen said.

After researching programs in other states, the Kewaskum teachers said they believed they could create an engineering class specifically for girls that would prepare the students for advanced courses.

via Kewaskum High School launches all-female engineering class.

Virtual Faculty Communities of Practice to improve instructional practices

Posted to the SIGCSE-Members list — I really like this idea! Our work on DCCE showed that communities of teachers was an effective way of improving teacher’s sense of belonging and desire to improve.  Will it work for faculty?  ASEE is the organization to try!

Greetings SIGCSE,

This is a great opportunity for CS faculty to work with like-minded faculty from across the country to explore and share support for introducing new instructional practices into your classroom.  Please consider this for yourself and pass it on to your colleagues.

Engineering education research has shown that many research-based instructional approaches improve student learning but these have not diffused widely. This is because (1) faculty members find it difficult to acquire the required knowledge and skills by themselves and (2) sustaining the on-going implementation efforts without continued encouragement and support is challenging. This project will explore ways to overcome both obstacles through virtual communities.

ASEE is organizing several web-based faculty communities that will work to develop the group’s understanding of research-based instructional approaches and then support individual members as they implement self-selected new approaches in their classes. We expect participants to be open to this technology-based approach and see themselves as innovators in a new approach to professional development and continuous improvement.

The material below and the project website (http://www.asee.org/asee-vcp) provide more information about these communities and the application process. Questions should be addressed to Rocio Chavela at r.chavela@asee.org.

If you are interested in learning about effective teaching approaches and working with experienced mentors and collaborating colleagues as you begin using these in your classroom, you are encouraged to apply to this program. If you know of others that may be interested, please share this message with them.

Please consider applying for this program and encouraging potentially interested colleagues to apply. Applications are due by Friday, September 13, 2013.

 

———————————– ADDITIONAL DETAILS ABOUT THE PROGRAM ——————————–

Format

Faculty groups, which will effectively become virtual communities of practice (VCP) with 20 to 30 members, will meet weekly at a scheduled time using virtual meeting software during the second half of the Fall 2013 Semester and during the entire Spring 2014 Semester. Each group will be led by two individuals that have implemented research-based approaches for improving student learning, have acquired a reputation for innovation and leadership in their course area, and have completed a series of training sessions to prepare them to lead the virtual communities. Since participants will be expected to begin utilizing some of the new approaches with the help and encouragement of the virtual group, they should be committed to teaching a course in the targeted area during the Spring 2014 Semester.

 

VCP Topics and Meeting Times

This year’s efforts are focusing on required engineering science and design courses that are typically taught in the second and third year in each of the areas listed below.

 

Computer science

Co-leaders are Scott Grissom and Joe Tront

Meeting time is Tuesday at 3:00 – 4:30 p.m. EST starting October 29, 2013 and running until December 17, 2013

 

Application Process

Interested individuals should complete the on-line application at https://www.research.net/s/asee-vcp_application_form_cycle2. The application form asks individuals to describe their experience with relevant engineering science courses, to indicate their involvement in education research and development activities, to summarize any classroom experiences where they have tried something different in their classes, and to discuss their reasons for wanting to participate in the VCP.

 

The applicant’s Department Head or Dean needs to complete an on-line recommendation form at https://www.research.net/s/asee-vcp_recommendation_form_cycle2 to indicate plans for having the applicant teach the selected courses in the Spring 2014 Semester and to briefly discuss why participating in the VCP will be important to the applicant.

Since demonstrating that the VCP approach will benefit relatively inexperienced faculty, applicants do not need a substantial record of involvement in education research and development. For this reason, the applicant’s and the Department Head’s or Dean’s statements about the reasons for participating will be particularly important in selecting participants.

 

Application Deadline

Applications are due by Friday, September 13, 2013. The project team will review all applications and select a set of participants that are diverse in their experience, institutional setting, gender, and ethnicity.

