I’ve been fascinated by the rapid progression of ChatGPT article fads:
Look at this weird thing that writes stuff!
I asked ChatGPT a question—and here’s what it answered!!
I asked ChatGPT to write this article—and it totally did!!!
Students could use ChatGPT to student essays write essays!!!! End of the world or totally awesome?????
I asked ChatGPT for suggestions about preventing students from using ChatGPT to cheat—and it gave me five great suggestions (and five terrible ones)!!!!!!
Waitaminute. Let’s back up.
Students finding easy ways to cheat is not exactly a new thing. Remember, “to Chegg” is a verb now. Let’s back up to fad #3. Writers are asking ChatGPT to write their articles, publishing those articles, and then advertising that the articles published under their by-line were written in 30 seconds by a machine.
Do they want to get replaced by an algorithm?
It seems to me we’re thinking about the problem that these algorithms present in the wrong way.
At the moment, ChatGPT is a toy
Language-generating algorithms ChatGPT and their image-generating cousins are toys in both good and bad ways. In a good way, they invite people to play. Anyone, whether or not they know anything about programming, can explore the possibilities and limitations of a certain kind of cutting-edge AI. It’s fun. And, like all good games, you learn by playing. You can learn what they can and can’t do well. How to write a good prompt. And so on. They make AI more accessible and, in a certain way, more understandable.
They’re toys in the bad sense because they weren’t designed for any particular purpose and tend to do weird things for any given task. For example, I tried to get DALL-E 2—ChatGPT’s image-generating sibling—to create a retro comic book cover depicting ChatGPT as a scary vampire. I tried half a dozen different prompts. The best result I got was with “An evil vampire on the cover of a retro comic book called ‘ChatGPT’”. Here’s the best image it came up with:
“An evil vampire on the cover of a retro comic book called ‘ChatGPT’” as interpreted by DALL-E 2
The algorithm does evil comic-book vampires pretty well. Spelling a word that was explicitly spelled for it in the prompt? Not so much.
Remember, a large language model algorithm like the one behind DALL-E 2 and ChatGPT don’t understand anything. It’s taking a bag of words and putting together pieces it found on the internet to construct something that seems like an appropriate response.
ChatGPT, for example, is notoriously bad at math. Because it doesn’t calculate. That’s not what it is designed to do. Its developers have been working on improving this limitation. So I asked it about how good at math it is:
Me: How good are you at math?
ChatGPT: I am able to perform basic arithmetic operations, such as addition, subtraction, multiplication and division. My training data includes mathematical concepts and I have been trained to recognize and respond to arithmetic questions. However, it is important to note that I may make mistakes in complex or unconventional mathematical computations.
Me What is 2794 + 83948?
ChatGPT: 87,742
That answer is incorrect.
So ChatGPT, the miracle of modern technology destined to destroy all education, is not as “smart” as a calculator. Note that you can ask Google or your Apple search prompt the same question and reliably get a correct answer. OpenAI (the makers of OpenAI) are likely trying to combine much simpler but mathematically reliable algorithms with the GPT-3 algorithm that underlies ChatGPT and DALL-E 2. The fact that they haven’t solved this problem yet suggests it is non-trivial. These different types of algorithms can be challenging to mesh. Many of the more impressive applications of artificial intelligence and machine learning are made possible by an ensemble of different algorithms working together, some of which fit together more naturally than others.
Don’t get me wrong; the math problem will be solved. The OpenAI engineers will crack it just as they are now pretty good at preventing ChatGPT from making stock predictions or answering questions about current events when its data set hasn’t been updated since 2021. But it’s a game of whack-a-mole. Because you can ask ChatGPT anything, people do ask it anything. The creators are learning a lot about the questions people ask and what can go wrong with the answers. This new knowledge will help them design more specific solutions. But a general-purpose prompt tool like ChatGPT will be hard to make good at solving any one particular problem.
I’m not convinced that ChatGPT, as it exists today, represents a big leap forward in essay cheating. It has length limitations, has to be fact-checked, can’t produce references, and spits out highly variable quality of reasoning and argumentation. Students would learn more by trying to fix the problems with a ChatGPT-generated draft than they would by going to a traditional essay mill.
Short answer questions are a different matter. ChatGPT is already dangerous in this area. But again, students can already “Chegg” those.
Yes, but…
Could somebody write a better program specifically for writing school essays? Or magazine articles? Yes. That work is already underway.
So what do we do about the essay cheating problem? Let’s start with the two most common answers. We can develop algorithms that detect prose that was written by other algorithms. That too is already underway. So we’ll have yet another flavor of the cheating/anti-cheating arms race that benefits nobody except the arms dealers. The anti-cheating tools may be necessary as one element of a holistic strategy, but they are not the ultimate answer.
Second, we can develop essay-writing prompts and processes that are hard for the algorithms to respond to. This would be useful, partly because it would be good for educators to rethink their stale old assignments and teaching practices anyway. But it’s a lot of often uncompensated work for which the educators have not been trained. And it ends up being another arms race because the algorithms will keep changing.
We miss the point if we respond to language-generating AI as a static threat that might become more sophisticated over time but won’t fundamentally change. ChatGPT is just a friendly way for us to develop intuitions about how one family of these algorithms works at the moment. You’re wrong if you think it is a one-time shock to the system. We’re just at the beginning. The pace of AI progress is accelerating. It is not just going to get incrementally better. It is going to radically change in capabilities at a rapid pace. It will continue to have limitations, but they will be different limitations.
So what do we do?
How about talking to the students?
When adaptive learning hit peak hype, a glib response to teacher hysteria started making the rounds: “If you [teachers] can be replaced by a computer, then you probably should be.”
Doesn’t that apply…um…generally?
If all students learn is how to use ChatGPT to write their essays, why wouldn’t their hypothetical future employer use ChatGPT instead of hiring them? Why would students spend $30K, $40K, $50K, or more a year to practice demonstrating that a free-to-use piece of software does their best work for them? Students need to learn the work these tools can do so they can also understand the work the tools can’t do. Because that is the work the students could get paid for. Technology will make some jobs obsolete, leave others untouched, change some, and create new ones. These categories will continue to evolve for the foreseeable future.
