Not so much this year. Most of the conferences I usually spend my summer at — all but one, as near as I can figure — have either gone explicitly back to fully in-person meetings or included a very vague reference to “hybrid” in the CFP that didn’t offer any details or allow a submission to be flagged for online only. Of course, mask mandates and vaccination expectations are a thing of the past, so safety plans for these gatherings, which are almost never available at the time you have to respond to a CFP, will likely be limited to handwashing and hope.

I know we’re supposed to be pretending the pandemic is over, but where I live currently 1 in 17 people has Covid-19 and the excess death rate is astonishing, especially in people under 44. The BC Human Rights Commissioner (who evidently no one listens to) is flagging the equity implications of the race to unmask.

I am at a point in my career (more about this later) where I can decide to put a pause on conference presentations until such a time as I feel safer being in a room with infinite unmasked humans — I don’t know when that will be, but it isn’t soon. I manage and mitigate risk as best I can with a school-aged child, which means that to minimize the viral load we are all exposed to, I mask indoors (including at the office), we don’t eat indoors at restaurants, and our playdates are outdoor affairs. We use our whole risk budget up on our kiddo going to school, and organize our lives accordingly. If I do travel, it’s to see my mum, not to sip lukewarm coffee while a man asks me a comment-not-a-question. Conferences — which, I confess, I barely enjoy — absolutely don’t make the cut.

The truth is that travel for conferences has always disadvantaged some in the academy, whether because they are disabled, underwaged, or have caregiving responsibilities. But right now, as an active pandemic rages, the choice to return to in-person programming is even more explicitly a choice to exclude.

That said, I don’t actually expect or even necessarily want everything to be online. I know people like to meet in person, and I know that for some there are benefits. And the reality is that I’d rather have no hybrid than bad hybrid, which is a uniquely miserable experience.

But it’s a hell of a privilege to just say, “No thanks.” Neither my CV nor my presence in the discipline will be meaningfully harmed by my decision to not engage in conference travel for the forseeable, and I have three virtual keynotes lined up in place of whatever conferencing I might have done instead. Which isn’t to say I won’t miss out — I will. On collegial conversations, on engaging with critical, in the moment interventions, on those kinds of special collections and book projects that seem to spring to life at conferences. I’ll miss out on all of that. But I will be okay.

It’s important to be honest, however: we can’t pretend that moving away from online conferencing doesn’t contract the available opportunities for many people, and, depending on the discipline, may erase them entirely. This raises a question, though, for all of us in the academy:

What’s The Plan?

I am asking this non-snarkily of all who are attending conferences with many missing colleagues, but more significantly and earnestly about those who sit on hiring committees, or performance review committees, or tenure and promotion committees? As conferencing opportunities dry up for members of our community, how are we working to actively shift our expectations of what a productive academic summer looks like?

I think we have to be able to talk about this, because silences in the university are a breeding ground for inequity. When I see troubling language around the return to on-campus events and a defensive rejection of online options, I worry about all the unsaid assumptions and who will be unmoored by them. If we remember that the academy’s default mode is ableist, it can help us to work harder to fight against it and offer other options. But if we don’t remember that, if we just reconnect with old ways of doing things because we’ve missed them without looking around to see who isn’t in the room, we will exclude more and more of our colleagues.

So how will you advocate for those colleagues when you hire or review or adjudicate? How will you remind people that voices in the room are missing? Where will your equity lens be when someone asks why a candidate seems to have a gap in their CV, or questions how they mobilize their research? How will you respond when someone circulates a job posting at a conference or flags a promising candidate from an in-person event? Will you be a voice for the folks who aren’t in the room?

For me, the biggest change in my practice is that I am committing to posting all my speaking notes and slidedecks here, going forward. It was always a good practice that I neglected to get around to; it seems to me that it’s now urgent. I hope others will consider doing the same.

But mostly, I hope we keep noticing who isn’t in the room and finding ways to mitigate those absences and make visible the hidden opportunities of the conference space. The second we stop, we’ve already done irreparable harm.

Thank you for the invitation to speak today. For our team, this is wonderful timing, as we’ve just come off our eight-week Digital Detox program, which this year focused on artificial intelligence in education. The Learning Technologies and Innovation team at TRU pride ourselves on not only providing excellent technical and pedagogical supports to our faculty here, but also leading conversations about ethical, care-centred, and accessible approaches to teaching and learning with technology across the sector within and beyond BC. Indeed, we’ve had participation in our Digital Detox and other programming from faculty and staff at almost every institution present here.

