OLDaily

National Microcredentials Framework
Government of Australia, Department of Education, Skills and Employment, 2022/03/29

Released this week by the Government of Australia Department of Education, Skills and Employment, this document (20 page PDF) sets out a national definition for microcredentials, unifying principles for microcredentials, and a minimum standard for microcredentials that will sit on a "Microcredentials Marketplace." The definition essentially says microcredentials apply only to assessed outcomes, though the statement of what this means is unfortunately Australia-centric. The underlying principles are arguably more universal (though I'm sure people would argue with them): microcredentials are, says the report, "outcome-based, responsive to industry need, tailored to support lifelong learning (and) transparent and accessible." So no microcredentials in philosophy, then, which seems a shame. I don't see any particular reason why microcredentials need to be so industry-focused, though that seems to be a major requirement here. Via Don Presant, who also links to last year's Saskatchewan Guide to Microcredentials (6 page PDF).

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Why has higher education decided on Zoom?
Bryan Alexander, 2022/03/29

This is an interesting list of reasons in its own right, and well worth a read. But at the same time it feels to me like an example of how you can ask a question of fifty or so well-informed people and still not really land on the right answer. From where I sit, there was one overwhelming reason for Zoom's success: anyone could use it without having to be logged into an institutional network. You could set up short meetings for free or a quite small monthly fee that even people like me can afford. You could, in other words, use it from home. Users didn't actually have to log in at all! Compare with the cost, login and domain nightmares we may still experience with alternatives from Google, Microsoft, Blackboard and the rest, and you can see why nobody really wants to let go of Zoom.

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Development of a Technology-Assisted Assessment for Sign Language Learning
Tobias Haug, Sarah Ebling, Katja Tissi, Sandra Sidler-Miserez, Penny Boyes Braem, International Journal of Emerging Technologies in Learning, 2022/03/29

It makes sense to use AI to recognize visual interaction such as sign language. But it is another thing again to use it to assess sign language, because here we have a case where we're trying to recognize something when it's being done incorrectly. This study (18 page PDF) looks at such a system being piloted with 100 words (out of more than 3,000) in the Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS). "The assessment system that we present here," write the authors, "provides adult sign language as a second language (L2) learners of DSGS with feedback on the correctness of the manual parameters (hand-shape, hand position, location, movement) of isolated signs they produce." The productions deemed incorrect "were then further linguistically analyzed to inform future sign language instruction practice." A future iteration of the system will be offered as an online assessment people can use in their homes.

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Selection of Audio Learning Resources Based on Big Data
Peng Wang, Xia Wang, Xia Liu, International Journal of Emerging Technologies in Learning, 2022/03/29

We're so focused on text-based content recommendation systems it's easy to forget about other types of data, such as audio. Looking at such systems takes us into a world of what are to me new concepts and entities. This paper (16 page PDF) looks at audio similarity based on "mel-frequency cepstral coefficient features" (read about this in Wikipedia here). These audio features are used by speech recognition engines; they are combined to build up words and phrases. The recommendation system described here then adds other, more familiar, elements, such as language, scene, genre and mood to fine-tune the categorization. A discussion for the future will be to ask whether feature-based audio recognition and recommendation will be superseded by more general transformer neural network algorithms, which I talked about here.

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Artificial Intelligence in Education and Ethics
Benedict du Boulay, Handbook of Open, Distance and Digital Education, 2022/03/29

This is the first of a series of articles in the open access Springer Nature Handbook of Open, Distance and Digital Education. I will no doubt list a number of these overview articles in OLDaily and I have a contribution of my own coming up (my deadline is in June). This particular article (16 page PDF) gives us a brief history of AI in education and then looks at learner, teacher and administrator-facing tools (such as chat agents and dashboards) before looking in to the ethical issues, which are organized according to the same three categories. In such a short space it is difficult to do the topic justice, but it serves well as an introductory look. Image: Hwang, et.al., Vision, challenges, roles and research issues of Artificial Intelligence in Education.

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Data Patterns for the Edge: Data Localization, Privacy Laws, and Performance
Anoop Koloth, InfoQ, 2022/03/29

This article gets into the weeds a bit but it's a really important concept and even if you don't follow the details it's good to become familiar with the broad outline. And that outline is this: with increasingly restrictive (and necessary!) data protection laws, more and more data is being stored locally, rather than in central data repositories. This is called 'the Edge', and in this article, "anything outside your data center can be considered 'the edge' of the network." So how is that managed? That's what this article is about, as it explores the interactions between end users, edge data, and synchronization with central data repositories (which have to at least know about the data, even if they don't store it).

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