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“We call on the field to recognize that applications that aim to believably mimic humans bring risk of extreme harms,” she co-wrote in 2021. “Work on synthetic human behavior is a bright line in ethical Al development, where downstream effects need to be understood and modeled in order to block foreseeable harm to society and different social groups.”

chatbots that we easily confuse with humans are not just cute or unnerving. They sit on a bright line. Obscuring that line and blurring — bullshitting — what’s human and what’s not has the power to unravel society

She began learning from, then amplifying, Black women’s voices critiquing AI, including those of Joy Buolamwini (she founded the Algorithmic Justice League while at MIT) and Meredith Broussard (the author of Artificial Unintelligence: How Computers Misunderstand the World). She also started publicly challenging the term artificial intelligence, a sure way, as a middle-aged woman in a male field, to get yourself branded as a scold. The idea of intelligence has a white-supremacist history. And besides, “intelligent” according to what definition? The three-stratum definition? Howard Gardner’s theory of multiple intelligences? The Stanford-Binet Intelligence Scale? Bender remains particularly fond of an alternative name for AI proposed by a former member of the Italian Parliament: “Systematic Approaches to Learning Algorithms and Machine Inferences.” Then people would be out here asking, “Is this SALAMI intelligent? Can this SALAMI write a novel? Does this SALAMI deserve human rights?”

Tech-makers assuming their reality accurately represents the world create many different kinds of problems. The training data for ChatGPT is believed to include most or all of Wikipedia, pages linked from Reddit, a billion words grabbed off the internet. (It can’t include, say, e-book copies of everything in the Stanford library, as books are protected by copyright law.) The humans who wrote all those words online overrepresent white people. They overrepresent men. They overrepresent wealth. What’s more, we all know what’s out there on the internet: vast swamps of racism, sexism, homophobia, Islamophobia, neo-Nazism.

One fired Google employee told me succeeding in tech depends on “keeping your mouth shut to everything that’s disturbing.” Otherwise, you’re a problem. “Almost every senior woman in computer science has that rep. Now when I hear, ‘Oh, she’s a problem,’ I’m like, Oh, so you’re saying she’s a senior woman?”

“We haven’t learned to stop imagining the mind behind it.”

In March 2021, Bender published “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” with three co-authors. After the paper came out, two of the co-authors, both women, lost their jobs as co-leads of Google’s Ethical AI team.

“On the Dangers of Stochastic Parrots” is not a write-up of original research. It’s a synthesis of LLM critiques that Bender and others have made: of the biases encoded in the models; the near impossibility of studying what’s in the training data, given the fact they can contain billions of words; the costs to the climate; the problems with building technology that freezes language in time and thus locks in the problems of the past. Google initially approved the paper, a requirement for publications by staff. Then it rescinded approval and told the Google co-authors to take their names off it. Several did, but Google AI ethicist Timnit Gebru refused. Her colleague (and Bender’s former student) Margaret Mitchell changed her name on the paper to Shmargaret Shmitchell, a move intended, she said, to “index an event and a group of authors who got erased.” Gebru lost her job in December 2020, Mitchell in February 2021. Both women believe this was retaliation and brought their stories to the press. The stochastic-parrot paper went viral, at least by academic standards. The phrase stochastic parrot entered the tech lexicon.

Tech execs loved it. Programmers related to it. OpenAI CEO Sam Altman was in many ways the perfect audience: a self-identified hyperrationalist so acculturated to the tech bubble that he seemed to have lost perspective on the world beyond. “I think the nuclear mutually assured destruction rollout was bad for a bunch of reasons,” he said on AngelList Confidential in November. He’s also a believer in the so-called singularity, the tech fantasy that, at some point soon, the distinction between human and machine will collapse. “We are a few years in,” Altman wrote of the cyborg merge in 2017. “It’s probably going to happen sooner than most people think. Hardware is improving at an exponential rate … and the number of smart people working on AI is increasing exponentially as well. Double exponential functions get away from you fast.” On December 4, four days after ChatGPT was released, Altman tweeted, “i am a stochastic parrot, and so r u.”

