What’s unique about CS education compared to other DBERs?

December 23, 2019 at 7:00 am 20 comments

I was recently asked by an NSF program officer to answer the questions, “What makes CS education different than other discipline-based education research (DBER, like math ed, physics ed, or engineering ed)? What research questions might we ask (that we might not currently be asking) to improve practice in CS education?” If I’m going to write a long-form email reply, I might as well make it a blog post. I’m using the specific term, computer science education, not my preferred and more general computing education because the question was framed specifically about CS programs, not necessarily about information technology, information systems, cybersecurity, or other computing programs.

Computer science education research has a quadruple-whammy right now that isn’t facing any other DBER that I know:

  • ONE: We know less about how to do CS education well than we know about math, physics, science, or engineering education. A point I made in my SIGCSE keynote is that ASEE is 126 years old, NCTM started in 1920, and AAPT in 1950. CSTA started in 2004. With Ben duBoulay, I wrote the history chapter for the new Handbook of Computing Education Research. The field only dates back to 1967. Because CS is so new, there are few mechanisms to track progress at a systemic level. Most US states don’t gather data on CS like they do reading, science, mathematics, and other school subjects. We have less knowledge of how to teach and what’s going on because we’ve been at it for a shorter time.
  • TWO: Below are two slides that I built but decided to edit out of my SIGCSE keynote talk. These are about the relative sizes of CS Ed and other DBER conferences. CS departments are desperately seeking more faculty (see the latest job ads analysis here). We have fewer practitioners and researchers than these other fields.

  • THREE: Perhaps a natural consequence of the first two: CS teachers know less of what we do know about evidence-based methods than STEM teachers in other fields. Charles Henderson showed that the vast majority of physics teachers in the US know about evidence-based teaching methods (over 80%) and try to use them (over 60%). Christopher Hovey presented evidence that it’s a small percentage for CS teachers (closer to 10%, see paper here). This might be expected given that we’re new (e.g., haven’t had the time to develop dissemination mechanisms that actually reach teachers) and there are relatively few teachers (compared to other disciplines) so it’s a smaller target to reach.
  • FOUR: We are facing enormous economic demand for computer science. Undergraduate University CS enrollments are skyrocketing in the US. There’s a great story and infographic from UNC this last week on their enrollment demands.

The result is that we’re providing CS education to many students with few resources (teachers) and without a whole lot of data or use of evidence-based methods. From a research perspective, it’s also interesting that lots of students are resisting CS education — which is pretty common across STEM education. Students complain about algebra, calculus, physics, chemistry, and so on. The interesting twist is that students resisting CS ed are also then resisting the economic benefits, which makes it a bit more intriguing to study. The incentives are there, but many students still find the costs greater than the benefits.

Some of the research questions that I find interesting which are unique to CS education research:

