define conceptual problem solving

What is Conceptual thinking? Unleashing Your Creative Potential

Have you ever come across the term “conceptual thinking” and pondered its true essence?

Well, it’s far from being just another trendy phrase; it’s a cognitive prowess capable of transforming the way you tackle problems, foster innovation, and ignite your creativity.

In this article, we’re diving deep into the core of conceptual thinking, unveiling its profound significance across various aspects of life, and even serving up some down-to-earth advice on how to nurture this invaluable skill.

So, come aboard as we embark on a captivating journey through the realms of imagination and inventive thinking.

What is Conceptual Thinking?

Conceptual thinking is the ability to visualize abstract ideas, think beyond the surface, and connect seemingly unrelated concepts.

It enables individuals to see the bigger picture and find innovative solutions by rearranging ideas and sparking creativity .

It’s like having the combined abilities of an artist, inventor, and philosopher, allowing one to explore profound questions, create groundbreaking innovations, and approach problems with unique solutions.

The Importance of Conceptual Thinking

Problem-solving .

When it comes to problem-solving, conceptual thinking is your reliable ally.

It’s not just some abstract concept but rather a mental Swiss army knife that equips you to tackle intricate problems from every conceivable angle.

Imagine yourself as a detective armed with a magnifying glass, meticulously examining every clue to unveil new and innovative solutions that might have eluded you otherwise.

This skill opens doors to a world of creative problem-solving .

Creativity thrives on conceptual thinking; it’s not just about solving problems.

If you’re an artist striving to paint a masterpiece, a writer weaving an engaging story, or an entrepreneur seeking the next groundbreaking idea, conceptual thinking becomes your muse.

Imagine it as possessing an endless well of imagination, capable of conjuring new ideas and concepts like a magician who continuously pulls surprises out of a hat.

This skill fuels the fires of creativity in all your endeavors.

Innovation 

Innovation owes its existence to conceptual thinking – it’s not merely a cerebral workout.

Visualize it as the driving force behind trailblazers, the catalyst sparking groundbreaking inventions, and the North Star guiding revolutionary business strategies.

It’s what separates the visionaries from the masses, enabling them to perceive opportunities where others perceive obstacles.

Conceptual thinking births innovation , propelling us towards uncharted territories of progress and change.

Decision-making 

In the realm of decision-making, conceptual thinking serves as your reliable compass.

Crafting well-informed decisions extends beyond superficial considerations; it necessitates a profound grasp of the underlying concepts.

It’s akin to possessing a map that not only unveils the destination but also unveils the entire landscape, enabling you to make choices that are not solely logical but also all-encompassing.

Conceptual thinking acts as your guiding star, illuminating the path to sound and comprehensive decision-making.

How to Develop Your Conceptual Thinking Skills

Now that you grasp the importance, here are some practical steps to enhance your conceptual thinking:

Diversify your knowledge

To supercharge your conceptual thinking, it’s time to become a knowledge explorer.

Picture your mind as an adventurous traveler, and the world of information as your vast playground.

The more you expose yourself to various fields and subjects, the broader your conceptual playground becomes.

It’s like having an ever-expanding map of ideas, where each new piece of knowledge adds a new dimension to your mental landscape.

Practice mindfulness 

Just like a fitness regimen keeps your body in shape, practicing mindfulness exercises can keep your mind sharp and focused .

It’s like having a mental gym membership, where you strengthen your ability to connect ideas and concepts.

Imagine it as doing mental push-ups for your brain, improving your cognitive flexibility and creativity.

Ask questions 

Never shy away from posing questions that challenge the status quo. Ask the ever-potent “why” and “what if” inquiries.

It’s akin to assuming the role of a detective within the realm of ideas.

These penetrating questions are not mere surface-level inquiries; they are profound dives into the vast ocean of concepts.

As you pose these inquiries, picture yourself unearthing hidden treasures and unfurling the enigmatic tapestry of the mental landscape.

Inquisitiveness becomes your guiding star, illuminating uncharted territories of knowledge and insight.

Mind mapping 

Imagine it as your visual toolkit.

With mind maps , you can connect and organize ideas in a way that’s both creative and structured.

It’s like building a roadmap for your thoughts, where every idea has its place, and you can see the intricate web of conceptual relationships.

It’s a powerful tool to make sense of complex ideas and visualize the bigger picture.

Practice solving problems

The content emphasizes the importance of practicing problem-solving to enhance conceptual thinking skills.

It likens problem-solving to a training ground where one can apply conceptual thinking techniques.

Starting with simple puzzles and gradually moving to more complex challenges is compared to climbing a mountain, and each problem solved is likened to a puzzle piece contributing to improved conceptual thinking.

In a world that ceaselessly evolves, placing a premium on innovation and ingenious problem-solving, conceptual thinking emerges as a critical skill poised to elevate you to unparalleled heights.

It possesses the transformative potential to unlock your innate abilities, whether you don the hat of an artist, a scientist, or an entrepreneur.

As you diligently nurture and foster your conceptual thinking prowess, you’re not merely liberating yourself from the constraints of conventional thought – you’re taking on the role of an architect, crafting new and uncharted paradigms that define the future.

Absolutely! Conceptual thinking is a skill that can be honed through practice and a curious mindset.

Yes, exercises like brainstorming, mind mapping, and lateral thinking puzzles can sharpen your conceptual thinking abilities.

Not at all. Conceptual thinking is valuable in all fields, from business and science to everyday problem-solving. It’s a universally beneficial skill.

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What Is Creative Problem-Solving & Why Is It Important?

• 01 Feb 2022

One of the biggest hindrances to innovation is complacency—it can be more comfortable to do what you know than venture into the unknown. Business leaders can overcome this barrier by mobilizing creative team members and providing space to innovate.

There are several tools you can use to encourage creativity in the workplace. Creative problem-solving is one of them, which facilitates the development of innovative solutions to difficult problems.

Here’s an overview of creative problem-solving and why it’s important in business.

Access your free e-book today.

What Is Creative Problem-Solving?

Research is necessary when solving a problem. But there are situations where a problem’s specific cause is difficult to pinpoint. This can occur when there’s not enough time to narrow down the problem’s source or there are differing opinions about its root cause.

In such cases, you can use creative problem-solving , which allows you to explore potential solutions regardless of whether a problem has been defined.

Creative problem-solving is less structured than other innovation processes and encourages exploring open-ended solutions. It also focuses on developing new perspectives and fostering creativity in the workplace . Its benefits include:

• Finding creative solutions to complex problems : User research can insufficiently illustrate a situation’s complexity. While other innovation processes rely on this information, creative problem-solving can yield solutions without it.
• Adapting to change : Business is constantly changing, and business leaders need to adapt. Creative problem-solving helps overcome unforeseen challenges and find solutions to unconventional problems.
• Fueling innovation and growth : In addition to solutions, creative problem-solving can spark innovative ideas that drive company growth. These ideas can lead to new product lines, services, or a modified operations structure that improves efficiency.

Creative problem-solving is traditionally based on the following key principles :

1. Balance Divergent and Convergent Thinking

Creative problem-solving uses two primary tools to find solutions: divergence and convergence. Divergence generates ideas in response to a problem, while convergence narrows them down to a shortlist. It balances these two practices and turns ideas into concrete solutions.

2. Reframe Problems as Questions

By framing problems as questions, you shift from focusing on obstacles to solutions. This provides the freedom to brainstorm potential ideas.

3. Defer Judgment of Ideas

When brainstorming, it can be natural to reject or accept ideas right away. Yet, immediate judgments interfere with the idea generation process. Even ideas that seem implausible can turn into outstanding innovations upon further exploration and development.

4. Focus on "Yes, And" Instead of "No, But"

Using negative words like "no" discourages creative thinking. Instead, use positive language to build and maintain an environment that fosters the development of creative and innovative ideas.

Creative Problem-Solving and Design Thinking

Whereas creative problem-solving facilitates developing innovative ideas through a less structured workflow, design thinking takes a far more organized approach.

Design thinking is a human-centered, solutions-based process that fosters the ideation and development of solutions. In the online course Design Thinking and Innovation , Harvard Business School Dean Srikant Datar leverages a four-phase framework to explain design thinking.

The four stages are:

• Clarify: The clarification stage allows you to empathize with the user and identify problems. Observations and insights are informed by thorough research. Findings are then reframed as problem statements or questions.
• Ideate: Ideation is the process of coming up with innovative ideas. The divergence of ideas involved with creative problem-solving is a major focus.
• Develop: In the development stage, ideas evolve into experiments and tests. Ideas converge and are explored through prototyping and open critique.
• Implement: Implementation involves continuing to test and experiment to refine the solution and encourage its adoption.

Creative problem-solving primarily operates in the ideate phase of design thinking but can be applied to others. This is because design thinking is an iterative process that moves between the stages as ideas are generated and pursued. This is normal and encouraged, as innovation requires exploring multiple ideas.

Creative Problem-Solving Tools

While there are many useful tools in the creative problem-solving process, here are three you should know:

Creating a Problem Story

One way to innovate is by creating a story about a problem to understand how it affects users and what solutions best fit their needs. Here are the steps you need to take to use this tool properly.

1. Identify a UDP

Create a problem story to identify the undesired phenomena (UDP). For example, consider a company that produces printers that overheat. In this case, the UDP is "our printers overheat."

2. Move Forward in Time

To move forward in time, ask: “Why is this a problem?” For example, minor damage could be one result of the machines overheating. In more extreme cases, printers may catch fire. Don't be afraid to create multiple problem stories if you think of more than one UDP.

3. Move Backward in Time

To move backward in time, ask: “What caused this UDP?” If you can't identify the root problem, think about what typically causes the UDP to occur. For the overheating printers, overuse could be a cause.

Following the three-step framework above helps illustrate a clear problem story:

• The printer is overused.
• The printer overheats.
• The printer breaks down.

You can extend the problem story in either direction if you think of additional cause-and-effect relationships.

4. Break the Chains

By this point, you’ll have multiple UDP storylines. Take two that are similar and focus on breaking the chains connecting them. This can be accomplished through inversion or neutralization.