At a time when students are more conscious than ever about the price-to-value of a college education, they ought to be open to the argument that they will only make a decent living at jobs they can do better than the machine. So they should learn those skills. Why learn to write better? So you can learn to think more creatively and communicate that creativity precisely. Those are skills where the primates still have the advantage.
Once we engage students openly and honestly on that point, we will start building a social contract that will discourage cheating and establish the foundational understanding we need for rethinking the curriculum—not just to keep from falling too far behind the tech but to help students get out in front of it. The current limitations of these AI toys demonstrate both the dangers and the potential. Suppose you want to apply the technology to any particular domain. In that case, whether it’s math, writing advertising copy, or something else, you need to understand how the software works and how the human expertise and social or business processes work. Whole echelons of new careers will be created to solve these problems. We will need thinkers who can communicate. Learning how to formulate one’s own thoughts in writing is an excellent way to learn both skills.
Fighting the tech won’t solve the problem or even prevent it from getting worse. Neither will ignoring it. We have to engage with it. And by “we,” I include the students. After all, it’s their futures at risk here.
(Disclaimer: This blog post was written by ChatGPT.)
(I’m kidding, of course.)
(I am able to perform basic humor operations, such as generating dirty limericks and “your momma is so ugly” jokes. My training data includes humorous concepts, and I have been trained to recognize and respond to knock-knock questions. However, it is important to note that I may make mistakes in complex or unconventional humor.)
ChatGPT is creating allkinds of buzzaboutstudentscheating on essays. So it got me thinking. If I had had access to a tool like ChatGPT when I was in college, would I have used it to cheat?
“A robot writing an essay” as interpreted by DALL-E 2
Yes. Absolutely. One hundred percent. But I wouldn’t have thought of it as cheating.
Cheating is a state of mind
When I was in college, I had a massive chip on my shoulder. If I caught the slightest whiff that the professor didn’t care whether I was learning or the assignment was not well-designed to help me learn something, I would immediately flip into “grudge” mode.
I never cheated. The whole point, in my immature mind, was to prove that I was smarter than the professor. So I would stay up late, party the night before the assignment was due, and give myself three or four hours to write it the next morning, hung over, before I had to turn it in. To give you a sense of just how far these grudges went, I didn’t actually enjoy partying very much. Sometimes I would go out of my way to do so because of the stupid assignment. It was all part of a game to challenge myself. No pain, no game.
I wasn’t always so self-destructive about it. If I felt the professor was genuinely interested in my learning but had simply written an assignment I wouldn’t learn from, I’d take the writing prompt and try to be as creative—and subversive—as possible. But that only worked when the professor would understand and appreciate the joke. If I had a professor who had (in my judgment) given me a coloring book and was going to grade me on whether I colored inside the lines, that triggered my worst adolescent self. While I didn’t cheat in the conventional sense, my goal was to minimize effort required to get an adequate grade and show myself how smart I was in the process. My social contract with the teacher was no less broken than the student who copied somebody else’s work. I once defined cheating as “engaging in behaviors that are intended to facilitate passing without learning.” By that standard, I cheated my ass off.
Something like ChatGPT would have been part of the game to me. If you’ve played around with it at all (or read some articles about it), you’ll know that writing prompts that lead the AI to generate good text is something of an art. I would have spent a lot of time crafting the best prompt possible. Then I would have edited the output. I still would have wanted to produce a good essay. If this process took more time to produce the end result than just writing it from scratch myself, it wouldn’t have mattered to me. I would have subverted the assignment into something that actually challenged me while flipping the bird to the instructor who had the temerity to underestimate or underappreciate their students in general and me in particular. That was the whole point of the game. I wanted to learn. And I wanted to care. Nothing pissed me off more than a professor who wasted my educational time.
ChatGPT wouldn’t have violated my “no cheating” rule because I wouldn’t have been cheating according to myrules.
Faculty tend to think that cheating is defined by their rules and the college honor code. The reality is far more complex. For me, it was heavily influenced by my social contract with each teacher, whether I felt they were holding up their end of the bargain, and how I could turn every assignment into a game that was a fun mental challenge. Other students may be influenced by which assignments they think are important for their career goals, how much work is reasonable to expect of them and, very often, or whether they think the instructor cares about their learning.
I can’t emphasize that last point enough. I’ve conducted a fair few focus groups with students over the years. The results consistently supported the research evidence that students are heavily influenced by whether they believe their teacher cares about their learning. And this manifests in surprising ways. I remember one particular focus group vividly. We were talking about what factors cause students to engage in with a class more than they expected or planned to. They all agreed that having a teacher that cared about their learning was a major factor. I asked them how they can tell if a teacher cares. I got some of the usual, expected answers like “she knows my name” or “he talks to me after class to see how I’m doing.” One surprising answer that elicited a lot of nods from the group was, “She picked a random student during class and asks them a question about the reading. She didn’t grade us on the answer but it’s embarrassing if you don’t know it.”
“You liked that?” I asked, somewhat incredulously.
“No,” he replied. “I hated it. But I know she did it because she wanted us to learn. So I did the readings.”
ChatGPT as a tutor
The current fad with journalism about ChatGPT is to ask the bot some version of, “If you were writing this article, what would you write?” The value of that exercise depends on the incisiveness of the commentary regarding the output, the prompt, and the relationship to writing craft. Examples of this genre can range from pretty interesting to pretty terrible, depending on the quality of thinking of the human “writer” involved. Bad articles end either in “WHEEEEE!” or “NO HUMAN WILL EVER LEARN HOW TO WRITE A COMPLETE SENTENCE AGAIN!” Better articles give us insight into what the tool is good for as a writing aid and how that reflects on writing craft.
Let’s see how well I fare at this genre. Can ChatGPT write this blog post for me? The base question I’m after is how to use a tool like ChatGPT to engage students in the craft of writing the way it would have engaged me. As you’ll learn by playing around with the tool or even reading some of the better articles on it, part of the craft in getting useful answers out of ChatGPT is writing a good prompt. My prompts in the experiments below are in bold. ChatGPT’s answers follow.