  • Why isn’t the enrollment boom extending to high schools?. Undergraduate education is exploding, but over 90% of US high school students are avoiding computer science, even when it’s being offered (which Miranda Parker explored in her dissertation). These are much lower numbers than in other STEM fields. (See blog post here about the low CS numbers in high schools, and this blog post comparing CS to other STEM fields.) We need ethnographic work and design work, to understand what’s going on and to document what might influence students to find CS more attractive.
  • What would computing education look like for the other 90%? If we wanted to invent computing education that would reach the rest of US high school students, what would it look like? I suspect that the answer is going to be mostly about integrating CS into other-than-CS classes (like Bootstrap, STEM-CT, and Project GUTS). It’s an issue both of engaging students and getting teachers to adopt. I’m working on task-specific programming with teachers informing the design (see post here), to create programming that they’ll actually adopt and integrate into their non-CS classes. Katie Cunningham is working on inventing CS education that is focused on user needs rather than programming language demands. This is an area where we need a lot of design studies to explore a wide range of possibilities.
  • What’s going on in community college CS? I know of studies of CS education at the high school level, the four year college, and the university level. I know of few studies at the community college level. How do they manage the economic imperative of CS education with preparing the students to go on to university? How do they motivate students to complete degrees if students just want to get a good job?
  • What’s going on in undergraduate CS classes, especially during the enrollment boom? One of Lecia Barker’s greatest hits was her definition of the “defensive climate,” how students in CS are more about competing than collaborating, and even questions in class are more about showing-off than gaining knowledge. We published a paper that drew on defensive climate research at ITICSE last year (see blog post) — and that was one of the few papers published on the subject since Lecia’s ground-breaking work in 2002-2004. As I search in Google Scholar, I see a couple papers from Colleen Lewis (2011 and 2013), but other than those, there are very few papers testing or extending these notions over the last 15 years. Is “defensive climate” still an issue? I bet it is. How do we test for it? How do we address it? Is there a difference in climate between liberal arts and engineering based CS? How does the climate impact diversity? We have few studies of what’s going on in CS classes under these extreme conditions these days.
  • How do we improve teaching quality in CS education? CS education has an issue like Engineering, but unlike science and math. I bet few calculus teachers are seriously swayed by, “How do professional mathematicians use calculus?” But in CS Ed, we’re always swayed by that economic benefit — many CS teachers worry about preparing students for current jobs, for current tools and languages. That focus on industry may inhibit a focus on pedagogy, but that’s a hypothesis to be explored. How do we teach CS teachers to know and use better teaching methods? What influences adoption of new teaching methods? This is particularly an important question in post-secondary where we have such extreme enrollment pressure. When I talk to CS teachers about new methods, the most common response is, “Sure, but when could I learn to do that?!?”
  • What influences access to CS education?. When I teach my class on CS education research, I ask my students to identify open research questions. Last semester (see blog post here), a lot of their questions were about access to CS classes, which is complicated by the unique issues of CS education: How do parents’ education level/career influence student choices in CS, e.g. ,to take a CS class, to get a CS degree, to seek a CS job, etc.? Do students with learning disabilities (e.g., dyslexia) view code differently, and does that influence their participation in CS? Could we use fMRI or eye tracking to measure this? Why don’t more lower-income students go into CS, especially since it has such a large economic benefit? What percentage of current CS students are lower-income? How many lower-income students have the opportunity to learn CS and don’t take it?

With this post, I’m taking a break from the blog, both for the holidays and to deal with some intense proposal writing. It’s been an exciting year. I’m going to end with a picture from the recent Georgia Tech PhD graduation ceremony. Not only did I get to hood Dr. Miranda Parker, but Barbara and I watched our son, Dr. Matthew Guzdial, get his doctoral hood. It’s a nice bright spot to close out the year. I wish you a happy holiday season and a successful 2020.

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Why don’t high schools teach CS: It’s the lack of teachers, but it’s way more than that (Miranda Parker’s dissertation) Computing Education Lessons Learned from the 2010’s: What I Got Wrong

20 Comments Add your own

  • 1. orcmid  |  December 23, 2019 at 11:18 am

    I’m intrigued about “defensive climate.” I wonder how much this permeates the world of computing practitioners, not just those seeking education in CS, and how much the (passive-aggressive?) competitiveness is something that feeds CS avoidance even more than STEM avoidance.

  • 2. gasstationwithoutpumps  |  December 23, 2019 at 1:45 pm

    Interesting that you spent a paragraph on differences in demographics (SES and learning disability) without mentioning gender, which seems to have the biggest unexplained imbalance. Perhaps you were focusing on less-studied and perhaps more tractable problems, where there is some hope of grant funding?

    I was just looking at the UCSC numbers, https://mediafiles.ucsc.edu/iraps/student-majors/fall-term/2019-20/fall-undergraduate-majorsbygender-mc.pdf and https://mediafiles.ucsc.edu/iraps/student-majors/fall-term/2019-20/fall-undergraduate-majorsbygender-mf.pdf, which show computer science at 20% female (in line with most of the rest of the school of engineering, but out of line with the rest of the university, which is majority female). Outside engineering, the most male-dominated majors are music, philosophy, mathematics, and physics (all around 30% female).

    I don’t have access to any numbers for SES or disability (are those numbers collected at public universities?), but the ethnicity figures at https://mediafiles.ucsc.edu/iraps/student-majors/fall-term/2019-20/fall-undergraduate-majorsbyethnicity-mc-87643.pdf show that CS at UCSC is dominated by Asian-Americans (43.8%). Even after adjusting for that, Hispanics are underrepresented (about 60% of the numbers one would expect).

    In California, other under-represented minorities are small enough percentages that single-campus and single-major numbers are too small to show statistical significance—one person adding or dropping makes a large change.