“This is one of the moves that turn up ridiculously frequently. People saying, ‘Well, people are just stochastic parrots,’” she said. “People want to believe so badly that these language models are actually intelligent that they’re willing to take themselves as a point of reference and devalue that to match what the language model can do.”

The membrane between academia and industry is permeable almost everywhere; the membrane is practically nonexistent at Stanford, a school so entangled with tech that it can be hard to tell where the university ends and the businesses begin.

“No wonder that men who live day in and day out with machines to which they believe themselves to have become slaves begin to believe that men are machines.”

what’s tenure for, after all?

LLMs are tools made by specific people — people who stand to accumulate huge amounts of money and power, people enamored with the idea of the singularity. The project threatens to blow up what is human in a species sense. But it’s not about humility. It’s not about all of us. It’s not about becoming a humble creation among the world’s others. It’s about some of us — let’s be honest — becoming a superspecies. This is the darkness that awaits when we lose a firm boundary around the idea that humans, all of us, are equally worthy as is.

The AI dream is “governed by the perfectibility thesis, and that’s where we see a fascist form of the human.”

“Why are you trying to trick people into thinking that it really feels sad that you lost your phone?”

This generation, the first not to have known life before the internet, has acquired a battery of skills in navigating digital environments, but it isn’t clear how well those skills line up with the ones traditionally accredited by universities.

From the perspective of students raised in a digital culture, the anti-plagiarism taboo no doubt seems to be just one more academic hang-up, a weird injunction to take perfectly adequate information, break it into pieces and refashion it. Students who pay for essays know what they are doing; others seem conscientious yet intimidated by secondary texts: presumably they won’t be able to improve on them, so why bother trying? For some years now, it’s been noticeable how many students arrive at university feeling that every interaction is a test they might fail. They are anxious. Writing seems fraught with risk, a highly complicated task that can be executed correctly or not.

Many students may like the flexibility recorded lectures give them, but the conversion of lectures into yet more digital ‘content’ further destabilises traditional conceptions of learning and writing

the evaluation forms which are now such a standard feature of campus life suggest that many students set a lot of store by the enthusiasm and care that are features of a good live lecture

the drift of universities towards a platform model, which makes it possible for students to pick up learning materials as and when it suits them. Until now, academics have resisted the push for ‘lecture capture’. It causes in-person attendance at lectures to fall dramatically, and it makes many lecturers feel like mediocre television presenters. Unions fear that extracting and storing teaching for posterity threatens lecturers’ job security and weakens the power of strikes. Thanks to Covid, this may already have happened.

In the utopia sold by the EdTech industry (the companies that provide platforms and software for online learning), pupils are guided and assessed continuously. When one task is completed correctly, the next begins, as in a computer game; meanwhile the platform providers are scraping and analysing data from the actions of millions of children. In this behaviourist set-up, teachers become more like coaches: they assist and motivate individual ‘learners’, but are no longer so important to the provision of education. And since it is no longer the sole responsibility of teachers or schools to deliver the curriculum, it becomes more centralised – the latest front in a forty-year battle to wrest control from the hands of teachers and local authorities.

an injunction against creative interpretation and writing, a deprivation that working-class children will feel at least as deeply as anyone else.

There may be very good reasons for delivering online teaching in segments, punctuated by tasks and feedback, but as Yandell observes, other ways of reading and writing are marginalised in the process. Without wishing to romanticise the lonely reader (or, for that matter, the lonely writer), something is lost when alternating periods of passivity and activity are compressed into interactivity, until eventually education becomes a continuous cybernetic loop of information and feedback. How many keystrokes or mouse-clicks before a student is told they’ve gone wrong? How many words does it take to make a mistake?

This vision of language as code may already have been a significant feature of the curriculum, but it appears to have been exacerbated by the switch to online teaching. In a journal article from August 2020, ‘Learning under Lockdown: English Teaching in the Time of Covid-19’, John Yandell notes that online classes create wholly closed worlds, where context and intertextuality disappear in favour of constant instruction. In these online environments, readingis informed not by prior reading experiences but by the toolkit that the teacher has provided, and ... is presented as occurring along a tramline of linear development. Different readings are reducible to better or worse readings: the more closely the student’s reading approximates to the already finalised teacher’s reading, the better it is. That, it would appear, is what reading with precision looks like.