I’m a college philosophy professor. I have created an assignment asking students to write an essay on a particular topic. They are allowed to use ChatGPT to generate their first draft but will be graded on the accuracy, originality of thought, and writing quality of their final draft. They will also be evaluated on the quality of the ChatGPT prompt that they write. What are the educational strengths and weaknesses of this assignment?
There are both strengths and weaknesses to using a tool like ChatGPT for a college philosophy assignment. One potential strength is that it can help students generate ideas and get started on their essays. This can be particularly helpful for students who may be struggling to come up with a topic or approach to the assignment.
Another potential strength is that using ChatGPT can help students practice using technology in their writing. This is an important skill in today’s world, and it can help students develop the ability to effectively use tools like ChatGPT in their future academic and professional endeavors.
On the other hand, there are some potential weaknesses to this assignment. One is that ChatGPT is a tool that generates text based on prompts, but it does not have the ability to understand the meaning or significance of the words it generates. This means that the essays produced using ChatGPT may not be as original or thought-provoking as those produced by the students themselves.
Another potential weakness is that using ChatGPT could discourage students from engaging with the material in a deep and meaningful way. Instead of reading and thinking critically about the assigned texts, they may simply use ChatGPT to generate a first draft and then submit it without further thought or analysis.
Overall, the use of ChatGPT in a college philosophy assignment has both strengths and weaknesses. It can be a helpful tool for generating ideas and getting started on an essay, but it should not be used as a substitute for careful reading, thinking, and writing.
My dialogue with ChatGPT
Meh. ChatGPT’s answer has all the hallmarks of an adequate but unimaginative high school essay, right down to the form. It has not produced an essay that is “original or thought-provoking” or written content that engages with the prompt “in a deep and meaningful way.”
We can marvel at the ability for the technology to do this well. It is genuinely miraculous and merits a brief pause for a moment of wonder. That said, the content itself is not enlightening or even particularly interesting by human writing standards.
Let’s try again. I’ve altered the prompt using language from ChatGPT’s output in our first try. To make the changes more obvious, I’m showing the text I deleted from the original prompt and underlining the text I added:
I’m a college philosophy professor. I have created an assignment asking students to write an essay on a particular topic. They are allowed to use ChatGPT to generate their first draft but will be graded on the accuracy, originality of thought, and writing quality of their final draft. They will also be evaluated on the quality of the ChatGPT prompt that they write. What are the educational strengths and weaknesses of this assignment?How can I improve this assignment to encourage students to produce more original, thought-provoking assignments in which the students engage with the material in a deep and meaningful way?
One way to improve this assignment would be to provide more guidance on how to use ChatGPT effectively. For example, you could provide students with examples of good and bad ChatGPT prompts, and explain how to write a prompt that will generate thoughtful, original responses from the tool.
Another way to improve the assignment would be to require students to engage more deeply with the material before using ChatGPT. For example, you could ask them to read and annotate the assigned texts, or to participate in a class discussion, before using ChatGPT to generate their first draft. This will help ensure that they have a strong understanding of the material and are able to generate original, thought-provoking ideas on their own.
In addition, you could also consider modifying the grading criteria for the assignment. For example, you could place less emphasis on the accuracy of the ChatGPT-generated text, and instead focus on the student’s own writing and analysis in the final draft. This would encourage students to take the time to engage with the material and produce original, well-written essays.
Overall, there are several ways you could improve this assignment to encourage students to produce more original, thought-provoking work. By providing more guidance on how to use ChatGPT effectively, requiring deeper engagement with the material, and modifying the grading criteria, you can help students develop the skills and habits needed to produce high-quality work in their philosophy studies.
My dialogue with ChatGPT
This answer is more interesting. The first suggestion—teach students how to write effective prompts—is a good start. I do think this is a skill that will be taught widely in schools within five years. (It should be sooner than that and it realistically might be later.) I don’t agree with the suggestion to de-emphasize the accuracy of ChatGPT’s text. Evaluating the quality of source material is an essential skill which becomes more important, complex, and interesting with the introduction of tools like this one. That said, a lot depends on context. I could imagine assignments for which I would take ChatGPT’s advice on this point. Notice, though, that the structure of the “essay” ChatGPT generated is still pretty stiff and formulaic. Leaning too heavily on a tool like this could have the effect of cultivating competent writing at the expense of stifling great writing.
Still, I can see the shape of a pedagogical process—and preferably a supporting end-to-end tool—that teaches many of the skills involved with good writing, including some hard ones like checking sources and editing—while including some elements of creativity. If it is scaffolded properly—again, with the right tool and process but also with a good, solid rubric—it could enable educators to spend more of their time honing in on specific aspects of the writing process with less drudgery. Particularly if used judiciously as part of the writing curriculum rather than the whole thing, it could be quite useful.
It’s also honest. It strengthens rather than weakens the social contract between student and educator by allowing the students to use a tool as long as they are open about it and are using it as part of a genuine learning process rather than a shortcut around thought work.
Would I use ChatGPT to help me write blog posts?
In principle, I have no problem with the idea of using machine-generated text in e-Literate posts as long as it is properly attributed. In practice, I haven’t been able to figure out a way to make it useful.
Part of the value of e-Literate is that it can be surprising in both content and form. Novelty teaches while entertaining. Could a tool like ChatGPT develop the right sort of novelty to fit with this blog? On the surface, it probably could. We’re already starting to see examples of the tool being asked to write an article on X subject in the style of Y person. If trained on the thousands of posts I’ve written, I suspect that a tool like ChatGPT, or maybe the next generation of it, could learn to use more em dashes, write convoluted sentences, and be more snarky. It might even incorporate some themes that show up in my posts.