 

————————————————–

Scott Grissom

Professor

School of Computing & Info Systems

Grand Valley State University

 

The challenges of integrated engineering education

I spent a couple days at Michigan State University (July 11-12) learning about integrated engineering education. The idea of integrated engineering education is to get students to see how the mathematics and physics (and other requirements) fit into their goals of becoming engineers. In part, it’s a response to students learning calculus here and physical principles there, but having no idea what role they play when it comes to design and solving real engineering problems. (Computer science hasn’t played a significant role in previous experiments in integrated engineering education, but if one were to do it today, you probably would include CS — that’s why I was invited, as someone interested in CS for other disciplines.)  The results of integrated engineering education are positive, including higher retention (a pretty consistent result across all the examples we saw), higher GPA’s (often), and better learning (some data).

But these programs rarely last. A program at U. Massachusetts-Dartmouth is one of the longest running (9 years), but it’s gone through extensive revision — not clear it’s the same program. These are hard programs to get set up. It is an even bigger challenge  to sustain them.

The programs lie across a spectrum of integration. The most intense was a program at Rose-Hulman that lasted for five years. All the core first year engineering courses were combined in a single 12 credit hour course, co-taught by faculty from all the relevant disciplines. That’s tight integration. On the other end is a program at Wright State University, where the engineering faculty established a course on “Engineering Math” that meets Calculus I requirements for Physics, but is all about solving problems (e.g., using real physical units) that involve calculus. The students still take Calculus I, but later. The result is higher retention and students who get the purpose for the mathematics — but at a cost of greater disconnect between Engineering and mathematics. (No math faculty are involved in the Engineering Math course.)

My most significant insight was: The greater the integration, the greater the need for incentives. And the greater the need for the incentives, the higher in the organization you need support. If you just want to set up a single course to help Engineers understand problem-solving with mathematics, you can do that with your department or school, and you only have to provide incentives to a single faculty member each year. If you want to do something across departments, you need greater incentives to keep it going, and you’ll need multiple chairs or deans. If you want a 12 credit hour course that combines four or five disciplines, maybe you need the Provost or President to make it happen and keep it going.

Overall, I wasn’t convinced that integrated engineering education efforts are worth the costs. Are the results that we have merely a Hawthorne effect?  It’s hard to sustain integrated anything in American universities (as Cuban told us in “How Scholars Trumped Teachers”). (Here’s an interesting review of Cuban’s book.) Retention is good and important (especially of women and under-represented students), but if Engineering programs are already over-subscribed (which many in the workshop were), then why improvement retention of students in the first year if there is no space for them in the latter years? Integration probably leads to better learning, but there are deeper American University structural problems to fix first, which would reduce the costs in doing the right things for learning.

Call for proposals on systemic reviews on computer science education

I met Jeff Froyd at the MSU Workshop in Integrated Engineering Education, and he asked me to share this call for a special issue of IEEE Transactions on Education.  The whole notion of a “systemic review” is pretty interesting, and relates to the Blog@CACM post I wrote recently.  His call has detailed and interesting references at the bottom.

Request for Proposals

2015 Special Issue on Systematic Reviews

Overview

The IEEE Transactions on Education solicits proposals for a special issue of systematic reviews on education in electrical engineering, computer engineering, computer science, software engineering, and other fields within the scope of interest of IEEE to be published in 2015. The deadline for 2,000‐word proposals is 9 September 2013. Proposals should be emailed as PDF documents to the Editor‐in‐Chief, Jeffrey E. Froyd, at jefffroyd@ieee.org. Questions about proposals should be directed to the Editor‐in‐ Chief, Jeffrey E. Froyd, at jefffroyd@ieee.org.

Special Issue Timeline

• 9 September 2013: Interested interdisciplinary, global teams of authors should submit proposals for full papers by 9 September 2013.
• 14 October 2013: The editorial team for the special issue will review proposals and notify authors of the status of their submission by 14 October 2013.
• 31 December 2014: For proposals that are accepted, the authors will be asked to prepare manuscripts that will go through the standard review process for the IEEE Transactions on Education in the Scholarship of Integration. Completed draft manuscripts will be due on 31 December 2014. Papers are expected to be between 8000‐10,000 words in length.
• Xxx – 31 December 2014: Plan (timeline, milestones, activities…) will be collaboratively developed to support manuscript completion by 31 December 2014. Steps in the process of preparing a systematic review include: (i) establishing the research questions, (ii) selecting the databases to be searched and the search strings, (iii), establishing inclusion/exclusion criteria, (iv) selecting articles to be studied, etc. Meetings, in‐person or virtual, will be scheduled to provide support for systematic review methodologies. Meetings will be intended to help develop systematic review expertise across the teams and to improve quality of published systematic reviews.
• 2015: Manuscripts accepted for publication are expected to be published in 2015.