Constant interaction across an interface may be a good basis for forms of learning that involve information-processing and problem-solving, where there is a right and a wrong answer. The cognitive skills that can be trained in this way are the ones computers themselves excel at: pattern recognition and computation. The worry, for anyone who cares about the humanities in particular, is about the oversimplifications required to conduct other forms of education in these ways.

Blanket surveillance replaces the need for formal assessment.

Confirming Adorno’s worst fears of the ‘primacy of practical reason’, reading is no longer dissociable from the execution of tasks. And, crucially, the ‘goals’ to be achieved through the ability to read, the ‘potential’ and ‘participation’ to be realised, are economic in nature.

since 2019, with the Treasury increasingly unhappy about the amount of student debt still sitting on the government’s balance sheet and the government resorting to ‘culture war’ at every opportunity, there has been an effort to single out degree programmes that represent ‘poor value for money’, measured in terms of graduate earnings. (For reasons best known to itself, the usually independent Institute for Fiscal Studies has been leading the way in finding correlations between degree programmes and future earnings.) Many of these programmes are in the arts and humanities, and are now habitually referred to by Tory politicians and their supporters in the media as ‘low-value degrees’.

studying the humanities may become a luxury reserved for those who can fall back on the cultural and financial advantages of their class position. (This effect has already been noticed among young people going into acting, where the results are more visible to the public than they are in academia or heritage organisations.)

given the changing class composition of the UK over the past thirty years, it’s not clear that contemporary elites have any more sympathy for the humanities than the Conservative Party does. A friend of mine recently attended an open day at a well-known London private school, and noticed that while there was a long queue to speak to the maths and science teachers, nobody was waiting to speak to the English teacher. When she asked what was going on, she was told: ‘I’m afraid parents here are very ambitious.’ Parents at such schools, where fees have tripled in real terms since the early 1980s, tend to work in financial and business services themselves, and spend their own days profitably manipulating and analysing numbers on screens. When it comes to the transmission of elite status from one generation to the next, Shakespeare or Plato no longer has the same cachet as economics or physics.

Leaving aside the strategic political use of terms such as ‘woke’ and ‘cancel culture’, it would be hard to deny that we live in an age of heightened anxiety over the words we use, in particular the labels we apply to people. This has benefits: it can help to bring discriminatory practices to light, potentially leading to institutional reform. It can also lead to fruitless, distracting public arguments, such as the one that rumbled on for weeks over Angela Rayner’s description of Conservatives as ‘scum’. More and more, words are dredged up, edited or rearranged for the purpose of harming someone. Isolated words have acquired a weightiness in contemporary politics and public argument, while on digital media snippets of text circulate without context, as if the meaning of a single sentence were perfectly contained within it, walled off from the surrounding text. The exemplary textual form in this regard is the newspaper headline or corporate slogan: a carefully curated series of words, designed to cut through the blizzard of competing information.

Visit any actual school or university today (as opposed to the imaginary ones described in the Daily Mail or the speeches of Conservative ministers) and you will find highly disciplined, hierarchical institutions, focused on metrics, performance evaluations, ‘behaviour’ and quantifiable ‘learning outcomes’.

If young people today worry about using the ‘wrong’ words, it isn’t because of the persistence of the leftist cultural power of forty years ago, but – on the contrary – because of the barrage of initiatives and technologies dedicated to reversing that power. The ideology of measurable literacy, combined with a digital net that has captured social and educational life, leaves young people ill at ease with the language they use and fearful of what might happen should they trip up.

It has become clear, as we witness the advance of Panopto, Class Dojo and the rest of the EdTech industry, that one of the great things about an old-fashioned classroom is the facilitation of unrecorded, unaudited speech, and of uninterrupted reading and writing.

The open model of participation calls into question where value is created in the education system. Gutenberg created a means to duplicate content. The social web creates the opportunity for many-to-many interactions and to add a global social layer on content creation and knowledge growth.

Whatever can be easily duplicated cannot serve as the foundation for economic value. Integration and connectedness are economic value points.

