Author Archives: David Zabner

I had to write an OpEd

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One of my classes required me to write a “provocative issue paper” and I decided to write about why we should stop collecting student data. In the interest of putting out something this week here is what I wrote:

It is becoming cliche to comment on the degree to which we live in an increasingly virtual world. However, this is a fact that policy makers and voters should be repeating as a mantra. It is without a doubt the most important shift of the 21st century. Sure, by the end of the year 2000 the dotcom bubble had already burst but the five years leading up to that were just a short preamble to the incredible and continuing changes wrought by our increasingly technological world.

One of the consequences of our new virtual reality is that corporations, governments, and researchers have the ability to collect data at unprecedented scale. Netflix collects a data point every second you are on its site or app, even if you do nothing, the NSA had, for a time, the ability to access metadata on every call and text we make and Pearson… actually its not clear what Pearson does with its data. I am not picking on Pearson either, whether its Khan Academy, Coursera, or Houghton Mifflin Harcourt educational technology companies have incredible reach into the educational lives of students and very little oversight or transparency on what data they are collecting, much less what they do with it. Because of the dangers inherent both in the existence of large volumes of student data and its use in AI and ML we would be better off, as a society, with strict restrictions on its collection.

Of course these educational companies and not-for-profits will claim that they collect data for only for the purposes of improving student outcomes but a decade in to the big data revolution no big data research has fundamentally changed the way we teach or learn. In his book, “Failure to Disrupt”, Justin Reich covers the few discoveries that have come from data collected from MOOCs and “personal” tutoring systems and finds that they are either rediscoveries from the 1990s and earlier or intuitively obvious. It is hard to prove a lack of progress from educational big data but I would challenge any reader to ask their local education Ph.D. for a paper that used big data and that changed their mind about how teaching and learning should occur.

The lack of progress to date alone, however, is no reason to curtail the collection of data and the attempts to make progress with research. The reason we need to slow or stop collection is instead because of the inherently toxic nature of data.

We do not, cannot, and will never know all of the things that can be predicted about us with data. Maybe students who log into Khan Academy earlier in the morning are more likely to become senators and the ones who log in later are more likely to go to jail. Maybe the student whose username includes numbers are more likely to suffer from depression or anxiety. These are correlation hypotheses and they are unaccountably infinite but each is a chance for a company or government to make choices about students. To sell them product or to sell them as products, to hire them, fire them, or raise the cost of their car insurance.

We really don’t know what power this data holds but what we do know, from books like “Weapons of Math Destruction” and “Algorithms of Oppression” is that this data can be combined with AI and ML algorithms to do immense harm. We know that, in general, the artificial intelligences we train reproduce and exaggerate the inequalities and biases of our society. That they tend to assume the best of the more privileged population and the worst of those who have less.

In short, the collection of this data and its use for any purpose represents a serious risk to our students, especially students who are children, and to our society with no noticeable upsides to date. I am no policy wonk but I have a rough idea of how we could protect from these risks: FERPA needs to be reformed to specifically force creators of educational technology to clear all data collection with an external board, similar to an IRB, and to delete that data after a reasonable and relatively short period of time. For data approved for analysis it should be stored and analyzed in a manner that provides for differential privacy. Ultimately, we need to treat student data as we treat medical data not as an afterthought.

Books

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I finished two books this weekend!

The first, “Failure to Disrupt” by Justin Reich took me quite a while to read but what a pleasure! If it were an education textbook it would be the best one I have read. It is a book I have been meaning to read ever since I started listening to the TeachLab podcast which did a “book club” on it. If you are interested in educational technology I cannot recommend it highly enough but if you just want a review and some sparky-notes (I should have actually taken notes but didn’t so this is all from memory) here is what I got out of it:

  • Technology can, has, and will continue to incrementally improve education.
  • Great education happens between people meaning that the best use of technology in education is often as a tool to widen and tighten a network of teachers and learners.
  • MOOCs are not going to replace schools any more than the radio did (and a lot of people thought radio would replace schools or at least teachers).
  • In fact, technology is very unlikely to “disrupt” education.
  • Educational technology is plagued by “the Matthew effect” (i.e. The rich get richer and the poor get poorer). Even “free” educational technology often requires expensive tools (ipads, internet, laptops, etc.) and therefore is more likely to serve already well-served students.
  • The results of research on MOOCs is
    • “students who do more, learn more, do better, get better grades”
    • People who are good at school (i.e. have 2+ degrees) do great on MOOCs everyone else, not so much
    • Most, 95%+, of people who start a MOOC give up after 3 lectures

I have a few critiques:

  • The book is insufficiently critical of the inhumanity of autograding. I have seen many students reduced to tears because of them
  • LMS systems are universally atrocious in design and deserve more hate/s

I had planned to do a more in depth review of “Failure to Disrupt” but life gets in the way of such things sometimes.

The second book I read was “Unapologetically Dope” by A. Nicki Washington, PHD. I made it through this one in about 90 minutes. Partly because it is relatively short and mostly because it’s easy to read and pretty engaging.

“Unapolgetically Dope” didn’t do much for me personally, as a 30 year old white man who has already done fine in tech but it was emphatically not written for me and I could definitely see myself recommending it to students. It is clearly written with love and filled with solid advice for anyone. Plus the autobiographical bits that the author includes paint an inspiring picture of a successful and dope black woman in tech.

My only critique is that I kind of wish it came with a workbook, checklist, or similar. Such a tool could be useful for students reading it. Maybe it’s unnecessary.

I attended SIGCSE for the first time!

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I just got back home from my first academic conference as a PhD student. I absolutely love conferences and I had a really wonderful time attending talks and meeting all kinds of people. I wanted to share some of the things I learned at SIGCSE:

  1. There is incredible excitement among the younger folks I met to make CS more inclusive and accessible. No one under the age of 35 would shut up about how important it was to make CS truly for all. I loved it
  2. Attendees seemed to have surprisingly little background in ED theory. I think there is a need to educate the community of CSEd researchers and practitioners on the broad strokes of learning sciences and how they relate to CSEd. Ideas like Behaviorism and Social Constructivism are helpful both in discussing and generating new ideas and in figuring out ways to measure the success of interventions.
  3. There is an incredibly wide range of opinions on what CS students need.
  4. There is a continuing crisis of (CS) educator numbers in K-12 and at smaller colleges and universities. This seems to be one of the drivers of the interest in teaching CS “at scale”.
  5. The big drama seems to be about the two AP computer science courses. Although I didn’t hear anyone angry or arguing, almost everyone seemed to care deeply about the content, purpose, and above all programming language of these courses.
  6. Everyone I met seemed more than happy to discuss my ideas about CS1 but few seemed interested in critiquing them.

Here are some of my favorite things I saw and read that I think are worth looking at:

  1. Subgoal Labeling for CS1
    1. Cool research on adding “Subgoal Labeling” as a scaffold for students. The idea is to label, for students, chunks of sample code in “worked solutions” to help them build a higher level mental model used to understand code and its constituent parts.
    2. They have some really impressive results over 3 papers
    3. https://www.cs1subgoals.org/
    4. They are working on adding Python and are looking for research partners
    5. They have a runestone textbook that supports this work
  2. CS + Ethics
    1. Teaching ethics by teaching ethics pedagogy The idea here is to create a CS Ethics course whose final project is an ethics module for another CS course. Seems like a very cool thing to replicate in the engineering school at Tufts.
    2. The House of Computing: Integrating Counternarratives Fascinating and difficult to summarize. Argues for integrating counternarratives (in all CS courses) to undermine the dominant narrative that CS is “objective, apolitical, and unbiased, with little need for ethics education”

Also everyone should check out CSEdResearch.org!

