Student Success & The Average Student
These are two things that we strive to understand. Want to crack the code behind and want to understand; the key to student success and the average student. But there is a problem. A fundamental problem with the concept from the start which affects the way we determine any of the above.
There is no such thing as the average student.
Yes, I know you will say, “well you’re wrong there, we know there is, we have the figures etc.”
Which is where the problem starts; there is no such thing as the average student as the data capture points for determining the average, is flawed. Say we are taking the mean number of page views within a course (I’m talking online learning, it’s my jam), you have a figure, which leads you to be able to cross reference that against their test scores at the end announce that, the average passing student has X page views within a course and all that sounds very compelling and useful. This can then be used to highlight potential student success, or failure when a student’s activity does, or doesn’t conform to this broken metric.
But let’s take it back one second. When I was growing up, you’d hear “who wants to be normal anyway?” (I was a weird kid, obviously) And that’s the same for averages. If you’re seen as above average that’s supposed to be good, below average bad. Average… meh. Yet you’re missing out the key parts in all of us. None of us are “normal”, because what is normal anyway? What is average anyway? We are all different and because of that any metrics we take need to be personal to us and contextual.
What I mean by that is; if a student has 4 page page views in a course, and another has 25, you’d, at first glance look at that and think the higher would be better and more likely to pass. But how about this? The first student understands the content, finds his way around the course simply and knows the content. The second student is struggling to get the material so keeps going back over it and still is struggling… you see? You can interpret that data in any way that you want and that’s not helpful. The lack of context behind any of this data leaves it open to misinterpretation and manipulation by its very nature and that helps no one.
So what’s the answer? This is a good question and it’s as simple as “a solution”. First, we need to understand what question we are trying to answer in the first place with our data. Then, we need to ensure that the data we collect has enough data points to make it meaningful, that data needs to be not only contextual, but also personalized too. Comparing students can be like comparing avocados and rocks. They might look similar on the outside but very different make up inside.
My passion is understanding how we can make learning anything for anyone possible, by understanding how we are all learning in the first place.
We continue to try and understand how to ask the questions asked of us, in a way that gives us meaningful, accurate and actionable data. This is where you’ll find me. Now you can get back to your day.
Originally posted on Linkedin here