• Inversion: Inversion changes the relationship between two UDPs so the cause is the same but the effect is the opposite. For example, if the UDP is "the more X happens, the more likely Y is to happen," inversion changes the equation to "the more X happens, the less likely Y is to happen." Using the printer example, inversion would consider: "What if the more a printer is used, the less likely it’s going to overheat?" Innovation requires an open mind. Just because a solution initially seems unlikely doesn't mean it can't be pursued further or spark additional ideas.
• Neutralization: Neutralization completely eliminates the cause-and-effect relationship between X and Y. This changes the above equation to "the more or less X happens has no effect on Y." In the case of the printers, neutralization would rephrase the relationship to "the more or less a printer is used has no effect on whether it overheats."

Even if creating a problem story doesn't provide a solution, it can offer useful context to users’ problems and additional ideas to be explored. Given that divergence is one of the fundamental practices of creative problem-solving, it’s a good idea to incorporate it into each tool you use.

Brainstorming

Brainstorming is a tool that can be highly effective when guided by the iterative qualities of the design thinking process. It involves openly discussing and debating ideas and topics in a group setting. This facilitates idea generation and exploration as different team members consider the same concept from multiple perspectives.

Hosting brainstorming sessions can result in problems, such as groupthink or social loafing. To combat this, leverage a three-step brainstorming method involving divergence and convergence :

• Have each group member come up with as many ideas as possible and write them down to ensure the brainstorming session is productive.
• Continue the divergence of ideas by collectively sharing and exploring each idea as a group. The goal is to create a setting where new ideas are inspired by open discussion.
• Begin the convergence of ideas by narrowing them down to a few explorable options. There’s no "right number of ideas." Don't be afraid to consider exploring all of them, as long as you have the resources to do so.

Alternate Worlds

The alternate worlds tool is an empathetic approach to creative problem-solving. It encourages you to consider how someone in another world would approach your situation.

For example, if you’re concerned that the printers you produce overheat and catch fire, consider how a different industry would approach the problem. How would an automotive expert solve it? How would a firefighter?

Be creative as you consider and research alternate worlds. The purpose is not to nail down a solution right away but to continue the ideation process through diverging and exploring ideas.

Continue Developing Your Skills

Whether you’re an entrepreneur, marketer, or business leader, learning the ropes of design thinking can be an effective way to build your skills and foster creativity and innovation in any setting.

If you're ready to develop your design thinking and creative problem-solving skills, explore Design Thinking and Innovation , one of our online entrepreneurship and innovation courses. If you aren't sure which course is the right fit, download our free course flowchart to determine which best aligns with your goals.

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What Are Conceptual Skills? (Example List Included)

Jeff Gillis 0 Comments

By Jeff Gillis

When people think about the skills they need to excel at work, they usually focus on problem-solving , collaboration , and other classics. But conceptual skills are also crucial.

Conceptual thinking helps you understand the big picture, examine abstract ideas, and so much more. If you’re wondering, “What are conceptual skills, and why do they matter?” here’s what you need to know.

What Are Conceptual Skills?

Before we take a deep dive into the various conceptual skills, it’s important to answer one question: what are conceptual skills? Well, to understand what they are, it’s helpful to break everything down a bit.

First, according to the Cambridge Dictionary , “concept” means “a principle or idea.” In some cases, concepts are considered thoughts or notions.

Conceptualizing is the act of coming up with these principles, ideas, thoughts, or notions. Usually, in the business world, conceptualizing is identifying potential solutions to a problem or creative strategies by thinking in an abstract way. It involves understanding and visualizing complex situations to get to an innovative answer.

So, knowing that, conceptual skills are capabilities that help you come up with those solutions or strategies, usually through abstract thinking.

More often than not, conceptual skills are soft skills . Things like creativity, strategic thinking, and adaptability play a big part in the conceptual thinking equation. However, that doesn’t mean specific hard skills aren’t valuable.

Usually, you also need the proper technical knowledge to get a complete understanding of the complex workplace scenario you want to navigate. Which hard skills matter depends on the nature of the job and the problem you’re trying to solve.

In the end, any ability, trait, or area of expertise that allows you to conceptualize effectively and come up with critical answers can qualify as a conceptual skill.

How Are Conceptual Skills Relevant to a Job Search?

At this point, you should have a reasonable understanding of what conceptual skills are, so it’s time to talk about why they matter during a job search. Let’s begin with the actual job search itself.

Conceptual skills are relevant to a job search in a few ways. First, if you think about it, finding a new job isn’t unlike problems in the workplace. Your goal is to secure a new position. To make that happen, you have to analyze the situation, identify potential paths toward success, and take strategic action.

With conceptual thinking, your approach can be more effective. You can envision the various pathways you can take and estimate how each method may (or may not) get you the desired result. You’ll be able to see the forest for the trees, ensuring you take the big picture into account.

Conceptual skills may also help you identify the right opportunities. While you might not know exactly what a position involves simply by reading a job ad, conceptual thinking allows you to come up with a solid guess.

Plus, they can help you create a better resume and higher-quality interview answers. Again, conceptual thinking involves the ability to assess scenarios and visualize solutions. In this case, the situation is finding a job, and the solutions are creating standout applications and responses to the hiring manager’s questions.

By taking in data about the situation – in this case, details from the job description and information about the company from its website, social media pages, and other resources – you can visualize what the hiring manager wants to find in a candidate. As you do that, you can determine how to position yourself as the ideal fit, making it easier to stay ahead of the competition.

After all, 80 percent of companies believe that soft skills are increasingly important to business success. So, by showing off your conceptual skills the right way, you can look like a stronger candidate for nearly any job type.

Okay, now it’s time to move onto the second part. Ultimately, conceptual skills are valuable in a wide range of jobs. But if you have your sights set on a management or leadership position, they are outright critical.

With management positions, conceptual thinking is typically part of the role. Upper-level roles commonly have to solve higher-level problems for the organization. Strategy development and innovation can be core responsibilities.

In those cases, having conceptual thinking capabilities is essential if you want to land the job and perform well in the position. They’ll make you a more effective problem-solver for issues at that level.

So, what are the conceptual skills hiring managers are looking for in 2022? Well, two of the biggest are analytical skills and problem-solving skills. Creativity and innovative thinking are also in demand.

But that really only scratches the surface. Remember, any skill that makes you effective at conceptual thinking can be valuable, especially if it helps you separate yourself from the pack.

How to Highlight Conceptual Skills for a Job Search

At this point, you probably have a solid idea about why conceptual skills are important to your job search. That means it’s time to move on and talk about how to showcase those capabilities when you’re looking for a new position.

In most cases, squaring away your resume and cover letter is what you’ll need to tackle first. Those are both parts of a typical application, so getting them right is essential.

When you’re creating your resume and cover letter, being achievement-focused is the better approach. By focusing on accomplishments, you can show the hiring manager how you put your skills to work, as well as highlight the results of your efforts.

If your goal is to highlight conceptual skills, you need to choose achievements where conceptual thinking played a big role in your success.

Okay, but what if you have several accomplishments that fit that bill? How do you pick the right ones to include? Well, by using the Tailoring Method .

The Tailoring Method is all about relevancy. It helps you choose achievements that will mean the most to that specific hiring manager. You take the employer’s needs and preferences into account, ensuring you’re sharing details that matter to them.

Once you’d done with your resume and cover letter, it’s time to start practicing job interview answers. You can use the Tailoring Method to help create responses for both traditional job interview questions and tricky behavioral interview questions .

For behavioral interview questions, adding a healthy dash of the STAR Method is a good move. You’ll turn your answers into engaging stories, making your responses informative and interesting in the eyes of the hiring manager.

How to Develop Conceptual Skills If You Don’t Have Them

If you don’t have conceptual skills, developing them is a good idea. It can help you stand out from other candidates and prepare you for the kinds of problem-solving you’ll likely need to do as you advance in your career.

The thing is, most people have some experience with conceptual thinking. For example, if you had to do science projects while you were in school, you’ve probably used some conceptual skills.

But whether you think you’re starting from scratch or that you have a bit of a foundation, that doesn’t mean you can’t acquire and hone these capabilities. If you aren’t sure how to go about it, here are some tips for building your conceptual skills.

1. Observe Conceptual Thinkers You Admire

Observation can be an incredibly powerful tool. By watching conceptual thinkers that you admire analyze problems and devise solutions, you can get amazing insights into the process.

While it may seem like observing conceptual thinkers in action would be difficult to do, that isn’t always the case. If there is a manager you admire at work, you may get to see them in action during staff meetings or planning sessions.

However, if you don’t have access to a suitable person in the workplace, then go online. For example, you could look up YouTube videos featuring people creating solutions to unique problems.

Mark Rober is an excellent example of a conceptual thinker in action. While his focus is on engineering, he presents information in a straightforward fashion and openly discusses his thought process. Plus, the results of his work are often quite entertaining.

2. Identify a Workplace Problem and Use It as a Case Study

If you want to put your conceptual skills to work, here’s one way to go about it. Identify a problem in your workplace – big or small – and treat it like a case study. Examine the issue from several angles. Talk with colleagues about it. See if you can create potential solutions that align with the company’s broader mission and goals.

You don’t necessarily have to succeed in finding an answer to make this approach worthwhile. It’s all about teaching yourself to think conceptually.

But if you do find a solution, that’s a great bonus. You can present your idea to the appropriate leaders and might be able to create meaningful, beneficial change, giving you a new achievement to add to your resume.

3. Volunteer for Cross-Departmental Projects

When a project involves several departments, it’s an opportunity to learn more about how different organizational areas view problems and devise solutions. It’s a chance to broaden your horizons and learn new ways to find answers by engaging with people who have different skillsets and perspectives.

List of Conceptual Skills

Alright, now is the moment you’ve been waiting for: the list of conceptual skills. Ultimately, there are a lot of capabilities that can fall into this category. By knowing which ones potentially land in this group, you can pick ones to highlight on your resume or cover letter – or in your answers to interview questions – to showcase your conceptual thinking abilities.

Here is a list of conceptual skills examples:

• Problem-Solving
• Innovative-Thinking
• Abstract-Thinking
• Critical-Thinking
• Idea Formulation
• Resourcefulness
• Adaptability
• Strategic-Thinking
• Negotiation
• Flexibility
• Prioritization
• Organization
• Active Listening
• Open-Mindedness
• Logical-Thinking

All of the capabilities and traits above could qualify as conceptual skills. However, that doesn’t mean they are the only ones. Any ability to lets you assess big-picture problems and develop unique solutions could also be a part of that list, so don’t limit yourself to just those included above.