Volunteering at TS was a blast! I just wish I had taken more pictures

Legitimate Peripheral Participation

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I haven’t posted yet this year and felt the need to post something so here are some thoughts I am having here at the end of January. They may be more appropriate to twitter but I am posting them here nonetheless:

I have started reading Failure to Disrupt by Justin Reich and I cannot recommend it highly enough. I also recommend Justin Reich’s podcast “TeachLab” where he goes into a lot of detail about how to improve educational systems in the US. One of his guests recommended a large edit to his book, replacing the concept of “scalable education” with “public education”. The book would be much improved by this change. I may do a book review on this down the line.

I grew up in a community and family full of chances for legitimate peripheral participation. I got to experience it within my synagogue, my dad’s lab, and at summer camp. I got to experience it again in some ways within the startup community here in Boston and a lot during my masters in CS at Tufts. However, I feel that it is missing from my current Ph.D. experience. I think Covid is largely to blame here. It is such an important way to learn and I miss it dearly.

Relatedly, I think that the curriculum idea I have about CS1 could be improved by designing it to explicitly create environments that support LPP. If anyone is reading and has ideas about how to engineer for it, or why it is impossible to induce, I would love to hear them.

I found my first “expert” who agrees with me that Computational Thinking (CT) is a load of crap! I just joined the class Children & Technology taught by the wonderful Dr. Marina Bers and we had a short chat about it today. The short version on why CT is a bad concept, in my opinion, is that it describes too broad a set of skills and ideas to be useful to anyone. You can teach programming, algorithmic thinking, computation modeling, proof, etc. but the idea that you can teach a student something that will improve their skills across all of these things seems wrong to me.

I am working with Dr. Trevion Henderson to develop my CS1 curriculum within a course at Tufts called ES2! It has already been a spectacular experience to work with him and to watch someone teach my curriculum. The best part has been watching and listening to students while someone else stands at the front of the room. I have already learned a lot doing it as well. I’ll post more about it when appropriate.

This is going to be a very busy semester but I am very hopeful that I will have the time and energy to update this blog a bit more regularly. A belated happy new year to all!

Process is culture and values

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A truism in software engineering, and I think engineering in general, is that your process IS your values. If you value code correctness your process will require the creation of unit tests. If you value UX then your process will involve user testing and feedback and the inverse is true. In my reading about racism, CRT, and anti-racism, time and again I find paragraphs, pages, chapters, and whole books about racist and damaging processes, procedures, and policies.

What I find difficult, however, is that in discussions with folks people are very comfortable identifying these bad processes and policies but are deeply uncomfortable with the idea of crafting good ones. It is maybe not surprising that this happens but I remain annoyed by how quickly people seem to run from attempts to craft antiracist policy. We would rather talk about how things went wrong, instead of crafting rules to attempt to guarantee that they will go right next time. I understand this fear – what if the process or policy we create is ineffective or counterproductive?

A problem with this, however, is that when we educate and change hearts and minds it is largely a one-by-one process. What’s worse is that antiracist research and behavior asks a lot of each person who undertakes it and puts them at a comparative disadvantage to those who do not. This work often requires the creation of large networks that are hard to maintain. They require not using populations of convenience in your research and more work before and after your research to make sure you are properly giving back to your participants.

Additionally, when people join your community, how are they to know that it is an antiracist one if they missed last years trainings, readings, and meetings? How will you transmit to them the shared understanding, the implicit rules that were set when the community put time and effort into thinking about how to do better?

The answer to all of these things is to turn implicit rules, shared understandings, and values into explicit rules policies and procedures. Give them some wiggle-room and update them regularly but make sure that they are explicit and required. Have your new members read and understand them. Use confusion and discomfort with these rules as signals and opportunities for teaching and self-education.

p.s. I wrote this post rapidly and out of an immediate sense of annoyance. Let me know how I can improve it?