In education, content can easily be produced (it’s important but has limited economic value). Lectures also have limited value (easy to record and to duplicate). Teaching – as done in most universities – can be duplicated. Learning, on the other hand, can’t be duplicated. Learning is personal, it has to occur one learner at a time. The support needed for learners to learn is a critical value point.

Learning, however, requires a human, social element: both peer-based and through interaction with subject area experts

Content is readily duplicated, reducing its value economically. It is still critical for learning – all fields have core elements that learners must master before they can advance (research in expertise supports this notion). - Teaching can be duplicated (lectures can be recorded, Elluminate or similar webconferencing system can bring people from around the world into a class). Assisting learners in the learning process, correcting misconceptions (see Private Universe), and providing social support and brokering introductions to other people and ideas in the discipline is critical. - Accreditation is a value statement – it is required when people don’t know each other. Content was the first area of focus in open education. Teaching (i.e. MOOCs) are the second. Accreditation will be next, but, before progress can be made, profile, identity, and peer-rating systems will need to improve dramatically. The underlying trust mechanism on which accreditation is based cannot yet be duplicated in open spaces (at least, it can’t be duplicated to such a degree that people who do not know each other will trust the mediating agent of open accreditation)

The skills that are privileged and rewarded in a MOOC are similar to those that are needed to be effective in communicating with others and interacting with information online (specifically, social media and information sources like journals, databases, videos, lectures, etc.). Creative skills are the most critical. Facilitators and learners need something to “point to”. When a participant creates an insightful blog post, a video, a concept map, or other resource/artifact it generally gets attention.

Intentional diversity – not necessarily a digital skill, but the ability to self-evaluate ones network and ensure diversity of ideologies is critical when information is fragmented and is at risk of being sorted by single perspectives/ideologies.

The volume of information is very disorienting in a MOOC. For example, in CCK08, the initial flow of postings in Moodle, three weekly live sessions, Daily newsletter, and weekly readings and assignments proved to be overwhelming for many participants. Stephen and I somewhat intentionally structured the course for this disorienting experience. Deciding who to follow, which course concepts are important, and how to form sub-networks and sub-systems to assist in sensemaking are required to respond to information abundance. The process of coping and wayfinding (ontology) is as much a lesson in the learning process as mastering the content (epistemology). Learners often find it difficult to let go of the urge to master all content, read all the comments and blog posts.

e. Learning is a social trust-based process.

Patience, tolerance, suspension of judgment, and openness to other cultures and ideas are required to form social connections and negotiating misunderstandings.

An effective digital citizenry needs the skills to participate in important conversations. The growth of digital content and social networks raises the need citizens to have the technical and conceptual skills to express their ideas and engage with others in those spaces. MOOCs are a first generation testing grounds for knowledge growth in a distributed, global, digital world. Their role in developing a digital citizenry is still unclear, but democratic societies require a populace with the skills to participate in growing a society’s knowledge. As such, MOOCs, or similar open transparent learning experiences that foster the development of citizens confidence engage and create collaboratively, are important for the future of society.

DLA is explic­itly res­i­den­tial and dia­log­i­cal

Not so much a replace­ment as a sup­ple­ment to dig­i­tal human­i­ties, DLA broad­ens the scope and relo­cates the cen­ter of grav­ity of what I have referred to as the dig­i­tal human­i­ties sit­u­a­tion, the recur­ring, play­ful encounter of human­ists with tech­nol­ogy.  Instead of focus­ing on what may bet­ter be described as the com­pu­ta­tional human­i­ties (a use­ful term recently pro­posed by Lev Manovich), the dig­i­tal lib­eral arts seeks to locate dig­i­tal media squarely within the frame of the lib­eral arts, broadly con­ceived as a cur­ricu­lum, not a dis­ci­pline or even set of dis­ci­plines, and as a dis­tinc­tive mode of edu­ca­tional expe­ri­ence, not a set of received the­o­ret­i­cal con­cerns. It is a fram­ing par­tic­u­larly suited to lib­eral arts col­leges — America’s great con­tri­bu­tion to higher learn­ing — but also to uni­ver­si­ties, such as UVa, whose souls are in the lib­eral arts as well.

