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I was thrilled when I got this message two weeks ago:
We have been working for months now on a big meeting organized by ECEP with the Research+Practice Collaboratory and Ruthe Farmer of the White House Office of Science and Technology Policy (OSTP). The goal is to organize state and federal leaders in growing CS for All in the states. Here’s my written-for-ECEP description of the agenda (not official, not vetted by OSTP, etc.):
CS for All: State-Level Research and Action Summit
The first part of the Friday sessions at the White House Office of Science and Technology Policy (OSTP) is aimed at strengthening connections between research and practice. The NSF’s CS10K efforts and the President’s CS for All Initiative have created an unprecedented rise in the implementation of CS education efforts across the United States. Making education reform systematic and sustainable requires cross-sector efforts with shared goals and meaningful data collection that can inform practice. We need to make sure that we are building and using evidence-based knowledge about what’s happening in our CS for All efforts.
CS for all is a rare education research opportunity. The American education canon does not change often. We need to create research-practice partnerships to improve our understanding of what works and why. The Research+Practice Collaboratory (Bronwyn Bevan, Phil Bell, Bill Penuel) will be bringing in a group of learning sciences researchers (including Shuchi Grover, Nichole Pinkard, and Kylie Peppler) and practitioners to work with the ECEP state teams. The goal is to learn how research-practice partnerships can help the field identify key questions and areas for building and sustaining evidence-based practice.
The afternoon session is focused on understanding where the state’s are today. ECEP Evaluators, Sagefox, will share with state groups benchmark data. We will review data on the evaluation of the efforts to make Exploring CS, CS Principles, Bootstrap, and Code.org curricula and professional development available across the country. As a group, we will review state efforts in computer science education implementation and reform. States identify their greatest successes and identify their most pressing needs.
The evening session at OSTP is focused on making the President’s CS for All initiative work at the state level. In the United States, K-12 curriculum and policies are decided at the state-level. Obama Administration officials will help the state teams to understand the goals of the CS for All initiative. Four state teams will share their successes and efforts, which differ considerably from one another as they meet the unique challenges and objectives of their state’s education system.
The CS for All initiative means that we all students in all schools in all districts get access to CS education. Each of our 16 states and Puerto Rico will summarize their successes and lessons learned in 3 minute madness talks. We’ll have two panels — one on negotiating state structures and processes when implementing CS for All, and one on how to make sure that we broaden participation while we aim for CS for All (to avoid being CS Just For Rich Kids). We will have a luncheon keynote from Cameron Wilson of Code.org on how they are aiming to create CS education that reaches all students.
The CS for All initiative requires us to reach all students in a system and sustainable way.
- Reaching Broader: We can see from the benchmark data where CS initiatives are focused and where there are gaps. Not all districts are implementing CS education yet. We need to develop strategies for filling in the gaps.
- Reaching Deeper: The data also show us where CS initiatives are starting but shallow. In most districts, a handful of teachers are getting short professional learning opportunities with little follow-up. Teachers need effective learning opportunities that give them the knowledge and self-confidence to make CS a sustainable topic. We need to develop strategies to make CS change deep, systemic, and sustainable.
State teams develop and share their strategies to reach broader and deeper.
Google’s latest reports from their collaboration with Gallup lines up with Miranda Parker’s research interests in privilege in CS education (see preview of her RESPECT 2015 paper here). I invited her to write a guest blog post introducing the new reports. I’m grateful that she agreed.
Google, in collaboration with Gallup, has recently released new research about racial and gender gaps in computer science K-12 classrooms. A lot of the report confirms what we already knew: there are structural and social barriers that limit access to CS for black, Hispanic, and female students. I don’t mind the repeated results though–it helps form an even stronger argument that there is a dearth of diversity in computing classrooms across the country.
The report does highlight interesting tidbits that may not have been as obvious before. For example, black and Hispanic students are 1.5 and 1.7 times more likely than white students to be “very interested” in learning computer science. This knowledge, combined with the data that black and Hispanic students are less likely to have access to learning CS, creates a compelling argument for growing programs focused at these groups.
Research like this continues to push the envelope of what is known about racial and gender gaps in computer science. However, it may be time to dig deeper than visible identities and explore if there are other variables that, independently or together with the other traits, create a stronger argument for why the diversity gap exists. Does socioeconomic status better explain racial gaps? What about spatial ability? These are variables that we at Georgia Tech are looking at, as we hypothesize about what can be done to level the playing field in computing.
