OLDaily

Welcome to Online Learning Daily, your best source for news and commentary about learning technology, new media, and related topics.

Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom
Louis Deslauriers, Logan S. McCarty, Kelly Miller, Greg Kestin, PNAS, 2024/02/13

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The abstract describes this item perfectly: "Comparing passive lectures with active learning using a randomized experimental approach and identical course materials, we find that students in the active classroom learn more, but they feel like they learn less. We show that this negative correlation is caused in part by the increased cognitive effort required during active learning. Faculty who adopt active learning are encouraged to intervene and address this misperception, and we describe a successful example of such an intervention." So... maybe more cognitive load is good for you? Via Tom Worthington.

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Generative AI - the latest scapegoat for research assessment
Danny Kingsley, Impact of Social Sciences, 2024/02/13

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Danny Kingsley argues that "in the same way that a decade ago, open access was a scapegoat for scholarly communication, now generative AI is a scapegoat for the scholarly publishing system. These concerns have an underlying assumption – the current system is working. We need to ask: is it?" My answer - as readers know well - is that it is not. As Kingsley argues, "there is already a groundswell against the current research assessment system," because the current system - which depends on things like unpaid reviewers, citation counts,  and impact factors - is too easily gamed. "Researchers who agree to manipulate citations are more likely to get their papers published.... prolific authors in high impact scientific journals (mostly with journalism degrees) who were themselves not researchers found a startling level of publication across multiple research areas."

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A third of Aussie children can't read proficiently
Rhiannon Bowman, Education Matters Magazine, 2024/02/13

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What the data shows, contra the report (107 page PDF) cited in this article, is that disadvantaged students perform significantly worse in reading than advantaged students (illustrated). Therefore, if a country wishes to address a weaker literacy score, as suggested here, the appropriate remedy is to address the needs of the disadvantaged population. And by this what I mean is not some sort of specialized 'education for poor people' such as ' a strong focus on phonics-based decoding skills in the early years' and ' a knowledge-rich curriculum' but rather concrete efforts to ensure disadvantaged students share the same advantages as their wealthier peers (which is far more than the 'whole language' straw man set up in the document).

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The OpenAI Endgame
Mike Loukides, O'Reilly Media, 2024/02/13

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This is a reasonably intelligent discussion about the potential outcomes of the court case between the New York Times and OpenAI on the use of content as training data. Mike Loukides makes an interesting prediction: "OpenAI will settle with The New York Times out of court, and we won't get a ruling." This creates a good thought experiment because, as he says, such a settlement "will set a de-facto price on training data. And that price will no doubt be high... sufficiently high enough to deter OpenAI's competitors." It also keeps the pool of those who provide training data pretty small. "This is chilling: if all AI applications go through one of a small group of monopolists, can we trust those monopolists to deal honestly with issues of bias?" Ultimately, the argument here is in favour of open data supporting open AI, but if a settlement were recognized as de facto law this would be that much more difficult to achieve.

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Philosophy's Digital Future
Richard Y. Chappell, Daily Nous - news for & about the philosophy profession, 2024/02/13

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We could substitute any discipline for 'philosophy' in the paper. Here, Richard Chappell argues, first, that "The 'filter then publish' model was designed for a non-digital world of high publication costs. Online publishing removes that constraint, enabling the shift to a superior 'publish then filter' model," and then, second, "AI will make it easier to "map" our collective knowledge, identifying the most important contributions and highlighting gaps where more work is needed." Both arguments are sound, in my opinion.I would add that a 'publish then filter' model doesn't require reviews per se; any response to the original article in a subsequent publication serves the function equally well. So we don't need to be concerned about incentivizing reviews.

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We publish six to eight or so short posts every weekday linking to the best, most interesting and most important pieces of content in the field. Read more about what we cover. We also list papers and articles by Stephen Downes and his presentations from around the world.

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