The pedagogical approach to task design in which one engages a student in deep learning – an approach I am quite passionate about – has been growing in popularity over the last few years. Yet, as with many educational concepts, asking one to define what deep learning is or “looks like” is quite difficult. When I first began teaching on this topic, my explanation of deep/surface learning were as follows:
- Surface learning: the acceptance of information and memorization as isolated and unlinked facts. It leads to superficial retention of material for examinations and does not promote understanding or long-term retention of knowledge and information.
- Deep learning: the critical analysis of new ideas, linking them to already known concepts (transference), leading to understanding and long-term retention of concepts so that they can be used for problem-solving in unfamiliar contexts. Deep learning works best when connected to motivations and interests. Deep learning promotes understanding and application for life. You know deep learning is achieved when a person can explain something in a different, but related, context.
The exemplar I would use to support these definitions is the video below from 2013 titled Teachers Embrace ‘Deep Learning,’ Teaching Practical Skills:
Recently, I have been exposed to some new learning and new perspectives on what deep learning is.
Metra and Fine (2015) characterize deep learning as when one can demonstrate “significant understanding of core content, exhibit critical thinking and problem-solving, collaborate, communicate, direct their own learning, and possess and academic mindset” (p. 4). Students can transfer knowledge from one context to another and not only possess significant factual knowledge, but can also develop interpretations, arguments and conclusions from that knowledge. This is accomplished by connecting understanding to student motivations and interest, argue Metra and Fine (2015), which then emerges when mastery, identity and creativity intersect.
Explaining deeper learning through a list of attributes, Berger, Wooden and Vilen (2016) state this approach challenges, engages and empowers student. They categorize deeper learning into six outcomes:
- mastery of core academic content
- critical thinking and problem solving
- effective communication
- self-directed learning
- academic mindset
In the book Deeper Learning: Engage the World Change the World, Fullan, Quinn and McEachen (2017) conceptualize deep learning as an environment where students are challenged, provoked and stimulated, and learning is celebrated. Similarly to Berger et al. (2016), these authors propose a list of six global competencies (Fullan, Quinn & McEachen, 2017):
- critical thinking
Upon this inquiry, it is evident that every definition or explanation of deep learning is different, and my original explanation is in due of a revision. But before I share my revised perspective, I feel it is best to share an example of the most recent deep learning I witnessed.
At my current school, I am transforming an elementary library to a learning commons. This school is characterized as traditional, rural and small. However, it is not lacking opportunities for innovation. A recent purchase of a Ditto Pro 3-D printer kept my lunches and recesses quite busy. After grabbing the attention of my early adopters, word of “how cool” the printer was spread quickly. In a grade 5/6 drama class, I allowed students to use EZ-Robots in their green screen newscasts. This prompted two boys to ask to print a sword for Roli. This involved the students to learn to group objects together, and learn how to best format an object so it resulted in a successful print. Doing most of this on their free time, the two began to explore the program Tinkercad, communicate and collaborate on how to design the sword (a bit of critical thinking in there), and then head to the printer. Their first pint resulted in the following:
If one looks close enough, the glue from a hot glue gun can be seen. Unfortunately, the boys were not successful in grouping the shapes together. This prompted in a quick tutorial on how to better align objects for a stronger bond, as well as a lesson in how to use a glue gun.
So, back to the drawing board again – and at this point I knew the students were interested and motivated because now Roli needed a hat. So the boys set out again with their feedback in hand and a new challenge: make the hat hollow. The next morning, they were back before school starts with a new design. I checked it over and saw that not only was grouping better, but the hat was hollow. I then asked if they were sure on the size. Watching the brains initiate, one of the boys went over to the robot’s head and held up a yellow ruler. After a quick estimate, he decided the size was perfect. Below is the hat:
It looked pretty good, but unfortunately, it was too big for Roli’s head. Alas, it must rest on a slant in order to stay put. However, the valuable lesson of size and scale was learnt.
In December 2017, an opportunity arose for me to co-plan and co-teach with the grade 5/6 teachers on a final interdisciplinary project on natural resources in British Columbia. Once these two boys learnt there was a presentation component, they immediately asked if they could 3D print a model. I was happy to support. However, what emerged was completely breath taking! Designing 2 separate files, the boys managed to produce the following… which required very little support from teachers. Please not the symmetry, scale (the two pieces joined together at the hitch), and attention to detail.
But what was even more exciting for me was what happened next. Being the last day before winter break, these two boys were at school well before the day began to check on their second print. As they worked to free the logging trailer from the print bed, others began to accumulate in the learning commons to see the result. Some students had yet to dabble in the printing world while others were struggling to learn the program. However, magic occurred as those two boys began saying phrases like “it isn’t that hard”, “want me to show you?” and “let me help”. Laptops were pulled out and the students began immersed in learning… deep learning. The two boys, now the teacher, demonstrated mastery as they were now leading and supporting others in creating designs. They took pride in me “not having to check it over” before sending to the printer because they had already done so. And, when the bell rang for students to get to class, I had to ask for them to be excused because the learning was so powerful.
The two boys were sharing their journey with visiting admin, parent council members and other teachers, feeling empowered and proud. And to imagine, it only took 3 prints!
All together, when I look at the current literature and combine this with my past definition and action research evidence, moving forward, my new explanation of deep learning is as follows: Deep learning occurs when careful attention to task design engages learners in creative learning experiences that require the use of effective communication skills so collaboration, critical thinking, problem solving and self-directed learning can occur. Design must consider prior knowledge, student interest and student motivation, with the opportunity to demonstrate mastery in both competency and content development by allowing transference of knowledge to others and to different contexts.
Berger, R., Wooden, L. and Vilen, A. (2016). Learning that lasts: Challenging, engaging, and empowering students with deeper instruction. San Francisco: Jossey-Bass.
Fullan, M., Quinn, J. and McEachen J. (2017). Deep learning: Engage the world change the world. Thousand Oakes: Corwin, A SAGE Company.
Mahata, J. and Fine, S. (2015, December). The why, what, where, and how of deeper learning in American secondary schools: Deeper learning research series. Boston: Jobs for the Future.