    You also don’t address one of the biggest cultural factors: the high prevalence of cheating (both plagiarism from the web and unauthorized collaboration) in programming courses. Other engineering courses see much lower cheating rates, though enforcement is approximately as strict.

    • 3. Mark Guzdial  |  December 23, 2019 at 1:47 pm

      Thank you for adding to the list of issues. Mine was not a comprehensive list. Your issues are excellent additions!

    • 4. BKM  |  December 24, 2019 at 11:03 am

      I have become convinced that plagiarism is one of the biggest issues we face in our field. I teach several upper level CS courses, and semester after semester, am faced with students who got A’s and B’s in the introductory sequence but cannot write even the simplest program on their own. The only way this could have happened is if they had others writing their programs for them. And indeed, I catch several cases of plagiarism each semester. My oldest kid is now a CS major at another school and several of his friends are CS majors at schools ranging from a state flagship to an engineering school to an elite liberal arts college, and while home on break, they all tell me that plagiarism is pervasive at their schools. One kid told me that one of the requirements to be accepted into an engineering fraternity at his school was to do a programming assignment for one of the fraternity members. And, on the other side of the fence, one of the reasons companies do those much hated interviews in which they bombard candidates with programming questions is because hiring managers do not trust that recent grads in CS actually have the skills they claim.

    • 5. Mark Guzdial  |  December 24, 2019 at 11:47 am

      On the issue of gender, I didn’t want to claim CS as unique since computer engineering is (at many institutions) even more male-dominant than CS. It looks like it’s that way at UCSC. Anybody else have contradicting data?

      I was just in charge of cheat-finding for a large (650+) intro to programming for engineers class. We accused a lot of students, but I felt that it could be pretty easily reduced by some adjustments to the course. The assignments were too hard. I had the sense that students tried then gave up, e.g., most of the cheating we saw occurred in the last 45 minutes before the assignment was due. We make cheating too easy — the assignments haven’t changed in four years, so the corpus of previous students’ code to draw from is enormous.

      A question I have: Are CS students more prone to cheating, or can we just catch them more easily?

      • 6. orcmid  |  December 24, 2019 at 12:23 pm

        < A question I have: Are CS students more prone to cheating, or can we just catch them more easily?

        I am not certain how to structure it, but having students provide walk-throughs of their solutions (with strict guidance of how to listen/guide a walk-through) would be very revealing and perhaps most important — demonstrating fluency and how some code is fit for a purpose. It probably takes peer/group settings for a typical class size. It particularly requires good preparation of the instructor.

        I can even imagine how this could work when students teamed up to solve a problem.

      • 7. BKM  |  December 24, 2019 at 12:50 pm

        What I tend to see is that students wait too long to start, and then realize that they can’t possibly get the program working in the time they have available. I think rather than making assignments easier, it would be better to spend more time teaching students practical skills like time estimation, how to divide a program into tasks that can be scheduled, and testing skills. Those are the things that tend to cause students to give up, and those are skills that from what I observe, are often not explicitly taught.

        One of the reasons cheating is so rampant in programming classes is because it is so easy to do. A lot of instructors never look at submitted code closely, so they can’t spot it, and while there are Turnitin style plagiarism detectors out there, they aren’t in widespread use. Also, it is easy (and quite common) to get around those detectors by simply hiring someone to write the program. From what I have seen, this is very common.

        Hiring people to write essays is a problem in writing intensive courses too. However, one way they get around that problem, especially in comp courses, is to have students write a paper in stages, with feedback at each stage. We should consider moving to a model like that.

        • 8. orcmid  |  December 24, 2019 at 2:59 pm

          Progressive development. Great idea. There are applications of Systematic Program Design that feature test-first constructions that provide progressive unit testing also. Language choice can be an impediment though. I saw it beautifully done using Dr.Racket. Automatic checking of solutions was part of it.

          With regard to procrastination and all that (something that hounds my life), there are some valuable aids in the Watts Humphrey “Personal Software Process.” It promotes journaling and measurement to give reality on time spent, progress made, etc.

          Valuable observations, BKM.

          • 9. gasstationwithoutpumps  |  December 26, 2019 at 1:44 pm

            Automatic checking of solutions is part of the reason that cheating is so rampant in CS. If no one is looking at the solutions, then students have the attitude that no one cares—it is just a game of getting the solution past the automatic grader. Automatic grading is also one reason that CS students are not learning how to document their programs—no one is reading their documentation, so they either don’t bother doing it or or do it all wrong and no one calls them on it.

