April 13, 2019

Data-Mongering: Platform U and Other News of the Week

It's Saturday and part of me wants to write a happy blog post about open-ended pedagogy and colearning... and maybe I'll do that later (see #TotalCoLearner at Twitter), but I think I need to review the data-mongering articles I read this week. In fact, I might try to make this a weekly round-up of sorts, scrolling back through my Twitter feed and sharing links here. These are articles that I read this week; some are new, but some are old which I only now got around to reading.

And what is "Platform U" you might ask? Read on:

The platform university: a new data-driven business model for profiting from HE by Ben Williamson. This article discusses exactly what I see happening at Instructure, and why I am so unhappy about it: 
Despite studies repeatedly showing Turnitin’s high error rate, and considerable concern over the mistrust it creates while monetising students’ intellectual property, its acquisition clearly demonstrates huge market demand for data-driven HE platforms. It changes how students are valued—not for their independent intellectual development but as raw material to be mined for market advantage.

How Ed Tech Is Exploiting Students [premium at Chronicle of Higher Ed] by Chris Gilliard. This is an article from last year warning about the dangers discussed in Williamson's article, focusing specifically on the students' lack of consent in the exploitation of their data:
When we draft students into education technologies and enlist their labor without their consent or even their ability to choose, we enact a pedagogy of extraction and exploitation. It’s time to stop.

Colleges Are Banding Together Digitally to Help Students Succeed. Here’s How [premium at Chronicle of Higher Ed] by Alexander C. Kafka. A truly horrifying piece about Canvas data mining, this time in the context of the Unizin consortium (my school does not belong). This is exactly what Goldsmith at Instructure promised (Soylent Canvas), and now with endorsement from the educational administrators themselves: they really believe in this data nightmare. Sad to see Jared Stein quoted here; I guess the whole Instructure crew really is on board with the new predictive-analytics push where students are reduced to their clickstreams and pageviews (my thoughts on AI Overreach: students are more than the data they leave behind in an LMS!).
Take students’ clickstreams and pageviews on the learning-management system, their writing habits, their participatory clicks during classroom discussions, their grades. Then combine that with information on their educational and socioeconomic backgrounds, their status as transfer students, and so on. You end up with "a unique asset," says Wheeler, in learning what teaching methods work.

Counting the Countless: Why data science is a profound threat for queer people by Os Keyes. The observations here about the state apply to educational institutions also, and it is surely the most vulnerable students who are going to be hurt most by tracking based on predictive analytics driven by LMS data-mining:
So: trans existences are built around fluidity, contextuality, and autonomy, and administrative systems are fundamentally opposed to that. Attempts to negotiate and compromise with those systems (and the state that oversees them) tend to just legitimize the state, while leaving the most vulnerable among us out in the cold. This is important to keep in mind as we veer toward data science, because in many respects data science can be seen as an extension of those administrative logics: It’s gussied-up statistics, after all — the “science of the state.”

Margin of error in data-driven decisions by Robin De Rosa. This is a great piece on the gap between quantitative and qualitative data, especially in education... data has to be more than number-crunching!
When we ask whether there is evidence for something related to learning, we are presuming that we all agree 1) what learning is and 2) what constitutes evidence. I contend that “learning” is broader and messier than what we generally assess, and also that “evidence” has been reductively equated with quantification and with the assumption that environments in education are controlled. At the core, I think the biggest problem is that we forget that humans aren’t just giant brains walking around: we are also a jumble of social contexts, emotions, and circumstances.

10 technologies that will impact higher education the most this year by By Macy Bayern. Yep, predictive analytics, AI, nudges, it's all there. instead of a pull-quote, I will share Robin's tweet. What she said.

And this cartoon that Bob Calder shared with me is a great way to express how all this top-down data-mongering looks very different from the point of view of teachers and students who are being surveilled. The cartoon circulates in lots of languages, but I think it may have started with the Polish version (?):

He likes it!
Have fun playing, little one.

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