It’s also critical to understand that you don’t have to get all of the skills above squeezed into your resume, cover letter, or interview answers. If you did, you probably went a bit overboard.

Instead, review the job description and company information. Then, use the Tailoring Method to pick the skills and traits that align with the hiring manager’s priorities. That way, you can discuss achievements that matter in their eyes, increasing the odds that you’ll look like an exceptional fit for the position.

Plus, it ensures you have room to discuss other essential capabilities. If you’d like to find out more about the different skills to put on a resume , check out our in-depth piece on the topic. It’ll give you valuable insights into what to highlight, allowing you to take your job search to the next level.

Putting It All Together

In the end, conceptual skills are incredibly valuable, especially if you want to work your way up into a management or leadership role. By honing yours now, you’ll be ready to tackle all of that big-picture, innovative thinking, ensuring you can come up with solutions to a range of challenging problems.

Plus, by reviewing the information above, you know how to showcase your conceptual thinking abilities effectively. Use that to your advantage. That way, when a new job opportunity comes around, you can position yourself as the ideal candidate for the role.

Co-founder and CTO of TheInterviewGuys.com. Jeff is a featured contributor delivering advice on job search, job interviews and career advancement, having published more than 50 pieces of unique content on the site , with his work being featured in top publications such as INC , ZDnet , MSN and more.

Learn more about The Interview Guys on our About Us page .

About The Author

Jeff gillis.

Co-founder and CTO of TheInterviewGuys.com. Jeff is a featured contributor delivering advice on job search, job interviews and career advancement, having published more than 50 pieces of unique content on the site , with his work being featured in top publications such as INC , ZDnet , MSN and more. Learn more about The Interview Guys on our About Us page .

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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10 Steps for Mastering Conceptual Thinking

Step 1: Define the Problem

The first step in mastering conceptual thinking is to clearly define the problem you are trying to solve. This may seem obvious, but many people skip this step and jump straight into brainstorming ideas. By defining the problem, you can focus your creative thinking towards finding a solution that addresses the root cause of the issue.

Identify the Key Challenges

When defining the problem, it’s important to identify the key challenges that need to be addressed. This involves looking at the issue from different angles and understanding the various factors that contribute to it.

“The definition of a problem is half the solution.” - Unknown

Determine the Scope

Once you have identified the key challenges, determine the scope of the problem. Is it something that can be solved with a simple fix, or does it require a more complex solution? Understanding the scope of the problem will help you determine the level of effort required to solve it.

Define Success Criteria

Finally, define what success looks like. What is the desired outcome of solving the problem? This will help you stay focused on the end goal and ensure that you are working towards a measurable outcome.

“If you don’t know where you’re going, any road will get you there.” - Lewis Carroll

Step 2: Research and Gather Information

Before you can start generating ideas, it’s important to thoroughly understand the problem you’re trying to solve. Here are some tips for how to conduct effective research and gather information:

Identify the key questions

To guide your research, start by identifying the key questions related to the problem. What do you need to know in order to develop a solution? Some of these questions may be obvious, while others may require more exploration. Write down your questions to help you stay focused during your research.

Search online resources

The internet is a vast trove of information, and there are likely many resources available online that can help you better understand the problem you’re working on. Use search engines to find relevant articles, blog posts, and research papers. Look for information from authoritative sources, such as academic journals or industry publications.

Talk to experts

Depending on the nature of the problem, there may be experts in the field who can offer valuable insights and advice. Reach out to these experts and ask if they would be willing to speak with you. You can also use social media platforms like LinkedIn to find experts in your area of interest.

Conduct surveys or interviews

Sometimes, the best way to get information is to ask people directly. Consider conducting surveys or interviews with stakeholders who are affected by the problem. This can provide valuable qualitative data that can help you generate more accurate and effective solutions.

Compile your findings

As you gather information, keep track of your findings in a central location, such as a spreadsheet. This can help you stay organized and ensure that you don’t miss any important details. Once you have compiled all your research, review it carefully to identify any key insights or trends that can inform your conceptual thinking.

Step 3: Brainstorm Ideas

Brainstorming is an essential part of conceptual thinking because it allows you to generate a wide range of ideas in a short amount of time. Here are some tips to help you get the most out of your brainstorming sessions:

Tip 1: Quantity over Quality

During the brainstorming process, it’s crucial to focus on quantity over quality. Don’t worry about whether an idea is good or bad; just write it down and move on to the next one.

Tip 2: Be Open-Minded

Approach brainstorming with an open mind and embrace unconventional solutions. Sometimes, the most surprising ideas can be the best ones.

“If you always do what you always did, you will always get what you always got.” - Albert Einstein

Tip 3: Set a Time Limit

Give yourself a specific amount of time to brainstorm and stick to it. This will help you stay focused and productive.

Tip 4: Build Upon Other Ideas

If someone shares an idea that sparks inspiration in you, build upon it. Don’t be afraid to collaborate and bounce ideas off of each other.

“If I have seen further than others, it is by standing upon the shoulders of giants.” - Isaac Newton

Tip 5: Take a Break

If you’re feeling stuck, take a break and come back to brainstorming later. A fresh perspective can often lead to new ideas.

Remember, the goal of brainstorming is to generate as many ideas as possible. So, let your imagination run wild and write down every idea that comes to mind, no matter how silly or unrealistic it may seem.

Step 4: Refine Your Ideas

Now that you have generated a list of potential solutions, it’s time to refine those ideas. The goal here is to select the most promising ideas and develop them further.

Evaluate Each Idea

Start by evaluating each idea on its own merit. Consider factors such as feasibility, cost, and how well it aligns with your overall goal. Keep in mind that some ideas may need to be combined or modified to be successful.

Seek Feedback

Share your ideas with other people and seek feedback. This can include colleagues, friends, or family members. Be open to criticism and suggestions, as they may help you refine your ideas even further. Keep track of the feedback you receive so you can refer to it later.

Develop a Plan

Once you have selected your top ideas, it’s time to develop a plan for each one. This plan should outline the steps you will take to turn your concept into a reality. The plan should include details such as timelines, resources required, and how you will measure success.

Create a Prototype

Building a prototype can help you determine the feasibility of your idea and identify any potential issues. The prototype can be as simple or complex as your idea requires. You can use materials such as cardboard or foam to create a rough model or use 3D printing technology for a more polished prototype.

Refine Your Ideas Again

Based on the feedback you receive from testing your prototype, it’s likely that you will need to refine your ideas even further. This may involve making changes to the design, functionality, or marketing strategy. Don’t be afraid to go back to the drawing board if necessary.

By following these steps, you can refine your ideas and turn them into a successful concept. Remember, the key is to stay open to feedback and keep refining until you are satisfied with your idea.

Step 5: Develop a Plan

Once you have refined your ideas, it’s time to develop a plan of action. This plan should outline the steps you will take to achieve your goal. Here are some points to consider while developing your plan:

Identify Your Goals and Objectives

Start by identifying your goals and objectives. What do you want to achieve with your concept? Be specific and make sure your objectives are measurable and achievable.

Set a Realistic Timeline

Develop a timeline for your project. This timeline should include all the important milestones, deadlines, and deliverables. Make sure the timeline is realistic and achievable.

Allocate Resources

Identify the resources you will need to achieve your goals. This can include people, equipment, materials, and funds. Make sure you allocate resources efficiently to avoid any unnecessary delays or costs.

Identify the Risks and Challenges

Identify the risks and challenges that you may encounter during the implementation of your plan. This can help you develop contingency plans and be prepared for any unexpected events.

Develop a Communication Plan

Develop a communication plan that outlines how you will communicate with your team members, stakeholders, and customers. Make sure your plan includes regular meetings, progress reports, and updates.

Review and Refine Your Plan

Review and refine your plan regularly. This can help you identify any issues or obstacles that need to be addressed. Make sure you are flexible and willing to make changes to your plan as needed.

By developing a comprehensive plan, you can ensure that your concept is implemented effectively and efficiently. So take the time to develop a plan that works best for you and your team.

Step 6: Visualize Your Concept

Visualization is an essential step in the process of mastering conceptual thinking. This involves creating a mental image of your concept and thinking about how it can be brought to life. Here are some tips for effectively visualizing your concept:

Use Your Imagination

Let your imagination run wild and picture your concept in as much detail as possible. Think about the features, design, and functionality of your concept and how they will work together.

Sketch Your Idea

If you’re having trouble visualizing your concept, try sketching it out on paper. This can help you see your idea in a more concrete form and identify any gaps or areas for improvement.

Create a Vision Board

A vision board is a visual representation of your concept and can include images, words, and other visual elements. This can help you stay focused on your goals and motivate you to bring your idea to life.

Imagine the End Result

When visualizing your concept, it’s important to think about the end result. Imagine what it will look like, how it will function, and how it will impact the world around you.

Think Outside the Box

Don’t be afraid to think outside the box when visualizing your concept. Consider unconventional approaches and ideas that may help your concept stand out from the crowd.

Remember, the goal of visualizing your concept is to develop a clear understanding of what you want to achieve and how you will get there. With a strong visualization, you will be better equipped to turn your concept into a reality.

Step 7: Build a Prototype

Building a prototype is a crucial step in the conceptual thinking process. It allows you to test your ideas and see how they function in the real world. Here are some tips for building a successful prototype:

Start Simple

When building a prototype, it’s important to start simple. Don’t worry about creating a perfect, finished product right away. Instead, focus on creating a minimum viable product (MVP) that demonstrates the core features and functionality of your concept.

Use Low-Cost Materials

You don’t need to spend a lot of money on materials when building a prototype. In fact, using low-cost materials like cardboard, foam, or 3D-printed parts can be a great way to save money and test different iterations of your design.

Incorporate User Feedback

As you build your prototype, it’s important to incorporate feedback from potential users. This can help you identify any issues with your concept and make improvements before launching it.

Test, Test, Test

Testing is a critical part of the prototyping process. Be sure to test your prototype thoroughly to identify any technical issues or areas for improvement. This may involve conducting user tests, running simulations, or performing stress tests to ensure your prototype can handle real-world conditions.

Iterate and Improve

Based on the results of your testing, iterate and improve your prototype. This may involve making changes to the design, functionality, or materials used. Be willing to go back to the drawing board and make changes until you have a prototype that meets your needs and those of your potential customers.