            • 10. orcmid  |  December 26, 2019 at 2:05 pm

              I wasn’t talking about automatic grading, but something a student could use to check their solutions, find improvements, know what tests failed. That sort of thing until a student is fluent at creating their own verification.

              The notion of getting past the teacher starts long before CS classes. I was dismayed to see youngsters not interested in learning to check their work, but just satisfying the teacher, who did not want evidence of that.

              • 11. gasstationwithoutpumps  |  December 26, 2019 at 5:05 pm

                The problem with providing beginning students with test sets it that they make almost random changes in response to the results, in an attempt to evolve a correct solution by chance. Teaching students to debug requires more than just automated tests.

                • 12. orcmid  |  December 26, 2019 at 6:09 pm

                  I have not claimed exclusive anything. I have seen solution test procedures that work far better than that, usually by displaying the test case that fails.

                  With regard to how students learn to verify their code and provide test-first cases does take something. I don’t deny it.

                  None of this happens in a vacuum. Why are we circling around like this?

      • 13. gasstationwithoutpumps  |  December 26, 2019 at 1:49 pm

        “A question I have: Are CS students more prone to cheating, or can we just catch them more easily?”

        Both. The classes are structured so that cheating is easy (problems reused for years and automatic grading on I/O behavior only), many students are in CS only for a job ticket with no interest in actually learning anything, class sizes are huge so students feel anonymous, and detection of copying with trivial changes is relatively easy because of the highly structured nature of programming languages.

  • 14. orcmid  |  December 24, 2019 at 11:25 am

    I don’t know that plagiarism is distinguishing with respect to CS. I do see that there is a tendency to look for solutions and examples and do so in satisfying subject-matter requirements of a new job. I also don’t know that practice in industry is particular to CS graduates.

    One problem may be the degree to which applied CS practice is not recognized as a social activity (e.g., software engineering) as much as for other STEM disciplines. The degree of imposter syndrome as well as cluelessness may be exacerbated here.

    Something else nags at me, especially with respect to K-12 CS/computing teachers. What is the gender (and perhaps age) distribution among, say, high school CS teachers? I wonder how much they reflect aspects of behaviors found in defensive climates?

    With regard to “showing-off,” I am dismayed by the awful folklore that comes out of the mouths of CS instructors on occasion, as well as absolutism about tools and platforms.

  • 15. David Cavallo  |  December 29, 2019 at 10:44 am

    Thanks as always for not only raising important issues in your blog, but also for always bringing a thoughtful approach for thinking about these issues.

    That the other fields have both a longer history of study for how to learn them and that they have significantly more researchers examining how to teach what has been the curriculum is certainly true.

    However, if longer history and larger community were the factors most for critical success, then it would imply that the other fields are more effective in what they do. Yet, in this case thinking primarily about K-12 education, particularly regarding structural inequality in achievement and success at a large scale beyond the elite, the other sciences can claim neither that they are successful nor that they are more effectively addressing the issues. The level of achievement is not significantly increasing over time and inequality may actually be growing.

    I would aver that we need to consider whether shorter history and fewer practitioners is a bug or a feature.

    How to teach what has been the curriculum for a longer time is certainly more developed. However, what should be taught is relatively unexamined with fresh eyes. Thus, the basis in evidence basis is constrained to what has been done, not to what could be done. Moreover, resistance to change and fundamental questions of past practice is perhaps stronger and more entrenched because of the longer history. The lack of ability to scale quality and advance equity, to me, is more due to not re-thinking curriculum and school processes than to lack of human capacity. This, to me, is one of the primary areas where computational approaches can potentially ameliorate many of the problems, not only for learning computation itself but also for learning in mathematics and the sciences.

    One key lesson from Evelyn Fox Keller’s wonderful book “Making Sense of Life: Explaining Biological Development with Models, Metaphors, and Machines” was that while many were inspired by the models of people like D’Arcy Thompson, biologists rejected them because while they were beautiful and inspiring, they weren’t true. However, at some point the field changed and new fields emerged by what computation brought to understanding biological processes. Indeed, one could easily claim that the rapid growth of knowledge in the world in the previous decades was due to the growing presence and power of computation, and the growing number of people who understand computation and can apply it in the fields and to better understand complex phenomena. Computation as facilitating learning and facilitating different learners has changed the world but has not yet changed education.