White Supremacy is a You problem (if you are white)

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“Me and White Supremacy”, by Layla F. Saad (Saad, 2020) is a 28 day exercise in educating yourself on white supremacy and the role it plays in your life. Through 28 chapters of theory, examples, and writing prompts, it creates a framework for self reflection; helping a reader come to terms with white privilege and their continuing role in maintaining and benefiting from white supremacy. It is a book explicitly intended for a white, or white-passing, audience and it promises that audience that through reflective journaling they will grow into an ability to make positive change in the world and “be better ancestors”. Saad keeps chapters short and snappy, with many examples as aids for the journaling portion. However, her focus on her audience’s internal state causes her to ignore white supremacy as a structural and political system.

Saad kicks off her book by signaling to the reader over and over again that they are neither alone nor under attack. The endorsement from Robin DiAngelo, a white antiracist educator, is followed by a comforting and inspiring introduction to the author and a user guide to the book which drives home the importance of “the work” and signals to the reader that they are ready to do it and that it is for them. It promises that “white supremacy is a racist ideology” and that you can overthrow that ideology by recognizing it in yourself. Additionally, the “three things you will need to do this work” are undoubtedly written to be comforting and familiar to the “spiritual white women” who were this projects original target audience.

Saad does a spectacular job throughout the book of choosing topics and prompts that push a reader towards practices of good allyship. To be an ally, one must understand the damage racism does to people of color (week 2), as well as all the different ways that one can be racist (week 1), get in the way (week 3), or fail to help (week 4). She also illustrates each week and chapter with extensive and incredibly useful examples of what each concept covered looks like in real life. These examples are especially powerful when they are taken from her life.
All that being said, Saad’s focus on the reader’s personal growth and self-awareness leaves neither time, nor philosophical space, for the greater issue of white supremacy as a political and economic system of oppression. This failure means that she leaves the reader without many of the tools to deconstruct it. In particular, she does not craft any argument for the exis- tence of white supremacy, which may leave readers unable to convince others of its continuing existence.

This book is written for those who hope to be on the path of allyship. As a person who is trying to be an ally I have often been asked why I care, more often than not in a very roundabout way. “Me and White Supremacy” suggests that the reason is to be “a good ancestor”, an answer many of the unconvinced will find unconvincing. A linked, but more fulfilling answer is that white supremacy is a global evil that must not be allowed to continue. To give that answer, however, one must be able to show, as Mills does very effectively, that global white supremacy exists (Mills, 1997). In day 1 of Saad’s book she does ask the related question “How do we know White Privilege is real?” The answer she gives is that her mother told her. It is certainly not necessary to devote hundreds of pages to proving the existence of the Racial Contract, Mills already did that, but the book would have been greatly strengthened by having day 1, and maybe even week 1, actually cover the existence and effects of global white supremacy.

An alternative to a new day 1 prompt would be references to further resources to help the reader fill in gaps. This is a more general problem through the book. Over 28, quite short, chapters the book contains 58 references, nowhere near enough to help a reader answer questions left at the end of a day’s reading and while each chapter’s brevity is in most ways a benefit to the book’s goals it does make it likely that the reader will have questions.

That being said, even with further resources, a reader is likely to be left with questions because the dismantling of White Supremacy is neither simple nor straightforward work. It is filled with tension between competing aims and priorities. The book in no way addresses these tensions. A particular example is this pair of facts: White saviorism is a form of white supremacy and it is the job of white people to dismantle white supremacy. While this tension is handily resolved through the concept of allyship many other problems of praxis are not so easily resolved, and to leave them unaddressed in such an action oriented work is an oversight. Continuing on the theme of praxis, the book contains almost nothing about how to combat racism outside of your own mind. It advises us not to be silent (day 4), to call out (or in) racism in our friends, family, and leaders (day 23-25) on their racist behaviors, and many times how not to be bad allies, but it completely ignores politics and policy. White supremacy will not end when people stop behaving in racist ways. It will end when the political, power system is brought down. Although battling racist thought and behavior is one step towards that goal, it is only one step, and it is also a move to innocence(Tuck & Yang, 2012). I don’t know if to make the book longer or replace some suggested chapter titles to fix the issue might be “Me and the Police State”, “Me and Colonial Capitalism”, or “Me and Schooling”. Possibly equally effective would have been to simply include a disclaimer from time to time reminding the reader that internal work is only the first step.

