Will big data transform education?

I think technology has great potential to transform education, but I am frustrated by how ineffective so much educational technology really is. For more on this, see my Guardian article here. Recently, I read a fascinating book about how big data could transform education, which described a lot of what I think are the more effective uses of education technology. It’s called Learning with Big Data, by Kenneth Cukier and Viktor Mayor-Schonberger, and it gives some really good examples of how data analysis of in-class formative assessment could improve teaching.

The best part of the book is where it follows the work of Professor Ng, a computer scientist at Stanford who is a co-founder of Coursera.

By tracking homework and tests done on a computer or tablet, he can identify specific areas where a student needs extra help. He can parse the data across the entire class to see how the whole cohort is learning, and adjust his lessons accordingly. He can even compare that information with other classes from other years, to determine what is most effective…For example, in tracking the sequence of video lessons that students see, a puzzling anomaly surfaced. A large fraction of students would progress in order, but after a few weeks of class, around lesson 7, they’d return to lesson 3. Why?

He investigated a bit further and saw that lesson 7 asked students to write a formula in linear algebra. Lesson 3 was a refresher class on math. Clearly a lot of students weren’t confident in their math skills. So Professor Ng knew to modify his class so it could offer more math review at precisely those points when students tend to get discouraged— points that the data alerted him to.

This, I think, is very powerful stuff. It really does have the potential to dramatically improve teaching and learning and to help identify what the most effective teaching methods are. There is more on this type of activity in the book, as well as interviews with the founders of Duolingo and Khan Academy, two of my favourite educational apps.

What I liked less about that book is that at times, it fell into lazy and entirely erroneous clichés of the ‘Shift Happens’ sort. The worst example was a throwaway comment in an otherwise excellent discussion how Professor Ng uses quizzes.

He interlaces the video classes with pop quizzes. It’s not to see if his charges are paying attention; such archaic forms of classroom discipline don’t concern him. Instead, he wants to see if they’re comprehending the material— and if they’re getting stuck, exactly where, for each person individually.

So, according to this, checking to see if pupils are paying attention is an archaic form of classroom discipline which great educators should not concern themselves with. Really? Not only does this just feel wrong, it also contradicts lots of the modern (and very un-archaic) research on the topic. Paying attention to things is how we remember them, and remembering things is how we learn. If we don’t pay attention, we don’t learn. In fact, research on the importance of paying attention is probably the most practically useful research for teachers. Dan Willingham goes so far as to say that ‘the most general and useful idea that cognitive psychology can offer teachers’ is to ‘review each lesson plan in terms of what the student is likely to think about’. Far from being an archaic form of classroom discipline, making sure that students pay attention is absolutely vital. One of the main reasons why the pop quizzes described above are so powerful is precisely because they help focus attention on the right things.

To be fair to this book, it is relatively free of this kind of error – certainly much freer than most of the books and articles I read about ed tech, where mere possession of an iPad will transform your intellectual capacities. This book does, for example, include the following important and salutary warning:

Learning will continue to require concentration, dedication, and energy.

But still, whilst the error about paying attention may be a brief one, it is there and it is important. Before we even start to think about how to use technology in the classroom, we need a clear understanding of what causes learning to happen. Only then can we start to think about how technology can enhance or improve that process. If we start with magical thinking about education, then we will end up applying technology in unhelpful ways, and the technology itself will get a bad name amongst many educators. We can see this happening at the moment – because so many uses of technology are based on misapprehensions and fail badly, many teachers become sceptical about all uses of technology. So, whilst I remain a fan of edtech, books like this actually convince me that whilst it’s important, it’s a second-tier issue: the most important issue is to establish clearly what causes learning.

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4 thoughts on “Will big data transform education?

  1. Neil Brown

    I think there’s two issues entwined here. One is whether monitoring your own class’s progress can help in teaching — the excerpt you give describes this situation. It seems clear to me that moving this progress monitoring to electronic might make it faster and easier for teachers, but really it is a more comprehensive version of walking round the class, listening in and looking over shoulders. However, this is small data, and I imagine it will primarily be “manual analysis” of the data (teacher looks at some display of student progress, draws their own conclusions).

    The big data issue is whether we can get any useful information out of performing this analysis at a large scale (e.g. across all UK schools, or across the whole of a MOOC). Such analysis would have to be done in an automated or semi-automated way. I believe this is a different kettle of fish. Multiple choice is not so bad if students are trying to learn facts, but how can you automatically decide what students are understanding if their output is an essay? Even for something fairly well-defined like rearranging an equation, it would be a challenge to automatically infer what the student is having difficulty learning.

    As you say, at this point you need a more rigorous model of what learning is. A computer is only as good as the algorithm you give it, and I feel like we are a long way from having the right algorithms and models to really analyse learning that is more complex than memorisation/recall. (Personal plug, but relevant — we are trying to do bits of this for students learning programming, so I know a little about this: https://academiccomputing.wordpress.com/2014/03/11/blackbox-observing-student-behaviour/ ).

    Reply
  2. discreetteacher

    Are you (and the books author) confusing performance and learning? At such a short interval between teaching and test, there is no time for seeing if the information is in the long term memory. Also, there is the problem of just because you get something right doesn’t mean you understand it?

    Reply
  3. teachwell

    However, I know that even 15 years ago there was software that would enable analysis of documents (admittedly typed) for the type of language used and number of times a word repeated itself, etc. I believe that technology can capture a lot but we need the coders to be working with teachers not people who claim to represent teachers. In addition, we as teachers need to realise that we need to ask for features and improvements, we can’t expect software companies to come up with these from thin air.

    Reply
  4. Pingback: My five favourite blogs of 2015 | David Didau: The Learning Spy

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