by Stephen Downes
Apr 13, 2017
The point of this article is that we have to get past old systems of thinking when we employ new technology. Couldn't agree more. But does the example make that point? George Couros writes that ionstead of saving all his stuff in folders (which is the old way of thinking) he just saves it wherever and makes sure it is tagged (that's the new way). But tagging - ie., metadata - seems to me to be fraught with peril. At least you can reorganize or split folders, but if you need to adapt materials to a new tag then you have no way to do it. My own method is to write a short description and then use regular expressions; this allows me to make new categories out of old materials even if the new tag hasn't been developed yet.
Creating an API for a university system is more of a challenge than you might think. ""The inconsistency was such that if you wanted to write a mobile app that dealt with a student, it was possible you would have to deal with 25 different APIs using five different identifiers, and multiple data formats." Yeah. And that's for a small university. Picture now a large enterprise or a government. But you start with some services, and eventually you have a single consistent interface. Start simple and expand as you go.
The interesting part of this post occurs when Christy Tucker says "Maybe you don’t need to scaffold within a microlearning module. Maybe the microlearning itself is the scaffolding." Indeed, if we can get past the idea that learning resources are single unified wholes, then we can imagine a constellation of resources around a particular activity, where each resource provides a bit of scaffolding and support.
I've been on Mastodon for a while and have written about it previously, but if you're just looking at it now the first instance, mastodon.social, is now closed to new users. But the idea is that Mastodon is distributed so there are other instances you can join. When you do, you can find me at https://mastodon.social/@Downes.
This is a technical article with some good less-technical points. First, we have the idea of how straightforward machine learning has become, as noted in the title. Second, though, those seven lines embody considerable depth of function. The data is run through several layers of neural networks (five of the seven lines in question). Finally, this: "The essence of machine learning is recognizing patterns within data." But it's not just the essence of machine learning, it's the essence of learning in general. To know is to recognize. To recognize is to be connected in a particular configuration. To learn is to form those connections from experience and reflection.
Let us welcome our new robot overlords. "ASIMO is an autonomous robot, meaning that it performs without the need for a human controlling its movements, and was billed as the first humanoid robot capable of human-like running (along with being a pretty fine dancer, too). The technology for ASIMO made its way into Honda’s version of walking assist robotic legs in 2015."
Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research
Daniel Z. Grunspan, Benjamin L. Wiggins, Steven M. Goodreau, Life Sciences Education, 2017/04/13
The paper is a couple of years old but I thought it was a good introductory deep dive into social network analysis (SNA) for education. It contains good practical advice for people who want to apply SNA in their own educational research (for example, recognizing and dealing with survey fatigue). It also describes how to test for specific hypotheses, for example, whether there is a correlation between link-formation and learning outcomes. "Conceivably, network analysis can be used to describe the structure of seemingly ethereal concepts such as reputation, charisma, and teaching ability through the social assessment of peers and stakeholders."
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