Deep learning is where students make connections between facts and procedures and develop enduring understandings and essential principles within a discipline. In other words, deep learning is where students understand the “why” behind what they are learning. For instance, students understand that the human body self-regulates its temperature, that correlation does not imply causation, and that scarcity is a general tenet of economics.
Most, if not all, standards require deep learning to master. But the challenge is, if we are not careful, we can skip deep learning and have very poor transfer-level results. The question is, which path do we take in the future?
- Path 1: Shallow transfer. Students learn surface-level knowledge and apply those facts and procedures within and across contexts.
- Path 2: Deep transfer. Students apply principles and conceptual understandings from a discipline within and across contexts.
Shallow transfer happens when we value each level differently and aim squarely at getting to transfer as fast as possible. We hear the term “gradual release of responsibility” as a means for relinquishing control of the learning and positioning students with the weight of responsibility as quickly as possible. As a result, we “skip the deep,” which results in shallow transfer. When we skip the deep, we omit the possibility of teaching through the standard and ensuring rigorous learning.
To ensure deep learning, we need to invest in a set of strategies that ensures that collaboration occurs. We need strategies that enable teachers and students to co-construct an understanding of the core principles of a discipline; evaluate and reflect on work samples, opinions, and perspectives; and give and receive feedback.
3 Strategies to Teach Deep Learning for Deeper Transfer
Strategy 1: Default with approximate feedback. Deep learning is the epicenter of the “we do” mentality. Feedback is one of those places where teachers should make sure there is balance in effort by students and teachers. Here’s how:
Start with giving students approximate feedback, and then shift to precision when necessary. Prior to giving students corrective feedback, do the following:
- State how many errors they have, and see if they can identify them.
- Use questions on their papers instead of comments.
- Place a dot on their paper, and tell them it means they did something either right or wrong and they need to figure out which is right or wrong, why, and what’s next.
- Place a set of comments from multiple students in a pile, and ask students to work together to figure out which comment goes to whose paper. (It’s a good idea prior to this activity to review classroom agreements to ensure that everyone is focused on learning rather than performance.)
Strategy 2: Leverage comparisons in tasks. Transition students’ surface learning to deep learning by making routine comparisons in their math and writing assignments.
In math, when students show their solutions to a problem using the standard algorithm (or any other tool, for that matter), ask them to complete the following sentence frames:
- To put that in context…
- That means...
- By comparison…
Or, create a small table with two columns. In column one, have students share the procedures and solutions calculated, and then ask students to create a comparison with the following prompts:
Connect your numbers to what people already know:
- Create a one-sentence comparison story using whole numbers.
- Create a one-sentence comparison story using estimation.
- Create a one-sentence comparison story using estimation with the number 1.
In literacy, use sentence frames to infuse higher levels of complexity with conjunctions. For example, provide students with a relationship such as these:
- Ratios are like proportions…
- Viruses are often considered living organisms…
- Democracy is often contrasted with dictatorships…
And then have them use one or more conjunctions:
You could do the same by adding subordinating conjunctions:
Strategy 3: Engage in collaborative interval training. To build students’ deep learning in conversations, consider engaging in multiple, short discussions. Here is one approach:
Step 1: Assign students to answer a question or complete a task in the group. Have them focus on ensuring that everyone speaks in the group. Consider using the three-before-me technique to accomplish this step.
Step 2: Ask them to repeat the conversation again and include academic language.
Step 3: Repeat a third time. This time, ask students to engage with a few of the following prompts:
- What might happen if you combined … and …?
- Do you agree that …? Why or why not?
- What evidence can you present for/against …?
- How does … contrast with …?
- Explain your response with examples.
- Why is … significant? Explain your reasoning.
- What is the point or big idea of …?
Step 4: Ask students to connect this discussion to their writing. Question prompts include the following:
- How will you incorporate this learning into your writing?
- What is markedly similar and different in how we talk from our writing?
Step 5. Finally, ask students how this work transfers within and across contexts. And what surface-level learning do they need to go and pick up?
Rinse and repeat this interval training as much as possible. Over time, intervals will become quicker, and the quality will gradually increase. The mantra here is to “go slow to go fast.”