Building a prototype can be time-consuming and challenging, but it’s an essential step in the conceptual thinking process. By following these tips, you can build a successful prototype and take one step closer to bringing your idea to life.

Step 8: Get Feedback

Once you have built a prototype, it’s important to get feedback from others. This step is crucial to ensure that your concept meets the needs of your target audience and is well received. Here are a few ways to get feedback:

1. Conduct Surveys

Surveys are a great way to gather feedback from a large number of people. You can create online surveys using tools like Google Forms or SurveyMonkey or conduct a face-to-face survey. Make sure to design your survey in a way that elicits detailed and honest feedback.

2. Host Focus Groups

Another way to get feedback is by hosting focus groups. Focus groups are small, moderated discussions with a group of people who fit your target audience. This allows you to gather more in-depth feedback and observe how people interact with your concept.

3. Seek out Expert Opinions

Reach out to experts in your industry and ask for their feedback. Their professional insights can help you identify areas of improvement and fine-tune your concept.

4. Ask for Colleague or Friend Feedback

Ask your colleagues or friends to provide feedback on your concept. They may provide a fresh perspective that you hadn’t considered before.

5. Analyze User Data

If you already have users using your concept, analyze their behavior and feedback to identify areas of improvement. You can use user data to identify which features or aspects of your concept are working well and which areas need improvement.

Remember to approach feedback with an open mind and a willingness to make changes. Incorporating feedback can help you refine and improve your concept, making it more successful in the long run.

Step 9: Refine and Improve

After receiving feedback, it’s important to take the time to refine and improve your concept. This may involve making changes to the design, functionality, or marketing strategy.

Analyze Feedback

Start by analyzing the feedback you received. Identify common themes or issues that were raised and prioritize them based on importance. Be open to constructive criticism and willing to make necessary changes.

Iterate and Test

Once you have identified areas that need improvement, iterate and test your concept again. Make any necessary changes and test it once more. This process may take several iterations before your concept is ready for implementation.

Consider Customer Needs

When refining your concept, it’s important to consider the needs of your target customers. What features or benefits are they looking for? How can you improve your concept to better meet their needs?

Revisit Your Plan

As you make changes to your concept, you may need to revisit your plan of action. Make sure it still aligns with your new vision and adjusts accordingly.

Collaborate with Others

Collaborating with others can lead to new ideas and perspectives. Consider getting input from colleagues, mentors, or other experts in your field.

Remember, refining and improving your concept is an ongoing process. Don’t be afraid to make changes and continue iterating until you achieve success.

Step 10: Implement and Launch

Congratulations! You have successfully refined and improved your concept and it’s time to bring it to market. However, before launching, there are a few things to consider.

Conduct Market Research

Before implementing your concept, it’s important to conduct market research. This involves analyzing the market to determine if there is a need for your product or service. You should also research your target audience and competitors to identify potential opportunities and challenges.

Develop a Marketing Strategy

Once you have conducted market research, it’s time to develop a marketing strategy. This involves determining how you will promote your product or service to your target audience. Some common marketing strategies include social media advertising, email marketing, and search engine optimization (SEO).

Choose a Launch Date

Choosing the right launch date is crucial for the success of your concept. You should consider factors such as holidays, industry events, and seasonal trends when selecting a launch date.

Monitor Progress

After launching your concept, it’s important to monitor its progress. This involves tracking key metrics such as sales, website traffic, and customer feedback. This information will help you identify areas for improvement and make necessary adjustments.

Make Adjustments as Needed

Based on the progress you monitor, you may need to make adjustments to your product, marketing strategy, or launch date. Don’t be afraid to make changes if necessary to ensure the success of your concept.

Continuously Improve

Remember, launching your concept is just the beginning. As you gain more customers and feedback, continue to improve and enhance your product or service. This will help you retain customers and remain competitive in the market.

By following these steps, you can implement and launch your concept with confidence and set yourself up for success. Good luck!

How to Stay Focused: The Ultimate Guide

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What Are Conceptual Skills?

Definition and Examples of Conceptual Skills

Types of Conceptual Skills

• Communication

Creative Thinking

Problem solving.

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Conceptual skills help employees avoid the pitfall of not “seeing the forest for the trees,” as the saying goes. If you possess conceptual skills, you can both envision problems and brainstorm solutions. Having these skills likely means that you're a creative type, and can work through abstract concepts and ideas. 

Employers value conceptual skills, and in some roles, having them is essential. 

Find out more about the various varieties of conceptual skills, and why they're important. 

Conceptual skills allow someone to see how all the parts of an organization work together to achieve the organization’s goals. 

They're essential for leadership positions, particularly upper-management and middle-management jobs. Managers need to make sure everyone working for them is helping to achieve the company’s larger goals. Rather than just getting bogged down in the details of day-to-day operations, upper and middle managers also need to keep the company’s “big picture” aims in mind.

However, conceptual skills are useful in almost every position. 

Even when you have a particular list of duties, it is always helpful to know how your part fits into the broader goals of your organization. Plus, if you have conceptual skills, you can tackle big challenges that come up for your team and devise creative and thoughtful solutions that go beyond fulfilling rote tasks. 

Take a look at this list of the most important conceptual skills sought by most employers. It also includes sublists of related skills that employers tend to seek in job applicants.

Develop and emphasize these abilities in job applications, resumes, cover letters, and interviews. 

You can use these skills lists throughout your job search process. Insert the soft skills you’ve developed into your  resume  when you detail your work history, and highlight your conceptual abilities during interviews. 

A very important conceptual skill is the ability to  analyze  and evaluate whether a company is achieving its goals and sticking to its business plan. Managers have to look at how all the departments are working together, spot particular issues, and then decide what steps need to be taken.

• Analytical abilities
• Analysis and diagnosis of complex situations
• Cognitive abilities
• Defining strategies for reaching goals
• Diagnosing problems within the company
• Forecasting for the business or department
• Questioning the connection between new initiatives and the strategic plan
• Recognizing opportunities for improvement
• Seeing the key elements in any situation
• Selecting important information from large data sets
• Understanding relationships between departments
• Understanding relationships between ideas, concepts, and patterns
• Understanding the organization’s business model

Without strong  communication skills , an employee won’t be able to share their solutions with the right people. Someone with conceptual skills can explain a problem and offer solutions. They can speak effectively to people at all levels in the organization, from upper management to employees within a specific department. 

People with conceptual skills are also good  listeners . They have to listen to the needs of the employers before devising a plan of action.

• Active listening
• Contextualizing problems
• Effectively communicating strategy
• Implementing thinking
• Interpersonal
• Interrelational
• Presentational
• Verbal communication

People with conceptual skills must be very creative. They must be able to devise creative solutions to abstract problems, which involves thinking outside of the box. They must consider how all the departments within an organization work together, and how they can work to solve a particular problem.

• Abstract thinking
• Being open-minded 
• Creative thinking
• Examining complex issues
• Formulating ideas
• Formulating processes
• Intuitive thinking
• Organization

Someone with conceptual skills also has strong leadership skills. They need to convince employees and employers to follow their vision for the company. They need to inspire others to trust and follow them, and that takes strong leadership.

• Commitment to achieving company goals
• Persuasiveness
• Strategic planning
• Task direction
• Task implementation
• Team building
• Visualizing the company as a whole

Once an employee analyzes a situation and identifies a problem, they then have to decide how to solve that problem. People with conceptual skills are good at solving problems and making strong, swift decisions that will yield results.

• Able to ignore extraneous information
• Broad thinking
• Critical thinking
• Breaking down a project into manageable pieces
• Decision making
• Executing solutions
• Formulating effective courses of action
• Logical thinking
• Multitasking
• Prioritization
• Resolving industry problems

Key Takeaways

• Conceptual skills allow you to foresee issues, brainstorm solutions, and understand the strategic big picture behind a company's day-to-day operations. 
• Possessing conceptual skills is particularly important for people in managerial roles, but they're helpful if you're in any role. 
• Include relevant types of conceptual skills in your resume and cover letter. Plus, use skills keywords during your job interviews. Be prepared to give examples of how you've used each one.

The Stuff of Problem Solving: Discovering Concepts and Applying Principles

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Few would disagree that building critical thinking and creative problem solving skills is imperative for today's students. However, subject matter knowledge is equally important. The 21st Century Skills framework includes a Core Subjects section in addition to skills such as critical thinking and problem solving. The Framework for K-12 Science Education includes Core Ideas in addition to process skills.

You might say, "Of course! Everyone knows students need knowledge as well as skills. We have to teach content, too."

But what exactly IS content? Is it the stuff found in a textbook? Is it information? Is it the definitions, formulas and facts of a subject area?

The answer is: Yes -- but not really.

Understanding What Understanding Is

As an instructional designer, I constantly wrestle with figuring out what it means to really understand something. The answer to that question determines how I will design the instruction to help students build that understanding.

Consider what a person who really understands a subject matter does:

A definition isn't just a string of words to be memorized and repeated; it's an attempt to state the meaning of a concept. A formula isn't a string of variables; it's a relation between concepts -- a principle. Understanding a concept is not the same as recalling a definition, and knowing a principle is not the same as recalling a formula.

"Must Have" vs. "Can Have"

Think about a triangle, for example. If your kindergarten student told you that a triangle was a shape with three sides, would you be satisfied that she understood the concept? Maybe. But you would probably want some more evidence. For example, can she pick out the triangle when you show her a triangle, a square and a circle? Does she identify right triangles as triangles or only equilateral triangles? How about a triangle that isn't a closed figure -- something with three sides but not three angles? Does she call both a small line drawing and a big red felt cut-out "triangle"?

A concept like "triangle" really refers to a category of things. There are some features that it "must have" in order to be placed in that category. A triangle "must have" three sides, and it must be a closed figure. But the sides can be different lengths, the angles can be different sizes, and it can be any color or made out of any material. These are some of the "can haves" -- the features that vary across examples of the category. 2

Part of "understanding" a concept, then, is to classify it -- paying attention to the "must haves" across a variety of "can haves."

Given this, how should concepts be taught?

Analyzing Your Concept

First, analyze your concept. Figure out what the "must have" features are. Think of a typical example and then change one of its features. Is it still an example? If so, then that feature is a "can have." Is it now an example of something else? If so, then the feature you just changed is a "must have."