Today, we’re releasing new research from our partnership with Gallup that investigates the demographic inequities in K-12 computer science (CS) education in two reports, Diversity Gaps in Computer Science: Exploring the Underrepresentation of Girls, Blacks and Hispanics and Trends in the State of Computer Science in U.S. K-12 Schools. We surveyed 16,000 nationally representative groups of students, parents, teachers, principals, and superintendents in the U.S. Our findings explore the CS learning gap between white students and their Black and Hispanic peers as well as between boys and girls and confirm just how much demographic differences matter. We’re excited to share this data to bring awareness to issues on the ground in order to help expand CS education in meaningful ways.
We have to teach where the students are: Response to “How We Teach Should Be Independent Of Who We Are Teaching”
Valerie Barr has great insights into computing education, especially with regards to diversity (e.g., see the blog post last CS Ed Week about alternative ways to view data about diversity in computing). I like what she has to say in her most recent Blog@CACM blog post, but I think the title is somewhat misleading.
“How we teach should be independent of who we are teaching” is clearly not true. No one would argue for teaching Linux kernel developing via all day long bootcamps in C to middle school students. Few people use CS Unplugged with machine learning graduate students. What Valerie is explicitly addressing in her blog post is an issue called essentialism.
As we continue efforts to diversify computing, we cannot afford to paint any group in a monochromatic way. We have to embrace the richness of today’s student population by making what we teach meaningful and relevant to them. There are women who want to geek out about hard-core tech, and there are men who care deeply about computing for the social good. There are students of all genders and ethnic and racial backgrounds who will be happy with an old-fashioned lecture, and those who will thrive on active learning with examples drawn from a range of cultures and application areas. Many students will be motivated by knowing how the techniques and subject matter they’re learning fit into their future workplace or life goals.
Here’s a definition of essentialism (from the Geek Feminism Wiki):
The concept of Essentialism states that there are innate, essential differences between men and women. That is, we are born with certain traits. This is often used as an explanation for why there are so few women in science and technology.
In contrast, the critical issue is who is in your classroom, what do they know, and what are their motivations. As How People Learn describes it:
There is a good deal of evidence that learning is enhanced when teachers pay attention to the knowledge and beliefs that learners bring to a learning task, use this knowledge as a starting point for new instruction, and monitor students’ changing conceptions as instruction proceeds.
This is hard to do. We can’t redesign every class for each new student population. What I think Valerie is admonishing us to do is to actually check and not assume certain interests and motivations because of the demographics of the students. When we were developing Media Computation, we did focus groups with students to get their feedback on our developing designs. We surveyed the students to get a sense of what they were interested in and what motivated them. Great work like Unlocking the Clubhouse suggested our starting point, but we did not assume that the majority-female class would have stereotypical responses. We checked with our student population, and we provided different kinds of media interactions to attract different kinds of students within our population.
It would be best if we could provide educational opportunities that meet each student’s needs individually. Short of that, we can design for the students who enter our classrooms, not for the stereotypes that we might expect.
My Blog@CACM post for this month is about imagining the remedial teaching techniques of a school-based “Computing Lab” in the near future.
It’s becoming obvious that computing is a necessary skill for 21st Century professionals. Expressing ideas in program code, and being able to read others’ program code, is a kind of literacy. Even if not all universities are including programming as part of their general education requirements yet, our burgeoning enrollments suggest that the students see the value of computational literacy.
We also know that some students will struggle with computing classes. We do not yet have evidence of challenges in learning computation akin to dyslexia. Our research evidence so far suggests that all students are capable of learning computing, but differences in background and preparation will lead to different learning challenges.
One day, we may have “Computing Labs” where students will receive extra help on learning critical computational literacy skills. What would happen in a remedial “Computing Lab”? It’s an interesting thought experiment.
I list several techniques in the article, and I’m sure that we can come up with many more. Here’s one more each DO and DON’T for “Computer Lab” for struggling computationalists.
- DO use languages other than industry standard languages. As I’ve mentioned before in this blog, CS educators are far too swayed by industry fads. I’m a big fan of Livecode, a cross-platform modern form of HyperCard. An ICER 2016 paper by Raina Mason, Simon et al. estimated Livecode to have the lowest cognitive load of several IDE’s in use by students. If we want to help students struggling to learn computing, we have to be willing to change our tools.
- DON’T rely on program visualizations. The evidence that I’ve seen suggests that program visualizations can help high-ability students, and well-designed program visualizations can even help average students. I don’t see evidence that program visualizations can help the remedial student. Sketching and gesture are more effective for teaching and learning in STEM than diagrams and visualizations. Sketching and gesture encourage students to develop improved spatial thinking. Diagrams and visualizations are likely to lead remedial students into more misconceptions.
The CS K-12 Framework was released Monday. This has been an 11 month long process — see first blog post about the framework, first blog post on the process, and the post after my last meeting with the writers as an advisor. The whole framework can be found here and a video about the framework can be found here:
A webinar about the Framework will be held on Wednesday, October 19, at 12 PM Pacific / 3 PM Eastern. Visit https://www.youtube.com/watch?v=wmxyZ1DFBwk for more details and to watch the webinar on the 19th.