I think that we can’t talk meaningfully about Generative AI in our institutions without three key concepts at the centre:

• Algorithmic bias: the idea that datasets already include the same biases as exist in society. 
• Automation bias: the tech utopian imaginary that machine-led decisions are inherently less biased than human-led decisions.
• Accountability gap: the distance between the entity making the decision and the human (hopefully) responsible for it.

There is no good data to suggest that machine-led decisions are more equitable, but there’s lots to suggest we believe it to be. As we approach discussion of Generative AI in our communities, these three concepts are central – especially how we take accountability (or fail to) for the work produced by AI.

How do we intend to use Generative AI? All users (students, researchers, writers) need more education about what generative AI can and cannot do. Remember that when it doesn’t “know,” it fabricates. (A really fun way to test this out is to ask one of these services about your own career and publication record.) But it’s important to note that the potential for misinformation is very high. Consider last week’s social media storm because a right-wing agitator generated a response from ChatGPT that asserted Pierre Polievre’s wife was a drug company executive. When that story couldn’t be corroborated, but ChatGPT repeatedly offered “citations” to prove its accuracy, conspiracy theorists asserted that the story had been scrubbed from reputable news sites. 

We have an opportunity to get ahead of how our communities understand generative AI. If we are thinking about training future citizens, this work is critical and urgent.

You invited me here today, though, to talk about academic integrity, and the truth is that widespread social adoption of Generative AI dramatically changes the term “academic integrity.” Machine learning data sets are built on scraped data used either non-consensually or with a limited understanding of informed consent. Generative AI does not “cite” its sources (indeed, it actively suppresses them and jealously guards the contents of the data set). Dr. Sarah Eaton calls this the “postplagiarism” moment, and this suggests we need to reimagine what we mean by plagiarism as these tools become acceptable components of working life.

We also need to consider our values and ethics when we look at the kinds of labour practices that have been accepted at the cost of doing business in bringing tools like ChatGPT to market. The reason ChatGPT is less overtly racist than GPT-3 was is because of underpaid, traumatized labour in the Global South, exposed to the worst the internet has to offer in the name of sanitizing the data sets for the Global North. Where do institutional values of equity and decolonization fit here?

This isn’t an argument for a ban; it’s an argument for urgent conversations about what “integrity” means.

I beg you not to pursue an arms race here. If I say nothing else to you today, I want to say that. Just as Turnitin begat contract cheating and created a massive new set of problems and risks, there is no detector tool that will resolve these questions or enable us to ignore Generative AI in our teaching, learning, and research practice.

It’s about assessment integrity, not academic integrity. This is a moment for reassessing assessment and for considering how we can centre process, and not only the final product, in our assessment practice if we really wish to evaluate learning. It’s a good time for asking where and when essays are the most appropriate assessment and devoting time to writing instruction when we say it’s valuable. And it’s a time for frank conversations about what is and is not possible to achieve at scale.

This is a rare opportunity to start from a position of radical transparency, not fear. Rather than revising academic integrity policies, I think we need to establish disclosure policies. Outline the expectations of disclosing AI use for faculty, staff, and students. We talk about “cultures” of academic integrity in our institutions, but will we hold faculty, staff, policy writers, and in-house communications to the same standard as we hold students? Is it acceptable for faculty to use AI to write assessments or to draft syllabi, for example, and will disclosure be required?

We can look to Nature and other Springer Journals for policy direction here. Nature’s policy is two-fold: an AI can not be an author, because it cannot be held accountable; and anyone using AI in a research or writing capacity must disclose in acknowledgements, methodology, or introduction. We also need to consider the data privacy implications of using AI in the classroom, especially as faculty experiment with these tools. I think laying out what ethical use of AI looks like in our institutions needs to come first, before deciding how AI integrates with academic integrity policy and practice.

Living and learning ethically alongside AI will not be seamless or easy, but it begins with understanding how definitions of academic integrity will necessarily shift with competing understandings of intellectual property, plagiarism, and ownership. And it begins with transparency and an honest conversation about institutional values, ethics, and integrity.

Thank you so much for your time today

Fight for the things you care about, but do it in a way that will lead people to join you.