    The way longer history can work against better understanding and practice is that when these fields began being taught in primary and secondary schools, our understanding of human learning in its complexity was much poorer and more inaccurate. How to organize learning and other creative environments was also much less diverse and effective. Perhaps, most importantly, due to first scarcity and then cost, the tools for learning were extremely limited. Too much curriculum was based upon what one could do with paper and pencil or in the science labs of the times, and not on what was learnable with computation and what was actually fundamental for developing knowledge in the field.

    Computational approaches combined with environments that facilitate more active, engaging, and meaningful learning, could provide the fresh eyes with which to address the pressing issues in STEM learning, and particularly to address the inequalities. Using engineering and software projects as a means for connecting, contextualizing, and concretizing theory and practice has at least some solid evidence behind its use. These fresh eyes could, perhaps, better examine what is taught, how it is taught, with whom it can be learned, and how it could be different. At the very least, new approaches deserve solid opportunities to demonstrate their potential.

    • 16. Mark Guzdial  |  December 30, 2019 at 10:28 am

      Thanks for engaging, David.

      Other fields are much better at providing equitable access than computer science. Check out Barbara Ericson’s analyses of Advanced Placement test-takers. Computer science will take a decade at least to be as female-friendly as Physics. No, they’re not equitable, but they’re far better off than CS.

      Have you read Morgan Ames’ The Charisma Machine yet? She does a terrific job of dissecting the idea that computation can be used to correct the ills of schools. Negroponte and Papert sold us a story about how bad off schools are, when that’s simply not true. I do believe that computing can make possible new ways of learning, and that constructionism is a powerful learning theory, but it can’t be rooted in the ideals of technically precocious boys. Logo and the XO Laptop failed, for good reasons, and we should learn from those mistakes. Computing can contribute to making education better, but by listening to teachers and engaging with the system, not by deriding the whole system.

  • 17. En defensiv kultur i datalogi – hanshuttel.dk  |  January 13, 2020 at 2:53 pm

    […] Mark Guzdial, der forsker i datalogiens didaktik, har nogle interessante kommentarer om det typiske … En artikel fra 2002 af Lecia Jane Barker, Kathy Garvin-Doxas og Michele Jackson fra University of Colorado dokumenterer en meget defensiv kommunikationskultur, der er fremherskende på datalogiuddannelsen ved det pågældende universitet. Guzdial spekulerer på, hvor meget forskning der siden er fundet sted for at afdække hele denne kultur og konkluderer, at der desværre ikke er ret mange senere forskningsresultater her. Det er trist, for hvis der er tale om et generelt fænomen, er det bekymrende. […]

  • 18. CS Rocks  |  January 15, 2020 at 4:43 am

    Having taught CS and what you reference as computer education for the past 18 years (US grades 9-12, prior to that corporate training and an occasional adjunct course at University ), the data points you utilize to form your opinions are puzzling to me.

    The answer to ‘What makes CS education different than other discipline-based education research (DBER, like math ed, physics ed, or engineering ed)?” was actually answered in the sentences following in your 1st paragraph. You simply failed to address that elephant in the room.

    A well defined (and universally accepted) definition of CS education does not exist – at least not in the US. This is not true of the other DBERs you mention.

    Until this definition can be better established, confusion between use of high level software tools (Word Processor, Graphic Design Software, Web Browser [view port]) and back office high level software tools (IPS, IDS, Network Analyzers, NOS, ROS) and the creation and augmentation of such tools (programming). Even this is an over simplification. Yet, it gets to the root of the issue.

    For example in the state of Indiana in 2016, K-8 CS standards were adopted. In 2018 the first state-wide iLEARN exam was given which was touted as having 20% of the content covering CS standards. One of such questions was actually about an egg drop experiment, but because the question said ‘preformed internet search’ it was considered a CS question.

    In my experience, deriving usefulness from education conference attendance numbers is questionable at best. The largest factor in attendance after marketing factors is funding dedication from institutions that employee those that would be interested in attending.

