Stephen Downes

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.