Next, figure out what kind of variety your concept has. What are all the different "can haves"? Change all of them and see how different two examples can be from each other and still be examples of the concept.

Teaching Your Concept

First, make the "must haves" obvious. See if you can find an example and a non-example that share all of the same "can haves" and vary in just one "must have." The single difference between the example and non-example will allow your students to infer the critical must-have feature. Or, better yet, help them discover a concept and practice thinking skills at the same time by taking them through the process of analysis. Change a variable and ask, "Is it still x ?"

Then extend across the range of "can haves" by asking students to identify a variety of examples with widely different features. You might assign them to look for the most "far out" or different example they can that's still an example of the concept.

Concepts and Principles: The Stuff of Problem Solving

When we look at a new phenomenon, we're comparing it to what we already know -- the classes of things and the relations of things already discovered. We might classify it as another example of an already established category, or we might invent a whole new classification system to incorporate it. We might discover a relation between concepts and recognize it as the application of an already known principle, or we might discover a new principle by analogy to what's already known.

Content is important, but it's more than the definitions and formulas found in a textbook. Content is made up of the concepts and principles of our world. Teaching concepts and principles with examples and non-examples can go a long way in helping students develop true understanding. In doing so, we offer our students more than something to be memorized. We offer them the "stuff" they need for problem solving.

1 Markle, S. M. & Tiemann, P. W. (1970). "Behavioral" analysis of "cognitive" content. Educational Technology, 10, 41–45.

2 Thanks to Joe Layng for coming up with the terms "must have" and "can have" for necessary and variable features, respectively.

Problem-Solving Framework: 7 Techniques Product Teams Should Follow

What is a problem-solving framework?

The problem-solving framework is a set of tools and techniques used to identify the cause of a problem and find the right solutions.

These frameworks use both rigorous data analysis and heuristics (mental shortcuts that let you apply what you already know in a new situation), which is useful when detailed research isn’t practical.

7 frameworks to find potential solutions to complex problems

There are plenty of frameworks that organizations use to solve problems. Here are 7 that we find most suitable for product teams at SaaS companies.

1. McKinsey’s Problem Solving Framework

McKinsey’s structured problem-solving approach is ideal for SaaS product teams looking to tackle complex challenges and drive growth .

The process consists of 7 steps:

• Define the problem : Work with all stakeholders to clearly outline the challenge to solve.
• Break down the problem : Use tools like hypothesis trees and the MECE principle (Mutually Exclusive, Collectively Exhaustive) to deconstruct the problem into manageable chunks.
• Prioritize issues : Focus on high-impact, easy-to-implement solutions by using a prioritization matrix like value vs. effort.
• Develop hypotheses : Form hypotheses to guide data analysis and ensure focus on the most likely solutions.
• Analyze data : Use a data-driven approach to test hypotheses, validate assumptions, and uncover insights.
• Synthesize findings : Summarize insights using the pyramid principle—start with the key recommendation supported by detailed analysis.
• Communicate : Present clear, concise recommendations and solutions to stakeholders; make sure they understand the reasoning and data behind them to secure their buy-in.

2. Root Cause Analysis

Root Cause Analysis (RCA) is a powerful problem-solving technique that focuses on identifying the underlying cause of an issue rather than just addressing the symptoms, which is essential to prevent recurring problems and ensure long-term improvement.

RCA consists of 5 key stages:

• Define the problem : Clearly articulate the issue, whether it’s a product bug , customer complaint, or poor metric performance.
• Collect data : Gather relevant information to understand the scope and impact of the problem.
• Identify possible causal factors : Analyze the data to find factors that may have led to the issue.
• Identify the root cause : Keep digging deeper into the causal factors—using techniques like 5 Whys—to find the underlying issue that caused the problem.
• Recommend and implement solutions : Develop actionable recommendations to tackle the root cause, put them into action, and monitor performance to ensure the problem doesn’t come back.

3. CIRCLES method for problem-solving

The CIRCLES method was created by Lewis C. Lin, known for his best-selling book Decode and Conquer. It’s a go-to problem-solving framework for companies like Google because it’s versatile and lets product managers solve all kinds of problems.

CIRCLES stands for the 7 steps it takes to solve a problem:

• C omprehend the situation: Understand the context and details of the problem you’re trying to solve.
• I dentify the customer: Define your target audience.
• R eport the customer’s needs: Conduct user research to define customer pain points and requirements. Record them as user stories .
• C ut, through prioritization: Use a framework of choice to pinpoint the most critical issues.
• L ist solutions: Brainstorm solutions that address the customer’s needs .
• E valuate tradeoffs: Weigh the pros and cons of possible solutions, considering their impact, riskiness, and feasibility.
• S ummarize recommendations: Choose the best solution and clearly explain your decision.

4. The Phoenix Checklist

Developed by the CIA, the Phoenix Checklist is another solid framework.

It consists of sets of questions grouped into different categories. Going through the checklist allows the agent… I mean the product manager to view the problem from multiple perspectives and come up with innovative solutions.

Here are some of the questions to ask at different stages of the process:

• Clarify the need : Why is solving this problem necessary? What benefits will come from addressing it?
• Gather information : What information do you have? Is it sufficient? What are the unknowns?
• Frame the problem : What are the limits of the problem? What are the constants?
• Break down the problem : Can you distinguish the different parts of the problem? What are the relationships between the different parts of the problem? Can you describe the problem in a chart ?
• Leverage past solutions : Have you seen this problem before? Can you use solutions to similar problems to solve this problem?
• Visualize solutions : What’s the best outcome you can imagine? What’s the worst? The most probable?

5. Lightning Decision Jam

Lightning Decision Jam (LDJ) is another effective problem-solving framework.

It consists of 9 steps, each of them time-boxed, so the team moves through the process quickly and efficiently.

Here’s the breakdown of the 9 steps:

• Start with the problems : Identify and write down the problems that need addressing.
• Present problems : Each participant presents the problems they’ve identified.
• Select problems to solve : Use a prioritization method to choose the key problems to focus on.
• Reframe problems : Convert the selected challenge into an actionable problem statement. For example, “How might we streamline the onboarding process to reduce user drop-off and increase completion rates?”
• Produce solutions : Brainstorm and generate solutions for the reframed problems.
• Vote on solutions : Use dot-voting or a similar method to prioritize the solutions.
• Prioritize solutions : Finalize the top solutions based on voting.
• Decide what to execute : Choose which solutions to act on immediately.
• Turn solutions into actionable tasks : Break down the chosen solutions into clear, actionable tasks with ownership and deadlines.

DMAIC is a problem-solving tool from the Six Sigma methodology, one of the best quality-improvement frameworks used across different industries. Not just the automotive sector for which it was initially developed.

The name is an acronym for the 5 main stages of Six Sigma projects:

• Definition stage : Identify the problem, collect the necessary people and resources, and capture the Voice of the Customer . To ensure the solution aligns with customer needs .
• Measure phase : Map out the process and measure current performance to establish a baseline for improvement.
• Analyze stage : Use data to identify the root causes of the problem and highlight any waste or non-value-adding activities.
• Improve stage : Generate, test, and optimize solutions. And plan for implementation if the tests are successful.
• Control phase : Ensures that everyone follows the new processes and assesses the outcomes to ensure long-term improvement.

7. The Fishbone Diagram

The Fishbone Diagram, aka the Ishikawa Diagram or Cause-and-Effect Diagram, is a tool used to identify the root causes of a problem—by visually organizing potential contributing factors.

Why the name?

The diagram looks like a fish skeleton, with the “head” representing a particular problem and the “bones” —categories of causes.

Common cause categories include:

• People : Human factors or workforce issues.
• Processes : Workflows or procedures that may cause problems.
• Materials : Input materials or resources that could affect outcomes.
• Equipment : Software or technology involved in the process.
• Environment : External factors like market trends, competitors, regulations, or workspace conditions.
• Management : Leadership , policies, or decision-making practices.

By using a Fishbone Diagram along with techniques like 5 Whys, you can systematically brainstorm and drill down into the various factors contributing to the problem and gradually identify the root cause.

How to choose the right problem-solving framework?

To choose the right problem-solving framework, consider the problem’s complexity, time constraints, and the level of stakeholder involvement.

• Simple problems : Use quick tools like 5 Whys or the Fishbone Diagram for fast root cause analysis.
• Complex problems : For multi-layered issues, opt for structured methods like Root Cause Analysis (RCA) or McKinsey’s 7-Step Framework to cover all angles.
• Time constraints : If time is limited, the Lightning Decision Jam (LDJ) helps make rapid decisions. For more thorough exploration, frameworks like The Phoenix Checklist offer a comprehensive approach.
• Stakeholder involvement : If multiple departments or customers are involved, frameworks like CIRCLES or McKinsey’s Framework align cross-functional teams and customer insights .
• Recurring issues : Use RCA to prevent repeated problems.
• Creative challenges : For innovative solutions, The Phoenix Checklist encourages diverse perspectives.

The essential elements of the problem-solving process

Having looked at a few of the most popular frameworks for solving problems, why don’t we look at the steps that they have in common?

Ramp Up Your Problem-Solving Framework with Code-Free Tools in Userpilot

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Identify and understand the problem with user research

First, it’s necessary to identify and understand the problem.

To do that, conduct solid user research and capture the Voice of the Customer (VoC) .

How to do it?

You can track user in-app behavior , run in-app surveys, conduct interviews, and analyze user social media feedback and online reviews.

To get a complete picture, collect both quantitative and qualitative data .

Brainstorm ideas to develop solutions

There’s no problem-solving framework out there that wouldn’t include brainstorming of some sort.

And there’s a good reason for that: it’s one of the most effective ways to generate a lot of different solutions in a short time.

To make the brainstorming sessions effective, give all your team members a chance to contribute. Your software engineer may not be the most vocal team member, but it doesn’t mean she has nothing to offer, and not recognizing her input can be costly.

The Delphi method and silent brainstorming are techniques that prevent groupthink and the less outspoken team members from being talked over.

No matter how ridiculous or outrageous some ideas may seem, don’t discard any unless they’re completely irrelevant. It’s not the time to evaluate ideas. Just come up with as many of them as possible.

Decide on a solution and implement it

Having brainstormed solutions, evaluate them and choose the one that solves the problem better than others. And put it into practice.