    Let’s face the facts, educators by a large do not have large disposable incomes and neither do the institutions for which they work, that would allow for attendance to most conferences. Unless paid of the teachers pocket directly, it would likely be charge to a PD budget. In my district for example the entire PD budget for year is $3500 for a staff of 75. SIGCE would cost more than half that for one teacher to attend and that would not cover transportation costs or per diem – only hotel and conference fees. Since the average k12 teacher salary in my state is around $42k with 10 years experience. It is not likely they would have an extra an extra $1800 or the desire to write substitute lesson plans for 4 days and take a hit on loss time pay if their school would not approve the conference days.

    Couple that with a lower number of possible attendees and that is what produces your numbers for your removed slides. For example a school district of a few thousand students k12 might have say 18 dedicated teachers in mathematics, or 20 in science, (not including k-3 where the teacher licensing tends to be by non specific subject) compared to 1 to 3 teaching CS related (not software use – since the majority of states have yet to even adopt legislation allowing for a CS teaching license [as opposed to computer education]). Larger school districts numbers would hopefully be larger, with slow acceptance of CS and lack of qualified teachers the CS teacher numbers are not a sure increase based on school population size.

  • 19. davidcavallo  |  January 15, 2020 at 2:36 pm

    Thanks for pointing out Ericson’s research and congratulations to both of you for the attention to equity and the means and methodologies to achieve it. Just as more work in recent years in economics focused on structural and systemic inequity, we need to be just as diligent regarding education and the role of computation for enabling high-quality learning environments for all.

    Sticking with influence from economics, Jeff Sachs credits his wife, Sonia Ehrlich, a pediatrician, with inspiring and him and influencing their work with the idea of being more clinical, in their case about thinking about development. In our field we also need to be more clinical, and less prone to more sweeping statements that often lead to false dichotomies or overly broad analysis.

    Before I would come to the conclusion that our lack of achieving equity in learning computation is primarily due to having less of a history and fewer devoted researchers and practitioners, putting on my clinical hat I would try to answer:

    Why is equity along ethnic/racial and class dimensions so bad across the board, not just in CS? If time and number of people were the most important, this would be more equitable as well.
    How would one explain the period in the 80s when, if I remember correctly, the proportion of women receiving bachelor’s degrees in Computer Science grew more quickly than in other STEM fields, and then fell off a cliff to the dismal levels now? One could make the claim that when Computer Science was a new field, improving equity was better.
    Why would US scores on PISA and like assessments decrease recently? Why would there be a difference internationally if the basis is time and number of people?
    How would we disentangle the improvement in females taking AP Computer Science from the increase in females going to college compared to males?

    As to the Ames book and critique, rather than enter into that in the detail it merits now, please allow me to just address how you framed the issue in your response.

    “the idea that computation can be used to correct the ills of schools”
    “Negroponte and Papert sold us a story about how bad off schools are, when that’s simply not true.”
    “… it can’t be rooted in the ideals of technically precocious boys.”
    “Logo and the XO Laptop failed”
    “Computing can contribute to making education better, but by listening to teachers and engaging with the system, not by deriding the whole system.”

    Responses, trying to be clinical, not in order:
    “Computing can contribute to making education better, but by listening to teachers and engaging with the system, not by deriding the whole system.”

    In principle, olpc only worked with countries, their systems, and their teachers. Any charge to the contrary is just not true.

    When we worked with the schools who would use the first prototypes, on our first visit to a public school on the periphery of São Paulo, one teacher told us “Government officials and university researchers come to us with some new initiative. We tell them why it won’t work as they have planned. They don’t listen to us. Then, when it doesn’t work, they blame us.” We did listen and incorporate their ideas into the actual implementation, not only for them and not only in their school. A major implementation idea in olpc was how sufficient access to computation combined with more progressive ideas about active learning could be empowering for students AND teachers. This is consistent with what I have done whether in educational efforts, community organizing, or facilitating organizational change and what I have written about regarding emergent design.

    Every potential laptop country formed workgroups to help in the development of the laptop, its software, and the overall program. We met regularly and held workshops to build capacity. We worked to build teams in each country and region. Those teams then helped support teachers, schools, kids, and communities, with our collaboration.

    Likewise, we worked to build bridges across countries so that countries trying to improve their systems could collaborate with each other and fortify their efforts. Isn’t this engaging with the system to help remove dependencies and build capacity?

