OLDaily

HyFlex Resources
Julie Johnson, pearltrees, 2021/04/08

'HyFlex' is one of those concepts that was developed in a hurry in response to the need to adapt teaching and learning during the pandemic. It's a bit like what we used to call 'blended learning' in that it incorporates both online and in-person activities (sometimes, unfortunately, at the same time). Anyhow, there isn't really a huge body of literature on the concept yet, but Julie Johnson has collected a good 70 resources on the topic in this pearltrees document.

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Igniting Change: Final Report and Recommendations
Malinda S. Smith, et.al., Federation for the Humanities and Social Sciences, 2021/04/08

I would be among the first to acknowledge that the issues raised around equity and social justice in this report (192 page PDF) are important, but seeing the site flagged by my security software for ad tracking reminds me that these are not the only issues on the table. And the document as a whole reads as through it was written from a single point of view, which I think is a problem. For example, virtually no one outside academia - or even outside the social sciences and humanities - seems to have been consulted. To be clear: I want the authors to be successful, but it's not clear how they would define (or even recognize) success. The definitions offered at the top of things like 'diversity' and 'inclusion' are muddled and jargon-filled, while the list of recommendations seems more like a grab-bag than a coherent plan. Inclusive web design, for example, is important, and worth mentioning, but this document tries (half-heartedly) to tell us what it is and how to do it (with reference to a 13-year old W3C document instead of something from 2021).

See also the Federation's response and action plan as well as its blog (well worth reading, sadly no RSS). There's also a Charter on Equity, Diversity, Inclusion, and Decolonization in the Social  Sciences and Humanities that uses much of the same language found in the report itself. Image: Gorodenkoff (Estonia), the original of the image used on the report, from iStock images (Getty Images).

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Being in a big factory, why don't I speak human words?
Tang Yahua, Deep Burn, 2021/04/08

This is an automated translation (original in Chinese here) of an article describing the rise of specialized jargon in the Chinese internet community. The authors in Protocol give some examples: "Some jargon is industry-specific: 'user perception' (用户感知), 'closed-link loop' (链路闭环), 'bottom-level logic' (底层逻辑) and 'top-level thinking' (顶层思考). Others are specific to a tech company: 325, for example, means 'needs improvement' within Alibaba" (Actually, in the article it says '325' means "3.25, which means it is judged as having no potential, no year-end bonus, and may be dissuaded"). So there's a bit of jargon in Protocol as well, and no doubt, some ambiguity introduced by the translation. Still, it's a great fun read and no doubt describes a common feeling experienced on both sides of the world.

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AI-enabled Adaptive Learning Systems: A Systematic Mapping of the Literature
Tumaini Kabudi, Ilias Pappas, Dag Håkon Olsen, Computers and Education: Artificial Intelligence, 2021/04/08

A lot of this paper is dedicated to the usual description of how the 147 papers being surveyed were selected and classified, but there is some interesting discussion in what follows, including a section (4.2) on problems and AI-enabled learning interventions. Problems addressed include "difficulty sharing learning resources, the high redundancy of learning materials, learning isolation and inappropriate information load" as well as "high levels of demotivation, passive attitudes, boredom, poor engagement and frustration", but problems not addressed by AI interventions include "the use of outdated and highly complex models" as well as "personalisation issues, designing and assessing adaptive courses, high instructor workload" and more. And there's more to be done to actually make AI work. "Users do not understand how to extensively use such systems. At the same time, such systems—when implemented—have not actually overcome the complex challenges faced by students."

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Comparison of learning analytics and educational data mining: A topic modeling approach
David J.Lemay, Clare Baek, Tenzin Doleck, Computers and Education: Artificial Intelligence, 2021/04/08

It takes a lot of reading to get to this point, but here's the outcome: "Learning Analytics (LA) papers focused more on student engagement, teaching tools, and social network analysis whereas educational data mining (EDM) papers focused more on techniques and methods of data analysis." The bulk of this paper (14 page PDF) is devoted to a description of the paper selection process, topic-modeling methodology, and discussion of the results. The lists of topics for the two types are almost identical, with only expected differences ('mining' vs 'analysis') in the ordering of the topics. The authors also point to areas overlooked by the two types of papers - "disciplinary blinds spots such as big data and AI ethics" - and suggests "they ought to focus on theory and knowledge building." The last thing this discipline needs is Yet Another Theory, but hey, who am I to talk?

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