But it doesn’t actually understand anything that it writes. ChatGPT distills and synthesizes answers that have already been given. It can only write about ideas that I have already thought of and written about. It can’t write about the idea I’m going to have tomorrow. e-Literate isn’t about what I know. It’s about what I’m learning. As such, I don’t yet see how I could get much value out of a tool like ChatGPT in the foreseeable future, at least for this blog. The only exception I can think of is for posts like this one that are about the tool. ChatGPT didn’t really write part of this post for me. It generated artifacts for me to analyze in my own writing.
These lines of demarcation—the lines between when a tool can do all of a job, some of it, or none of it—are both constantly moving and critical to watch. Because they define knowledge work and point to the future of work. We need to be teaching people how to do the kinds of knowledge work that computers can’t do well and are not likely to be able to do well in the near future. Much has been written about the economic implications to the AI revolution, some of which are problematic for the employment market. But we can put too much emphasis on that part. Learning about artificial intelligence can be a means for exploring, appreciating, and refining natural intelligence. These tools are fun. I learn from using them. Those two statements are connected.
Would I teach writing using ChatGPT?
If I were teaching writing today, would I use an AI tool? In practice, probably not, simply because it would be too much work to cobble together the pieces. ((I have now guaranteed that I will get an avalanche of emails from EdTech startups claiming to solve this problem. Please direct your messages to my chatbot.)) This is the perennial challenge of EdTech, which, on balance, creates a vastly underestimated drag on the amount of time educators have to put into the thought work of delivering high-quality education. In principle, though, yes, I absolutely would. I have a clear picture in my head of what I would need in terms of the EdTech and what sorts of writing work I would (and wouldn’t) use it for.
Do I think higher ed is ready for widespread adoption of these tools? That’s a harder question. Teaching this way requires a new skillset. Higher ed has abysmally under-resourced professional development support for teaching, on the whole. Also, teaching this way isn’t what our PhD system trains young academics to aspire to. Many will see it as a dumbing down of the work they have dedicated their lives to. And if implemented poorly, it can easily turn into that.
So will AI text generation tools revolutionize or kill college writing? Both! Neither! For sure! Probably! Eventually! Somewhat! It’s…complicated.
Phil Hill recently tweeted out two slides from Coursera’s quarterly conference call. They made me sit up straight. Then they made me read the transcript from the call.
I haven’t been paying enough attention. Having watched the rise of the MOOCs closely and having watched 2U/EdX chase EdX’s inspiration and rival, I assumed I understood Coursera as well.
I was wrong. Coursera is evolving into a different and much more interesting company that is well-positioned to take advantage of the tectonic shifts I’m tracking.
Coursera as a marketplace
One of the two slides Phil shared out was further confirmation of a trend that everyone in higher education has already been struggling with:
Coursera, like US higher education institutions, has seen a decline in degree program enrollments. This seems like a significant drop relative to the drop across degree programs as a whole, particularly against primarily degree-granting institutions, which have fared better than their on-campus counterparts. So it’s curious.
But this is the slide that attracted my full attention:
The point of this slide is to show the diversification of Coursera’s business. Degree programs may be down, but enterprise licenses and direct-to-consumer certificates are up. But it also indicates Coursera’s ability to diversify revenue streams for its university content providers. The enterprise business provides a distribution channel between universities and employers. From what I can tell, it’s a Guild competitor, even though the two companies look very different on the surface. The consumer segment started as the MOOC business and has expanded into the “tweener” space between courses and degrees: certificates, microdegrees, whatever.
On the surface, it may appear that Coursera and 2U/EdX are evolving toward each other, with Coursera having grown out its degree programs and 2U acquiring EdX to get into Coursera’s MOOC space. In reality, 2U/EdX is moving much more toward Coursera than the converse. And it’s easy to see why if we look at the economics of their original models.
2U historically made its money by helping premium-brand universities bring expensive graduate degree programs online. And they famously succeeded in attracting multiple premium university brands to offer the same degree through them by showing that, historically speaking, geographic brand reach is smaller than you might think. Students in the Northeast United States have been much more likely to apply for degrees from Harvard, Yale, or Princeton than from Stanford or even Duke, even if the degree programs are all online. The same is even more true, a notch or two down the reputation scale, where institutions may have strong regional brands but are not even broadly recognized nationally.
Roughly about the same time that it became apparent that the growth of expensive online graduate degree programs would inevitably slow—there’s only so much demand in the world for $40,000 MSW degrees—2U got into the short course business. And then the BootCamp business. And then they bought EdX. But, as I have written about in the past, 2U has tended to publicly imply that its “free to degree” product range is a sales funnel. Of course they’re interested in diversifying their revenue. But outwardly, at least, the mindset has appeared to be that of a diversifying OPM.
In contrast, Coursera has always thought of itself as a two-sided market. For those unfamiliar with the term, a two-sided market is one where the company’s primary business is to connect buyers with sellers. Amazon, Etsy, Airbnb, and Uber are all examples of this sort of business. Yes, they were attached in the early days to selling a particular form of MOOC as an individual product, much as Amazon only sold books in the early days. Much of the attention during the early years of MOOCs was on the pedagogical model of the MOOC itself, which is not very effective, and on the MOOC course delivery platforms, which directly translated lecture-model courses into an infinite lecture hall, with some relatively modest technological improvements. The innovation that got the least attention at the time was Coursera’s nature as a two-sided market. I remember talking to Daphne Koller about this circa 2014 (in front of one of those ridiculous fountains in the Swan and Dolphin Hotel at a Sloan-C conference).
I’m not sure that EdX ever fully grasped the implications of the two-sided market model. 2U might; it’s hard to tell right now. The company’s in-process rebranding is confusing and their clearest marketing point so far has been that MOOCs lower the advertising costs for degree programs. Coursera, on the other hand, understood the business model implications early on, one of which is that two-sided markets tend to produce one big winner in any given space. Who is the second-largest competitor to Amazon? Walmart? Wayfair? The distributed network of stores that Shopify powers? I don’t know. The answer isn’t obvious. It’s not like Coke and Pepsi.
Multi-sided platforms have unusual characteristics.