Proposal Guidelines

Proposals for systematic review manuscripts must provide the following sections:

1. (i)  Contact information and institutional affiliation of the lead author
2. (ii)  An initial list of the team members who will prepare the systematic review, indicating howthese team members provide requisite expertise and global representation. Given the requirements for systematic reviews, it is expected that a qualified, interdisciplinary team will include one or more individuals with expertise in library sciences, one or more individuals with expertise in synthesizing methodologies (qualitative, quantitative, mixed method, or combinations of the three), and one or more individuals with domain expertisein the proposed content area. Given the need to promote global community in the fields in which ToE publishes, it is expected that a qualified team will represent the diverse global regions that comprise the IEEE.
3. (iii)  Description of the proposed content area, why a systematic review of education in the proposed content area is timely, why a systematic review will enhance development of the field, and how future initiatives might build on the systematic review.
4. (iv)  Initial description of the proposed systematic review methodology. The project will provide support to promote development of systematic review methodology across all participating teams. However, demonstration of initial familiarity with systematic review methodology will strengthen a proposal.

Brief Overview of Systematic Review Methodology
Diverse fields are developing systematic review, a study of primary (and other) studies to address a crafted set of questions, as a research methodology in and of itself. With risks of considerable oversimplification, systematic review methodology rests on two basic ideas. First, interdisciplinary systematic review teams can use large databases of journals, conference proceedings, and grey literature that have been constructed to search the literature using keywords. Then, the team systematically evaluates returned items using explicit criteria to identify the set of articles that will be reviewed. The first basic idea provides a transparent, unbiased, replicable process to identify relevant articles. Second, teams can apply synthesizing methodologies that have been developed in the last 50 years to extract trends, patterns, themes, relationships, gaps… from the identified set of articles. Synthesizing methodologies draw from a wide variety of quantitative (e.g., statistical meta‐analysis, network meta‐analysis), qualitative (e.g., meta‐ethnography, content analysis), mixed method approaches, and combinations of the three. Systematic, transparent use of literature search and synthesizing methodologies can produce systematic reviews of the literature that may be seminal contributions to the community that has created the literature. Good introductions to systematic reviews can be found at:

• Texas A&M University Libraries, Research Guides, Systematic Reviews,
  http://guides.library.tamu.edu/systematicreviews
• Oxford LibGuides, Subject resources. Information Skills. Research Guides. Systematic Reviews, http://ox.libguides.com/systematic‐reviews
• University of Toronto, Gerstein Science Information Centre, Systematic Reviews in the Sciences & Health Sciences, http://guides.library.utoronto.ca/systematicreviews

ToE has already established review criteria for the scholarship of integration, the area addressed by the proposed special issue. These review criteria can be found at http://sites.ieee.org/review‐criteria‐toe/.

Examples

This section offers examples of systematic reviews that have been done in STEM education. Generally, topics for these examples are outside topical areas that would be considered for the IEEE Transactions on Education, but they show examples of good practices for some steps in systematic reviews.

L. Springer, M. E. Stanne and S. S. Donovan, “Effects of small‐group learning on undergraduates in science, mathematics, engineering, and technology: A meta‐analysis.” Review of Educational Research, vol. 69, no. 1, pp. 21‐51. 1999 (doi: 10.3102/00346543069001021)

F. B. V. Benitti, “Exploring the educational potential of robotics in schools: A systematic review,” Comput. & Educ., vol. 58, no. 3, pp. 978‐988, 2012

N. Meese, and C. McMahon, ”Knowledge sharing for sustainable development in civil engineering: A systematic review,” AI and Soc., vol. 27, no. 4, pp. 437‐449, 2012

N. Salleh, E. Mendes, Emilia, and J. Grundy, “Empirical studies of pair programming for CS/SE teaching in higher education: A systematic literature review,” IEEE Trans. Softw. Eng., vol. 37, no. 4, pp. 509‐525, 2011

