OLDaily

Online learning, videoconferencing and energy consumption
Tony Bates, Online learning and distance education resources, 2021/09/17

So here's the argument, from Renee Obringer: "A standard video conference uses 2.5 gigabytes per hour and has a carbon footprint of 157 g of CO2. If one individual has 15 one-hour meetings in a week, his monthly carbon footprint would be 9.4 kg." I see this sort of argument a lot. And I want to be clear that it has nothing to do with individual choices about videoconferencing. To illustrate that point, here's a chart of Ontario Electricity Production. As you can see, less than 3 percent of the electricity in Ontario is produced from fossil fuels. So we're not dumping carbon into the air when we videoconference. True, the situation is very different in other countries. But this is now a matter of national energy policy, not individual choices about videoconferencing (it does matter how you vote, however).

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How does CORE substitute Microsoft Academic Graph?
Melanie Heeley, JISC, 2021/09/17

Microsoft Academic Graph (see next post) does a lot of what numerous education-led projects (including Jisc's CORE) intended to do. Where does this leave these projects? " CORE already fulfils a number of the solutions supplied by MAG, but critically CORE is not a direct replacement for MAG. In fact, there is no direct replacement for MAG. As OurResearch clearly says in their announcement – a “perfect” replacement is very hard to come by." In other words, this is a concession that Microsoft looked at what the sector was trying to do and did it better (it helps a lot I think that Microsoft didn't have a prior interest as a journal publisher). CORE there fore is now focused on more specific projects, such as "Methods for detecting citation intent and purpose embedded and applied on citation data" and "Full-scale near-duplicate detection to recognise different versions of manuscripts."

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Next Steps for Microsoft Academic – Expanding into New Horizons
Microsoft Academic, 2021/09/17

I missed this when it came out (one can't be everywhere) but I definitely want it entered into the record: " Microsoft Academic has been on a mission to explore new ways to empower researchers and research organizations to achieve more. The research project is characterized by two sets of technologies: one that reads all the Bing-indexed web pages and organizes the most up-to-date academic knowledge into a knowledge base called Microsoft Academic Graph (MAG), and the other that performs semantic reasoning and inference to serve that knowledge through the Microsoft Academic search website and API."

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Is “Teaching Loss” a Myth, Too?
Pav Wander, Chey & Pav, 2021/09/17

"Did teachers miss out on being able to have a fulfilling teaching experience?" asks Pav Wander. "So much happened that prevented us from truly being able to enjoy, or even be a part of, the full teaching experience. We were knocked off our foundations, we were just barely managing to keep learning meaningfully, and many of us, including myself, describe this past year as 'just trying to stay afloat.'" The article lists six facets of teaching loss, and is worth considering (you may need to convert it into a more readable font first, though). Via Doug Peterson. Image: EdCan network (found via Google image search, which credits it to Anthony Jess on Adobe Stock and gives this link to tell us "where this information came from").

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The Use and Misuse of Counterfactuals in Ethical Machine Learning
Atoosa Kasirzadeh, Andrew Smart, ACM Conference on Fairness, Accountability, and Transparency, 2021/09/17

This paper (9 page PDF) dives into the weeds a bit, but it's interesting and potentially very useful. When we use AI in learning, one of the criteria people ask for is for an explanation of decisions or recommendations. But for various reasons, which I mentioned here and will discuss in detail in the future, explanations will either be very difficult to get or not very useful. Instead, a lot of writers are recommending the use of counterfactuals; sometimes, what people need, rather than an explanation per se, is a statement of what could have been done instead to produce a different outcome. But counterfactuals introduce their own issues. How do you know that a counterfactual is true? This article looks at the semantics of counterfactuals and offers a table of the decisions we need to make in order to use them. And this gives us an interesting way to talk about the ethics of using AI in learning.

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