It’s no secret that students are distracted. But what type of pedagogical approach can we take that helps students engage in sustained, focused learning that leads to endurance rather than distraction? In my latest article in the series The Concentration Code I shared an overview of Deeper Learning.

Listen to the Podcast

If you enjoy this blog but you’d like to listen to it on the go, just click on the audio below or subscribe via iTunes/Apple Podcasts (ideal for iOS users) or Spotify.

 

How Do We Respond to New Challenges?

When the iPhone first came out, I was excited about the creative and connective capacity of the device. I knew that it would be a few years before it became affordable enough to impact our low-income school. Still, I was excited to be part of a pilot program called the 21st Century School Initiative where each student had their own iPod Touch. At the time, it held promise. Students could do fluency work, blog, create podcasts, and make videos. And they did. But even then, I saw a hint at the shadow side of these powerful devices.

At the time, people called our students “digital natives,” but that wasn’t very accurate. Our students were “consumer natives,” just like we had been. Over the next few years, I watched as these tools became distraction devices, with social media apps and endlessly scrolling media platforms engineered to snag people’s attention. To use these for creativity, curiosity, and learning would require something deeply counter-cultural. Eventually, I set tight parameters on smartphones. As enthusiastic as I had once been with those iPod Touches, I now faced a battle for attention.

I think we are in a similar period of disruption, with two significant challenges to student focus. The first is the culture of distraction and the second is the proliferation of generative AI tools. I’m actually hopeful. I continue to watch moments of focused engagement and I believe that the solution lies in deeper learning. But first, let’s address the challenges.

 

Challenge #1: A Culture of Distraction

Coming out the pandemic, many teachers described the challenge of engaging distracted learners. For some, the issue seemed to relate to the incessant usage of smart phones and the “fear of missing out” I described in this article. In other cases, the distraction seemed to connect with students failing to complete assignments, struggling to work within their groups, and acting out in class when they should be focused on the teacher. Some pointed to technology, others to collective trauma, and others to parenting styles. What remained common was a sense that students were disengaged, reluctant, and distracted.

In other words, students were failing to be self-directed in their learning.

Some students struggled with self-starting. They froze up when given directions out of fear of doing things wrong. Or they ran to the teacher and asked for immediate help. Or they simply procrastinated in their learning. Other students struggled with self-management. They grew distracted once they started a learning task. Or they couldn’t work collaboratively without intense conflict. Or they simply gave up when faced with a challenge.

And yet, if we want students to be successful in an unpredictable world, they need to be self-directed. However, teachers have faced a new challenge to self-direction in the form of machine learning.

 

Challenge #2: The Rise of AI

The arrival of ChatGPT ushered in a new era for AI in education. What once felt a far off fantasy became an instant reality in the classroom. And while it was more of an AI evolution than revolution, teachers were suddenly faced with a new challenge of academic integrity and cheating. Distracted students could now use AI tools to complete tasks in seconds. Schools almost immediately jumped into two different camps: the “Lock It and Block It” approach and the “Techno-Futurism” approach.

 

Unfortunately, both approaches had a down side. The Lock It and Block It approach of hyper-traditionalism failed to teach students ethical use of AI. It failed to grasp the powerful ways that generative AI could save time on learning tasks, guide students toward mastery, and help with curiosity and creativity. However, the Techno-Futurism approach failed to grasp the dangers of cognitive atrophy and the reality that we need to embrace many timeless and traditional types of learning.

Here’s the thing. We are in the early phases of generative AI. In a recent blog post, AJ Juliani describes the rise of AI agents:

We are entering a phase where the kids don’t even have to “go to [insert AI app]” anymore to get help with or do assignments. With Lindy AI anyone can make an AI agent that does work behind the scenes. Anthropic AI has Claude AI agents that take over your computer to do work.

AJ goes on to describe two responses that schools take:

AI-Resistant: paper and pencil, in class and in person tests, no outside HW, etc.

AI-Compatible: use AI as a resource, cite your use, defend learning, performance tasks with AI as a partner in the process.

I would describe this AI-compatible approach to be similar to the blended approach of being human-centered and AI-informed.

I’ve written before about how we can take a blended approach to AI in writing, how we can integrate it into PBL, how we can use AI as a co-creation tool, and how we can embrace AI in student inquiry. And yet, I also see some value in turning away from all things technology and embracing the lo-fi and the human. I see the need to create an AI-resistant approach to learning that helps students improve their focus and engagement.

But how do we get there? The solution involves deeper learning, which can help students engage in focused learning in a way that is both AI-resistant and AI-compatible.

 

Deeper Learning for Wicked Problems

I recently worked with a university shifting their engineering program to incorporate a more project-based and problem-based approach. One key takeaway from a survey with industry partners was that their students were great at solving narrow problems. They struggled, however, with “wicked problems.”

