When I was in the first grade, I was a tiger at reading. Tigers were fast. We knew how to tear through the text with speed and precision. But we weren’t nearly as fast as jaguars. The jaguars sat at the front table, where they got to to do choice reading every single day. To me, that sounded amazing. I loved to read and the choice-based reading felt like I was cheating the system; like I was sneaking desert into dinnertime.
However, on the rare occasion that jaguars slowed down, after the Very Important Test, I would meet up with the dejected-looking new tiger who would join our second-best reading group as a punishment for slowing down in their reading fluency. I get it. They were Coke. We were Pepsi. But at least we weren’t Shasta Cola.
We weren’t supposed to know our reading levels. After all, our teacher told us that all the big cats were equally important. But kids talk. They compare. They rate one another. And, our cat-based ranking system didn’t fool anyone. We knew better. We knew that the jaguars were the best readers and the lions were the losers — much like the football team that bared the same name. For what it’s worth, I have a tattoo on my bicep of every Super Bowl victory the Lions have had. It’s a bare arm.
So that was reading. In math, I was a shark. That sounds tough, right? But don’t be fooled. Everyone knew the math wizzes were the dolphins. Dolphins aced the timed test every single week. I spent a year trying to be a dolphin. I made flashcards and practiced with my twin brother. I knew if I worked hard enough, I could become a dolphin. But I never even made it to the whale group. I languished as the lone shark and eventually defined myself as “not much of a math person.” At six years old, I had thrown my lot in with reading and writing and history instead.
Fast forward thirty years and I am sitting down analyzing data. I’m planning out my dissertation study and thinking about a regression analysis for a journal article. I know about p-values and z-scores. I geek out on the subtle way language shapes a null hypothesis. In other words, I love math. It’s still slow for me. I still struggle with computational fluency. However, that no longer matters. If grade school math was all about being a hare, I’m finally in a place where being a mathematical tortoise is finally acceptable.
The System Values Speed and Accuracy
I recently listened to a Revisionist History podcast where Malcolm Gladwell deconstructs the LSAT test. Here, future lawyers do logic and language problems at breakneck speed with the goal of proving their merit. Their scores are then used to determine law school and eventually clerkships and job prospects. The only problem? In this podcast, Gladwell analyzes how speed and accuracy are often less important than slow thinking and problem-solving.
For what it’s worth, this issue begins far earlier than the LSATs. Our system places a high premium on getting the right answer and getting it quickly. If you’re reading, the goal is speed and accuracy. If you’re doing math facts, the goal is to solve the problems through basic recall as quickly as possible. On some level, I get this. It’s hard to comprehend text if your fluency is so low that you’re stumbling on every word. It’s challenging to do math if you’re struggling with basic facts.
Moreover, sometimes life requires speed and accuracy. If you’re Steph Curry shooting a three-pointer, you need speed and accuracy; something that evaporated from the Warriors in the final. If you’re a venture capitalist working on a merger, you might want to move swiftly and accurately. If you’re doing improve, you have to think on your feet. And if you’re a middle school teacher, you need to be nimble and modify lessons in the moment.
However, there’s a cost to the focus on speed and accuracy. As we shift toward AI and machine learning, we increasingly depend on tools that will always work quicker and more accurately than ourselves. Case in point, I struggled through math in elementary school. I struggled through Honor’s Algebra I and II (I’m not sure why we used Roman numerals for a class that relied so heavily on the Hindu-Arabic system). But Trigonometry? That was fun. I loved solving matrices and finding cosines and doing proofs because I could finally use a calculator. Suddenly, math wasn’t about speed and accuracy. It was about problem-solving and complexity.
The Need for Slower Thinking
While it’s hard to predict the future, many of our students will need to engage in slower, deeper work. They will work on projects that take weeks or even months to accomplish. They’ll need to find unique solutions that require divergent thinking. Computers do an amazing job of quickly analyzing data and running through tasks accurately. Meanwhile, the human mind, while slower than the computer, is able to think creatively. We can dream up new possibilities and engage in the functional novelty that AI still struggles to attain. We can move beyond pre-programmed tasks and think divergently to solve problems:
However, if our students are going to engage in in complex creative thinking, they will need to have longer, sustained periods of deep focus. Deep work is an especially relevant skill in a distracted world, where we can get sucked smaller task that divert our attention. We live in a world of instant media, where the constant pinging from our devices beckons us from one urgent task to the next. If we want them to become makers and philosophers and researchers, they need to have the mental endurance to stick with a task long-term.
This is a concept that Cal Newport refers to as “deep work” in his landmark book by the same name (it’s a great read that I highly recommend). It’s what happens when you delve into meaningful, sustained work that requires full cognitive attention. Newport argues that the constant interruptions of email and social media reduce our attention while also increasing cognitive load. This ultimately reduces our ability to get into deep work.
When students engage in deep, meaningful, sustained work, they are more likely to hit a state of flow:
In other words, while the system continues to value speed and accuracy, our students will often need to work more slowly and think outside the box. If A.I. is the proverbial hare, our students will need to succeed as tortoises.
Where do we go from here?
- Use multiple measures for assessment. If you’re looking at reading levels, use fluency data but also balance it with comprehension tests, reading analysis tests, and standards-based assessments. Help students see that reading fluency is just one small measure in what it means to be a reader.
- Allow for aids. Let students listen to audiobooks in reading. Let them use calculators in math. Give them manipulatives if they need it. Although I never learned about my dyscalculia until college, I knew there was something different about how I processed numeric information.
- Provide flexibility on your assignments. If it’s a writing assignment, try shifting from the five-paragraph essay toward a more holistic focus on quality writing. If it’s a math class, move from “do 20 problems” toward a more flexible approach of “work through these problems that begin at an easier level and move to more difficult.” I remember doing this with blogging and then tracking actual writing fluency. To my surprise, when I got rid of the length deadlines, my students actual read more. The same was true of silent reading.
- Be flexible with deadlines. I’ve learned that sometimes students simply need a longer amount of time to work on an assignment and that’s okay.
- Shift toward project-based learning. Project-based learning allows for longer blocks of time for deep work. Although it takes time, you make up for it by doing less direct instruction and having fewer transitions. It can help to incorporate design thinking into PBL. Design thinking forces students to slow down in their work. They have to do research to build background knowledge. They spend time on ideation. As a teacher, you will often chunk out additional time for revision before launch.
- Vary your grouping. Instead of using fluency-based groups for literary circles or math levels, consider using mixed-level groups and playing to student strengths.
- Redefine success. Celebrate things like creative struggle and deep work. Help students see that it’s possible to be successful even when fluency is a little lower. This doesn’t mean you ignore computation fluency or reading fluency. But it does mean you celebrate things like creative thinking and problem-solving.
In the end, there’s nothing wrong with working quickly and being accurate. However, when we shift toward a PBL model that incorporates elements of design thinking, we can help students embrace deep work and internalize the idea that learning is about more than just speed and accuracy.
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