Ruth Bader Ginsburg

I am no massive RBG fangirl — how much can you love a representative of carceral state power? — but this really speaks to me, because it’s how I try to do my job. It’s also how I think the job has to be done: higher education needs a culture shift in how we think about so many things, including accessibility, integrity, and data privacy, so it’s not enough for me to fight for better practice. I need my faculty to care about these issues too, and I need them to join the fight.

It’s not an excuse for tone-policing, though, and I seem to be seeing a lot of that online lately when it comes to folks earnestly engaged in the struggle for equity in higher education. People who don’t like the message begin shared have a tendency to complain about the manner rather than the content — especially if they feel attacked by the content. It’s how we got to a place where white people are more scared of being called racist than they are of doing racist shit. Going hard over the way people ask for change is one of those super-duper red flags.

I do acknowledge there’s a certain amount of privilege in being a short, white, feminine, soft-voiced person who does this work; not every body is perceived as non-threatening. I used to get upset at not being taken seriously, but now I treat it like a superpower that allows me to have difficult conversations — and say no! — with a gentle tone. I think I have the power to be persuasive in many spaces because of this. A kind of weaponized gentleness, in the words of a close friend. It feels gross, and at the same time, our privileges are our privileges. We can’t pretend they don’t exist; we just have to mobilize them in the service of positive change.

But!

I am aware of others who share my superpower and use it for evil. The ingratiating emails to faculty. The well-funded conferences. The smiling face. The kinder, gentler surveillance technology. I’ve been increasingly aware of the way pleasantness in edtech in particular is weaponized as an entré. I know, welcome to the world of sales, Brenna — but I guess I am still naive enough to think that educational tools shouldn’t be commodified in this way.

When I get these emails forwarded to me by concerned faculty, I am always troubled by how good they are. It gives Lionel Hutz pitching the monorail vibes — it’s persuasive stuff. They use language to show they understand the difficult position of faculty and the challenge of the role, and honestly, they come off a lot like contract cheating firms: I’m your friend, I know things are hard, I can fix this. The problem is that it’s not academic integrity that hangs in the balance but student data. You can’t un-ring that bell.

Anyway, it’s all got me thinking about how we lead people and how we share our message, and then need to be more persuasive and more honest (because we can’t be more slick) than the kind faces of EdTech’s bullshit. Centring the fight — the values! the reasons! the goals! — and telling the story in a way that makes people want to join the fight. I have to believe that’s the best remedy.

Although the assholes are clearly winning at the moment. So who knows.

Increasingly, it seems like both educational technology companies and our own administrators are interested in outsourcing the judgement part of our teaching to algorithms. I have written about these algorithms before, and my most significant concern with them is this notion of outsourcing something which is, for better or for worse, a bit part of what it means to teach — at least insofar as evaluation and grading goes.

It’s intuitively clear to most of us, I think, that what we use to train an algorithm matters, and that biased content in will result in biased content out. But recent research into algorithmic recommendation suggests that the mechanism itself, not simply the content used for training it, can be the source of bias. This is especially dangerous in a culture that pretends at the neutrality of algorithmic tools for it own convenience.

Perhaps the greatest source of harm is that the illusion of neutrality algorithms have can be exploited in attempts to roll back protections against discrimination. Appeals to the neutrality of algorithms as a cover for discriminatory outcomes has become a fairly common trope.

Catherine Stinson, Algorithms Are Not Neutral

The truth is that I think a lot of learning analytics are pointless but hopefully largely harmless at the level of collection (I’m talking, like, who clicked where in the Learning Management System stuff), but I’m still troubled that we hand so much data over to faculty without any kind of training on how to make use of it. I give this maybe-trivial example all the time, but if I can track when my students do their homework, and I have an in-built bias for morning people (because of our inherent superiority, obviously), how do I ensure that doesn’t impact my evaluations — and is anyone checking?

But that becomes much less trivial when what is given to faculty moves out of the realm of data and into the realm of judgement. For example, some tools profess to tell us what kind of a student someone is based on this kind of data and some kind of secret sauce algorithm. So we get told the student who submits all their assignments one minute before the deadline is in trouble or in need of some kind of intervention when what they maybe just are is busy.

Of course, the ideal is for us to know our students, so we know when someone’s behaviour changes significantly or what pressures are weighing on them. But of course, most of us don’t work at institutions where that is possible, because we design universities not for learning, but for scale. 300-student lectures and precarious faculty teaching eight sections across three universities to make ends meet. These are not the conditions where it is possible to know our learners. And so, instead of fixing the real problems, our senior leaders are drawn to shiny technological solutions.