All in all, “Me and White Supremacy” is a powerful tool for self-education and reflection.

Its modular design of short, relatively stand-alone chapters also make it a great teaching tool allowing it to be easily mixed and remixed into curriculum and other readings. Especially as a book that started out as a series of posts it is a shame that it does not provide more citations, links, and other external resources. Additionally, Saad’s assertion that white supremacy is primarily personal and secondarily global and systemic sends a hopeful, but incorrect, message that education and self-reflection are all we need to fix it.

References

Mills, C. W. (1997). The racial contract. Cornell University Press.

Saad, L. F. (2020). Me and white supremacy: Combat racism, change the world, and become a good ancestor. Sourcebooks, Inc.

Tuck, E., & Yang, K. W. (2012). Decolonization is not a metaphor. Decolonization: Indigene- ity, education & society, 1(1).

“How to Be Less Stupid About Race” a critical book review

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One of my classes, “Philosophies” with Dr. Powell, had me write my first book report since high school this week and given that I have been too busy to write anything else I figured I would share that. I am proud that I wrote it given how out of practice I am but a bit disappointed in how rusty I feel writing anything.

“How To Be Less Stupid About Race” is a funny, personal, and powerful book that both explains critical race theory(CRT) and chronicles the author’s personal journey from relative ignorance about global white supremacy to a deep understanding of it’s role in shaping society. Its early chapters, while dry, act as a crash course in modern critical race theory, drawing heavily on the work of Mill’s and his “epistemology of ignorance”(Mills, 1997). Explaining in brief the history of global white supremacy and how it simultaneously perpetuates and camouflages itself. The book then spends five chapters explaining the author’s growth out of that epistemology while living through the Obama era and the beginning of the Trump presidency. Finally, the book explains 10 approaches to “Becoming Racially Literate”. Throughout, the book uses humor and personality as sugar to help the anti-racist medicine go down.

Dr. Fleming[1] uses humor to numb the pain of having the racial blindfold ripped off and she does rip it off effectively. The summary of CRT gets the reader up to speed on what is currently understood and what Fleming believes and her personal stories act as a framework for how to be convinced of CRT’s veracity. All in all, this makes the book supremely well pitched at a certain population: Those who, like the villains in “Get Out”, would have voted for Obama a third time(Jeffries, 2018) but who are at least a little aware that white supremacy is a problem and are interested in educating themselves about it.

Fleming explains clearly and with purpose that she herself fell into that category of person in 2008. She had been raised in “an environment that insulated me from the realities of racism” and spent her education in environments that “downplayed racial oppression or focused on conceptually vague ’cultural elements’ of race rather than systemic racism.” Her growth and realizations through the Obama era give the liberal but ignorant reader a script for realizing that systemic racism exists and white supremacy continues. First through examination of Obama’s policy as one of continued American imperialism and then the story of Trayvon Martin and finally through a re-examination of Obama’s whole political career. When Fleming says that Obama is “a highly strategic, ruthlessly ambitious Uncle Tom” we know as readers that this is coming from someone who loved him not so long ago. She is telling us, that she thought this racism thing was over too. That she thought Obama was going to fix everything. That she recently stood where the reader stands now.

Much of the rest of the book focuses on wig-snatching white supremacy. Walking through counterarguments from the left to the central thesis, that white supremacy continues to be a dominant force in this country, without too explicitly naming these arguments, sparing the reader some of the shame stemming of being more directly disabused. Fleming lets us know that Trump’s election was no aberration in an otherwise post-racial world and that no amount of miscegenation will solve the white supremacist structures of power in our society. Finally, she sets out 10, doable if not easy steps, one can take, after finishing her book, to increase one’s racial literacy (or decrease one’s racial stupidity).