I believe that this framework is about as good as we can expect right now. Pat Yongpradit did an amazing job engaging a broad range of voices in a short time. The short time frame was forced on the process by the state policymakers who wanted a framework, something on which they could hang their state standards and curricula. The NGSS veterans did warn us what could happen if we got it wrong, if we went too fast. Maybe the framework process didn’t go too fast.
The framework document is impressive — comprehensive, carefully constructed, with a rich set of citations. It’s teacher-centric, which may not be the best for a document to inform state standards, but that’s the constituency with the strongest voice in CS Ed today. There are too few CS Ed informed policymakers or district administrators to push back on things that might not work work. The CS Ed researchers are too few and too uncertain to have a strong voice in the process. Computer scientists (both professional and academic) generally ignored the process. The CS teachers had the greatest political influence.
I predicted in January that this would be a “safe list,” a “subset of CS that most people can agree to.” I was wrong. There’s a lot in there that I don’t see as being about computation. Like “Create team norms, expectations, and equitable workloads to increase efficiency and effectiveness” — that’s a high school computing recommendation? Like “Include the unique perspectives of others and reflect on one’s own perspectives when designing and developing computational products” — you can achieve that in high school?
Those “aspirational” statements (Pat’s word) mean that the writers went beyond defining a consensus document. They tried to push future CS education in the ways that they felt were important. Time will tell if they got it right. The framework fails if schools (especially under-resourced schools) decide that it’s too hard and give up, meaning that underprivileged kids will continue to get no CS education. If teachers and administrators work harder to provide more and better CS education because of this document, then the framework writers win.
This is an important document that will have a large influence. Literally, millions of schoolchildren in several states are going to have their CS education defined by this document.
Typing that statement gives me such a sinking feeling because we just don’t have the research evidence to support what’s in the framework.
When I went to meetings, I too often heard, “Of course, teachers and students can do this, because it works in my program.” So few computing education programs (e.g., packages of curriculum, professional development, assessment, and all the things teachers need like pacing guides and standards crosswalks) have scaled yet in diverse populations. Maybe it works in your program. But will it work when it’s not your program anymore? When it’s a national program? When states and districts take it over and make it their own? Will it still work?
And we want schools and districts to make things their own. That’s at the heart of the American educational system — we’re distributed and diverse, with thousands of experiments going on at once. I worry about how little knowledge about computing and computing education is out there, as guidance when schools and districts make it their own.
So, yeah, I’m one of those uncertain researchers, mumbling in the corner of this process, worrying, “This could go so wrong.” Maybe it won’t. Maybe this will be the first step towards providing a computing education for everyone.
The die is cast. Let’s see what happens.
Insightful interview with Richard Tapia. He understands issues about broadening participation in computing and talks about them frankly.
You have said, “Underrepresentation is a much greater danger to the health of the nation than to the health of the discipline.” Can you explain what you meant by that?
The disciplines of math and science are in good shape and will continue to flourish without the involvement of women and underrepresented minorities. Of course many of the applications will be impacted by the presence of women and minorities in these application areas. But the theory will continue to be healthy without the involvement of these groups. However, the backbone of America has been mathematics, science, and engineering. We have historically led the world in these areas. Our changing demographics show that the country is becoming not only more Hispanic but significantly more. If we do not involve women and minority groups in our backbone activity, we will have no one to do the work and the nation will most certainly suffer and lose global competitiveness.
Source: People of ACM – Richard Tapia
I’ve only just started reading this new report from National Academies Press, but am finding it useful and interesting. What do we mean when we say that we want people to be scientifically literate? It’s an important question to ask when considering the goal of computational literacy.
Science is a way of knowing about the world. At once a process, a product, and an institution, science enables people to both engage in the construction of new knowledge as well as use information to achieve desired ends. Access to science—whether using knowledge or creating it—necessitates some level of familiarity with the enterprise and practice of science: we refer to this as science literacy.
Science literacy is desirable not only for individuals, but also for the health and well-being of communities and society. More than just basic knowledge of science facts, contemporary definitions of science literacy have expanded to include understandings of scientific processes and practices, familiarity with how science and scientists work, a capacity to weigh and evaluate the products of science, and an ability to engage in civic decisions about the value of science. Although science literacy has traditionally been seen as the responsibility of individuals, individuals are nested within communities that are nested within societies—and, as a result, individual science literacy is limited or enhanced by the circumstances of that nesting.
Science Literacy studies the role of science literacy in public support of science. This report synthesizes the available research literature on science literacy, makes recommendations on the need to improve the understanding of science and scientific research in the United States, and considers the relationship between scientific literacy and support for and use of science and research.