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Topic: "Convergence: Big data, digital texts, and what?"

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Topic started by: Brian Drayton on 11/3/15

I’ve written once before in this space about digital curriculum; and about big data. Now, thanks to the wonders of human inventiveness, the impulse to engineer society for its own good, and the logic of the marketplace, it’s already hardtoexplorethe educational possibilities for digital curriculum, without adopting simultaneously the lenses of accountability and commerce. I wonder whether this instant move to control and commodification won’t impede progress in understanding the strengths and weaknesses of the new tools.

Let’s start with a recent column by Will Oremus in (“No more pencils, no more books“). The article reports, and reflects, on two big developments. “While the thinkers are arguing,” says Oremus, ” textbook publishers are acting.”

First is the current speedy transformation of Big Text (McGraw-Hill, Pearson, &Co) into Big Digital Content Delivery, or something. A McGraw-Hill imagineer (without, I suppose, the Disney twinkle) says that they aren’t a textbook company any more: it no longer thinks of itself as a textbook company. Were a learning science company, and, Oremus tells us, the logic of techno-economic development is impelling the ed products industry to incorporate more and more into their designs:

David Levin, CEO of McGraw-Hill Education, tells me his company views all of this as an imperative to reinvent its core products. To retain its value, Levin says, the textbook of the 21stcentury cant just be a multimedia reference source. It has to take a more active role in the educational process. It has to be interactive, comprehensive, and maybe even intelligent. It has to make students and teachers lives easier by automating things theyd otherwise do themselves. The smarter it gets, the more aspects of the educational experience it can automateand the more incentive schools and teachers will have to adopt it.

So what are these “smart things?” The early betting seems to be that more and more eduproducts will include “adaptive technology,” a digital environment that includes an artificial-intelligence layer which draws inferences about its user (customer, client, student) on the basis of her responses to various prompts and actions within the environment. Oremus digs into this development, one more manifestation of the apparently irresistible impulse to automate “differentiated learning” (or at least “differentiated instruction,” see an earlier blog post here for links and opinions).

Reading the article, you can’t help but see notice the little tell-tale buzzwords: “learning styles,” “personalized learning,” “efficiency,” “student outcomes,” “interactive” and so on, words that carry a freight that includes a bit of meaning and a lot of atmosphere. Despite the good intentions and deep backgrounds of many of the visionaries and experts in this sector of the Ed Industry (once, education was a social enterprise, now it’s an Industry), the mutually reinforcing powers of policy and capitalism drive us all to quickly to seize upon some ideas, hopes, hypotheses about learning, and reify them as products. Products are purchased, and then become imperatives we have to justify our investment. So where’s the room for critique? For learning from experience? For research to inform practice (not to mention purchasing)?

This is not just the moaning of a slow-coach Luddite the questions raised by ed tech are often deep and maybe revealing but research is itself a design process, requiring time, intuition, and serendipity as well as data (information collected as part of a theoretically-controlled inquiry) not “data”as in“a massive record of events” in which to go fishing.

Critiques are always being made, of course, and not always in ivory towers or little blogs in the hills. I recently read the comments of a voice new to me, whomyou might enjoy, as well, one Emmanuel Derman. It’s not that he is saying anything new, but in reflecting on worldwide fascination with Big Data (which drives much of the technological innovation that is coming to education), he reminds us of fundamentals.

Big Data is useful, but is not a replacement for the classic ways of understanding the world. Data has no voice. There is no “raw” data. Choosing what data to collect takes insight; making good sense of it requires the classic methods: you still need a model, a theory, or an intuition to find a cause. “Philosophy is a battle against the bewitchment of our intelligence by means of language,” wrote Wittgenstein. I take that to mean that language can deceive our natural intuition, and we need philosophy to reclaim it. In a similar sense, I would argue, science is a battle against the bewitchment of our intelligence by data.

In our work, we often use theories, because they help us design processes, tools, events, heuristics. We need also to encourage and challenge each other (and ourselves) to test the theories, even some thatwe take for granted, usingexperience, logic, and of course data.






Originally posted Nov 3, 2015

This archived topic is open to the public.

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This topic has 5 posts, showing all.

go faster and evolution

posted by: Louise Wilson on 11/4/2015 7:28 am

I teach geometry, which is pretty much the same geometry as it was 2000 years ago, with a bit extra for computerized drawing.
But i read Lewis Carroll's biography, and he was tutoring undergrads at Oxford in Geometry. The same geometry. Where the tutees had the same problems. But they were a few, mostly upper crust, gentry who were about 20 years old, with time to learn the material, and no 6 year olds to babysit.
We're teaching the same material to 14 year olds and expecting them to have the same maturity of understanding. I see the idea of text book evolution is to have the education go faster so that we can produce a race of brighter, faster innovators.
Don't know much biology, but I'm pretty certain that our brains have not evolved that much in 250 years. We might be able to output "lessons" at faster than the speed of sound, but we can't input them to our brains that quickly. There's no magic pill to swallow, no speed video to watch, that will speed up the growth of thinking in the brain. And surely we need thinkers, people who know some facts and can put them together in new ways. It takes time to learn how to do that. And the great media education specialist corporations (who actually really just want the money, thank you) have no way to help.
I guess Pearson et al have sped up the process of extracting money from taxpayers. Corporations are about making money, and they have done a great job.

Big Data Educational Industry

posted by: Anne McLaughlin on 11/13/2015 5:00 pm

Let us not forget something the Big Data proponents hope we don't figure out:
Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom. Clifford Stoll

data is information

posted by: Dean Livelybrooks on 11/14/2015 6:16 pm

As data IS information, the rest of Stoll's quote is nonsense. As a research-active geophysicist, I wonder how we would locate earthquakes without data from seismometers, how we would thence gain knowledge about most-frequent occurrence of earthquakes at the edges of tectonic plates, how we then understand different types of plate boundaries via the types of earthquakes (relative plate motions) they evince, and how we would develop wisdom enough to focus society's seismic mitigation efforts (eg., higher building standards to mitigate shaking hazards) at plate boundaries?
My great confidence in predictions that today's students will need to, on average, sift through greater amounts of data as part of their future careers derives from the observation that recent generations of students are already doing so in their careers.

There's data and data

posted by: Brian Drayton on 11/15/2015 4:28 pm

I think there are at least two strands of "big data" that can be discerned in this conversation.

1. Big Science Data. Here there is no question that big data is being used productively. I think that when people make a distinction between "data" and "information," what they mean is that "data" is information collected in relation to a line or field of inquiry. Information may not be. Thus: The seismometers etc. of which you speak are already surrounded by a lot of reliable science, and indeed so much so that anomalies and surprises can be recognized and thought about/worked on. A lot of educational software, by contrast, can create vast records of events from keyboards, mouse, etc., and this not be useful at all, because there is no reliable theory to sort out signal (of what?) from noise.

2. The Big Data that's being touted in education policy and marketing is largely of the latter sort this is the second strand of Big Data, that is, data about students and learning. In this arena, there is a lot of hope, some interesting research, and a lot of snake-oil. This might change, of course -- but until there's a way to sort out promising developments from pseudo science, we will have trouble making progress. I think.

As to students needing to sort thru vast amounts of data now and in the future, I would only note that this is true for some students (and people living their post-student lives), but a trend in automation is to take complex analyses of data and develop environments which are smart enough that users with relatively low levels of scientific or mathematical knowledge can make reliable and economical use of the data collected by the system.
(Also, at present and into the future, a very high proportion of students are going to go into fields that do not require big data skills-- transportation, retail, health care, etc. )