I am currently taking several online courses in an attempt to continue my development as an educator. One of them, Deep Learning Through Transformative Pedagogy has been incredibly insightful and is reacquainting me with a lot of the concepts and ideas in pedagogy and education that I was introduced to in my Bachelor of Education studies. It really is incredibly important to assess our development and come back to previous insights and understanding we sometimes take for granted. While they are still rattling around somewhere in my head, I often forget key ideas or theories which would really really benefit me as an educator and also my students. There is so much information out there, and so much new information being worked on everyday, that staying current and staying hungry are crucial to self development and improvement.
I wanted to share some ideas and insights as well as my journal responses in case anyone finds them interesting.
Professor Gregor Kennedy shared some ideas and insights on technology and its use for Deep Learning. What I found interesting was his very measured approach to synchronous and asynchronous technologies and how they can both be used effectively for communication.
He identifies that there is a temporality of collaboration that needs to be acknowledged, and this is why synchronous approaches such as skype or zoom are effective and beneficial. However, they are not always appropriate and at different stages of learning, such as review and reflection, asynchronous tools like recorded lessons and videos are much more beneficial.
I find that many school systems take either one approach or the other and do not properly leverage the benefits of both to encourage deep learning. Online learning should be approached in a way that maximizes its benefits, not simply trying to mimic in class teaching. A lot of educators need to re-evaluate the goals on online learning, what skills they have which can be used effectively and how to provide engaging learning opportunities versus simply pretending like its the same as in person lessons.
John Hattie is a Professor of Education and I found some of his insights quite instructive about Deep Learning and how it is applied. I remember reading a lot of articles by him in my B.Ed courses, so it was interesting to come back to him now.
The next quote is incredibly instructive. As an educator, we often think providing students with problems to apply some new information or solution will help them consolidate their understanding. The easiest example to think about is in mathematics, where we teach a concept and then we have students repeat using that concept over and over, varying the difficulty slightly as they practice. However, what he points out here is that what we really need to do, is TEACH students how to transfer knowledge and information properly. You teach them how to assess different problems, identify similarities and differences, and then decide how to apply the concept or skill they just learned, if its applicable. That is a world apart from assuming they’ll understand how to apply information to new problems or slightly different ones. I have often seen my own students get confused when variables are changed or introduced, and as an educator, I have failed to teach them how to transfer their knowledge properly, and even, how to properly assess the problems they are trying to solve. This was really eye opening to me.
This next quote is also very very insightful as we often over emphasize Deep Learning as this gold standard and goal from the very beginning. A lot of educators, parents and people in general often debate the value of teaching simple memorization skills like multiplication charts or spelling. How useful is knowing static, surface information like that when technology and other advances let us circumvent that need quite easily. His insight here provides perhaps a better way of framing the question, valuing the important of Surface Learning to lead into and develop Deep Learning.
Finally here is one journal entry I was asked to write. I thought I would share it as it holds some good insights into my own thoughts on Deep Learning:
1. List three features of each approach.
Three features of Surface learning:
-Information that is processed quickly and can be memorized, mostly related to ideas and content.
-Learning that is often self contained or independent. Usually related to facts, concepts or ideas that students are asked to learn and then apply with little critical thinking or problem solving.
-New information that is often needed to be understood before broader connections or applications can be applied
Three features of Deep learning:
-Develops connections of facts or information to broader understandings of the world or related content.
-Allows students to extend their thinking, revise their understanding and reflect on their learning, while also applying new information to their pre-existing knowledge of the world.
-Requires longer protein synthesis within the brain to consolidate information and create lasting understanding
2. Has your understanding of the nature of, and differences between, deep learning and surface learning changed? If so, how?
My understanding has not changed at a broader level, or perhaps, if I'm being honest, I can say that my surface understanding of deep learning has not changed since my introduction to it in my bachelor of Education program. However, my ability to extend that information and slightly refine it has changed somewhat. I now understand that deep learning is not simply extending knowledge or learning and having students demonstrate their understanding in a variety of ways. I have learned that students need to apply this information into their schema of how the world works or in their conceptual models of broader systems. By integrating new learning into frameworks that are robust and help them interact with the world, students are consolidating the information into a useable system that informs their ability to learn and their approach to problem solving. This ties in with the fundamental lessons in deep learning which extend beyond facts or figures and provides a robust system to interact with complex ideas, reflect on their own development of learning, and develop new strategies to approach novel and interesting problems and ideas.