Let’s outsource judgement.

I don’t love any kind of learning analytics, but I do see a world of difference between giving faculty the means to micromanage or microanalyze student behaviour — an ill-advised zero-sum game — and giving faculty a label on a student, whether that label is “at risk” or “strong collaborator” or “solitary worker.” I worry that once that label is heard, it’s hard to un-hear.

And then there’s the question of where those labels go. It would be one thing if they were course-specific, but what if that label gets to the next class you are taking before you do? Listen, in elementary school, a teacher read the roll and looked directly at me and said, “Oh no. Not another little Gray.” My big brother’s performance as a student prejudiced that teacher about how I would be in her class. (Sorry to throw you under the bus, Simon.) As it was, she was wrong about who I would be as student in a different course, but we never got along after that. Indeed, her preconception reframed who I was and how I behaved. How do algorithmically-defined labels on students circumscribe them any less than that?

And of course, as more enterprise-level tools masquerade as teaching and learning tools, we should ask if this data captured by Microsoft and LinkedIn and these other corporate-first products we’ve invited into our classrooms is going to follow our students into their working lives. And whether they have the right to make mistakes, to start again, to fail — all things we’re supposed to make room for as educators. And when all else fails, do these tools give students the right to be forgotten?

I always caution faculty that we should be mindful of what data we ask for, because once we have it, we become responsible for it and for its ethical use. When in doubt, don’t look at it. And I think that’s doubly true of the algorithmic judgements that are increasingly sold to us as some kind of neutral truth. The algorithm ain’t neutral, and neither is the judgement.

Just don’t look. It’s not radical, it’s not transformative, and it’s not the whole answer. But it is a start. When in doubt about what you will do with the information and how you will make ethical use of it, just don’t look.

I am going to start this post by pointing you to someone brighter, smarter, and more coherent today, because Tim Fawns theorizes everything I am about to rant about in his thoughtful “An Entangled Pedagogy.” Honestly, you can read Tim and skip the rest of what I have to offer here.

But if the ravings of a social media addict are more your speed, please, pull up a chair.

Okay, so the conference scene has been wild these past couple of weeks, if by wild you mean the silent nightmare of watching people talk about accessibility in spaces that are violently inaccessible to disabled people or really anyone who isn’t in denial about the pandemic we are living through, but Ann Gagné has you covered on that analysis. But it was also wild because there were a bunch of overlapping teaching and learning discussions happening, and in so many of them — at least the part of the discourse that filtered onto my corner of Twitter, anyway — the idea of “pedagogy before technology” kept coming up, as in we must approach teaching and learning from a pedagogy-first perspective (as opposed to, presumably, a technology-first perspective, which for the record no one is suggesting).

This is one of those phrases in education that we are encouraged to repeat without unpacking. It is a very popular concept in teaching and learning centres, especially those without a particular interest in the digital, because it asserts that true pedagogy is this thinking that happens separate from and in advance of our engagement with the sullying influence of technology.

It is not a phrase I have a great deal of respect for, not least because it seems to me that at least thirty percent of the time I encounter it, the person saying it acts like they are sharing some shattering new insight. I think part of the appeal of this phrase is as a rallying cry for folks who think a certain kind of way about education to find each other. Signals and signposts are necessary, but —

The Wikipedia definition of a thought-terminating cliché is actually excellent:

A thought-terminating cliché (also known as a semantic stop-sign, a thought-stopper, bumper sticker logic, or cliché thinking) is a form of loaded language, often passing as folk wisdom, intended to end an argument and quell cognitive dissonance.^{[1][2][3][4][5]} Its function is to stop an argument from proceeding further, ending the debate with a cliché rather than a point.^[2] Some such clichés are not inherently terminating; they only become so when used to intentionally dismiss dissent or justify fallacious logic.^[6]

https://en.wikipedia.org/wiki/Thought-terminating_clich%C3%A9

We are supposed to accept “pedagogy before technology” because we are supposed to accept prima facie that pedagogy is good and technology is less good and that the two operate in separate realms. It ends an argument I’m not interested in ending: namely, it defines a supposed hierarchy in how we think about the arrangement of our thinking about teaching. But if we think about it for even a second, surely it’s obviously untrue.