A “wicked problem” is a term used to describe a complex problem that is difficult to define, understand, and solve. The term was first coined by urban planner Horst Rittel and political scientist Melvin Webber in 1973 to describe social and cultural issues that are resistant to traditional problem-solving methods.

In engineering, wicked problems are the type that don’t have one simple solution. Attempts to solve one aspect of the problem may create new issues or unintended consequences in other areas. They require divergent thinking and constant iteration. One entrepreneur I met described it this way, “University graduates are great at solving problems. But we need people who know how to find problems and look at unintended consequences.”

If we think about what humans do well and AI does poorly, we can see that students will thrive in the following areas where AI lacks:

• Divergent thinking: the ability to think outside the algorithm by generating original ideas
• Contextual thinking: the ability to apply learning to new and relevant contexts in a way that is deeply personalized
• Empathy: the ability to feel what others feel and to see their perspectives
• Curiosity: the ability to ask meaningful questions
• Voice: your “fingerprint” on your work; your ideas, perspectives, unique lens, and style in what you do

In a world dominated by A.I., where problem-solving will occur at a rapid-fire pace, our students will need to solve wicked problems. They’ll need to do that with things like divergent thinking, contextual understanding, empathy, and curiosity. One way we can get there is by embracing deeper learning.

 

What Do We Mean by Deeper Learning?

Deeper learning is an approach to education that goes beyond the shallow level of facts and into critical thinking. The goal is to help students understand and apply what they are learning at a profound level. Deeper learning emphasizes mastery of the core subjects. That’s a key distinction, because critics will often claim it’s about “soft skills” and not “academic skills.” But that’s not true. Deeper learning embraces the idea of mastering the learning at a deep level through critical thinking and problem-solving. Along the way, however, students will ultimately develop those critical skills that they need in an unpredictable world.

The goal is to prepare students to tackle real-world challenges, adapt to new situations, and continue learning throughout their lives. In other words, the exact kind of focused learning that students will need in a world full of “wicked problems.” Deeper learning connects knowledge to practical scenarios. Here students think creatively, work with others, and approach problems with a combination of intellectual humility and persistence. The goal, then, is to make learning meaningful, not just for standardized tests (or even college admissions), but for life.

While people define deeper learning in different ways, there are a few key areas that typically define a deeper learning approach.

 

1. Mastery of Core Academic Content

Deeper learning focuses on helping students master the core academic content in a deeper way. The goal is to provide more time and space for adequate rehearsal and retrieval. If we think about the Information Processing Theory diagram, we want to move more information from short term to long term memory.

It follows the research on learning science. In How People Learn, Bransford’s highlighted how mastery moves beyond memorizing facts and into organizing knowledge in ways that facilitate retrieval and application. Thus, students might engage in curation of key information. Here, they move from passively consuming content to a place where they are actively engaged in critical consumption, which can then lead to creativity.

Students might also apply what they have learned in meaningful projects. Here, they see a direct connection between what they are learning and how it applies to the larger world around them. For example, they might use a design thinking framework as they learn about forces in motion in science.

Note that certain critics of this approach point out the potential for extraneous cognitive load. However, we can mitigate this additional load by using protocols, systems, and deadlines so that students focus on the learning rather than wasting too much time trying figure out what to do.

In some cases, a mastery-based process might involve compacting. Here, students essentially “test out” of key standards that they already mastered so they can spend more time engaging in deeper learning with the standards where they are struggling. In these moments, you might implement certain days where students engage in independent choice menus linked to the standards they still haven’t mastered.

And yet, mastery shouldn’t be a solitary endeavor. Deeper learning also includes collaborative learning.

 

2. Collaborative Learning

Research by Johnson and Johnson (1989) demonstrates the value of cooperative learning in improving understanding and while also boosting social skills. This is more vital than ever. If the corporate ladder has become a maze, our students will need to engage in meaningful collaboration. It’s a key skill that Google identified in Project Oxygen, when they tried to determine which skills led to higher results in their company.

However, collaborative learning can be challenging. In a recent blog post, I shared five of the challenges that we face with collaboration and offered proactive solutions we could pursue.

One of the key ideas is to incorporate interdependence into the work. If independent learning is fully autonomous and dependent learning involves students simply depending on another person, interdependence is the overlap, where students have autonomy but they must have mutual dependence on one another.

When students work interdependently, each member is adding value to the group project. Each member has something of value to add to the group. The goal here is to craft project protocols where each student is individually accountable but they also need to work with one another.