    Well before the hardware and software were even finished, we worked with countries to develop capacity to use the laptops effectively. In some countries, like Brasil, this was relatively straightforward because there were already so many excellent people (like José Valente and Lea Fagundes in Brasil) who had been working for so many years doing so much good work using computation to improve the learning environment. Other countries did not have the same wealth of expertise and experience as Brasil, but all the countries functioned in collaboration with each other. When that didn’t happen, those countries suffered, but not due to a lack of olpc’s engagement with the system at all levels.

    “Negroponte and Papert sold us a story about how bad off schools are, when that’s simply not true.”
    One of the consistent and inspiring things I’ve learned from both Seymour and Nicholas, is that rather than castigate a present, focus on the positive that can be achieved. Seymour would always quote Einstein’s dictum “Love is a better master than duty.” We focused on making what could be because of what was now possible that was not previously possible.

    Please let me give you pictures from Rwanda (but I also give some from urban and rural schools in Brasil, or Mexico, Costa Rica, Senegal, Mali, Cameroon, Thailand, Colombia, where I’ve worked). We based ourselves at Rwanda’s technical university. We helped develop a government team who would support their schools.

    The Rwandan Minister of Science and Technology told me of when he was previously Minister of Education he purchased a new mathematics curriculum and texts that still, 10 years later, had not been delivered and they still did not have means for distribution. His hope was that with devices and connectivity they would not be stuck with obsolete texts, high costs of distribution, and could have the best materials in each school at a reasonable cost.

    The State Minister of Primary and Secondary Education told me that he knows all the rivers in France and Belgium and none in Rwanda because the geography textbooks they used were from the colonial powers. We worked with teachers, the Education Ministry, the governmental support team, and pre-service teachers to develop local content, where the development of the content was a learning experience and the practice in developing content helped open eyes to other, less lecture-driven and more learner-centered activities.

    We led a long workshop in Rwanda for people from every laptop country (plus others such as South Africa, Cameroon, Mali, Senegal, Kenya, Tanzania, Uganda, Burundi, and perhaps some others I am forgetting that wanted to participate). A major objective was to develop teams that would go to schools in those communities to work for 3 months to help support them how education could improve. (We had previously run workshops at MIT and in laptop countries.) We had volunteers from college students internationally, as well as teachers and government officials from around the world who participated in the workshops and then worked in the communities alongside people from the countries and communities. These teams then helped bring a level of experience and expertise to the communities where they worked with teachers, parents, officials, and schools to implement what they wanted in their communities.

    In our workshops with the Rwandan team, we used school vacation periods to hold long workshops with teachers and students to use the laptops for learning. The point was not merely to teach some bit of software, but to help put into project-based learning into practice in places In one truly memorable workshop, we used the Freirean idea of generative themes on important local issues to guide student projects. We asked them to build projects to focus on prevention of malaria, prevention or remediation of HIV/AIDS, or on helping the environment.

    Because it was school vacation, it was not compulsory for anyone to participate. Yet we had a full turnout of students and teachers, using their time to learn and to work, not for extrinsic reward but for the joy of learning, self-improvement, and systemic improvement.

    The projects helped prototype real activities and systems that could help those communities. What perhaps was most memorable and gratifying was when parents came to us after the end of workshop exposition and told us how proud they were of their children and how they hadn’t previously appreciated how smart they are. Having the opportunity to design and construct something meaningful and socially impactful, and to successfully overcome the technical difficulties in construction helped the students also feel their capabilities.

    With the support of the local team and us, the laptops and connectivity enabled the development and dissemination of local content. It enabled collaboration on projects beyond classroom and school walls. Teachers were empowered to not just teach lessons by rote, as was the common practice, but to act as researchers and learners with their students.

    As you know, this is all a constructionist approach. I could go on with many more examples but that is not the point and this is not the place.

    Trying to be clinical, I think if real actions are ignored and data is cherry-picked perhaps to fit a pre-determined narrative, we will delay the progress of more equitable access to better education. Obviously, olpc deserves serious critique, but the critique needs to be accurate and more complete. The country teams and our team did beautiful work from a commitment to social justice and providing better learning opportunities for all, particularly the most excluded. To have our work denied leaves a rather sour taste.

    “… it can’t be rooted in the ideals of technically precocious boys.”
    Again, this needs more space than I can devote here. However, that this critique ignores the work we did on dance, mathematics, robotics, and programming; or on social/community projects in the US, Brasil, and Thailand; or on Fred Martin’s and Deirdre Butler’s work with robotics to represent and enact traditional Irish myths and legends, as well as much other work where by changing content we achieved greater equity in participation along all social dimensions.