A market is a platform
We often use the term “platform” in tech conversations to narrowly mean the features and architecture of a kind of software. A platform is distinguished from an application in the sense that a platform encourages people to build other things on top of it. Mapping software that can only provide directions is an application. If the same software can be integrated into search and discovery applications like Yelp is a platform. In hardware, a flip phone is a device. An iPhone is a platform.
But platforms can enable building more than just software extensions. When Phil Hill and I worked together at MindWires, the company was heavily involved with helping the 114-campus California Community College System onto one common LMS instance. ((Phil and some MindWires teammates did the lion’s share of the work, although I got to write a white paper for the state legislature, which was fun.)) The point wasn’t just to get everybody using the same LMS but rather to use that common base to build up a course exchange that would enable campuses to help each other with over- and under-subscribed courses as while enabling students to take courses that they need to graduate but that are oversubscribed on their own campuses:
(Shameless plug: The webinar I’ll be facilitating about university/workplace partnerships for OpenLMS on Thursday, November 2nd at 1 PM ET will include a similar story about how a common LMS adoption facilitated teacher support and recertification amidst a state-wide teacher shortage.)
With these examples in mind, I found the following anecdote from Coursera CEO Jeff Maggioncalda to be very interesting:
In July, I highlighted the recent partnership with Louisiana Tech University and the University of Louisiana Systems. Our initial partnership with Louisiana Tech started as a social impact initiative with the office of student financial assistance via the Louisiana Board of Regions. [sic]
It was designed to provide low-income high school students and graduates access to Coursera’s entry-level Professional Certificates from Google in order to prepare them for college or to begin a digital career. This was followed by Louisiana Tech summer programming series open to faculty and staff that is banded to include professional development opportunities for employees across all campuses within the University of Louisiana Systems.
Now the Louisiana Workforce Commission, a state agency responsible for enhancing workforce growth and well-paying jobs for Louisiana residents, is partnered with Coursera to launch Tech Ready Louisiana, a statewide workforce development initiative providing training to thousands of Louisiana.
The centerpiece of the program is Career Academy, as our entry-level professional professor certificates were specifically designed to prepare workers without a college degree or prior work experience. Using Career Academy, Louisianians can explore careers develop key skills and competencies, build a portfolio of hands-on projects using actual workplace tools and earn industry-recognized credentials.
And many of these credentials have ACE Credit Recommendations, which make it easier for learners to earn credit towards a local or online degree program when they’re ready to continue their education.
Finally, in recognition of the connectivity challenges that present a real barrier today, the Workforce Commission is making reliable Internet access at nearly 60 sites across the state for learners to complete their Coursera courses.
Traditional university degree programs and workforce development initiatives often lack a solid connection to today’s in-demand jobs and are often not equipped to adapt to the fast-changing skilled landscape and evolving employer expectations.
By leveraging Coursera, an entire system of higher education in coordination with government institutions can foster stronger collaboration with industry by unlocking new development opportunities for students, faculty and staff by diversifying and expanding talent pipelines for employers and by building a more competitive workforce.
This is the vision of Coursera’s three-sided platform at scale, connecting learners, educators and institutions in a global learning ecosystem designed to keep pace with our rapidly changing world.
Notice he calls it a three-sided platform, although I count more than three sides here. The Coursera platform, writ large, connects universities, learners, government agencies, megacorporations, and local employers. It helps open up new opportunities for universities to reach students without falling afoul of the “dialing for dollars” problem that plagued for-profit universities and create ethical and legal complexities for OPMs.
A multi-sided market also offers some benefits to the educational mission relative to other models. If, for example, you’re an OPM that has heavily subsidized the creation of a degree program in exchange for a share of the revenues for ten years, then you need to make sure your up-front investment pays off. This is true of any business that invests up-front in building products, including textbook publishers. In a multi-sided market, depending on how the compensation is set up, the marketplace owner shouldn’t care much about whether the company sells 100,000 units of one product or 1,000 units each of 100 products. For education, some programs are essential for students, local economies, and important business niches, even if they don’t require a lot of trained individuals. So a well-functioning multi-sided market should, all else being equal, offer more educational opportunities within a scalable model.
(Side note: Somebody other than me should write an analysis the strategies and pricing for 2U/EdX, Coursera, Guild, and other evolving university sales channels.)
Coursera sees evolution ahead
Maggioncalda also had an interesting question on the inevitable future-of-the-college-degree question:
I’m in Bangalore right now and I spent the last 10 days here in India. There is a demand — the government is trying to shoot gross enrollment ratio up to 50% by 2035.
They need 3.3 million more professors to serve 35 million more students. And so a lot of the demographics that folks are seeing in the U.S. is not consistent with what some of the demographic trends are in other parts of the world. And we are seeing college degrees globally be as revered as they have been historically.
So when we look at this on a global basis, I think a lot of it is going to be driven by demographic trends. I think that especially when you have rising middle classes, with a real premium on education, you’re going to see — we’re going to see a lot of demand for this. So we’re very bullish on the future value of a college degree.
Another thing is colleges are not sitting still. I don’t know, there might have been five or six examples in the scripts about universities putting Career Academy literally into their curriculum, so that when someone graduates, they don’t just have a degree, they’ve got a degree and an industry certificate.
I think a lot of people think of college degrees as this static thing that’s not going to change. But really, the competition among job seekers is going to be those who have maybe micro credentials and those who have college degree and micro credentials. So I think it’s going to almost always be worth it.
And on the affordability side, we see affordability really being driven by technology and, frankly, competition. So I think degrees are going to come down in price. They’re going to be a lot more job relevant, they’ll be much more broadly available and especially because they’re online, open up to the working adult population, which is a much, much bigger market than just young people who are between the ages of 18 and 24.
So we’re still very bullish on the long-term opportunity for degrees.
This answer hits many of the themes in e-Literate’s Changing Themes for Changing Times: rising economic value of the post-traditional student, merging of traditional and post-traditional student preferences, lifelong education, and regional maturation of education markets around the globe. (The company’s international story is interesting enough that it merits its own post.)
Coursera is moving in the direction of becoming a uniquely horizontal EdTech company, not because of its MOOC pedagogical model but because of how it has leveraged the reach of the initial MOOC growth to become a successful multi-sided market. They haven’t completed this transition yet. But if they pull it off, they could become the first hyper-scaled EdTech company on the planet.
But what about…you know…learning?
I’ve come to realize that I don’t have a clear sense of how much the Coursera pedagogical platform has evolved in the way I do with EdX. Nor have I seen much in the way of efficacy research coming out of the company (or out of its university partners but publicized by the company). I’m skeptical about the platform. Once you’ve baked a particular set of assumptions into your platform design (e.g., video lectures plus assessments as a primary learning mode), it’s hard to change those later. But since I haven’t meaningfully explored that platform in years—I don’t count a couple of random, poorly designed MOOCs that I never completed over the years—I am making a conscious decision to set aside previous assumptions and acknowledge that I don’t know much about its current state. The same can be said for its underlying data architecture, suitability for research, and actual efficacy research being conducted. I haven’t personally seen evidence of significant progress on any of these fronts. But as the saying goes, absence of evidence is not evidence of absence.
One of the tricky aspects about evaluating Coursera is that it isn’t just a multi-sided market (like Teachers Pay Teachers) or just a learning platform (like an enterprise-licensed LMS). It’s a fusion of both. It’s a compound, complex platform. Its marketplace can reach and connect many different stakeholders. Buyers, sellers, and enablers. Is their content production, delivery, and analytics platform equally able to serve these disparate learning needs and contexts? That’s a vastly more complicated feat to pull off than it sounds.
From an economic perspective, Coursera is clearly evolving from a second-wave to a third-wave EdTech company. They’ve gone beyond direct-to-consumer and are demonstrating what it looks like to design a company for an environment of increasingly global, lifelong, and universal education. I have less clarity about whether their core teaching and learning capabilities are up to the task. But despite the hit their stock has taken in the current market, they may be the best-positioned player to crack that code.
I recently ran across a white paper called Ideas for an Affordable New Educational Institution out of MIT’s J-WEL center. While the paper contains no groundbreaking new ideas, the gestalt of it is interesting and timely. My first thought after reading it was, “If I were designing the Cal State system in 2022, starting with a blank sheet of paper, this paper would be one decent blueprint to start from.” That kind of practical synthesis is valuable and, sadly, under-rewarded.
And as it turns out, I know and respect one of the authors. My call with him confirmed my sense that this paper is worth writing about.
The context of the paper
It’s often hard to know where these sorts of papers are coming from—and what they might lead to—without additional context. J-WEL is one of many funded centers at MIT. To what extent is the university’s leadership aware of the paper? What are the odds that MIT might act on the recommendations? What are the motivations of the authors? What do they think or hope to accomplish?
Through a quirk of fate, I am fortunate to know one of the five professors from different disciplines who are listed as the paper’s authors. S. P. Kothari was a Professor of Accounting at the University of Rochester during my brief stint as an English graduate student there. I helped him with a couple of writing projects as a way to earn a little extra cash. He was unfailingly kind and always expressed gratitude that seemed out of proportion with the modest help I offered. Beyond that, two things stuck with me about S. P. First, he was interested in designing software to teach students accounting. This was back in the 1990s, so it was early for a professor outside of an education or computer science department to be thinking about such things. Second, while I knew nothing about his field, the clarity of thought in his academic writing and the celebrated status of some of his co-authors suggested to me that he was an up-and-comer.
I was certainly right about his career prospects. He went on to chair MIT Sloan School’s Department of Economics, Finance and Accounting; then became Deputy Dean of the school; and eventually serve for three years as the Chief Economist for the Security and Exchange Commission of the United States before returning to MIT. Despite being at the top of his field,
S. P. clearly never lost his passion as an educator. I gather the same is true for his co-authors. The group started its work with some funding from J-WEL and continued on a volunteer basis afterward because they all cared about the project. I did not get the impression from S. P. that MIT will be picking up this work. But the authors are still trying to bring attention to their proposal in the hopes that somebody will take it forward.
That, then, provides the lens through which the paper should be examined. It is a passion project by highly accomplished scholars in diverse disciplines, none of which is education, and supported by some limited funding. Accordingly, I read it with the same spirit of generosity in which it has been offered. At the same time, it’s worth acknowledging that the document contains some tensions and elisions that are typical of such endeavors. The authors make clear that this paper isn’t a fully formed, shovel-ready proposal. Rather, it is a thought piece and discussion starter.
The context of the need
The authors state their rationale and goals up front:
The conventional model of higher education is facing growing skepticism. With rising tuition costs, ballooning debt, and concerns about preparedness for the work force, many are doubting the value of higher education. Alternative credentials and online offerings are gaining currency.
Viable solutions to these challenges will require abandoning some of our long-held assumptions about how institutions of higher education should operate. But our core educational values—that each learner deserves individual attention and encouragement, and that education should nourish the entire person and not simply provide short-term skills—must be preserved. So we reject solutions that involve replacing teachers by robots, taking all lessons online, or demoting the humanities.
We describe an alternate model for baccalaureate education that offers key levers for addressing these challenges. We list the features of this hypothetical “New Educational Institution,” or NEI, below. NEI is intended to be a new class of institution that is different from traditional R1 or R2 research universities. The focus of NEI will be on majors such as computer science and business, and eventually, broader areas of engineering and design.
This proposal might be called radically conservative in the sense that it is intended to conserve the best parts of an American-style liberal arts education by re-imagining it but not rejecting it. It will take on the challenges of cost and career value without rejecting the value of liberal arts of human teachers.
The authors confine the scope of their NEI to “eventually, broader areas of engineering and design.” In this respect, I think they may be too modest in their ambition. To understand the potential role this model could play, we need to review a little history.
The 1960 California Master Plan—the template from which the modern US higher education system has been built—had a variety of limitations and trade-offs, but one of the most glaring from the very beginning was the lack of identity for the middle tier. California created three systems for higher education. On the bottom was the California Community College System. Any student anywhere in the state would be within easy commute of a college that could offer a two-year degree, an on-ramp to skilled work, and the ability to transfer credit to four-year institutions. Community colleges accept the top 100% of students who apply.