Even the best ideas are not worth much if you can’t implement them, so pay attention to this stage.

Solving difficult problems often requires big changes, so prepare your team or your customers. Take your time, and focus on explaining the rationale for change and the benefits that it brings.

Make sure to provide the right training to your staff and support your users with onboarding and product education to reduce friction once the new solution goes live.

Collect feedback after the implementation process and evaluate its success

Once you implement the solution, collect feedback to assess its effectiveness.

Is it solving the problem? Does it help you achieve the objectives? If not, how can you modify it to improve its success ? If yes, is there anything else that would provide even more value?

You can do this by asking your users for feedback, for example, via a survey .

In addition to gathering feedback actively, give your users a chance to submit passive feedback whenever they feel like it.

In case of organizational changes, monitor whether the new processes or tools are used. Being creatures of habit, people tend to relapse to their old ways easily, often without realizing it.

Frequently asked questions about problem-solving frameworks

You still with me? Awesome!

I’m nearly done. Let’s finish with answers to a few problem-solving FAQs.

What are the benefits of using standard methods to solve complex problems?

Standard methods provide a structured approach. This means a more thorough and consistent analysis, a deep understanding of the problem, a lower risk of missing important factors, and more reliable, data-driven solutions.

Using proven frameworks also helps you track progress , improve collaboration between stakeholders, and ensure the solutions are sustainable.

What is the ideal problem-solving framework?

There isn’t one.

The choice of framework depends on factors like the problem’s complexity , urgency, and your team’s workflow. Simple issues may need quick methods like 5 Whys, while more complex ones require detailed frameworks like McKinsey’s 7-Step Process.

What are the 4 P’s of problem-solving?

The 4 P’s are Problem , Plan , People , and Process .

First, clearly define the Problem. Next, Plan your approach and ensure the right People are involved.

Finally, execute and refine the Process to solve the problem effectively.

The above frameworks guide problem solvers through the process of defining the problem, identifying causes, generating and implementing solutions, and assessing their impact.

To implement them in SaaS, product managers usually need the right tools, like Userpilot.

If you’d like to learn how Userpilot can help you capture the voice of the customer, analyze the data to identify root causes, help design user-centered solutions, and collect both active and passive feedback to test their effectiveness, book a demo !

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Problem Solving in Mathematics Education

• Open Access
• First Online: 28 June 2016

Cite this chapter

You have full access to this open access chapter

• Peter Liljedahl 6 ,
• Manuel Santos-Trigo 7 ,
• Uldarico Malaspina 8 &
• Regina Bruder 9  

Part of the book series: ICME-13 Topical Surveys ((ICME13TS))

95k Accesses

15 Citations

Problem solving in mathematics education has been a prominent research field that aims at understanding and relating the processes involved in solving problems to students’ development of mathematical knowledge and problem solving competencies. The accumulated knowledge and field developments include conceptual frameworks to characterize learners’ success in problem solving activities, cognitive, metacognitive, social and affective analysis, curriculum proposals, and ways to foster problem solving approaches. In the survey, four interrelated areas are reviewed: (i) the relevance of heuristics in problem solving approaches—why are they important and what research tells us about their use? (ii) the need to characterize and foster creative problem solving approaches—what type of heuristics helps learners think of and practice creative solutions? (iii) the importance for learners to formulate and pursue their own problems; and (iv) the role played by the use of both multiple purpose and ad hoc mathematical action types of technologies in problem solving activities—what ways of reasoning do learners construct when they rely on the use of digital technologies and how technology and technology approaches can be reconciled?

You have full access to this open access chapter,  Download chapter PDF

• Mathematical Problem
• Prospective Teacher
• Creative Process
• Digital Technology
• Mathematical Task

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Mathematical problem solving has long been seen as an important aspect of mathematics, the teaching of mathematics, and the learning of mathematics. It has infused mathematics curricula around the world with calls for the teaching of problem solving as well as the teaching of mathematics through problem solving. And as such, it has been of interest to mathematics education researchers for as long as our field has existed. More relevant, mathematical problem solving has played a part in every ICME conference, from 1969 until the forthcoming meeting in Hamburg, wherein mathematical problem solving will reside most centrally within the work of Topic Study 19: Problem Solving in Mathematics Education. This booklet is being published on the occasion of this Topic Study Group.

To this end, we have assembled four summaries looking at four distinct, yet inter-related, dimensions of mathematical problem solving. The first summary, by Regina Bruder, is a nuanced look at heuristics for problem solving. This notion of heuristics is carried into Peter Liljedahl’s summary, which looks specifically at a progression of heuristics leading towards more and more creative aspects of problem solving. This is followed by Luz Manuel Santos Trigo’s summary introducing us to problem solving in and with digital technologies. The last summary, by Uldarico Malaspina Jurado, documents the rise of problem posing within the field of mathematics education in general and the problem solving literature in particular.

Each of these summaries references in some critical and central fashion the works of George Pólya or Alan Schoenfeld. To the initiated researchers, this is no surprise. The seminal work of these researchers lie at the roots of mathematical problem solving. What is interesting, though, is the diverse ways in which each of the four aforementioned contributions draw on, and position, these works so as to fit into the larger scheme of their respective summaries. This speaks to not only the depth and breadth of these influential works, but also the diversity with which they can be interpreted and utilized in extending our thinking about problem solving.

Taken together, what follows is a topical survey of ideas representing the diversity of views and tensions inherent in a field of research that is both a means to an end and an end onto itself and is unanimously seen as central to the activities of mathematics.

1 Survey on the State-of-the-Art

1.1 role of heuristics for problem solving—regina bruder.

The origin of the word heuristic dates back to the time of Archimedes and is said to have come out of one of the famous stories told about this great mathematician and inventor. The King of Syracuse asked Archimedes to check whether his new wreath was really made of pure gold. Archimedes struggled with this task and it was not until he was at the bathhouse that he came up with the solution. As he entered the tub he noticed that he had displaced a certain amount of water. Brilliant as he was, he transferred this insight to the issue with the wreath and knew he had solved the problem. According to the legend, he jumped out of the tub and ran from the bathhouse naked screaming, “Eureka, eureka!”. Eureka and heuristic have the same root in the ancient Greek language and so it has been claimed that this is how the academic discipline of “heuristics” dealing with effective approaches to problem solving (so-called heurisms) was given its name. Pólya ( 1964 ) describes this discipline as follows:

Heuristics deals with solving tasks. Its specific goals include highlighting in general terms the reasons for selecting those moments in a problem the examination of which could help us find a solution. (p. 5)

This discipline has grown, in part, from examining the approaches to certain problems more in detail and comparing them with each other in order to abstract similarities in approach, or so-called heurisms. Pólya ( 1949 ), but also, inter alia, Engel ( 1998 ), König ( 1984 ) and Sewerin ( 1979 ) have formulated such heurisms for mathematical problem tasks. The problem tasks examined by the authors mentioned are predominantly found in the area of talent programmes, that is, they often go back to mathematics competitions.

In 1983 Zimmermann provided an overview of heuristic approaches and tools in American literature which also offered suggestions for mathematics classes. In the German-speaking countries, an approach has established itself, going back to Sewerin ( 1979 ) and König ( 1984 ), which divides school-relevant heuristic procedures into heuristic tools, strategies and principles, see also Bruder and Collet ( 2011 ).

Below is a review of the conceptual background of heuristics, followed by a description of the effect mechanisms of heurisms in problem-solving processes.

1.1.1 Research Review on the Promotion of Problem Solving

In the 20th century, there has been an advancement of research on mathematical problem solving and findings about possibilities to promote problem solving with varying priorities (c.f. Pehkonen 1991 ). Based on a model by Pólya ( 1949 ), in a first phase of research on problem solving, particularly in the 1960s and the 1970s, a series of studies on problem-solving processes placing emphasis on the importance of heuristic strategies (heurisms) in problem solving has been carried out. It was assumed that teaching and learning heuristic strategies, principles and tools would provide students with an orientation in problem situations and that this could thus improve students’ problem-solving abilities (c.f. for instance, Schoenfeld 1979 ). This approach, mostly researched within the scope of talent programmes for problem solving, was rather successful (c.f. for instance, Sewerin 1979 ). In the 1980s, requests for promotional opportunities in everyday teaching were given more and more consideration: “ problem solving must be the focus of school mathematics in the 1980s ” (NCTM 1980 ). For the teaching and learning of problem solving in regular mathematics classes, the current view according to which cognitive, heuristic aspects were paramount, was expanded by certain student-specific aspects, such as attitudes, emotions and self-regulated behaviour (c.f. Kretschmer 1983 ; Schoenfeld 1985 , 1987 , 1992 ). Kilpatrick ( 1985 ) divided the promotional approaches described in the literature into five methods which can also be combined with each other.

Osmosis : action-oriented and implicit imparting of problem-solving techniques in a beneficial learning environment

Memorisation : formation of special techniques for particular types of problem and of the relevant questioning when problem solving

Imitation : acquisition of problem-solving abilities through imitation of an expert

Cooperation : cooperative learning of problem-solving abilities in small groups

Reflection : problem-solving abilities are acquired in an action-oriented manner and through reflection on approaches to problem solving.

Kilpatrick ( 1985 ) views as success when heuristic approaches are explained to students, clarified by means of examples and trained through the presentation of problems. The need of making students aware of heuristic approaches is by now largely accepted in didactic discussions. Differences in varying approaches to promoting problem-solving abilities rather refer to deciding which problem-solving strategies or heuristics are to imparted to students and in which way, and not whether these should be imparted at all or not.

1.1.2 Heurisms as an Expression of Mental Agility

The activity theory, particularly in its advancement by Lompscher ( 1975 , 1985 ), offers a well-suited and manageable model to describe learning activities and differences between learners with regard to processes and outcomes in problem solving (c.f. Perels et al. 2005 ). Mental activity starts with a goal and the motive of a person to perform such activity. Lompscher divides actual mental activity into content and process. Whilst the content in mathematical problem-solving consists of certain concepts, connections and procedures, the process describes the psychological processes that occur when solving a problem. This course of action is described in Lompscher by various qualities, such as systematic planning, independence, accuracy, activity and agility. Along with differences in motivation and the availability of expertise, it appears that intuitive problem solvers possess a particularly high mental agility, at least with regard to certain contents areas.

