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that the deep learners in Marton and Säljö’s study were more intrinsically motivated than the surface learners, and were thinking about the usefulness of the topic, as suggested by the MUSIC model. Surface learners, in contrast, were extrinsically motivated, and more concerned with passing tests.

Surface learning and deep learning in higher education This research sparked the interest of higher education researchers, including an Australian educational psychologist called John Biggs. Building on the work of Marton and Säljö, Biggs (2001) developed a questionnaire which could be used to assess the extent to which a student adopts deep or surface approaches to learning. Biggs and others have conducted extensive research using this and similar questionnaires, and as a result, we now have a good understanding of how the approach you take to learning can influence how much and how well you learn. In general, it is agreed that deep approaches to learning are much better if you want to truly understand a subject, to remember information for a long time, and to be able to transfer what you have learned to new situations. According to educational researcher John Hattie (Hattie & Donoghue, 2016), deep learning therefore also contributes to educational success, with deep learners tending to achieve higher grades than those who adopt surface approaches to learning. Learning approaches in context However, it is important to note that your learning approach is not simply about your personal preferences; it is also affected by the situation in which you are learning, particularly the material which you are studying, and the way in which your learning is being assessed (Biggs, 1999). If you are learning basic information about a topic for the first time, and you will be tested by a multiple-choice examination, then it makes sense to memorise and to take a surface approach. If, however, you are trying to learn advanced subject knowledge, where critical thinking is required to produce a sophisticated argument in an essay, or you need to apply your knowledge to solve problems, for example, then deep learning is far more effective. If you have learned to drive, then you may have experienced this for yourself. Many countries require learner drivers to pass a theory test, showing that they can recognise road signs, understand the basic rules of the road, and recognise hazardous situations in a driving simulation. Passing the theory test usually requires simple memorisation of a set of facts. However, to gain a full driving license, the new driver must demonstrate skill in driving a real car on real roads; they must practice the skill of driving and show that they can respond to unpredictable situations (such as another driver’s behaviour, or a hazard on the road). This type of learning takes much longer, and the driver must experience driving in different road conditions and practice new strategies for each, until they are able to react quickly and automatically to unexpected events. In psychology, this might equate to knowing how to use a computer software package to calculate a particular statistical test (for a first- year laboratory class, perhaps), compared to being able to design a good experiment, collect data and choose the correct statistical test to analyse that data when we are conducting an original piece of research (such as for a final project for a degree or a postgraduate course). Effective learning in an academic context, then, can vary depending on what we are learning; we may learn the basic facts of our subject using surface learning, but true expertise is achieved when we have actively engaged with the subject and transferred what we have learned into a variety of different settings.

© Oxford University Press, 2020