    The critique should not ignore Papert’s and Turkle’s article on epistemological pluralism from the 80s, or ignore that Evelyn Fox Keller’s work on Barbara McClintock, referred to in the article. The seriousness and importance of the premise demands that we take a more clinical (and complete and accurate) approach.

    We did the RoBallet project because when we left it open for kids to choose projects, many kids, primarily girls, wanted to make a pet or a robot that could dance with them. This was much more complex than the typical robotics project, but it wasn’t technical precociousness that drove or repelled them. It was interest.

    In the juvenile jail, when we did cooperative robotics challenges as opposed to competitive ones, it not only was popular and engaging, but also it helped raise important socio-emotional issues that the collaborative construction helped address positively and effectively.

    I would maintain that too much of STEM content is boy-centered, abstract, un-engaging, not collaborative and cooperative, and asocial. Too much tends to be based upon what one can do with paper and pencil and not on what are the fundamental ideas and how we best learn.

    In the early 2000s, Seymour and I approached several major educational publishers proposing to develop a new approach to mathematics education in K-12 schools that would utilize computers all along the way.

    What was the response? No one questioned the quality of the ideas or the evidence upon which it was based.

    We were denied solely on market grounds, not on quality. We were told that, in the US, let alone in developing countries, that schools might have a computer lab but it was not used for math class. Math classrooms might have a few computers, but it would not be enough for the 25 or so kids in the class (and in the countries where olpc worked often ranged from 80-110.

    Some critiques of computers for learning say that without sufficient content purchasing of computers is not justified. However, without a critical mass of computers funders do not fund content development. olpc helped break that logjam. olpc also helped change the debate about what was reasonable to help truly transform learning in countries with fewer material resources, high inequality, and lower functioning economies. olpc helped challenge the idea that computers must cost thousands of dollars. In those ways at the very least olpc was not a failure.

    At a recent conference on technology and learning in Brasil, a group of researchers and teachers presented their study of the one laptop program in Ceara, in the northeast of the country. Their results were extremely positive and achieved on their own. I have witnessed too many American or European researchers come to conclusions without knowing the countries and the conditions and without studying the situation in full. I have also seen too many large global technology companies more worried about profits and market share and thus try to inhibit efforts towards equity of opportunity, and then push rather awful learning practices that they control and that do not achieve results.

    “Logo and the XO Laptop failed”
    I’ll just focus on the Logo failed aspect. Around 20 years ago (yikes!), Elliot chaired a wonderful workshop in Maine focused on computation and learning. As a small part of the workshop, Elliot gave a moving tribute to Seymour’s contributions to thinking about learning and what computation can bring. Seymour, Cynthia, Wally, Danny, and their then colleagues and students such as Hal, Andy, Ken, Paul, and others were primary to raise the ideas about how kids programming computers could potentially lead to rich learning experiences and differentiated the approach from the more typical Computer-Aided Instruction approaches of the time.

    As Cynthia points out in her talks and in an upcoming article she co-authored on the history of Logo, Logo was not only a programming language but, at least at the MIT AI Lab, carried with it a culture, ideas, and people. To call this a failure, when you, I, and many others could do not the work we do and think the way we think without Logo, Papert, Solomon, and all of their work and ideas would perhaps be to call ourselves failures also.

    Again, I think we need to be more clinical, more accurate, more thoughtful, more compassionate, and less absolute.

    I apologize for the length of this note but it just felt like it needed addressing, and, unfortunately, one of Seymour’s critiques of my writing (besides that I don’t do enough of it) was that I am far too verbose and need to construct text that is more “crisp and clear.” Sorry.

    • 20. orcmid  |  January 15, 2020 at 6:46 pm

      Thank you for all of that. I had forgotten about the importance of OLPC where text books and related materials are scarce and inadequate/outdated. Sharing an internet connection was another grand feature of the grid capability.

      I have two questions on two lines, one clinical, I think, and one personal.

      Multi-part: (a) How many OLPC XO’s were produced and put in the hands of students and teachers around the world? (b) what happened to them: (c) what has taken their place or otherwise preserved what was achieved with them?
      I donated my two XOen for the Haiti Earthquake relief effort. Was that of any use/value/benefit?


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