01: Deep and surface learning
It’s January. It’s cold. It’s dark. The Christmas decorations are packed away. Blue Monday – the most depressing day of the year – is on the horizon. What better time to start a blog.
To ease myself into this project I’m going to start with the basics: deep and surface learning. These terms were introduced by Ference Marton and Roger Säljö in their 1976 paper in the British Journal of Educational Psychology ‘On Qualitative Differences in Learning’. Unfortunately the original paper is hidden behind a paywall. Thankfully, you don’t have to look too hard to fund summaries of their research and findings.
Surface learning is knowledge without meaning or context. It simply exists free-floating in your head, learned through repetition and reproduced on demand. For example, I know that Bobby Stokes scored the only goal of the 1976 FA Cup Final (in the 83rd minute fact fans). This knowledge has not improved my life – outside of pub quizzes there is nothing I can do with it – it is just there. Well done Bobby Stokes. Well done me.
Deep learning is the opposite. It is knowledge that relates to other knowledge. It connects principle with experience, evidence and argument, and meaning with action. For example, I know that there is a force called gravity which is caused by physical bodies attracting each other (stop sniggering at the back) and results in things having weight. Put more prosaically, when I drop something, it falls. It falls because forces are being exerted on it, not because that is what things do. This knowledge is useful to me (sort of). It makes sense to me (sort of). It helps me understand the world around me. I think that makes it deep learning.
Is my knowledge of the seven times table an example of surface of deep learning? I learned it by rote and without context, which pushes it towards the surface camp. However, it has also proved useful to me when I apply it in a range of different contexts (erm…calculating change…playing scrabble?), which would perhaps suggest the latter. Are surface and deep learning mutually exclusive? Can one become the other? Or is surface learning merely a synonym for bad learning?
Perhaps the answer lies in my motivation to learn, which is another distinction that Marton and Säljö make between the types of learning. Surface learning is extrinsically motivated (e.g. I want to pass my driving test so that I can legally drive on the road). Deep learning is intrisically motivated, so that the rewards are personal (e.g. I want to learn to drive). Perhaps the most important factor in learning is simply the desire to learn.