Learning Analytics comes of age.

The term ‘big data’ has been around for a few years, but it has been difficult to apply in the field of education. Huge online companies like Google, Facebook and Amazon know so much about each of us because of how frequently we use them, and how we use them. Interestingly, while schools, LAs and MATs do not generate anywhere near the amount of data that those behemoths do, they do generate more than you might think.

The reason is that many schools now use online platforms for teaching and assessment and, crucially, many of these systems link up to others, such as those keeping track of students’ attendance, punctuality and behaviour. The result is that much more is known about each student than would previously have been possible. In particular, measures like student achievement can be placed in a wider context: what are their attendance and behaviour like, for example?

The challenge is how to make sense of all this data, and that’s where visual aids such as the dashboards in Groupcall Analytics come in. The old saying “a picture paints a thousand words” is definitely true in the context of data, especially when there is a large amount of it.

Used well, such programs can work on two levels. In the case of individual students, a teacher can look at someone’s progress and, critically, ascertain whether the general trend is upward, static, or downward. The teacher can then make adjustments if necessary: perhaps of their teaching, or possibly in the amount of extra tuition given or the resources made available to the student. The important thing is that the immediate accessibility of the data enables the teacher to make adjustments in almost ‘real time’. Contrast this with the old days, when a teacher would look at how well students did on a course and then give feedback — when it was, in effect, too late for the student to do anything about it!

The other level on which a good analytics program works is what we may call the macro level. That is to say, it’s possible for an LA or a MAT to see at a glance — literally — which schools are doing better than others. If school A is outperforming school B, then you can drill down to try and find out why.

That can still be quite challenging, but there’s some good news. It is almost certainly only a matter of time before artificial intelligence has been developed — by educationalists — that will do even more of the hard work for you. The beauty of applications like Groupcall Analytics is that they can present the data in such a way that, rather than causing you to ask “What?”, you can begin to ask “Why?”

Teachers may worry about AI, but it’s not likely that robots will start teaching any time soon. A much more likely scenario is one in which tools such as Artificial Intelligence and what has been called Educational Data Mining enable both teachers and schools to fine-tune what they do for the benefit of all concerned.

Terry Freedman is a freelance ed tech consultant and writer. 

For this article I have been inspired by, and drawn from, a chapter called Learning analytics, artificial intelligence and the process of assessment, by Rosemary Luckin and Kristen Weatherby, in a just-published book entitled Enhancing Learning and Teaching with Technology: What the Research Says, by Rosemary Luckin (Ed), published in 2018 by UCL-IOE Press.

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