R. M. Tamim, R. M. Bernard, E. Borokhovski, P. C. Abrami, and R. F. Schmid, “What forty years of research says about the impact of technology on learning: A second‐order meta‐analysis and validation study,” Review of Educ. Research, vol. 81, no. 1, pp. 4‐28, 2011

Resources

These resources provide guides to systematic review methodologies:

E. Barnett‐Page, and J. Thomas, “Methods for the synthesis of qualitative research: A critical review,” BMC Medical Research Methodology, vol. 9, no. 1, p. 59, 2009
Abstract:

Background: In recent years, a growing number of methods for synthesising qualitative research have emerged, particularly in relation to health‐related research. There is a need for both researchers and commissioners to be able to distinguish between these methods and to select which method is the most appropriate to their situation.

Discussion: A number of methodological and conceptual links between these methods were identified and explored, while contrasting epistemological positions explained differences in approaches to issues such as quality assessment and extent of iteration. Methods broadly fall into ‘realist’ or ‘idealist’ epistemologies, which partly accounts for these differences.

Summary: Methods for qualitative synthesis vary across a range of dimensions. Commissioners of qualitative syntheses might wish to consider the kind of product they want and select their method – or type of method – accordingly.

M. Borrego, , E.P. Douglas and C.T. Amelink, “Quantitative, qualitative, and mixed research methods in engineering education“ Journal of Eng. Educ., vol. 98, no. 1, pp. 53‐66, 2009
Abstract: The purpose of this research review is to open dialog about quantitative, qualitative, and mixed research methods in engineering education research. Our position is that no particular method is privileged over any other. Rather, the choice must be driven by the research questions. For each approach we offer a definition, aims, appropriate research questions, evaluation criteria, and examples from the Journal of Engineering Education. Then, we present empirical results from a prestigious international conference on engineering education research. Participants expressed disappointment in the low representation of qualitative studies; nonetheless, there appeared to be a strong preference for quantitative methods, particularly classroom‐based experiments. Given the wide variety of issues still to be explored within engineering education, we expect that quantitative, qualitative, and mixed approaches will be essential in the future. We encourage readers to further investigate alternate research methods by accessing some of our sources and collaborating across education/social science and engineering disciplinary boundaries.

D.A. Cook and C.P. West, “Conducting systematic reviews in medical education: a stepwise approach,” Medical Education, vol.46, pp. 943‐952, 2012
Abstract:

Objectives: As medical education research continues to proliferate, evidence syntheses will become increasingly important. The purpose of this article is to provide a concise and practical guide to the conduct and reporting of systematic reviews.

Results: (i) Define a focused question addressing the population, intervention, comparison (if any) and outcomes. (ii) Evaluate whether a systematic review is appropriate to answer the question. Systematic and non‐ systematic approaches are complementary; the former summarise research on focused topics and highlight strengths and weaknesses in existing bodies of evidence, whereas the latter integrate research from diverse fields and identify new insights. (iii) Assemble a team and write a study protocol. (iv) Search for eligible studies using multiple databases (MEDLINE alone is insufficient) and other resources (article reference lists, author files, content experts). Expert assistance is helpful. (v) Decide on the inclusion or exclusion of each identified study, ideally in duplicate, using explicitly defined criteria. (vi) Abstract key information (including on study design, participants, intervention and comparison features, and outcomes) for each included article, ideally in duplicate. (vii) Analyse and synthesise the results by narrative or quantitative pooling, investigating heterogeneity, and exploring the validity and assumptions of the review itself. In addition to the seven key steps, the authors provide information on electronic tools to facilitate the review process, practical tips to facilitate the reporting process and an annotated bibliography.

M. Petticrew and H. Roberts, Systematic Reviews in the Social Sciences: A Practical Guide. Malden, MA: Blackwell Publishing, 2006

A. C. Tricco, J. Tetzlaff and D. Moher, “The art and science of knowledge synthesis,” Journal of Clinical Epidemiology, vol. 64, no. 1, pp. 11‐20, 2011
Abstract:

Objectives: To review methods for completing knowledge synthesis.