the idea of Coursera-style MOOCs being part of the DLA is a non-starter, although dis­trib­uted and medi­ated forms of edu­ca­tion can, and I think must, become part of the lib­eral arts experience

DLA is as con­cerned with ped­a­gogy as it is with research

focus­ing on the real use of dig­i­tal col­lec­tions (for exam­ple) as much as on their cre­ation and publication

large-language models like ChatGPT are often extolled as the cutting edge of artificial intelligence, it may sound dismissive—or at least deflating—to describe them as lossy text-compression algorithms. I do think that this perspective offers a useful corrective to the tendency to anthropomorphize large-language models

Even though large-language models often hallucinate, when they’re lucid they sound like they actually understand subjects like economic theory

The fact that ChatGPT rephrases material from the Web instead of quoting it word for word makes it seem like a student expressing ideas in her own words, rather than simply regurgitating what she’s read; it creates the illusion that ChatGPT understands the material. In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.

starting with a blurry copy of unoriginal work isn’t a good way to create original work

If and when we start seeing models producing output that’s as good as their input, then the analogy of lossy compression will no longer be applicable.

Even if it is possible to restrict large-language models from engaging in fabrication, should we use them to generate Web content? This would make sense only if our goal is to repackage information that’s already available on the Web. Some companies exist to do just that—we usually call them content mills. Perhaps the blurriness of large-language models will be useful to them, as a way of avoiding copyright infringement. Generally speaking, though, I’d say that anything that’s good for content mills is not good for people searching for information.

Having students write essays isn’t merely a way to test their grasp of the material; it gives them experience in articulating their thoughts. If students never have to write essays that we have all read before, they will never gain the skills needed to write something that we have never read.

Sometimes it’s only in the process of writing that you discover your original ideas. Some might say that the output of large-language models doesn’t look all that different from a human writer’s first draft, but, again, I think this is a superficial resemblance. Your first draft isn’t an unoriginal idea expressed clearly; it’s an original idea expressed poorly, and it is accompanied by your amorphous dissatisfaction, your awareness of the distance between what it says and what you want it to say. That’s what directs you during rewriting, and that’s one of the things lacking when you start with text generated by an A.I.

What use is there in having something that rephrases the Web?

"The current system places too great a burden on individual faculty who would, under the circumstances, appear to have perverse incentives: Pursuing these matters lowers course evaluations, takes their severely limited time away from research for promotion, and unfortunately personalizes the issue when it is not personal at all, but a violation against the university."

In the humanities, professors have found technological tools to check for blatant copying on essays, and have caught so many culprits that the practice of running papers through plagiarism-detection services has become routine at many colleges. But that software is not suited to science-class assignments.

a "studio" model of teaching

The parents paid tuition in cash

The idea that students should be working in a shell is so interesting. It never even occurred to me as a student that I shouldn't work with someone else on my homework. How else do you figure it out? I guess that is peer-to-peer teaching. Copying someone else's work and presenting it as your own is clearly wrong (and, as demonstrated above, doesn't do the student any good), but learning from the resources at hand ought to be encouraged. Afterall, struggling through homework problems in intro physics is how you learn in the first place.

Jarvis never broaches such subtleties. His is a simple world:

why not consider the possibility that the incumbents may be using the same tools, Jarvis’s revered technologies, to tell us what to think, and far more effectively than before? Internet shelf space may be infinite, but human attention is not. Cheap self-publishing marginally improves one’s chances of being heard, but nothing about this new decentralized public sphere suggests that old power structures—provided they are smart and willing to survive—will not be able to use it to their benefit

Jarvis 1.0 was all about celebrating Google, but Jarvis 2.0 has new friends in Facebook and Twitter. (An Internet intellectual always keeps up.) Jarvis 1.0 wrote that “Google’s moral of universal empowerment is the sometimes-forgotten ideal of democracy,” and argued that the company “provides the infrastructure for a culture of choice,” while its “algorithms and its business model work because Google trusts us.” Jarvis 2.0 claims that “by sharing publicly, we people challenge Google’s machines and reclaim our authority on the internet from algorithms.”