Technology shapes my pedagogical choices every single moment. If I walk into a classroom and there’s a whiteboard — or more frighteningly, if I’m expecting a white board and there isn’t one — this shapes my pedagogy. If I arrive in class to find that not one student has brought paper or pens to class, this shapes my pedagogy. These technologies are ubiquitous and non-digital, so we don’t think of them as technologies, but they impact our teaching and learning in myriad ways. It’s asinine to pretend otherwise.

I’m not being intentionally obtuse here. I get the intention of the phrase, that the technology we have access to shouldn’t drive our pedagogical choices. I understand that premise: I just don’t buy it. To me, the two are so deeply intertwined and the only reason to hold up one over the other is ideology.

The truth is, as Tim points out in the article linked above, when we assert pedagogy first, even as we do it to eschew a kind of technological determinism, we are suggesting that technologies are neutral, waiting to be folded into any pedagogy. That’s simply untrue, but it’s also dangerous: imagining that technology is unproblematically being led by pedagogy because we will it to be so is denying the power of algorithm and analytic to impact and shape practice. If you are thinking critically about the technological tools you are using in your classroom, you can’t put it last in your analysis; you need to be clear-eyed about the changes the technology makes to your teaching. Because it does.

The LMS circumscribes your choices; the learning curve on any tool is access-limiting; the choice of video or audio or text has an impact on the user. Considerations of these factors happens alongside pedagogical planning, not as an afterthought — if we’re doing it right. There’s simply no way to engage in critical digital pedagogy if you always already think that technology is on the backfoot.

“Pedagogy before technology” comes from a good place, but it lacks nuance and it also lacks faith in our colleagues to be able to have complicated conversations. It’s not “pedagogy before technology.” It’s “technological considerations need to be made alongside pedagogical ones because ultimately the learner experience cannot extricate one from another,” but then I’ve never found myself accused of constructing my own thought-terminating clichés.

Two. Kids these days are [fill in the blank here]. No they aren’t, you just got old, and if we’re lucky that’s what happens to the best of us. It would be nice if people tasked with educating the next generation didn’t fall into the same thought-terminating clichés as the last generation of garbage teachers adopted, but here we are.

Three. I don’t have time to teach [writing, grammar, study skills, etc]; my content is very important. If you evaluate for it, you have to teach it. That’s the rule. Otherwise, your course becomes an opaque mess of a game of “guess what’s in the professor’s head” and that’s not actually learning. Stop it. Oh, and when you outsource this work to your colleagues, recognize the specific skillset you are tapping into and stop treating so-called service departments like they are departments filled with your own literal servants.

Four. Accommodations are a workload issue / impinge on my academic freedom. I have empathy for the former — institutions download work onto those least able to resist, it’s true, and I don’t know of a single Accessibility Services office that would say, “Hey, we have enough money to do everything we need to do.” But access is a value and a commitment, and often by the time we get to the point of accommodations, there are changes in practice that would benefit the whole classroom (and, often, the instructor). The idea that accommodations are an academic freedom issue is a load of horseshit.

Five. Ranting about my students is a necessary catharsis. That’s an argument for talking about your rough day with your close friend or spouse, not for your anonymous social media account with 15.7M followers where you post excerpts of student writing, you absolute soggy Rice Krispie of a person. Students are humans who you should treat with respect. Why is that controversial.

It makes me think about a lot of EdTech, honestly — these feature rollouts for more and more and more and more surveillance that no one actually asked for. Great, I get to know what time my learner usually opens their word processor. I never asked for this, and no one is teaching me how to use this data. Sure, it’s not a VR helmet that kills you, but it opens the door to potential bias. And again, much like the opportunity (?) to die inside a game, it’s not something anyone actually asked for.

Meta just got legs and this guy wants to take your head off.

Virtual Reality has been the “next big thing” in Educational Technologies for as long as I’ve been in the discipline and longer, and most of it is really bad. Like, the number of times I have seen the most straight-ahead lecture replicated, but I don’t know, we’re in space now, and we’re all supposed to pretend its innovative. If I could VR my way into a Magic-School-Bus-style fieldtrip, you could probably talk me into it. But investing in a VR headset so some guy can lecture at me, but from a cloud? Nah.

It feels like the educational equivalent of making fetch happen.

I actually kind of want to channel my inner Audrey Watters here to reflect on how positively Skinnerian this whole thing sounds. Someone somewhere is building a VR headset that shocks a kid if they close their eyes for too long. You know I’m right.