The top tier, the University of California System, would comprise universities that offered advanced degrees and conduct significant research. These institutions would be selective and model themselves after prestigious private universities in their range of activities and emphasis on scholarship.
In the middle was the California State University System. More selective than the community colleges but less selective than the UC universities, these were intended to be four-year universities for students who would benefit from a liberal arts education and perform skilled work but did not need the benefits of studying with top scholars in the field. To prevent Cal State from competing with UC, the former universities were not to offer graduate degrees (although that prohibition didn’t last long).
The problem with this approach is that the United States lacked (and still lacks) a distinctive identity for a Cal State-style institution. Other countries have various interpretations of a vocational university with distinctive goals and personalities for students with certain types of life and career goals. In contrast, the Cal State system and the middle-tier colleges and universities in other states generally lack a distinctive purpose that drives a different approach. This lack of differentiation from their research-oriented cousines creates a few problems. First, the tiers became castes. State colleges and universities that lack the R1 or R2 Carnegie designation are not generally perceived as places students go to pursue different goals but rather as “lesser” versions of research universities. Colleges want to become universities and universities want to become research centers. Why? Because that’s where the money and prestige are. Everything from college rankings to funding flow from the implications of this classed system.
It gets worse. Graduate students are encouraged to aspire to research while being taught nothing about teaching. And why not? Faculty generally advance in their careers based on their research, not their teaching. Meanwhile, because the middle-tier institutions are modeled after their R1 cousins, they make the same research demands on faculty and incur expenses for building out facilities that are not focused on creating well-educated citizens, successful professionals, and thoughtful human beings. Money that could be used to build better connections to employers, add more advisors, or cut tuition is instead spent on the same trappings as research universities.
I’m not claiming that all mid-tier institutions are this way or that Cal State universities are this way. Rather, I’m suggesting that the design of the system, and particularly the lack of a distinctive and valued purpose and identity for mid-tier institutions, creates enormous pressure on these colleges and universities to engage in behaviors that tend to raise tuition while lowering teaching quality.
Higher learning for the rest of us
What if universities were places where you go to learn? What I mean is, what if there were universities that exist tobe places where you go to learn stuff that you need or want to learn? What if that was their main purpose? Not to generate research. Not to be the place that coming-of-age movies are made about (although personal growth counts as “stuff you need to learn” and would not be ignored).
Education would be cheaper. The report’s authors point out that “the actual cost of instruction amounts to less than a third of total expenditures for four-year colleges today.” Exactly how the money flows is…complicated. The authors use the example of regulatory costs for a research university with a hospital to illustrate expenses. Now, given that S. P. is a celebrated financial expert and I am not, I want to be careful with this next bit. The authors imply that this extra cost is cross-subsidized in tuition. I strongly suspect this is true in many cases. The literature I’ve found on the topic over the years is murky. And this itself is a problem.
Clark Kerr, the architect of the aforementioned California Master Plan, also described the modern research university as “a multiversity” where comparison of costs across internal interest groups is discouraged. Kerr’s problem in 1960 wasn’t economic. The California State Legislature was in the mood to write big checks for education. His problem was political. He had to hold his coalition together. Infighting would not help. Which means that comparing how much money goes to one group over another was discouraged. That which doesn’t get measured doesn’t get managed. It’s likely that teaching in general and large lecture courses in particular cross-subsidize functions that don’t directly benefit undergraduates in many institutions. For some students, one could make an argument that being educated by leading researchers provides them with direct benefits. But others probably get little from it.
And the truth is that many academics get little from it as well. If you’re not a star researcher, life as a young academic can be brutal. And if you care about your teaching more than your research, your career prospects will suffer.
A good portion of the paper argues that all parties would be better off if professors were trained, supported, and rewarded for effective teaching of relevant (though not narrowly vocational) skills and tuition money were invested high-quality, well-trained, well-supported educators. It’s not a novel idea. Again, the value of this paper is not in its original approaches but rather in its synthesis of various well-established ideas into a coherent idea of college. The online, CBE-oriented mega-universities are one realization of this new balance. This paper offers a second, complementary vision. One that holds onto more of the residential liberal arts college tradition.
What a Neo-Cal State would be like
Regular e-Literate readers will be well familiar with most or all of the recommendations in the report:
Research requirements for professors should be de-emphasized and should focus on either applied research or research in the scholarship of teaching.
Multidisciplinary courses showcasing the practical value and contribution of the humanities would be the new core curriculum.
Deconstruction of degrees into stackable micro-credentials would enable students to get credit for progress and to move in and out of the workplace as needed.
An added benefit of stackable credentials is that each micro-credential could have more design coherence and value than a collection of individually chosen courses.
Active learning, with strong training and support for the faculty, should be the rule rather the exception.
All faculty would have status that is often referred to as “professor of practice,” meaning they have full employment status, promotion largely based on their teaching, and elimination of tenure/non-tenure distinctions (which is usually implemented as long-term contracts and participation in shared governance but no tenure).
Students would be encouraged to participate in co-ops, where full-time, paid jobs count toward their academic degree (and students may simultaneously take online courses).
The university shifts to a trimester schedule to more easily accommodate long internships.
Visiting faculty fellows augment the regular faculty to bring more research, innovation, and real-world experience into students’ academic work.
An active but balanced extra-curricular schedule is encouraged and supported.
Facilities are designed for use. The section on this topic doesn’t provide a lot of detail, but the reference to libraries, which have evolved from warehouses of paper books to collaboration spaces, is a give-away. S. P. mentioned to me verbally that more online education (presumably along with off-campus coops) will reduce the amount of physical plant investment the university will need to make. Taken together, we can imagine a campus that is the academic analogue of a WeWork facility.
(I’ll address the use of EdTech separately in the next section.)
Can you close your eyes and envision such an institution as a coherent and attractive mid-tier institution where you would be comfortable sending your own kid? I can. Many details are still missing, of course, which I’ll touch on in the last section of the post. But strikes me as coherent.