Jarvis has another reference point, another sacred telos: the equally grand and equally inexorable march of the Internet, which in his view is a technology that generates its own norms, its own laws, its own people. (He likes to speak of “us, people of the Net.”) For the Technology Man, the Internet is the glue that holds our globalized world together and the divine numen that fills it with meaning. If you thought that ethnocentrism was bad, brace yourself for Internet-centrism

Why worry about the growing dominance of such digitalism? The reason should be obvious. As Internet-driven explanations crowd out everything else, our entire vocabulary is being re-defined. Collaboration is re-interpreted through the prism of Wikipedia; communication, through the prism of social networking; democratic participation, through the prism of crowd-sourcing; cosmopolitanism, through the prism of reading the blogs of exotic “others”; political upheaval, through the prism of the so-called Twitter revolutions. Even the persecution of dissidents is now seen as an extension of online censorship (rather than the other way around). A recent headline on the blog of the Harvard-based Herdictproject—it tracks Internet censorship worldwide—announces that, in Mexico and Morocco, “Online Censorship Goes Offline.” Were activists and dissidents never harassed before Twitter and Facebook?

Most Internet intellectuals simply choose a random point in the distant past—the honor almost invariably goes to the invention of the printing press—and proceed to draw a straight line from Gutenberg to Zuckerberg, as if the Counter-Reformation, the Thirty Years’ War, the Reign of Terror, two world wars—and everything else—never happened.

even their iPad is of interest to them only as a “platform”—another buzzword of the incurious—and not as an artifact that is assembled in dubious conditions somewhere in East Asian workshops so as to produce cultic devotion in its more fortunate owners. This lack of elementary intellectual curiosity is the defining feature of the Internet intellectual. History, after all, is about details, but no Internet intellectual wants to be accused of thinking small. And so they think big—sloppily, ignorantly, pretentiously, and without the slightest appreciation of the difference between critical thought and market propaganda.

Google search results gives more weight to pxhere.com where the image has a mighty 4 views (some of which are me) over the original image, with almost 5000 views.

What kind of algorithm is that? It’s one that does not favor the individual. Image search results will favor sites like Needpix, Pixsels, Pixnio, Peakpx, Nicepic, and they still favor the really slimy maxpixel which is a direct rip off of pixabay.

did you know that the liberating world of “use any photo you want w/o the hassle of attribution” is such a bucket of questionable slime? And that Google, with all of their algorithmic prowess, gives more favorable results to sites that lift photos than to the ones where the originals exist?

So yes, just reuse photos without taking all of the severe effort to give credit to the source, because “you don’t have to.” Be a copycat. Show your flag of Lacktribution. Like everyone else. I will not. I adhere to Thanktribution.

Students are more likely to watch a series of shorter videos than a single, longer video, which is why I recommend instructors try to divide long screencasts into five-to-ten minute segments (whenever possible)

asynchronous discussion with audio and video. If you’re not familiar with this concept, I’m referring to discussion tools that allow students to respond with audio and video instead of just text. One tool that’s been a leader for a long time in this multimedia discussion space is VoiceThread. While VoiceThread’s defining feature is its user-friendly approach to audio-based commenting, it can also be used to create narrated presentations with PowerPoints slides, images, and video. If you find yourself overwhelmed by the interface of a traditional screencasting tool, VoiceThread is worth exploring as a simpler way of recording online lectures and fostering discussions that go beyond plain text

tools that require both high bandwidth and high immediacy, and the best examples of this are videoconferencing tools like Zoom or Skype.  Videoconferencing is a great way to engage with students when they truly need to see and hear each other in real time. It can also be useful for online office hours, since it’s easier to feel connected and avoid misunderstandings when you have the benefit of tone of voice, facial expressions, and body language.  Unfortunately, videoconferencing is one of the most inflexible and bandwidth-intensive activities we can ask our students to do. Before you rely on it too heavily, look at the other quadrants and ask yourself if there’s any other way to accomplish your learning objectives without it. 

None of this is to say that videoconferencing is inherently bad or that it has no place in an online course. It’s simply a reminder that seemingly small (and sometimes unconscious) choices about the technologies we use can have a big impact on how inclusive and effective our teaching is.