Anyway, while Joe Oculus there was explaining why I should pay him money to very literally explode my brain for me, I got to thinking about how much power we give technology entrepreneurs in education when they are walking around saying ideas like this with their whole chests. Like. I don’t understand why we don’t look at stories like this and laugh these folks out of the room when they offer us engagement clickers that also take attendance (I didn’t ask) or a productivity score for every student (I didn’t ask) or an AI-graded essay tool (why am I asking students to write an essay I don’t want to read, exactly?). Over and over again, the pitches show that there are never any educators in the room.

Get these doofuses out of the room where we’re trying to do the teaching and learning.

No one wants to die in real life if they die in the game. Literally no one asked for this.

It’s about community. I make three podcasts: two weekly shows and one longer form project that I am preparing for peer review. The main thing I have learned from my weekly shows (one of which I produce and one of which I strictly play the role of lazy talent on) is that community makes or breaks a show, and that a listenership doesn’t have to be large to be committed and vocal. It’s really rewarding to learn about the relationships that develop around the show you make — not just the oft-cited parasocial relationships to you as the host, but real, lasting friendships between people who wouldn’t have met without that shared space. And in the first year of the pandemic, I was delighted to see how our campus support podcast created a place of virtual community. I have always thought it was the intimacy of the podcast form that leads to this community-building; we listen to podcasts in our domestic spaces, often alone, often with a voice in our ears through earbuds. It’s certainly why I think Chuck and Josh from Stuff You Should Know are my actual friends.

Embrace a broad, diverse audience by being accessible first. Too often with academic projects, accessibility concerns are, at best, an afterthought. Part of building community is making your work usable for as many people as possible. For my work podcast, I insist on the show being day-one accessible, meaning that I make sure we provide transcripts in two modalities, a downloadable mp3 in addition to streaming on the web and on several platforms, and an accessible podcast website. I’m not perfect in these spaces and I’m always learning about how to improve our workflows to be mindful of the different access needs that exist for disabled listeners, rural and remote listeners, and other folks who may have specific needs. There are resources out there that you can use to make steps towards improving accessibility. With effort, podcasting really can be a democratic medium.

Making mistakes in public is okay. Often when I talk to faculty, they are concerned about the aspect of putting work out into the open and the criticism that may come from making mistakes in an open space. I have to say, as someone who makes a lot of mistakes in public, this hasn’t been my experience. Hand-in-hand with the community-building aspect of podcasting is the fact that the audience is looking to you to build something good and something meaningful. I have experienced my fair share of corrections and complaints since embarking on a podcasting practice, but almost entirely as an experience of calling in, not calling out. By that I mean that criticism and corrections have been generous and thoughtful, part of an act of solidarity and community to create the best possible product. I am grateful for what podcast audiences have taught me about inclusive language, in particular, and I don’t think I would be as careful with my words as I strive to be without that input. Don’t be afraid of making mistakes. Be afraid of not learning from them.

You have to get your hands dirty. In the podcasting course, there’s a direct corollary between people who get in and do the recording and editing assignments right away and those who finish the course. Audio is new to the majority of people who embark on the course, and there’s a lot of trepidation around committing the voice to a recording and then actually trying to manipulate it. To a one, participants are surprised and delighted to find out (at least in their self-report) that editing audio isn’t so hard, once you start playing with it, but that procrastinating on playing with it makes everything harder. We put the editing in Week 2 for this reason. In my experience, this is true of learning any new tool: at some point, you have to stop reading and thinking and start mucking in. Probably true for life, too.

You can DIY the whole thing. I am increasingly getting solicitations from sketchy folks claiming they will make “a podcast” of my articles or book chapters, so one of the most important things I think we do with the podcast course is showing folks how straightforward it is to self-host a podcast (especially because have great tools!). We support using WordPress with the Seriously Simple Podcasting plug-in.

I love supporting podcasting and building our capacity in this area — it’s fun and rewarding, and I still have so much to learn in this space.

And of course I am. I was a former drama kid who paid her way through her undergraduate degree doing cartoon voice work. I also had drunk the flavor-ade of every great teaching and learning movie and un-ironically hankered after my own oh-captain-my-captain moment à la Dead Poets Society. I loved literature but I also really loved it when people listened to me talk about literature. Lecturing fed my ego in a really satisfying way because I knew I was good at it: students laughed at my jokes, stayed after class to chat, and wrote me notes at the end of the term about how much fun they had. I loved it.