This is why I love incorporating Kagan Structures into the learning process. It creates a structured element with soft accountability so that students work interdependently. If you’ve ever used Round Robin, Rally Robin, Numbered Heads Together, Think-Pair-Share, Timed-Pair-Share, or Stand-up, Hand-Up, Pair-Up, you’ll see what I mean. These structures are clear, concise, and easy to implement.

Still none of this will lead to deeper learning if students don’t engage in critical thinking.

3. Critical Thinking and Problem-Solving

Deeper learning frameworks draw focus on helping students engage in evaluation and synthesis in order to solve complex problems. At a basic level, we can start with hitting higher levels of Bloom’s Taxonomy.

In the 1950’s, educational psychologist, Dr. Benjamin Bloom, led a team of researchers and educators who developed a model for educational learning objectives. Their goal was to create a taxonomy to help improve critical thinking in schools. The end result was the Taxonomy of Educational Objectives in 1956. More commonly referred to as Bloom’s Taxonomy, this model breaks learning objectives down into three domains.

The first was cognitive domain, which focused on the acquisition of knowledge. The second was the affective domain, which focused on emotions and attitudes and tends to tie into student engagement. The final domain is psychomotor, which focuses on actions and motor skills.

The most prominent is the cognitive domain, which is what many educators use when crafting objectives and learning targets, constructing questions, and designing assessments. Bloom’s Taxonomy is often represented as a hierarchy, though it was never explicitly meant to treat lower levels as “less than” or higher levels as “better than.” Instead, the bottom levels are foundational and build up progressively to higher levels.

Here is the original Bloom’s Taxonomy, which goes from knowledge to comprehension to application to analysis to synthesis to evaluation. More recently, scholars have updated Bloom’s Taxonomy by taking knowledge out of the cognitive domain and making a new knowledge domain with factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge. They have also revised the cognitive domain. Here’s a basic overview of how it works:

But we might also use an approach like Depth of Knowledge (DOK), a framework developed by Norman Webb to categorize the complexity of tasks and thinking required in educational activities.  The DOK framework consists of four levels:

1. DOK Level 1: Recall and Reproduction
   • Tasks require basic recall of facts, concepts, or procedures. Examples include defining a term, solving a simple math problem, or identifying parts of a diagram.
   • Example Question: Who was the president during the Civil War?
2. DOK Level 2: Skills and Concepts
   • Tasks involve applying skills, relating concepts, or solving problems with some steps or logic. This level often requires comparing, organizing, or summarizing information.
   • Example Question: Compare two of the inventions of the Industrial Revolution
3. DOK Level 3: Strategic Thinking
   • Tasks require reasoning, planning, and using evidence to support conclusions. Students must analyze information and apply strategies for problem-solving.
   • Example Question: How would you redesign this experiment to improve the results?
4. DOK Level 4: Extended Thinking
   • Tasks demand complex reasoning over extended time periods, often involving investigation, synthesis, or real-world application. These tasks integrate multiple skills and knowledge areas.
   • Example Question:

DOK emphasizes the depth of thinking required rather than the difficulty of the task. For example, a simple math problem could be DOK 1, but applying that math in a real-world scenario might be DOK 3 or 4. T

While this higher level thinking is important, I’d argue that we need to get into a place of deeper divergent thinking. This is where students learn to use creative constraints to generate new solutions. In 1956, the psychologist J.P. Guilford coined the terms convergent thinking and divergent thinking to describe the two contrasting approaches of divergent and convergent thinking. 

Convergent thinking is linear and systematic while divergent thinking is web-like, focusing on the connections between ideas. Convergent thinking narrows down multiple ideas into a single solution. On the other hand, divergent thinking expands outward by generating multiple ideas, often thinking like a hacker and using materials in original ways. Here, you treat barriers as design opportunities. Convergent thinking tends to be more focused while divergent thinking is more flexible and iterative. Convergent thinking is analytical and focused on what’s best.

By contrast, divergent thinking is open-ended. Participants are encouraged to take creative risks. even though some ideas might not work. Convergent thinking asks, “Why?” Divergent thinking asks, “Why not?” While these might seem like competitive approaches, they actually go hand-in-hand. Often, teams will use divergent thinking to generate multiple ideas followed by convergent thinking to analyze and narrow down ideas.

It can help to think of divergent thinking as learning to “think outside the box by repurposing the box.”

So the goal, then, is for students to engage in deeper critical thinking as they master the standards.

 

4. Curiosity

I always start each of my podcast episodes with the statement that, “I am passionate about seeing teachers transform their classrooms into bastions of creativity and wonder.” There’s something powerful that happens when students ask questions and chase their curiosity. I see this when I go into a science classroom and students are testing their hypothesis and making new observations. I see it in math classes, where students compare and contrast their strategies and ask conceptual and procedural questions. I see it in social studies classrooms, where students ask research questions about concepts that lead to a deeper understanding of events and systems in the past.