Study Design and Setting: We discuss how to complete a broad range of knowledge syntheses. Our article is intended as an introductory guide.

Results: Many groups worldwide conduct knowledge syntheses, and some methods are applicable to most reviews. However, variations of these methods are apparent for different types of reviews, such as realist reviews and mixed‐model reviews. Review validity is dependent on the validity of the included primary studies and the review process itself. Steps should be taken to avoid bias in the conduct of knowledge synthesis. Transparency in reporting will help readers assess review validity and applicability, increasing its utility.

Conclusion: Given the magnitude of the literature, the increasing demands on knowledge syntheses teams, and the diversity of approaches, continuing efforts will be important to increase the efficiency, validity, and applicability of systematic reviews. Future research should focus on increasing the uptake of knowledge synthesis, how best to update reviews, the comparability between different types of reviews (eg, rapid vs. comprehensive reviews), and how to prioritize knowledge synthesis topics.

Off to Michigan State, to talk Education and Engineering, then CSTA Conference for ECEP

I’m going to Michigan State University on Wednesday July 10 through Friday August 12.  On the 10th, I’m visiting with colleagues whom I knew in Education at the University of Michigan (Bob Geier and Joe Krajcik) and giving a brownbag talk.  I’m really looking forward to hanging out with Education folks for the day.  I’ve just learned that Danny Caballero has moved to MSU, so I’m hoping to meet up with him, too. On Thursday and Friday, I’m attending a workshop on integrated engineering education. Since I used to do work like that, and haven’t done much in Engineering Education in years, I thought it would be fun and interesting — something I might want to get involved in again.  Plus, it was a great chance to get back ‘home’ to Michigan.

The day after I get back, we are heading off to Boston and the CSTA Conference in Quincy, Massachusetts.  We are holding an ECEP Day on Sunday July 14, to connect with CSTA Chapter Leaders and Leadership Cohort in the states where we’re working.  On Monday, July 15, I’m just hanging out at the CSTA Conference, so if you’re there, I hope you will stop by the ECEP table and visit!

On the Value of Combining Education and Engineering

I did a Blog@CACM post on the value of combining Education and Engineering. I was impressed by my visit to Tufts’ Center for Engineering Education and Outreach. Then when I got back to Georgia Tech, I attended a meeting that was explicitly asking, “What should the relationship be between Engineering and Education?” Thus, this blog post, where I argue that the relationship is important and deep, and benefits each.

New Masters of Arts in Teaching Engineering (MAT) Program at Tufts: For CSEd too? (#CSedWeek)

I visited Tufts this week, and they got me thinking about the possibilities of using the umbrella of engineering education to advance computing education. Engineering Education is growing, with units devoted to that at Virginia Tech and Purdue. They have a new Masters degree for teaching about engineering (see below). Would this be a useful degree for the high school CS teacher?

I’m interested in exploring further the relationship between engineering education and computing education. The key difference that I see right now is that engineering education is focused on teaching engineering and design skills and concepts related to the physical world, where computing education has to teach about the virtual world. The design skills and methods are certainly in common. We can use methods from teaching about the physical world in the computational world, but they may not transfer. The laws are different. I believe that the greatest challenges of understanding computation are exactly outside the intersection set with understanding the physical world.

Tufts University Graduate School of Arts and Sciences and the Tufts University School of Engineering are proud to announce the new Master of Arts in Teaching Engineering (MAT) program, which will prepare teachers for teaching engineering. Engineering has become an essential component of STEM disciplines at the middle and high school levels. There is a clear need to prepare engineering teachers who have a strong academic background in engineering as well as a research-based understanding of how students learn the concepts and design process of engineering. Engineering teachers must also have an intellectual appreciation for the ways in which mathematics and science fields intersect with engineering.

The program builds on the successful teacher preparation programs of the Education Department and the successful collaborations the department has had with development of engineering curriculum and STEM outreach in the Tufts School of Engineering, in particular the work of the Center for Engineering Education and Outreach. The program is designed to create a deeply reflective, intellectual culture of considering engineering in schools that bridges the traditional tensions between research and practice in teacher preparation. Learn more about the program at the Education department’s MAT in Engineering site.

via CEEO Main Site – New MAT Program.