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Why Teachers Shouldn’t Be Scared of “Big Data”

Why Teachers Shouldn’t Be Scared of “Big Data”

By Dominic Norrish

“Big Data” is a term that’s been thrown around a lot. So much so that some have suggested it might have a beneficial role in education.

There is, however, a suspicion within the education profession that this may have negative connotations.

Let’s start at the beginning.

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Schools have long been collecting information about learners. Attendance and grades are the most obvious data points, but there’s plenty of other stats that they collect too.

For example, where students live, gender and ethnicity, extra-curricular activities and even family spending.

I’d describe this a “wide data.” If it were contained in a spreadsheet, it would spread across many columns, but only extend down as many rows as there are students.

It might tell you about student progress, for example, but is too granular to help you understand the cause of this progress, which could be the result of many factors.

When you combine the information that’s already being collected with new and more valuable data points, educators and administrators can become better at what they do.

If the term “big data” scares you, keep reading. Find out why you should be open to the idea and how it has the potential to you and your students.

More: How Gamification Helped 10,000 Teachers Improve Student Reading

What is “Big Data”?

For data to be properly described as “big,” it needs to cover millions of data points rather than thousands.

This distinction is an important one; once the numbers get to this size, the anomalies of student behavior, which is based on contexts, histories and individual characteristics, fade into the background.

This is when the most valuable information comes to the surface.

“This big-data approach could do the same for education as the scientific method has done for medicine over the past century.”

Consider an example:

Imagine that lesson observations, which schools use to monitor and improve the quality of teaching, are all carried out using a common framework. This wouldn’t need to have total uniformity, schools could also add their own data points, but many would mandatory for every school.

Now imagine that these uniform data points are combined with the information gathered about students’ progress. Once safely anonymized by removing all identifiers, and organized into a database of every school’s lesson observations, some intriguing findings might emerge.

Due to the sheer scale of this data, which would render it much more reliably generalizable, we could ask questions that would help inform practice on the classroom:

  • What teaching techniques are linked to rapid progress in Physics?
  • Which ways of delivering English appear most effective for girls?
  • Are there methods of explaining calculus that work well with dyslexic students?

Dissecting the Data to Find the Value

Let’s dissect the calculus example. First you need to look at the progress of dyslexic students over a year and identify where development is happening rapidly. Next, you need to compare these rapidly progressing students against the teaching techniques that were recorded in the tens of thousands of lesson observations during that period.

“When you combine the information that’s already being collected with new and more valuable data points, educators and administrators can become better at what they do.”

What might emerge would be a list of teaching methods that this evidence suggests works well with dyslexic students.

Perhaps using video examples voiced by their own teacher was hugely successful. Suddenly you have a technique you know works with a large portion of these students, and you can apply it right away.

This is just the starting point for proper qualitative research about why this method seems to bear fruit, but it would be a promising lead on something that could end up benefitting the lives of countless future children.

This big-data approach could do the same for education as the scientific method has done for medicine over the past century, which eliminates the need for guesswork. In epistemological terms, we’ve been blindfolded in our attempts to pin the tail on the educational donkey. In a dark room, that’s been painted black.

More: How to Be a Technology Risk-Taker in Your Classroom

Teacher Concerns: Are They Founded?

Despite the clear value of big data in education, many teachers and education professionals have concerns, and these are not groundless. Perhaps the most concerning of those are about data and identity protection, which demand that any data captured and used as described above must be totally anonymous and secured.

Secondly, there’s the suspicion that this tactic is aimed at undermining the essence of what it means to be an educator, to second-guess the countless thousands of interactions with children across a career which inform a teacher’s decisions about how to teach and about what they know works best.

The human brain is a remarkable thing and is probably capable, at a subconscious level, of aggregating a lifetime’s experience into effective decisions about children’s learning, but there are two parts of this viewpoint that I believe to be limiting:

  • It assumes that teachers have experienced using every method with every ‘type’ of student and can therefore comprehensively and reliably pick the best teaching style or tactic to employ.
  • It assumes that teachers are able to remove any personal prejudice from their decisions. Humans are bad at this and confirmation bias is a noted feature of educational practice.

“No tools capable of doing what is described above currently exist, to my knowledge, chiefly because better learning doesn’t translate easily into larger profits. But shouldn’t our sector be demanding these tools? Shouldn’t our pupils?”

A computer analyzing a suitably large set of data would not have its outputs confounded by either issue. This isn’t to say that teacher judgment doesn’t have a role (indeed, it’s the decisive role), but that doesn’t mean it needs to be the only weapon in our arsenal.

Of course, this is the type of pie-in-the-sky, best case scenario, future gazing with which self-indulgent bloggers routinely annoy actual practitioners dealing with the realities of today.

To be clear, no tools capable of doing what is described above currently exist, to my knowledge, chiefly because better learning doesn’t translate easily into larger profits. But shouldn’t our sector be demanding these tools? Shouldn’t our pupils?

Still, as a teacher, you can make a difference in your own classroom using the ideals of big data.

Work to improve your teaching by using a data-driven approach. Poll your students, track their reading scores with tools like Whooo’s Reading, notate exam scores, and use this data to inform and personalize your teaching.

Whatever you do, don’t be scared of data. The opportunity cost of not starting to make discerning and careful use of big data to help our students may be looked back upon, and frowned upon, years to come.

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