Competency model development: The backbone of successful stealth assessments
JCAL,
2024/06/13
You might not like this, but this, I think, is the future of assessment: "Stealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real-time inferences about their learning." According to the authors, the success of this approach depends on four sets of models: "Stealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real-time inferences about their learning.' The central question is whether these models can be developed algorithmically, that is, by using AI. The full version is behind a paywall, but don't bother - you get the sense from the outline and most of the relevant work was already openly published here. Image: Rahimi.
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No One is Coming to Save Us
Alex Usher,
HESA,
2024/06/13
Alex Usher paints a picture of generally declining government support for higher education since 1971. Through to 1996, this resulted in funding shortfalls altogether (this was ehen I was in higher ed). Since then, revenue generation has become the name of the game. But, he says - correctly, I think - this era is coming to an end. There's no support for continued tuition increases. And government has placed limits on international student recruitment. So institutions will have to focus on cutting costs. In the current model, this doesn't work - you can't cut your way to sustainability; each cut makes it more expensive to support the students that remain. The model - as I predicted in (checks) 1998 - has to change.
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Why AI in the classroom needs its own 'doll test' 70 years post-Brown
eSchool Media Contributors,
eSchool News,
2024/06/13
I think this is a good idea, independently of any preconceptions we may have about the outcome. "We need a comprehensive evaluation–a metaphorical "doll test"–that can reveal how AI shapes students' perceptions, attitudes, and learning outcomes, especially when used extensively and at early ages." Such studies should not look for specific things, should not cater to people's fears about AI, but should be widely designed so as to capture any effects, whatever sort they may be.
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How a widely used ranking system ended up with three fake journals in its top 10 philosophy list
Tomasz Żuradzki, Leszek Wroński,
Retraction Watch,
2024/06/13
It is of course not a surprise to find three fake philosophy journals making it into the top 10 in Scopus rankings. This sort of fraud is rampant. But note how they accomplish this: "The trick is simple: The Addleton journals extensively cross-cite each other." It's a trick not limited to AI-generated journals, and results in some very unserious work being taken seriously.
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Teaching Writing in the AI Era : Metawriting
Deanna Mascle,
Metawriting,
2024/06/13
Teachers won't win in a war against AIm writes Deanna Mascle. Nor is it an answer to encourage students to 'feed content into the capitalist techbro maw' (writers using expressions like that are hard to rake seriously). " The solution to AI's entry into the writing classroom is not hysteria but a return to the writing workshop. Know your students, know their writing, and create a community where your students know the writing of their peers (because you write together as a community) and slay the AI monster together."
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Verifiable Credentials Overview
W3C,
2024/06/13
As the summary states, " The Verifiable Credentials Working Group has just published a Working Group Note of Verifiable Credentials Overview." It's basically a three-part model: you have an issuer, a holder, and a verifier. There are two major approaches: "enveloping proofs or embedded proofs. In both cases, a proof cryptographically secures a Credential (for example, using digital signatures). In the enveloping case, the proof wraps around the Credential, whereas embedded proofs are included in the serialization, alongside the Credential itself. "
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