 

Curiosity is a natural human process. Toddlers are curious. They’re engaged in research years before they learn how to read. And yet, it’s way too easy for students to lose this curiosity. Through social pressure and the fear of being wrong, students quit asking questions. Through school systems that place an emphasis on getting the right answer quickly, students can too easily stop asking questions.

I used to think that students would ask questions naturally if given time and space. But then I had a student teacher conduct an audit of inquiry in my classroom and I learned that I asked the questions 97% of the time. It was a wake-up call for me. I realized that I needed to prioritize curiosity and create structures to incorporate it into my days. I’ll be sharing this in a future blog post. But one of the ways to do this is through a Socratic Seminar.

 

5. Self-Directed Learning

In our book Empower, AJ Juliani and I described the shift from compliance  (I’m doing this because I have to do it) to engagement (I’m doing this because I want to do this but it’s still initiated by the teacher) to empowerment (I’m doing this out of a sense of ownership and self-direction.

It’s the notion of empowering students in every aspect of the learning journey, including owning the assessment process, choosing topics, choosing strategies, self-selecting scaffolds, owning the creative process, managing the projects, and so many other areas of the learning. It’s not always easy and we often need to take a Gradual Release of Responsibility approach to voice and choice. But ultimately, if we want students to navigate the maze of an uncertain world, they will need to be self-directed.

 

6. Learning How to Learn (Metacognition)

Metacognition has a strong association with success in both college and career. We can’t prove that increased metacognition leads to success in those domains. It might just be that those who are successful also happen to have high metacognition. But studies indicate that college students with strong metacognitive skills achieve better academic outcomes because they can plan, monitor, and adjust their learning strategies based on task demands. For example, Zimmerman (1990) found that self-regulated learners who practiced metacognitive strategies were more likely to persist in challenging courses and achieve higher grades.

In the professional sphere, metacognition is equally critical, enabling individuals to adapt to changing workplace demands and engage in lifelong learning. Research by Bransford et al. (2000) highlighted how metacognitive skills support problem-solving and decision-making, particularly in leadership roles where evaluating strategies and outcomes is crucial. This can then lead to better self-directed learning and resilience.

It can help to think of metacognition as a cycle:

As educators, we can help students with metacognition by empowering them to own the assessment process.

 

7. Resilience

As students engage in these deeper learning practices, they ultimately develop resilience. They begin to see mistakes as learning opportunities where they can grow and improve. A mastery approach recognizes that students will not always get the answer correct on the first try. Instead, they have the permission to make mistakes as they pursue multiple options and ultimately solve the problems. This can help lead to a growth mindset.

 

Embracing Deeper Learning Approaches

While there are many different pedagogical frameworks that align with deeper learning approaches, I’d like to highlight three different frameworks.

 

Inquiry-Based Learning

An inquiry-based approach begins with student questions. They then engage in data gathering (research), data analysis, and sharing their results with a larger community. Students might do full-blown inquiry-based projects or they can engage in short inquiry-based sprints, like a Wonder Day Project.

 

Problem-Based Learning

With problem-based learning, teachers pose a problem or challenge that students then try to solve in a way that connects directly to their content. The focus here is on applied learning. The situation often connects to the community and might even require a full project-based learning unit. But not always. Sometimes students go through the problem, engage in inquiry, develop potential solutions, and share their findings with peers. The focus here is on the divergent thinking and contextual understanding that AI does poorly.

Project-Based Learning

Project-based learning includes some of the best elements of both inquiry-based learning and problem-based learning. Students engage in sustained inquiry and authentic research. They share their work in a public product while also engaging in community connections through various phases of the project. They solve complex problems and reflect on their learning. A core idea is that students learn through the project rather than doing a culminating project.

 

 

Deeper Learning Can Be “Traditional”

Note that many of the examples I shared seem to be a shift away from “traditional” practices of desks in rows, teacher lectures, or students filling out a worksheet. However, this is a false dichotomy. Deeper learning is about the depth of knowledge rather than the pedagogical framework. In other words, the depth in deeper learning is less about how much content you cover (depth versus width) and more about how deeply students think about the content. Here, the focus is on the “stickiness” of learning and how it endures over time.

Thus, a teacher might provide an overview of a big concept during direct instruction but do a question break for rapid-fire student inquiry. This allows for strategic confusion that can help improve the rehearsal and recall.

Looking for more? Check this out.

Join my email list and get the weekly tips, tools, and insights all geared toward making innovation a reality in your classroom.

Success! Now check your email to confirm your subscription.

There was an error submitting your subscription. Please try again.

Email Address

Subscribe We won't send you spam. Unsubscribe at any time. Powered by Kit

Sources: