OLDaily

The Digital Learning Ecosystem: What’s Missing?
James Wiley, Encoura, 2021/11/04

It's a bit of work to get to the main point of this post, which is this: "Learning Analytics Solutions are struggling to gain traction in the market; only 9% of the (full stack ecosystem) schools use this type of technology, while (online-focused) schools... have avoided implementing these solutions entirely." This may be because learning analytics is bundled with the other types of products, or (more likely) it may be because people don't really see the value in using learning analytics "to measure, collect, analyze, and report data about learning outcomes." The article also breaks down the learning technology market into eight categories, with links to category leaders.

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Newsela Provides Tons of Leveled Reading Materials for Students
Med Kharbach, Educational Technology and Mobile Learning, 2021/11/04

I found the Newslea website very frustrating because you need to sign in to see almost everything - the only freely accessible resource was the blog, which published infrequently. It's frustrating because what the site does should be subject to public scrutiny. It selects and offers curricula-aligned news resources to schools "from over 100 authority sources in the publishing realm namely:  History, The New York Times, The Washington Post, USA Today, PBS NewsHour, Scientific American, Smithsonian, World History Encyclopedia, among others." So what, exactly, is being shown to students? Fox News? National Geographic? The site stresses access and equality, anti-bias and anti-racism, but you can't actually read about any of this without signing in.

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Humans’ Invariance Assumption: Should Statistics and AI Adopt It?
Patricia Cheng, The Brains Blog, 2021/11/04

This is a complex post (and the comment which follows even more so) but it boils down to this: what if there are some biases that humans (and by inference, AIs) have to have in order to be able to learn and know things about the world? One such is the principle of causal invariance, which is the idea that causal relations don't change over time; "if a causal relation changes from the moment one induces it to the moment one applies it, that knowledge would be useless." It's the difference, we could say, between merely searching for phenomenal patterns in the world, and searching for truth. And this, the argument runs, is a good sort of bias to have. There may be others. "Introductory psychology courses teach the many ways intuitive reasoning is riddled with biases. Work on invariance may show that insight can flow in the opposite direction." Image: Causality for Machine Learning.

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