As potent and clear as the writing is, the book is not without its weaknesses. The book begins with its densest and most technical chapters and while being “less stupid” may strongly motivate the target audience, it also likely limits that audience, offending those who don’t feel stupid about race before reading the book. Finally, the book focuses heavily on the damage white supremacy inflicts on African Americans, and particularly African American women, while barely mentioning the harms perpetrated by white supremacy against non-whites both in the US and abroad.

It was a mistake to start this book with two chapters (the introduction and chapter 1) full of definitions, lists of misconceptions, and philosophical name dropping as if daring the reader to give up. In academic writing this structure is a strength. We often write with the idea that the reader may only make it through the abstract or introduction and if they are to read all of what we have written we hope to quickly arm them with the concepts and definitions necessary to understand what follows. However, I think most people would be better served by starting at chapter 3 “On Racial Stupidity in the Obama Era”. In 2008, after Obama’s election only half of Americans felt that there was “‘a lot’ or ‘some’ discrimination against blacks”(Valentino & Brader, 2011). The journey Dr. Fleming takes from “Obamania” to “critic of Barack, the Democrats, and US racism” is an incredible framing device for helping us understand how we continue to be stupid about race in the 21st century. It also serves as a spectacular introduction to Dr. Fleming as a person. Giving us a window into her background, optimism, liberal bona fides, and academic expertise.

Another strength of chapter 3 is that it shows, multiple times, that white supremacy is global and imperialist and that it has global effects. President Obama’s policy of drone strikes was enabled by the same ideas that enabled French colonialism, the subject of Dr. Fleming’s thesis work. Yet, the rest of the book seems to pay little more than lip-service to this idea. This is in important failure in a few ways. The first is that American Colonialism is a foreign policy issue, an easier space in which to convince people to change their mind. Whether convincing a liberal nimby, or a conservative, discussions about our behavior “over there” are ones that are much easier to start and to be productive about. More importantly, the issues of global white supremacy, global colonialism, and capitalism intersect and a more fully intersectional approach opens up important avenues of argument and thought. These are also areas in which Fleming is not short on expertise. It would have been fascinating to have her compare and contrast the legacies of black slavery in the US and French colonial slavery around the world. While it is possible that this expansion of topic would have lightened the focus on white supremacy as a problem here in the United States it is more likely that it would have provided American readers with an additional unflattering mirror – another angle from which to view our problems.

The final chapter of the book is titled “Becoming Racially Literate”. The title tells the reader that if they have made it this far in the book, they are no longer stupid about race, just unread. This, combined with the title of the book, tells a strong story about the books intended audience: people who feel stupid about race and want to fix that. On the one hand, it is clear that Fleming knows this is her audience and has written a book for precisely this group. On the other, it is likely that the title and framing has excluded potential readers. Outside of the academic environment at Tufts, I know few people who are willing to admit to knowing too little about race and many who would find the accusation of being stupid about race offensive, or at the very least off putting. It is difficult to know how many americans would have been willing to read this book with a slightly different title and framing but I imagine the number is not insignificant. In many ways this book is pop-science and if there is one thing we have learned from the attempts at public science education during the Covid-19 pandemic it is that calling the uneducated stupid is not a terribly effective way to get them to learn or change their behavior. While no change to title or structure could get Tucker Carlson to read this book, it is possible that a different title would have made this an easier sell for people like my parents.

In all, “How To Be Less Stupid About Race” left me with hope that progress can be made in dismantling global white supremacy. I hope to get many friends and family members to read this book and start them on the path to racial literacy.

References

Jeffries, J. L. (2018). Jordan peele (dir.), get out [motion picture] blumhouse productions,

2017. running time, 1 h 44 min. Springer.

Mills, C. W. (1997). The racial contract. Cornell University Press.

Valentino, N. A., & Brader, T. (2011). The sword’s other edge: Perceptions of discrimination and racial policy opinion after obama. Public Opinion Quarterly, 75(2), 201–226.

[1] I guess I’m nasty

Why Tufts

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I have chosen Tufts twice now. The first time was for a Masters in CS and, to be entirely honest, it was a choice of convenience. I had been working as a software engineer in the Boston area for 6 years at that point, and neither I nor my girlfriend were interested in moving at the time. So I looked at highly ranked CS Masters programs in the area and settled on Tufts. What I found when I got here was really amazing. The CS department had an amazing student culture. This was the before-times and you could find students from first years in CS1 to ABD Ph.D. students in Halligan Hall, the CS building, at all hours of the day and night doing homework, socializing, and educating themselves and one another. 

Spending so much time in that building listening, learning, and collaborating inspired me to be a TA in the third (and fourth) semester of my Masters. I also, somewhat on a whim, signed up for a course called Engineering/STEM Education taught constructively by Dr. Julia Gouvea. The one-two punch of teaching, and learning about education, made me realize that I really loved teaching and learning. So I decided that the next step was a Ph.D. in Education, with a focus on computer science ED, and started looking for schools. It quickly became apparent to me that Tufts would be the best place for me and the reason was Dr. Gouvea and what I was learning in her class. The class was very weird, especially for someone very used to a more standard, CS, educational style. Every week we were assigned readings and when we came to class we discussed them, and did fascinating, odd, thought provoking activities. There was no exam, and the few homework assignments we turned in were remarkably vague in specification. But every week I learned something fascinating, hard to explain, and important. It was a class about constructivist science education, in many ways, taught in a constructivist manner. It was a class about responsive teaching being taught by a professor who was constantly redesigning and rethinking the course based on what we were thinking and doing and then we read “Discovery Learning and Discovery Teaching” and I realized that the Tufts education department must be full of people wanting to teach and learn in this way. 

In a stroke of luck, I found out in the same week that Tufts would ask me to defer for a year and that my alma-mater, Cornell College, wanted me to come work as a lecturer and although I applied to other schools last December I was pretty sure that Tufts was where I wanted to be.

I am back at Tufts because I want to teach Computer Science this way. I want to find out if love (aka social caring) can help students better understand algorithms and data structures. I hope to prove that every student can discover how to code. I would love to find out what happens in a 5th grader’s head when you hand them an AI powered robot and tell them to explain its behavior. It also doesn’t hurt that at the Center for Engineering Education and Outreach I get to play with a seemingly infinite supply of legos.

This post should also show up here at some point soon: https://sites.tufts.edu/asegrad/

Theoretical justification for my curriculum

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This blog post is a placeholder of sorts. I had a late night urge to explain the theoretical justification for my “figure it out” curriculum (I need a better name for it).

There are a bunch of threads I want to bring in:

  • Responsive teaching: By not lecturing you are free to listen to the classroom and the students. To respond to changing conditions: breakthroughs, difficulties, and curricular deficits.
  • Krashen’s theories: Admittedly unscientific and discredited but I am really inspired and believe in the idea that exposure is key to learning any language. What better way to expose students then to have them constantly trying to make sense of programs during class.
  • Constructivism: Teachers cannot and should not try to pour information into students heads. What better way to have them construct their own understanding then through sensemaking activities.
  • Constructionism: Have students, alone and in groups, create and present artifacts of their work and learning. Sometimes those artifacts look like completed websites showcasing personality, artistic inclination, and humor. Other times it may simply be a class list of “the rules of python”.
  • Antiracism: This is something I need to work on. There are certainly reasons I think my curricular approach would support those underrepresented in CS but I need to do a lot more work to make sure this is the case and to improve my teaching and curriculum.

Hopefully, this will turn into some kind of theoretical framework paper.

At some point this week, I’ll write a blog post doing my best to detail student’s responses to the program 0 activity. I wish I had taken better notes of what happened.