According to Lompscher, “flexibility of thought” expresses itself

… by the capacity to change more or less easily from one aspect of viewing to another one or to embed one circumstance or component into different correlations, to understand the relativity of circumstances and statements. It allows to reverse relations, to more or less easily or quickly attune to new conditions of mental activity or to simultaneously mind several objects or aspects of a given activity (Lompscher 1975 , p. 36).

These typical manifestations of mental agility can be focused on in problem solving by mathematical means and can be related to the heurisms known from the analyses of approaches by Pólya et al. (c.f. also Bruder 2000 ):

Reduction : Successful problem solvers will intuitively reduce a problem to its essentials in a sensible manner. To achieve such abstraction, they often use visualisation and structuring aids, such as informative figures, tables, solution graphs or even terms. These heuristic tools are also very well suited to document in retrospect the approach adopted by the intuitive problem solvers in a way that is comprehensible for all.

Reversibility : Successful problem solvers are able to reverse trains of thought or reproduce these in reverse. They will do this in appropriate situations automatically, for instance, when looking for a key they have mislaid. A corresponding general heuristic strategy is working in reverse.

Minding of aspects : Successful problem solvers will mind several aspects of a given problem at the same time or easily recognise any dependence on things and vary them in a targeted manner. Sometimes, this is also a matter of removing barriers in favour of an idea that appears to be sustainable, that is, by simply “hanging on” to a certain train of thought even against resistance. Corresponding heurisms are, for instance, the principle of invariance, the principle of symmetry (Engel 1998 ), the breaking down or complementing of geometric figures to calculate surface areas, or certain terms used in binomial formulas.

Change of aspects : Successful problem solvers will possibly change their assumptions, criteria or aspects minded in order to find a solution. Various aspects of a given problem will be considered intuitively or the problem be viewed from a different perspective, which will prevent “getting stuck” and allow for new insights and approaches. For instance, many elementary geometric propositions can also be proved in an elegant vectorial manner.

Transferring : Successful problem solvers will be able more easily than others to transfer a well-known procedure to another, sometimes even very different context. They recognise more easily the “framework” or pattern of a given task. Here, this is about own constructions of analogies and continual tracing back from the unknown to the known.

Intuitive, that is, untrained good problem solvers, are, however, often unable to access these flexibility qualities consciously. This is why they are also often unable to explain how they actually solved a given problem.

To be able to solve problems successfully, a certain mental agility is thus required. If this is less well pronounced in a certain area, learning how to solve problems means compensating by acquiring heurisms. In this case, insufficient mental agility is partly “offset” through the application of knowledge acquired by means of heurisms. Mathematical problem-solving competences are thus acquired through the promotion of manifestations of mental agility (reduction, reversibility, minding of aspects and change of aspects). This can be achieved by designing sub-actions of problem solving in connection with a (temporarily) conscious application of suitable heurisms. Empirical evidence for the success of the active principle of heurisms has been provided by Collet ( 2009 ).

Against such background, learning how to solve problems can be established as a long-term teaching and learning process which basically encompasses four phases (Bruder and Collet 2011 ):

Intuitive familiarisation with heuristic methods and techniques.

Making aware of special heurisms by means of prominent examples (explicit strategy acquisition).

Short conscious practice phase to use the newly acquired heurisms with differentiated task difficulties.

Expanding the context of the strategies applied.

In the first phase, students are familiarised with heurisms intuitively by means of targeted aid impulses and questions (what helped us solve this problem?) which in the following phase are substantiated on the basis of model tasks, are given names and are thus made aware of their existence. The third phase serves the purpose of a certain familiarisation with the new heurisms and the experience of competence through individualised practising at different requirement levels, including in the form of homework over longer periods. A fourth and delayed fourth phase aims at more flexibility through the transfer to other contents and contexts and the increasingly intuitive use of the newly acquired heurisms, so that students can enrich their own problem-solving models in a gradual manner. The second and third phases build upon each other in close chronological order, whilst the first phase should be used in class at all times.

All heurisms can basically be described in an action-oriented manner by means of asking the right questions. The way of asking questions can thus also establish a certain kind of personal relation. Even if the teacher presents and suggests the line of basic questions with a prototypical wording each time, students should always be given the opportunity to find “their” wording for the respective heurism and take a note of it for themselves. A possible key question for the use of a heuristic tool would be: How to illustrate and structure the problem or how to present it in a different way?

Unfortunately, for many students, applying heuristic approaches to problem solving will not ensue automatically but will require appropriate early and long-term promoting. The results of current studies, where promotion approaches to problem solving are connected with self-regulation and metacognitive aspects, demonstrate certain positive effects of such combination on students. This field of research includes, for instance, studies by Lester et al. ( 1989 ), Verschaffel et al. ( 1999 ), the studies on teaching method IMPROVE by Mevarech and Kramarski ( 1997 , 2003 ) and also the evaluation of a teaching concept on learning how to solve problems by the gradual conscious acquisition of heurisms by Collet and Bruder ( 2008 ).

1.2 Creative Problem Solving—Peter Liljedahl

There is a tension between the aforementioned story of Archimedes and the heuristics presented in the previous section. Archimedes, when submersing himself in the tub and suddenly seeing the solution to his problem, wasn’t relying on osmosis, memorisation, imitation, cooperation, or reflection (Kilpatrick 1985 ). He wasn’t drawing on reduction, reversibility, minding of aspects, change of aspect, or transfer (Bruder 2000 ). Archimedes was stuck and it was only, in fact, through insight and sudden illumination that he managed to solve his problem. In short, Archimedes was faced with a problem that the aforementioned heuristics, and their kind, would not help him to solve.

According to some, such a scenario is the definition of a problem. For example, Resnick and Glaser ( 1976 ) define a problem as being something that you do not have the experience to solve. Mathematicians, in general, agree with this (Liljedahl 2008 ).

Any problem in which you can see how to attack it by deliberate effort, is a routine problem, and cannot be an important discover. You must try and fail by deliberate efforts, and then rely on a sudden inspiration or intuition or if you prefer to call it luck. (Dan Kleitman, participant cited in Liljedahl 2008 , p. 19).

Problems, then, are tasks that cannot be solved by direct effort and will require some creative insight to solve (Liljedahl 2008 ; Mason et al. 1982 ; Pólya 1965 ).

1.2.1 A History of Creativity in Mathematics Education

In 1902, the first half of what eventually came to be a 30 question survey was published in the pages of L’Enseignement Mathématique , the journal of the French Mathematical Society. The authors, Édouard Claparède and Théodore Flournoy, were two Swiss psychologists who were deeply interested in the topics of mathematical discovery, creativity and invention. Their hope was that a widespread appeal to mathematicians at large would incite enough responses for them to begin to formulate some theories about this topic. The first half of the survey centered on the reasons for becoming a mathematician (family history, educational influences, social environment, etc.), attitudes about everyday life, and hobbies. This was eventually followed, in 1904, by the publication of the second half of the survey pertaining, in particular, to mental images during periods of creative work. The responses were sorted according to nationality and published in 1908.

During this same period Henri Poincaré (1854–1912), one of the most noteworthy mathematicians of the time, had already laid much of the groundwork for his own pursuit of this same topic and in 1908 gave a presentation to the French Psychological Society in Paris entitled L’Invention mathématique —often mistranslated to Mathematical Creativity Footnote 1 (c.f. Poincaré 1952 ). At the time of the presentation Poincaré stated that he was aware of Claparède and Flournoy’s work, as well as their results, but expressed that they would only confirm his own findings. Poincaré’s presentation, as well as the essay it spawned, stands to this day as one of the most insightful, and thorough treatments of the topic of mathematical discovery, creativity, and invention.

Just at this time, I left Caen, where I was living, to go on a geological excursion under the auspices of the School of Mines. The incident of the travel made me forget my mathematical work. Having reached Coutances, we entered an omnibus to go some place or other. At the moment when I put my foot on the step, the idea came to me, without anything in my former thoughts seeming to have paved the way for it, that the transformations I had used to define the Fuschian functions were identical with those of non-Euclidean geometry. I did not verify the idea; I should not have had the time, as, upon taking my seat in the omnibus, I went on with the conversation already commenced, but I felt a perfect certainty. On my return to Caen, for conscience’ sake, I verified the results at my leisure. (Poincaré 1952 , p. 53)

So powerful was his presentation, and so deep were his insights into his acts of invention and discovery that it could be said that he not so much described the characteristics of mathematical creativity, as defined them. From that point forth mathematical creativity, or even creativity in general, has not been discussed seriously without mention of Poincaré’s name.

Inspired by this presentation, Jacques Hadamard (1865–1963), a contemporary and a friend of Poincaré’s, began his own empirical investigation into this fascinating phenomenon. Hadamard had been critical of Claparède and Flournoy’s work in that they had not adequately treated the topic on two fronts. As exhaustive as the survey appeared to be, Hadamard felt that it failed to ask some key questions—the most important of which was with regard to the reason for failures in the creation of mathematics. This seemingly innocuous oversight, however, led directly to his second and “most important criticism” (Hadamard 1945 ). He felt that only “first-rate men would dare to speak of” (p. 10) such failures. So, inspired by his good friend Poincaré’s treatment of the subject Hadamard retooled the survey and gave it to friends of his for consideration—mathematicians such as Henri Poincaré and Albert Einstein, whose prominence were beyond reproach. Ironically, the new survey did not contain any questions that explicitly dealt with failure. In 1943 Hadamard gave a series of lectures on mathematical invention at the École Libre des Hautes Études in New York City. These talks were subsequently published as The Psychology of Mathematical Invention in the Mathematical Field (Hadameard 1945 ).

Hadamard’s classic work treats the subject of invention at the crossroads of mathematics and psychology. It provides not only an entertaining look at the eccentric nature of mathematicians and their rituals, but also outlines the beliefs of mid twentieth-century mathematicians about the means by which they arrive at new mathematics. It is an extensive exploration and extended argument for the existence of unconscious mental processes. In essence, Hadamard took the ideas that Poincaré had posed and, borrowing a conceptual framework for the characterization of the creative process from the Gestaltists of the time (Wallas 1926 ), turned them into a stage theory. This theory still stands as the most viable and reasonable description of the process of mathematical creativity.