3. Describe a situation in which either you or your students were engaged in deep learning. What features of deep learning that you identified in question one were present in this example?
In a very simple example, students and I were trying to learn a new skill. The skill in question was juggling. This was an exercise to have students identify a target outcome and create a system to build towards completing that target skill. Juggling was a fun and engaging idea for the class. While juggling would be a surface learning skillset, mastering a set of simple motor commands and coordination, the process of identifying how to learn to juggle was the deep learning aspect. Before we began, students identified potential ways of learning that they thought would suit them. They came up with a framework to try and develop the necessary skills to be able to juggle. Hand eye coordination, throwing one ball up individually, first using one hand, then the other. Students slowly and systematically developed steps that would help them towards the eventual goal. This allowed them opportunities to stop, assess the progress, identify new and unforeseen challenges (how do we transfer the ball from one hand to the other, are there different ways to do this? Is that a separate skill from throwing the ball upwards, where should the eyes focus during the activity, etc.) and continue developing more and more robust systems of learning. After the juggling and all its stages were achieved, students then had to analyze and assess the mode for learning and create a plan that could be applied for other skills or target knowledge.
Through the acquisition of a single surface level skill, juggling, the students were developing a framework and scaffolding approach to learning that they could then apply to future skills or projects. Planning, creating plans of action, assessing their progress, returning to the planning stage and revising what they were doing, analyzing the outcome, sharing information, all these deeper learning skills were being used and then connections were made to other forms of learning that this could apply to (other subjects or disciplines).
This activity definitely ties into developing connections for broader understanding of concepts and systems in the world, students identified patterns and skills useful in the acquisition of knowledge that could be harnessed and used successfully to approach a wide range of problems in the future. It also allowed them to revise their understanding of how they normally approach problems or goals and how to develop effective plans to work towards future goals.
4. Do you intentionally incorporate deep learning processes in your teaching? If so:
How do your learners respond?
What do you find rewarding and challenging?
I do try to incorporate deep learning processes into my teaching. I often structure all my lessons/projects/units on Big Ideas and personal connections which fundamentally connect to deep learning as students need to apply information and knowledge to issues they care about and integrate it into how they view the world.
Students often respond quite positively because it creates natural and authentic engagement with topics that they normally find boring or uninteresting. Subjects that sometimes seem divorced from their real world experience take on a new meaningful character as they struggle to apply new information to how they view things within their world. I also find it very beneficial because students are no longer tasked with memorizing or reproducing a piece of knowledge, but rather asked to develop their own understanding or simply develop their own curiosity, which is much more rewarding and challenging for them. It removes any questions of “why do I need to learn this?” and instead empowers students to ask meaningful questions which fuels their desire for deep learning. Students are very happy to challenge me, the system, or previously held ideas and see if they can come up with novel ideas or alternate ways of doing things, really testing their creativity and problem solving and flexing those deep learning skills.
What I find most rewarding is the intrinsic motivation and confidence students develop. Reframing learning away from a value system that heavily prioritizes correct responses empowers students of different levels and abilities to succeed and develop rather than feel scared or discouraged when they can’t reproduce information on command. Deep learning, while incorporating surface understanding and knowledge, can happen at a level which encourages growth rather than results. A student struggling in math, who is several grades behind their peers, does not need to be focused on simply obtaining a certain result or score on a test. What they benefit most from is enhanced deep learning which builds on their fundamental understanding and helps them take ownership of their learning. Focusing on building deep learning means they can experience growth and development far greater than any numerical value on a test or exam. Similarly, students at a higher grade level are stimulated, inspired and challenged to go beyond what might be easy for them and develop more complex understandings of topics that fuels their development as well.
The most challenging aspect is building the confidence and resiliency in students to attempt deep learning and work within that framework since so many are used to the previous transmission model of education. I work mostly in East Asian countries and they rely heavily on memorization, reproducing information and tests. Students who have grown up in that system find open ended questions, reflections, collaborative work and open ended projects quite daunting. Building up their confidence and giving them the freedom to explore that newfound space for learning can be quite scary for them and challenging as an educator.