I was also very, very aware that lecturing wasn’t necessarily teaching. I was trained from my time as an undergraduate peer educator in meaningful active learning through the Centre for Initiative in Education at Carleton University. I knew what worked in the classroom to reinforce learning — and I knew the research that backed it up — and I knew that the lecture was not ideal. I could also share a million reasons why I chose lecturing: as a young graduate student, it felt like a more authoritative move; in the lecture theatre and with my student population, it felt expected; as someone learning how to convey information effectively, it felt safer to do all the delivery myself. But also, it has to be said: I loved lecturing. I still do.

Clearly, I don’t think my lectures were bad, as lectures go. There was lots of space for discussion and there was lots of interaction. But there wasn’t much opportunity to work with the material, and the implicit structure of the class made me the central focus. The reality is that I think the ego is part of the story of the persistence of the lecture as a form in spite of what we know to be the benefits of more active strategies. This was so clear to me in the pandemic closure period, when it was so clear that the video lecture was both not working and impossible to overthrow.

Let me be clear: I know there are gifted lecturers and I know that there are learners who prefer the form. But when a practice persists in the face of so much criticism, I think a lot about why. And certainly there are structural pressures — especially over-sized classes and precarious faculty — that invite the lecture as a kind of teaching-at-scale. I get all of that.

For me, putting aside the lecture in favour of an active, learning-centred, task-based classroom required putting my ego aside, and I was driven to it by nine years spent primarily teaching academic writing. You cannot lecture in an academic writing class and pretend anyone is learning anything: you have to get hands-on with the course material. You have to write and rewrite and edit and draft and mess up and laugh and fix your mistakes and write some more. You just do. And when I realized how much more learning — demonstrably more, because that’s how skills acquisition works — was happening in my composition classes than in my literature classes, well, I had to admit the lecture had to take a backseat.

Since that time, I’ve learned that the smartest person in the room is the room itself (concept and framing thanks to pal and co-conspirator David N. Wright), and that it’s more useful to learn how to acquire knowledge than to absorb it from a so-called sage on the stage. I like students checking my claims on their devices and I like being able to ask them to use those same devices to answer lower-order questions so we can work together on analysis, instead. There are lots of fine applications for lectures, for sure. For example, I actually prefer a lecture-style keynote, but there’s a very concrete reason why that is: I want to have a passive learning experience, because I need that from my professional development at this stage of the never-ending pandemic. I’m making a trade-off and I know it. But new knowledge acquisition often requires opportunities to practice and manhandle that knowledge, and there are few opportunities for that in the bog-standard, traditional lecture format.

But lectures are what they are, and not everyone is like me — an admitted egoist — about them. I can acknowledge that. And in fact, when I think about the trouble with the teacherly ego, it’s not really lectures I think about. I’m much more curious about how the teacherly ego plays into our relationship to information, data, and privacy.

I wonder a lot about why we feel entitled, as faculty, to the information we can access on the emergent edtech platforms. It’s a question I ask a lot in my faculty support work — though I choose less triggering language than “why do you feel entitled to this information?” I think about things like demanding (rather than gently inviting) cameras to be on in a video session, which interjects the professor into the personal home space in a way that feels, well, entitled. I think about why we want to know that a student handed in an essay 2 minutes or 2 hours or 2 days or 2 weeks before the deadline, and what conclusions we are drawing from that information that might be based on our own assumptions and biases. Some tools will offer you information about your students like what time they usually work on their assignments or how much time they spend in the word processor — again, why should we have access to that information? All of these tools are designed to centre the instructor’s assumptions about learning and not the needs of the learner. And they are all predicated on the sentiment that it is our right as faculty to know. It all feels a bit… ego-centric to me.

Indeed, I think academic ego-stroking is a general force for ill in the university. It must be at least part of what is motivating responses to fraudulent journals, whose solicitous emails always make me guffaw. (“Dr. Gray, we know you to be a globally-recognized, world-renowned leader in the field of TITLE OF CONFERENCE PAPER FROM TEN YEARS AGO HERE and as such we would be humbled if you shared your genius with the world and submitted to our journal FOR THE LOW PRICE OF A GAZILLION DOLLARS.”) I am a world-renowned expert in literally nothing, but there’s always a split-second between seeing that email and processing that it is spam where a little part I am not super proud of goes, “Finally, I am being recognized.”

