 From a neuroscience perspective, the learning that takes place in the classroom is essentially about memory formation, its storage, and its retrieval. So an understanding of brain functions will help us understand how that memory formation retrieval system operates. Now in terms of understanding of surface and deep learning, neuroscience tells us that the brain is a plastic organ with changes during learning. And there are changes to both the structure and function of the brain. These changes are different depending on whether the learning is surface or deep. There are many different views in neuroscience about what is surface learning and what is deep learning. One way of distinguishing them is to think of surface learning as immediate or short term learning, and deep learning as a consolidation process that leads to long term changes. So in terms of the biology of taking learning from surface to deep, we think what is happening in the brain during consolidation is protein synthesis and development of new connections. In surface learning, while proteins are modified, they change and decay over a short period of time. It is in the consolidation process that this surface or immediate learning becomes long term or deep learning through changes in gene transcription and new protein synthesis. In animal models, we found that if you block the protein synthesis, the long term or deep learning is blocked, but not the short term or surface learning. The world we live in contains a lot of information, a lot of facts, and we encounter new information all the time. My understanding of deep learning is that it is a process of integrating new facts about the world into our existing semantic framework. So we can perhaps conceptualise deep learning as creating a model of the world which is part of our overall predictive model. The rich set of facts that we have about the world are not independent or standalone items. It's not like storing and retrieving information from a computer. Deep learning is not about memorising things but integrating the facts that we have into aggregates of information and models about how the world works. I know that my memory isn't great, but I can know plenty of things by integrating and connecting information. In terms of surface and deep learning involving short or long term memory formation, it may be that in humans and animals the underlying neural basis is the same, but we have language and conceptualise our thoughts in linguistic terms and that doesn't happen in animals. So in animal models, there are a limited number of ways to create a complex, integrated set of memories that can be interrogated in the same way we can explore them in humans. The mechanisms might be similar, but as humans we have an additional way of accessing and examining these processes. Deep learning is the opportunity for people to engage with disciplinary thinking at a very sort of deep and focused level. So we're affording people, the supports, the resources and the skills for engaging in thinking about an interesting, relevant, complex dynamic problem that they really care about solving so that they get in and are actually engaging with really deeply understanding the nuances of some topic that interests them, and it actually doesn't even matter that much what the topic is, that becomes the fodder for them to learn what it feels like to really be an expert at something. From an educator's perspective, surface learning involves recalling and reproducing content and skills. Deep learning involves things like extending ideas, detecting patterns, applying knowledge and skills in new contexts or in creative ways, and being critical of arguments and evidence. So one way we can understand how students are making meaning from what they're learning is to see how they engage in problem solving, either by themselves or with others.