Deep learning in science

Students can approach any task from either a learning approach focused on understanding the material, or a learning approach focused on reproducing it. These two learning approaches were termed deep learning and surface learning respectively (Marton and Saljo, 1976). The University of Oxford have published an excellent essay summarising these two approaches to learning. I have summarised some of the best bits below.

Deep and surface learning approaches

Misunderstandings of deep learning

It is often assumed that deep learning involves discovery based, constructivist behaviours whilst surface learning involves learning lots of facts. This incorrect interpretation fails to take into account that deep learning also requires students to learn lots of facts, the difference is that deep learning requires the students to then make meaning from what they have learnt. Students are not deep or surface learners, rather they can adopt either of these approaches. Indeed the learning intention of a student may change as the student works e.g. if the work becomes too hard or time is short they may adopt a surface approach.

Why is deep learning better?

I have always been convinced that deep learning results in better learning. Deep learning requires students to make sense of the data, and use this knowledge in different contexts by making links with existing knowledge, integrating ideas and creating novel solutions – this has to be better! Research shows students who are adopting deep approaches tend to have higher quality learning outcomes. Students may adopt a superficial learning approach in the belief that it is better, but this is often just a pragmatic response to limited time.

How can we encourage deep approaches to learning?

  1. Clearly state academic expectations [well planned lessons that are objective and not task driven]
  2. Stimulating teaching – stress you own personal commitment to the subject matter [challenge the students]
  3. Give opportunities for choice in the method and content of study [motivate the students]
  4. Promote interest in the subject matter and take account of background knowledge [link to prior knowledge]
  5. Don’t overload the curriculum  – prioritise the important bits and remove the noise [link to the Big Ideas of Science]

The science of learning

The Science of Learning is an excellent summary published by Deans for Impact that reviews the evidence for how students learn. It is not specific to science education or deep thinking, but many points are particularly relevant to helping students learn science.

Further reading
  • Marton, F. and Säljö, R., 1976. On Qualitative Differences in Learning: I—Outcome and process*. British journal of educational psychology, 46(1), pp.4-11.
  1. Cognitive development 
  2. Motivation in science 
  3. Misconceptions and conceptual change
  4. Deep learning in science teaching