Confronting my relationship to the lecture made me acutely aware of how ego is the achilles heel of my own teaching, and it makes me attuned to how it functions in the broader post-secondary context. When we talk about student-centred learning, maybe we need to talk a little bit more about what sometimes gets in the way: the teacherly ego.

Not if you read that Times Higher Ed piece, which was penned by an edtech executive who once travelled in pedagogical circles. The thesis of the article seems to be that about five people in the world — conveniently, established names in the field of faculty support and conveniently, white-presenting — are doing ungrading right and everyone else is doing it wrong.

I mean, it’s a patently absurd thesis and I don’t need to spend time debunking it. Twitter did a good enough job in the intervening days. Learning is contextual and learners need greater or lesser amounts of structure depending on all sorts of factors (read the brilliant Karen Costa for more on this). And educators range dramatically in the freedom around assessment and grades that they can access. There are many ways to challenge the supremacy of the numerical grade.

But I instead want to look at the impulse underlying the article itself, and why it’s unhelpful: the impulse to assert that some people teach “right” and others teach “wrong” in a postsecondary system where almost none of us were actually trained to teach at all is prima facie alienating. Working in faculty support, even more than my life as a classroom teacher, has taught me that the number one thing I can do to facilitate learning is to be a safe place to utter the phrase, “I don’t know.” We implicitly understand this in the classroom: foregrounding the impossibility of or rampant failure to achieve a task is wildly counterproductive. No one likes to feel mocked or derided, but this dynamic can be even more acute when working with faculty: while students might at least reasonably expect to not know something, many faculty are acculturated to really fear admitting that.

So when I read an article that purports to tell the story of a better way to approach learning that is entirely framed by the premise that only a small handful of people are even in the ballpark of worthiness to use the term “ungrading” (which is a term and concept much older than that article suggests), I wonder who it is trying to reach.

Many pedagogical innovations in their early moments feel, and sometimes are, impossible for so many of use, due to curricular requirements, relative power or role, department culture, institutional policy, or a zillion other factors. No one is swayed or supported by someone descending from the mount to say, “If you don’t ascribe to my vision, you’re doing it wrong.” Turning it into a zero sum game means that the potential benefits of critically reflecting on learning or accurate self-appraisal or increased empowerment — all of which can be achieved with shades of ungrading, or with babysteps towards any number of innovative practices — are abandoned.

As so often occurs, perfect is the enemy of the good.

Anyone purporting to help faculty revise practice needs to recognize the importance of words like “support” and “development” in our titles. They also need to recognize the very real structural issues that stand between well-meaning faculty and ideal practice. And they need to recognize that they may only be seeing one part of the picture. (Like, as an example, if the only positive examples you can think of are white people, you might possibly not have the broadest possible perspective on the topic.)

It’s not only our learners we meet where they’re at. We meet our colleagues there, too.

Or, I guess, we don’t. The edtech executive in question works for Course Hero, which as an organization has shown much disdain for faculty: there’s no collegial process for getting your work taken down once Course Hero has it (unless you find the DCMA a collegial process) and they flatly refuse to engage with their critiques in any meaningful way. I appreciated Matt Crosslin’s outline of those issues. The approach of this article seems similarly disinterested in actually engaging with faculty or any meaningful effort to revise practice.

I don’t want to tell people they’re wrong: I want to be the safe place to disclose the real difficulties many of us experience in the classroom so that we can solve the problem together. I don’t respond well to scolding. Does anyone? I ask this after a fight to retrieve two pairs of socks from the mouth of the family dog. Scolding didn’t work there, either. A chewy milk bone though? Magic.

Am I asking you to be the chewy milk bone you wish to see in the world? Maybe. I think mostly I’m reminding all of us who hope to revise, renovate, or burn down the institution that we cannot do it without friends, and we cannot do it from a limited scope.

Holier than thou feels great sometimes, but it doesn’t move the needle in the places where it matters.

There’s a giant poster over my desk in my home office that reads, “Do the work.” The work is meeting people and conversation and iteration and gentle nudges. Save “you’re doing it wrong” for the people and policies and systems that are causing real harm — not the well-meaning folks aligned to your cause and trying hard within the same unforgiving system.

Be the chewy milk bone. Do the work. And don’t tell me you’re the only one who sees clearly, especially if you’re demonstrating a studied attention to whiteness. I simply will not believe you.