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Alex Usher interviews his "favourite commentator on all issues related to educational technology and higher education institutions," Phil Hill. The best bit is this, where Usher comments: "25 percent of post-secondary enrollments in the U. S. are now fully online and another 25 percent or so are at least partially online. I had no idea the market was that big." But they are, and as Phil Hill makes clear, a big part of the reason was MOOCs. "MOOCs, along with 2U's initial business model, those were the two things that forced traditional higher education to get over themselves and say, we need to take online education seriously." The two of them are far more interested in markets and money than I am, but on these they know their stuff. Image: Phil Hill.

Your college degree, sponsored by Google? " Interim (University of Nebraska) President Chris Kabourek announced Tuesday that the university will soon offer Google Career Certificates in a variety of fields... Google experts teach the programs, which are vetted by leading employers. NU students, alumni and Nebraska residents can get a special first-year rate of $20 per enrollment." According to Kabourek, "the new partnership advances a 2022 legislative goal, which NU supported, to increase the percentage of Nebraskans with postsecondary credentials by 2030 to 70%." I'm not sure that's what the legislature meant, but these days, who can be sure? Via The 74.

Offering a little light reading, this report (502 page PDF) "tracks, collates, distills, and visualizes data related to artificial intelligence (AI)." The authors ease you in; you can read the 'top 10 takeaways' (p. 5), chapter-by-chapter 'report highlights' (p. 14ff), or read the chapters themselves - each page is almost like an individual slide with some key points and a graph or table illustration, while some coloured 'highlight' pages (like 'Will Models Run Out of Data?', p. 52) are interspersed. It's worth the time to just flip through this document, where you'll find everything about AI considered, including, for example, "their capacity for moral reasoning, especially moral reasoning that aligns with human moral judgments, is less understood... Of all models surveyed, GPT-4 showed the greatest agreement with human moral sentiments," a statement that is remarkable on several dimensions (p. 122 ).

You may not think of children's vaccines as educational technology, but I think of it as the second educational technology, following only the first: proper nutrition. This tech stuff we do? That only helps once we've addressed the rally major issues created by child poverty and misinformation about health. I'm old enought that I got all the diseases when I was a kid - measles, German measles, chickenpox, mumps. Everybody got them, and a few of the kids died. What would have been better than running that gauntlet? Immunization. "We have forgotten how many children used to die before their fifth birthday," writes Kristen Panthagani. I haven't forgotten. That's why I get a flu shot every year and make sure I'm up on my Covid shots. I've remained flu-free for years and years, and entirely covid-free. I hope to stay that way. Via Robin DeRosa.

It's not that this article is wrong. But as Gerald Ardito comments, "there is more than a little irony in this post, given EdSurge's long time tech boosterism." Tanner Higgin writes, "Within this technocentrist frame, education is sick and edtech is like medicine." How many studies do we see like this, asking "what works?" in education, as though the same goal were shared by all. But as Seymour Papert writes, "The content for human development is always a culture, never an isolated technology." And in particular, "The potential of technology is significantly affected by the humans that use it and their context."

This short article references a 2022 paper that studies how the order of questions in a test impacts how well the test-takers do. "A perfectly logical test-taker should do equally well no matter the order of questions," writes the author, "but research shows that humans are influenced by the order." In particular, "students answered more... questions (correctly) when they began with easy questions compared to when they started with difficult ones." The relevant question to ask here is this: if the result depends so much on the order of the questions, what is it exactly that the test is measuring?

I often ask what it is we're doing when we're teaching and learning, that is, what counts as success? This paper offers some insight into the sort of question I have in mind. To 'learn' something is more than just to come to know that something is true. That's just memorization. No, as David Bourget would argue here, it's to grasp what is being taught. "It is one thing to believe something, and it is another to grasp it. For example, everyone knows that life is short, but most of us arguably do not fully grasp this fact." But what is it to 'grasp'? He argues, "we grasp to the extent that our thoughts are grounded in experience, whether occurrent or non-occurrent... , what we experience matters to how we reason because that is how we are wired: consciousness isn't a late addition to our minds; it is the
most central, causally potent form of mental activity." I think this argument works, overall. Image: Ding.

This paper (21 page PDF) is a review of papers proposing AI competence frameworks for teachers (CFT) and organizes them into five categories: integrating AI competencies in existing CFT; modelling new AI knowledge domains; process-driven; AI systems-driven; and competence level-driven. True to their field, the authors stress the need to theorize. "The empirical and design-based nature of AIED requires a solid theoretical foundation. The adoption of theoretical frameworks serves as a unifying force, fostering shared concepts and terminology among researchers and designers." But it's not clear there is (or is going to be) agreement on just what to theorize. This depends on what we want teachers to do, and as the authors note, "it is important to understand what kind of problems AI teachers' competencies will solve and what kind of solutions AI teachers' competencies convey."

This article offers three examples of the use of AI in a learning concext (it's a bit too early to call them 'success stories' since we don't really have a story of what counts as 'success' in this context). In one example, students are provided "with customisable prompts that they can cut-n-paste from a shared GoogleDoc to save them typing the prompt in." In the second, a 'learning lead' "used Microsoft Copilot to create imaginative discussion prompts for students." In the third, a senior lecturer aims "to normalise the use of GenAI" by putting it in the handbook and offering training sessions.

Since one assumes the 'President & Fellows of Harvard College' know better, we must conclude that their publication of articles like this is deliberately deceptive. Here's the set-up: Frederick Hess imagines himself in a conversation with an Uber driver about the plan to forgive billions in student debt. "I pay for this car," she (the Uber driver) said. "I paid for community college. These people borrowed money to go to college. They promised to pay it back. Why am I supposed to pay their bills, too?" Well, first, because living in a society isn't a quid pro quo. Second, at tax time an Uber driver is declaring business expenses like car payments, which offset the cost. Third, it is recognition that without addressing tuition fees college is something that could never be accessed by the working class, as used to be the case. And finally, neither students nor Uber drivers should be paying more in taxes than they get back: in a just society, those with the means (including Harvard and its $50 billion endowment) who benefit the most from society should be paying the freight.

"I take the position that a large portion of contemporary academic work is an appalling waste of human intelligence that cannot be justified under any mainstream normative ethics," writes Nathan Robinson. He's writing about philosophy but he could be writing about a lot of academia generally (not all, though, and that's an important distinction). I have asked this question of myself, and knew I'd never be able to just teach philosophy at an institution. It's more important than that. We discussed this morning how we achieve a 'work-life balance' and I replied that I don't have one: my life is my work, and my work is dedicated to making the lives of people better. Singer resonates with me: "we all know it would be wrong to watch a child drown in front of you and do nothing, so why is it okay to let children die far away from you and do nothing?" (P.S. I feel very self-conscious saying stuff like this so mostly I don't, but it's there, behind all my work.)

Justin Weinberg, writes about "anew website features autobiographical essays by philosophers who were first-generation college students" called First Generation Philosophers. It's of interest to me because I also fall into that category of 'first generation philosopher', though I'm uncomfortable with the division of the world into academics and non-academics and of the suggestion that one is of a better class than the other (though of course I'm not from Oxford, Cambridge or Harvard, which 4/5 of the writers here are). Still, it's interesting to see their success at navigating a thin sliver of opportunity afforded by progressive governments during their time and how they are for the most part opposed to the class-based system inherent in traditional academia (even while benefiting from it, though I don't begrudge them that).

It's so tiring reading stuff like this: Somewhere between the death of our favorite aggregator websites and the world surviving a pandemic, the modern internet was reduced to four companies in a trench coat. You don't have to visit those websites. RSS still exists, aggregators still work. Blogs are still being published by thousands of people around the world. The only person stopping you from stepping outside a paywall-defined and/or toxic meta-site is you. That other internet exists - you have just conditioned yourself to pretend it's not there. Via Dan Gillmor.

"Meta is searching for a killer app for its Quest headsets, which today are primarily used for gaming," writes Jennifer Kingson. "Could it be education?" Um, no. At least, not as the headsets are currently designed, and not for (as illustrated) simulating a meeting across a table (we really don't need more than Zoom for that, or possibly, just a table). As this story makes clear, the real problem being addressed here is Meta's desire to sell more units. That's not a problem education needs to solve. Via Doug Levin, who comments, "it is apparently silly season in edtech."

Without open data there is no open AI (and no open science generally). "However," the authors write (9 page PDF), "the challenge of producing and openly disseminating data that are easily discoverable, accessible, interoperable, and reusable (FAIR) has emerged as a significant concern for policymakers." This article attempts to explore the reasons for that. To make a long story short, even if the researcher intends to abide by FAIR principles, institutions and repositories often have policies and regulations that make compliance difficult. "While some institutional repositories enforce policies like restricted open access, which reduces the visibility and reusability of research outputs, others impose eligibility restrictions on deposits, raise copyright concerns, and require funding for maintenance." I've argued with people that the default should be 'open', while restrictions should be the exception that requires justification. But that's the opposite of most institutions today.

I wish I could say it was just Missouri that is blocking all these websites, but I'm quite sure it isn't. There are two aspects: first, the deliberate blocking of websites, which is a political issue, which I will leave to the particular societies concerned; and second, the accidental blocking of websites, which I'm sure is why a site like NASA would be blocked (as well as, for me, at the office, the Open Education Conference), which results from (a) bad categorization, and (b) category-based content filtering. This is an education technology issue, and it is having a widespread impact, not simply because it makes the sites more difficult to access (not impossible, unless you're poor; you can just use your own phone or home computer) but also because it undermines our respect for the institutions that are managing access to information in such a sloppy manner. Via Doug Levin.

According to this article, "online mathematics programs may benefit most the kids who need it least." It's a catchy way to introduce the main argument, which is to say, the program works "only if used as intended", however, only five percent of students use it as intended, and these are already likely to be high achievers. I think the point is well made, but what's the solution? Enforce proper use? Not practical. Return all math instruction to in-person instruction? Also not practical, and it's also not clear that this would solve the problem (how many teachers teach 'as intended')? Getting the research right would be a start.

The scoring was applied to the State of Texas Assessment of Academic Readiness (STAAR) for Science, Social Studies, Reading, and Language Arts. As George Siemens reported, "the state hired around 2,000 human scorers this year, a small margin compared to the 6,000 employed in 2023."

I was going to build my own tool (a simple link to a website that says "No!") but while I get that ready (I've applied for funding) you can rely on this tool that "offers a curated list of questions designed to provide visibility into factors related to the institution's readiness for identification and adoption of generative AI solutions across three core focus areas: strategy and governance, capacity and expertise, and infrastructure." Here it is: 12 page PDF.

We want to make decentralized, but if we start there, we break a key principle of software design, which is that we must start with the user experience. Which sounds great, but when you start with the user experience it's really easy to make software that is very hard to decentralize later. So what, asks Gordon Brander, is the minimum we need to build to make our user-focused software decentralizable? His answer: Immutable data, universal IDs, user-controlled keys. "If you have all three," he says, "you have a fair shot at building first, decentralizing later." Sounds easy - but how to get even this far is hard.