“What surprised me was the resurgence of many of the zombie ideas about online learning creeping into the discussions, such as the idea that online learning isn’t as personal, or that you can’t have interactivity, or that it just doesn’t work. And while it is true you need to change how you think about your course — you can’t just replicate what you used to do in the classroom — there’s an opportunity to evolve your teaching practices and create a better learning experience for your students.”

What’s happening now is going to reshape education for years, if not decades.

People want the old normal, not the new normal. We will, to some degree, get back to what we know and love, but it won’t ever look like it did before

“Like your physical buildings on campus, you also have a somewhat invisible set of resources called your educational technology. If you don’t understand it well and don’t treat it as important infrastructure, your ability to move online sustainably will be challenged. Sometimes institutions see eLearning as a project, not a strategy. Online learning isn’t a fly-by-the-seat-of-your-pants project; it has to be integrated into your academic plan and institutional strategy. I hope that COVID-19 underlined that for institutions.”

“We’ve known for over 30 years now that one-hour lectures are not a great way to teach: you can have a good one-hour session, but can you have 13 over a semester? It’s about cognitive load, and students can’t focus for more than 15 to 20 minutes at a time without being distracted. There’s room for synchronous discussion, but we can do it better. There’s a huge amount of research into online learning and what happens when students have access to online learning whenever they want it. And just like in real life, you have to know how to do both synchronous and asynchronous interactions well.”

We need to make space for the voices of communities who haven’t traditionally been heard: non-traditional learners, students who are food or housing insecure, students who are neurodivergent, students of colour, and Indigenous students. We must think of all these populations and the degree to which our educational system — our technology, our platforms — has not been built for them. We do a lot of work to make our methods accessible, but at the core, our systems, institutions, and platforms aren’t really built for — or by — those students

s challenging as it is, I’m seeing online pedagogy’s focus on equity and care resonating with many of those new to the medium

I’ve used really experimental styles over the past few years, but I won’t be doing that as much over the coming year because I shouldn’t. My classes are traditionally where students get to work with tools and platforms outside of the norm. If everyone moving online treats it that way, the cognitive load on the students will be absolutely overwhelming. My right to flex my academic freedom regarding platforms should be superseded by care and consideration for my students’ cognitive loads across a program. Navigating different platforms and tools is hard and distracting.

“One of the most vital tools and resources that I’ve seen people using is their human capacities for compassion and patience — the degree to which faculty are stepping up and approaching their students from a place of care, and a place of genuine desire for students to feel a sense of hope, safety, and flexibility.”

Reasons why people want to keep their cameras off include: Discomfort or shyness with showing their faces online. This is real, people. For most people, it gets better with time, but not always and not in every context.Noisy or busy home environments e.g. spouse or kids or siblings moving about. I have occasionally had to mute and turn camera off in the middle of a webinar I am personally giving for those reasons! Women and girls can be especially vulnerable to kids and spouses not respecting their work/learning timeNot being dressed for company (for me personally, I often don’t want to cover my hair for a meeting where I’m not presenting, I want to lounge around in comfy clothes). Or your home not being tidy enough for company. This is a thing.Slow/unstable internet connection. Turning off webcam can be the easiest way to get better quality audioDiscomfort over recording

Ask questions and ask everyone to respond in the chat. You will know if they are focused and engaged by their responses and every single person can participateAsk questions for them to answer orally. Either call on people round robin, or call out some people from the chat (also keeping in mind some people are voice shy, and some people have noisy home environments)If you can divide students into smaller groups go talk to each other and you can move between them like a butterfly, this can help some people engage/talk more and occasionally even turn their cameras onUse things like Annotation or Google docs to have folks contributeAsk students to have a profile picture up when their camera is off. This helps sometimes.You might learn to distinguish student voices as you would close friends on the phone (remember life pre-caller ID where close friends and family would expect that?) and use them as proxies for how they are feeling. You already have this skill, but are not expecting to use it.If you record, consider having an unrecorded portion. You will be surprised how much some people participate or are willing to turn cameras on in the unrecorded portion.

it seems we need to consider ways of allowing people to “be there” in alternative ways that they are comfortable with and that tell us they are really listening to us and responding in more explicit ways