Posts tagged ‘computing education research’
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.
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.
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 (email@example.com), and nominations for the Program chair to the 2017 Program Chair, Josh Tenenberg (firstname.lastname@example.org). We also encourage informal expressions of interest to the individuals just mentioned.
I have written individual blog posts for each paper or other contributions at conferences like ICER or SIGCSE. Then sometimes, like this year, that’s just overwhelming. So please excuse me for talking about a bunch (I may not even get all of it) of Georgia Tech related CS Education work at SIGCSE 2017 this year. (Conference website is here, and program is here. The on-line program is really nice, which is here.)
Workshop 101: GP: A General Purpose Blocks-Based Language
Wednesday 7-10 pm: Room 618-619
I’m helping to organize a workshop with John Maloney, Yoshiki Ohshima, and Jens Mönig on GP. I blogged about GP here, and about the use of GP for Media Computation in a minimal manuals structure here. The workshop will be the first SIGCSE activity with GP. The plan is to move it into a public form next summer, and the team is looking for people who want to start using it for their classes.
Panel: The Role of CS Departments in The US President’s “CS for All” Initiative
Thursday 10:45-12: Room 6E
I was part of an effort at last year’s CRA Conference at Snowbird to get CS departments to participate in President Obama’s “CS for All” initiative (see blog post here). This year, Barbara Ericson, Rick Adrion, and Megean Garvin will tell us about how their CS departments are working to promote CS for All. I’m the moderator.
EarSketch: A STEAM-based Approach for Underrepresented Populations in High School Computer Science Education
Thursday 1:45-3:00: Room 615
Brian Magerko and Jason Freeman will present on EarSketch, which I just blogged about here. They are also presenting on Creativity in Authentic STEAM Education with EarSketch on Friday 1:45-3 in Room 612. And then again Saturday 10-10:45 as a demo, EarSketch, a web-application to teach Computer Science through Music
CS Principle Ebooks for Teachers and Students building on Educational Psychology Principles
Thursday 3-4:30 pm: NSF Showcase in Exhibition Space
BOF: Researching the K–12 Computer Science Framework
Thursday 5:30-6:20 pm: Room 613-614
I’m part of a BOF led by Pat Yongpradit of Code.org with Leigh Ann DeLyser of CSNYC and Kathi Fisler at Brown. The BOF session will allow researchers to discuss opportunities in K-12 CS ed research within five areas related to the implementation and future of the framework:
- Equity and access
- Learning progressions
- Pedagogical content knowledge (Knowledge teachers need to teach CS)
- Facilitating learning in other disciplines
- Policy and implementation within K–12 education systems
Workshop 310: Using and Customizing Open-Source Runestone Ebooks for Computer Science Classes
Friday 7-10 pm: Room 612
Barb, Brad Miller, and Paul Resnick will present on the Runestone platform that we build our ebooks on. Brad built Runestone, and Paul uses and extends it frequently for his Informatics course at U. Michigan. This is the first time that they’re teaching others how to use the platform, which is a great sign of the maturation of Runestone — from researcher and early-adopters into something that all CS educators can use.
Designing and Studying of Maker Oriented Learning to Transform Advanced Computer Science
Saturday 10-11:30, NSF Showcase area in Exhibitions
Zane Cochran, a student of my colleague Betsy DiSalvo, will present some of his work on using maker spaces to improve CS education.
Concepts and Practices: Designing and Developing A Modern K12 CS Framework
Saturday 10:45-12: Room 611
My PhD student, Miranda Parker (who has been working on privilege issues and on the SCS1), and Leigh Ann Delyser (of CSNYC and CS for All fame) will present on the new K-12 CS Framework (see blog post here) and the research support for it.
Workshop 401: Evidence Based Teaching Practices in CS
Saturday 3-6 pm: Room 618-619
Briana Morrison is leading the effort with Cynthia Lee, Leo Porter, Beth Simon, and me to present CS teaching practices for which we have an evidence-base. We’re drawing a lot on our New Faculty Workshops material.
Workshop 404: How to Plan and Run Effective Teacher Professional Development
Saturday 3-6 pm: Room 612
(YES! Dueling workshops!)
Barb is working with Rebecca Dovi and Ria Galanos on how to teach CS teacher professional learning opportunities. Barb is using a lot of the material that she’s developed for “Train the Trainer” sessions as part of ECEP.
I am excited to get quoted (and correctly!) in an article about the Finnish approach to using programming to teach across the curriculum. The article gets the idea a little wrong — it’s not really about teaching CS without computers, as the title suggests. The key idea is that “Finnish children are taught to think of coding and programming more as tools to be explored and utilized across multiple subjects
Liukas pushes back at the idea that children are already tech-savvy simply because they seem to be able to navigate an iPhone intuitively. She’s particularly fond of this quote from the American computing professor Mark Guzdial:
We want students to understand what a computer can do, what a human can do, and why that’s different. To understand computing is to have a robust mental model of a notional machine.
In other words, knowing how to use something isn’t the same as understanding how it works. And because programming can be taught in so many ways, Liukas said, it can be an opportunity for kids to learn lots of related skills, such as how to collaborate, how to tell a story, and how to think creatively.
“This demands a lot from the teachers, obviously,” Liukas said during a presentation at the embassy event. This is true in the sense that incorporating coding and programming lessons across disciplines requires all kinds of educators, from the science teacher to the art teacher, to understand the basics.
The Computing Research Association has released their report on the surge in CS enrollments. They’re naming this surge “Generation CS.”
Across the United States and Canada, universities and colleges are facing a significant increase in enrollment in both undergraduate computer science (CS) courses and programs. The current enrollment surge has exceeded previous CS booms, and there is a general sense that the current growth in enrollment is substantially different than that of the mid-1980s and late 1990s. To investigate the current situation, the Computing Research Association (CRA) produced an enrollment survey to measure, assess, and better understand enrollment trends and their impact on computer science units, diversity, and more. The survey was administered in parallel with CRA’s annual Taulbee Survey of doctoral-granting units and ACM’s annual NDC Study of non-doctoral granting units in computing. Analysis of the survey is presented in a new report, “Generation CS: CS Enrollments Surge Since 2006,” available for download and online at: http://cra.org/data/generation-cs/.