OLDaily

Tony Hirst, OUseful Info, 2021/07/09

This post wanders through some compelling thoughts raised by the use of AI software like GPT-3 to write stories, relate facts, give instructions, program computers, and more. One question is: how reliable are they? Tony Hirst comments, "we might see Google as attempting to perform as a knowledge engine, returning facts that are true, and OpenAI as a belief engine, freewheeling a response based on what it’s just heard and what it’s heard others say before." But more: where does GPT-3 get its information? There's no attribution, no direct way to know whether it has been plagiarized, "is it possible to licence code in a way that forbids its inclusion in machine learning/AI training sets," and should we be prohibiting the use of such tools by students in coding exercises?

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High-Leverage Opportunities for Learning Engineering
Ryan S. Baker, Ulrich Boser, Penn Center for Learning Analytics, 2021/07/09

This document (48 page PDF) is undated but recent; I found it referenced in an otherwise unhelpful article in The 74. The topic of 'learning engineering' isn't popular among educators or learning designers, but there's a school of thinking which touts this as the way forward. This paper identifies ten 'opportunity' areas in post-pandemic learning engineering, but the topics are unremarkable, including such things as "enhance human-computer systems" and "support learning 21st century skills and collaboration". Some of the more specific suggestions actually are interesting, though I would hardly see them as being characteristic of learning engineering. Things like "methods for knowledge graph discovery" and "collect more complete data on learner identity" are interesting to me, for example. What readers will notice most of all is the lack of a systemic approach. We get a bunch of topics without any deep discussion about what educators are trying to do or what would count as success.

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Sharing and self-promoting: An analysis of educator tweeting at the onset of the COVID-19 pandemic
Jeffrey P. Carpenter, Torrey Trust, Royce Kimmons, Daniel G. Krutkad, Computers and Education Open, 2021/07/09

With the relatively sudden arrival of the pandemic in the spring of 2020 a brand new term sprang up: 'remote learning'. In a certain respect, the new term precipitated a rush for influence and status in social media circles (the closest comparison I would draw was the way influencers flooded Google+ when it was first launched). This article looks at the use of the term 'remote learning' as a hashtag on Twitter. "We identified four overlapping themes..." write the authors, "professional knowledge sharing, social sharing, self-promotion, and information broadcasting." On the one hand, they note, " hashtags offered affordances as ad hoc spaces where educators engaged in just-in-time knowledge and social sharing.” But on the other, "the sheer quantity of self-promotion in the #remoteteaching and #remotelearning spaces may also have been overwhelming or distracting for some users."

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Praxis
Radhika Dimri, Product Hunt, 2021/07/09

I offer no warrant whatsoever that this project is good, nor even that it actually exists. I do however want to point to the idea, which is a good one, and points the direction to a whole range of learning technology. Here's the idea: you select a workout video on YouTube, then set your webcam to show yourself doing the same workout, and the AI compares your form with the YouTube form to determine whether you're doing the exercise correctly. This may look like a pretty niche product, but you consider the wide range of activities that can be recorded on video, you get a sense of how large this learning technology could become. See also: the virtual fishing rod.

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