I gave a lightning talk

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This afternoon I gave a lightning talk at Tufts. The whole event was really wonderful and it was fascinating to learn about all the different kinds of research going on here at Tufts. Every talk I saw was unique and fascinating. Since I wrote a speech, I figured I might as well share it here:

Hello, my name is David Zabner and I am a first-year STEM Education Ph.D. student here at Tufts. I have spent the last few years teaching computer science, at bootcamps, here as a TA, at Cornell College as a lecturer and for two weeks in China as a visiting faculty member at a university there.

I am going to start by describing my experience of teaching in China and I will argue that we should teach computer science in as much the opposite way from how it is taught there as possible. This is necessarily an extremist argument and I will happily admit that the most perfect way almost certainly lies somewhere in between the two extremes.

So there I am at a small Chinese university, hired to lecture about Java programming for two weeks, 10 1.5 hour classes, to about 100 students. My plan had been to review Java basics over the first three days to get students used to listening to lectures in english, and then to move on to more advanced topics in Object Oriented Design. On the first day I found out three things:
1. I needed to speak slower than I thought possible in order to be understood,
2. The classroom I was teaching in had one wall covered in answers to a multiple choice exam on english and
3. My students knew absolutely nothing about how to program in Java or for that matter in any other language.
These students, many of whom brought laptops to class, had not even installed the tools necessary to actually program on their machines. This was very surprising to me as I was teaching at the end of a semester in which they had all been studying java and I knew that they had all already taken a class in C++.

I asked around and got a basic explanation for why this was the case: the programming classes these students had taken consisted of a semester of lecture, followed by a high stakes final, which was not changed from year to year and which every student cheated on. The faculty informed me that given that every single student cheated they had the option every semester of failing all the students, and losing their jobs or allowing them all to pass. 

In short, this classroom of pedagogic hell had the following characteristics:

  1. Education based purely on lecture
  2. No chances to practice the thing being learned
  3. No feedback from classmates or instructors
  4. A single event that decides a students grade

When I got home, knowing that I would get a chance to teach an introduction to Computer Science class of my own the next year at Cornell College so I set about designing the opposite class. It had these characteristics as goals:

  1. Education without any lecture at all
  2. Constant practice of the thing being learned
  3. Regular feedback from classmates and instructors
  4. Many chances to affect a students grade

To give the students practice, feedback, and many chances, I used mastery grading and a huge quantity of optional homework, which I highly recommend, but I want to focus here on “content delivery”.

This consisted of coming to class every day with some code that I wrote and handing it over to students with a button that ran it (I did this using a wonderful platform called Replit). I then asked the students, alone, in groups, and as a class to de-code what I had given them, explain it, identify patterns and rules, and make changes. I was inspired by a poetry class I took at Cornell that focused on learning to do close reading before writing your own poetry. Once they had decoded and were comfortable with reading the day’s code, the homework consisted mainly of prompts asking them to edit and extend the code.

I found that teaching this way was spectacular! Firstly, with a little structure and support from a partner, every student in my class was able to, in an hour and a half, go from never having seen code before to confidently explaining the behavior of 100 lines of Python. By the first midterm students were able to do the same in a programming language, Golang, that they had never seen before, while under the time pressure and stress of an exam. Secondly, by untethering myself from the lectern I was able to dedicate more time to supporting students alone and in groups. This meant not only individual attention for the students, it also meant that I could focus on discovering what my students were and were not learning. Finally, students reported that they loved this method of learning and I had zero incidents of students falling asleep in class. 

I also hope that this gives my students a durable ability and confidence to learn a new programming language, a skill critical to anyone who is programming regularly and even more important for those who program rarely, as every time they sit down to do it it will probably be in a new language!

One of my goals over the next few years is to find ways of answering the question of whether or not this way of teaching works as well as I think it does. Since I am just starting to learn about how to do educational research I am still a little unsure of how I will answer that question but if you have ideas, I would love to hear them.

Thanks for your time! Any questions?