The core argument in this paper (19 page PDF) is that students' perception of feedback from learning analytics has an impact on how they regulate their own learning (see the diagram), which means that (in theory, at least) if you can tailor the feedback to individuals you can have a greater impact on processes like goal-setting. As always (from my perspective) it's really hard to work from such vague terms and imprecise measurements such as, say, self-perception of motivation, to recommendations for specific individually tailored prompts. I mean, what do you make of this? "I think some of the ways it might have recommended might not have been part of the normal peoples' way of studying. ... personally I didn't do any of the [e-book] because that's not how I like to do it." Still - what we would do is exactly what the conclusion says we should do: talk the students through their dislike or particular tools and methods.
To be clear, you probably shouldn't do machine learning in Excel. As one Reddit comment said, it's like learning to drive in a Model-T. But still, if you're comfortable with Excel, why not? This article summarizes a book describing machine learning in Excel, and where there are bits and pieces in columns elsewhere (I searched), you'd probably want to pay the 30 Euros at APress (their "dedicated site for Canada" leads to the U.S. site, which is more of an insult than a convenience). That's a far sight cheaper than the $2795 course (delivered virtually) from Global Knowledge and a little cheaper than the $10/month for a bundle from Packt, but not as cheap as Microsoft's own documentation, which is free.
I am in my won way a futurist and so I feel obligated to post at least a couple sets of predictions from other people. The third and fourth predictions in this post are noteworthy (the rest are throwaways). The third looks at the difference between business-centered and people-centered leadership, and these (mostly) translate to education. The fourth looks at the topic of the 'employee experience' and while this might seem like a lark, the last year made clear just how important it is. This, again, translates to education, for both teachers and students. "It demands a whole nest of integrated digital tools (read about IBM’s focus here) that go from case management to knowledge management to safe workplace to daily productivity."
In a meeting a few days ago I was asked to predict what's upcoming. I had two items, one of which matches what Matthias Melcher in this post: the resurgence of anti-technology sentiment. A lot of it will be expressed as 'getting back to normal', and a lot of it will be expressed as 'what a disaster digital learning was in 2020'. As Melcher says, "people are craving for the real, the genuine and the authentic." Maybe. And maybe there's a point to what he describes as Luddism. But I think that a lot of people, for a lot of things, won't want to go back. (p.s. the second prediction? I think we'll begin in 2021 to consider the huge deficits the pandemic created, and that by 2022 education will be in a full-blown public funding crisis, the severity of which depends on your particular government). Image: Forbes predictions.
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