Further, while the NEI envisioned by the paper out of MIT is most easily applied to engineering, design, and business, I’m not sure why it would need to be so narrow. The biggest challenge I can see is that they call for co-ops, which are hard to implement broadly. But if we add in multi-term project-based learning as an acceptable substitute, I think it could work broadly.
Would it be a good education? The best longitudinal study of educational outcomes that I know of is the 2014 Gallup Purdue Index Report. It correlates aspects of students’ college experiences with the vast collection of employee research the company has conducted over many decades, looking at various dimensions of long-term well-being such as wealth, physical health, mental health, and social connections. Gallup collectively calls these measures “wellbeing.” The Gallup Purdue research correlated survey answers about various aspects of college life to various measures of success later in life. According to the study, students’ of thriving in all five areas of Gallup’s wellbeing index were
1.7 times higher if “I had a mentor who encouraged me to pursue my goals and dreams”
1.5 times higher if “I had at least one professor at [college] who made me excited about learning”
1.7 times higher if “My professors at [college] cared about me as a person”
1.5 times higher if “I had an internship or job that allowed me to apply what I was learning in the classroom”
1.4 times higher if “I was extremely active in extracurricular activities and organizations while attending [college]”
1.1 times higher if “I worked on a project that took a semester or more to complete”
Tellingly, the researchers found no correlation between the type of university respondents attended and their likelihood to answer positively to the questions above. Selective institutions were no better (or worse) than non-selective ones. Large universities were no better (or worse) than small ones.
The design of the NEI as described in the paper would optimize for every single one of these student experiences.
EdTech
Unsurprisingly, the part of the report which generated the most cognitive dissonance for me is the one I know the most about: EdTech. Overall, the paper is commendably broad in its survey of various strands of educational reform, given that it was written by a group of non-specialists mostly in their spare time. Which makes the technology section stand apart. It’s so very…MIT. Some years ago, the folks inside EdX began talking about “SPOCs”—small, private online classes. They invented a name for these classes, explicitly positioning them in relation to MOOCs and implicitly positioning them as if they were MIT investions. In fact, tradtional-sized online courses have been widespread for almost 30 years. Much of the innovation in the field has been at access-oriented colleges. As far as I have seen, MIT’s approach to this educational form hasn’t been particularly innovative—in fact, it has been retrograde in some ways—and its framing has been ahistorical.
Whoever was briefing the paper’s authors on EdTech apparently never got outside that frame, because the EdTech section largely centered on converting MOOCs to SPOCs to digital textbooks for use in flipped classrooms. This blind spot had the side effect of also blotting out the history of digital curricular products that are actually designed for use in a classroom which, when designed for classroom use often look quite different than MOOCs and can be used in more rich and interesting ways. And finally, the often video lecture-intensive nature of many MOOC designs tends to undercut the overall tendency of the NEI’s design to break out of the “second-class citizen” status that today’s mid-tier universities suffer under. Having students watch regular video lectures from a “real” professor would tend to undercut the authority of the instructor in the room.
That aside, the authors make a few astute observations about the promise and perils of the MIT-centric frame they work within. First, they note that disintermediating the textbook publishers—my phrase, not theirs—with university-created content can subsidize the continued investment by the universities creating the content. While the MOOC is not the right format for this model, a two-sided curricular materials market in which universities are both sides of the market absolutely is moving in the right direction.
Second, they note that “a social contract [for the use of digital curricular materials] needs to be established at the beginning of the class. And during the course, the coaching and in-person elements must reinforce the back-and-forth between the online content and its absorption and application.” The broken social contract is a core problem with analog curricular materials as well. Students are asked to purchase an expensive 400-page book of which they will be asked to read perhaps 15%. But the professor assumes that the students didn’t read and therefore lectures on the topic of the reading. The students feel like they’ve wasted their time so they stop reading, leaving the professor with the (correct) sense that students don’t do their homework. “Social contract” is exactly the right term. The paper’s authors then point out that educators need to be trained in how to integrate these materials into the give-and-take with their students, which is true. But at its heart, the issue can be drawn from those top three effect sizes in the Gallup Purdue research: Students want to know their professors care about what they do, what they learn, and how (and who) they are.
Finally, the authors talk about co-creation of curricular materials with the “SPOC” provider. They list a few benefits of the approach, but two I didn’t see are (1) infusing the teaching expertise of the NEI professors of practice into the curricular materials design, and (2) bringing both diversity and localization to the students’ curricular experiences. e-Literate followers will recognize both this co-creation function, the renewal of the social contract around homework, and the two-sided market as hallmarks of my recently deceased start-up, Argos Education.
The paper leaves a lot more to be explored in the potential for technology to support what I’ll call “medium-residency” universities. While the envisioned NEIs aren’t quite low-residency programs like Goddard College (or no-residency online programs), the sense of campus community and the nature of student touchpoints changes in an environment where students are routinely off-site for long-duration co-ops and are routinely engaged in hybrid or online learning. This has far-reaching implications for how students are supported, how work is organized, and how technology enables these changes. But that’s another topic for another time.
Who will do this?
The authors of the papers were very clear that they had not created a business plan and are aware of numerous practical implications that were beyond the scope of this work. That’s fair. I’ll raise one practical concern at this stage.
A major disadvantage of this approach relative to scaled online universities is the up-front cost. Regardless of any benefits in ongoing operating costs, the upfront costs of either building or radically rebuilding a physical campus are considerable. Further, if this is intended to be a model rather than a one-off demonstration, then somebody would need to take it on at scale.
The obvious candidate is an existing state university system. That would require a legislature with foresight, unity, and budget, not to mention a faculty union ready to embrace the idea. It’s a big lift.
In normal times, I would write this off as a good but impractical idea. These are not normal times. We are approaching a tipping point of punctuated equilibrium in higher education. It’s hard to know what will come next, except that it will be necessarily different from what’s come before. So now is the time to put all good ideas on the table. However improbably they may seem from the perspective of the world as we lived it yesterday.