1.2.2 Defining Mathematical Creativity

The phenomena of mathematical creativity, although marked by sudden illumination, actually consist of four separate stages stretched out over time, of which illumination is but one stage. These stages are initiation, incubation, illumination, and verification (Hadamard 1945 ). The first of these stages, the initiation phase, consists of deliberate and conscious work. This would constitute a person’s voluntary, and seemingly fruitless, engagement with a problem and be characterized by an attempt to solve the problem by trolling through a repertoire of past experiences. This is an important part of the inventive process because it creates the tension of unresolved effort that sets up the conditions necessary for the ensuing emotional release at the moment of illumination (Hadamard 1945 ; Poincaré 1952 ).

Following the initiation stage the solver, unable to come up with a solution stops working on the problem at a conscious level and begins to work on it at an unconscious level (Hadamard 1945 ; Poincaré 1952 ). This is referred to as the incubation stage of the inventive process and can last anywhere from several minutes to several years. After the period of incubation a rapid coming to mind of a solution, referred to as illumination , may occur. This is accompanied by a feeling of certainty and positive emotions (Poincaré 1952 ). Although the processes of incubation and illumination are shrouded behind the veil of the unconscious there are a number of things that can be deduced about them. First and foremost is the fact that unconscious work does, indeed, occur. Poincaré ( 1952 ), as well as Hadamard ( 1945 ), use the very real experience of illumination, a phenomenon that cannot be denied, as evidence of unconscious work, the fruits of which appear in the flash of illumination. No other theory seems viable in explaining the sudden appearance of solution during a walk, a shower, a conversation, upon waking, or at the instance of turning the conscious mind back to the problem after a period of rest (Poincaré 1952 ). Also deducible is that unconscious work is inextricably linked to the conscious and intentional effort that precedes it.

There is another remark to be made about the conditions of this unconscious work: it is possible, and of a certainty it is only fruitful, if it is on the one hand preceded and on the other hand followed by a period of conscious work. These sudden inspirations never happen except after some days of voluntary effort which has appeared absolutely fruitless and whence nothing good seems to have come … (Poincaré 1952 , p. 56)

Hence, the fruitless efforts of the initiation phase are only seemingly so. They not only set up the aforementioned tension responsible for the emotional release at the time of illumination, but also create the conditions necessary for the process to enter into the incubation phase.

Illumination is the manifestation of a bridging that occurs between the unconscious mind and the conscious mind (Poincaré 1952 ), a coming to (conscious) mind of an idea or solution. What brings the idea forward to consciousness is unclear, however. There are theories of the aesthetic qualities of the idea, effective surprise/shock of recognition, fluency of processing, or breaking functional fixedness. For reasons of brevity I will only expand on the first of these.

Poincaré proposed that ideas that were stimulated during initiation remained stimulated during incubation. However, freed from the constraints of conscious thought and deliberate calculation, these ideas would begin to come together in rapid and random unions so that “their mutual impacts may produce new combinations” (Poincaré 1952 ). These new combinations, or ideas, would then be evaluated for viability using an aesthetic sieve, which allows through to the conscious mind only the “right combinations” (Poincaré 1952 ). It is important to note, however, that good or aesthetic does not necessarily mean correct. Correctness is evaluated during the verification stage.

The purpose of verification is not only to check for correctness. It is also a method by which the solver re-engages with the problem at the level of details. That is, during the unconscious work the problem is engaged with at the level of ideas and concepts. During verification the solver can examine these ideas in closer details. Poincaré succinctly describes both of these purposes.

As for the calculations, themselves, they must be made in the second period of conscious work, that which follows the inspiration, that in which one verifies the results of this inspiration and deduces their consequences. (Poincaré 1952 , p. 62)

Aside from presenting this aforementioned theory on invention, Hadamard also engaged in a far-reaching discussion on a number of interesting, and sometimes quirky, aspects of invention and discovery that he had culled from the results of his empirical study, as well as from pertinent literature. This discussion was nicely summarized by Newman ( 2000 ) in his commentary on the elusiveness of invention.

The celebrated phrenologist Gall said mathematical ability showed itself in a bump on the head, the location of which he specified. The psychologist Souriau, we are told, maintained that invention occurs by “pure chance”, a valuable theory. It is often suggested that creative ideas are conjured up in “mathematical dreams”, but this attractive hypothesis has not been verified. Hadamard reports that mathematicians were asked whether “noises” or “meteorological circumstances” helped or hindered research [..] Claude Bernard, the great physiologist, said that in order to invent “one must think aside”. Hadamard says this is a profound insight; he also considers whether scientific invention may perhaps be improved by standing or sitting or by taking two baths in a row. Helmholtz and Poincaré worked sitting at a table; Hadamard’s practice is to pace the room (“Legs are the wheels of thought”, said Emile Angier); the chemist J. Teeple was the two-bath man. (p. 2039)

1.2.3 Discourses on Creativity

Creativity is a term that can be used both loosely and precisely. That is, while there exists a common usage of the term there also exists a tradition of academic discourse on the subject. A common usage of creative refers to a process or a person whose products are original, novel, unusual, or even abnormal (Csíkszentmihályi 1996 ). In such a usage, creativity is assessed on the basis of the external and observable products of the process, the process by which the product comes to be, or on the character traits of the person doing the ‘creating’. Each of these usages—product, process, person—is the roots of the discourses (Liljedahl and Allan 2014 ) that I summarize here, the first of which concerns products.

Consider a mother who states that her daughter is creative because she drew an original picture. The basis of such a statement can lie either in the fact that the picture is unlike any the mother has ever seen or unlike any her daughter has ever drawn before. This mother is assessing creativity on the basis of what her daughter has produced. However, the standards that form the basis of her assessment are neither consistent nor stringent. There does not exist a universal agreement as to what she is comparing the picture to (pictures by other children or other pictures by the same child). Likewise, there is no standard by which the actual quality of the picture is measured. The academic discourse that concerns assessment of products, on the other hand, is both consistent and stringent (Csíkszentmihályi 1996 ). This discourse concerns itself more with a fifth, and as yet unmentioned, stage of the creative process; elaboration . Elaboration is where inspiration becomes perspiration (Csíkszentmihályi 1996 ). It is the act of turning a good idea into a finished product, and the finished product is ultimately what determines the creativity of the process that spawned it—that is, it cannot be a creative process if nothing is created. In particular, this discourse demands that the product be assessed against other products within its field, by the members of that field, to determine if it is original AND useful (Csíkszentmihályi 1996 ; Bailin 1994 ). If it is, then the product is deemed to be creative. Note that such a use of assessment of end product pays very little attention to the actual process that brings this product forth.

The second discourse concerns the creative process. The literature pertaining to this can be separated into two categories—a prescriptive discussion of the creativity process and a descriptive discussion of the creativity process. Although both of these discussions have their roots in the four stages that Wallas ( 1926 ) proposed makes up the creative process, they make use of these stages in very different ways. The prescriptive discussion of the creative process is primarily focused on the first of the four stages, initiation , and is best summarized as a cause - and - effect discussion of creativity, where the thinking processes during the initiation stage are the cause and the creative outcome are the effects (Ghiselin 1952 ). Some of the literature claims that the seeds of creativity lie in being able to think about a problem or situation analogically. Other literature claims that utilizing specific thinking tools such as imagination, empathy, and embodiment will lead to creative products. In all of these cases, the underlying theory is that the eventual presentation of a creative idea will be precipitated by the conscious and deliberate efforts during the initiation stage. On the other hand, the literature pertaining to a descriptive discussion of the creative process is inclusive of all four stages (Kneller 1965 ; Koestler 1964 ). For example, Csíkszentmihályi ( 1996 ), in his work on flow attends to each of the stages, with much attention paid to the fluid area between conscious and unconscious work, or initiation and incubation. His claim is that the creative process is intimately connected to the enjoyment that exists during times of sincere and consuming engagement with a situation, the conditions of which he describes in great detail.

The third, and final, discourse on creativity pertains to the person. This discourse is space dominated by two distinct characteristics, habit and genius. Habit has to do with the personal habits as well as the habits of mind of people that have been deemed to be creative. However, creative people are most easily identified through their reputation for genius. Consequently, this discourse is often dominated by the analyses of the habits of geniuses as is seen in the work of Ghiselin ( 1952 ), Koestler ( 1964 ), and Kneller ( 1965 ) who draw on historical personalities such as Albert Einstein, Henri Poincaré, Vincent Van Gogh, D.H. Lawrence, Samuel Taylor Coleridge, Igor Stravinsky, and Wolfgang Amadeus Mozart to name a few. The result of this sort of treatment is that creative acts are viewed as rare mental feats, which are produced by extraordinary individuals who use extraordinary thought processes.

These different discourses on creativity can be summed up in a tension between absolutist and relativist perspectives on creativity (Liljedahl and Sriraman 2006 ). An absolutist perspective assumes that creative processes are the domain of genius and are present only as precursors to the creation of remarkably useful and universally novel products. The relativist perspective, on the other hand, allows for every individual to have moments of creativity that may, or may not, result in the creation of a product that may, or may not, be either useful or novel.

Between the work of a student who tries to solve a problem in geometry or algebra and a work of invention, one can say there is only a difference of degree. (Hadamard 1945 , p. 104).

Regardless of discourse, however, creativity is not “part of the theories of logical forms” (Dewey 1938 ). That is, creativity is not representative of the lock-step logic and deductive reasoning that mathematical problem solving is often presumed to embody (Bibby 2002 ; Burton 1999 ). Couple this with the aforementioned demanding constraints as to what constitutes a problem, where then does that leave problem solving heuristics? More specifically, are there creative problem solving heuristics that will allow us to resolve problems that require illumination to solve? The short answer to this question is yes—there does exist such problem solving heuristics. To understand these, however, we must first understand the routine problem solving heuristics they are built upon. In what follows, I walk through the work of key authors and researchers whose work offers us insights into progressively more creative problem solving heuristics for solving true problems.

1.2.4 Problem Solving by Design

In a general sense, design is defined as the algorithmic and deductive approach to solving a problem (Rusbult 2000 ). This process begins with a clearly defined goal or objective after which there is a great reliance on relevant past experience, referred to as repertoire (Bruner 1964 ; Schön 1987 ), to produce possible options that will lead towards a solution of the problem (Poincaré 1952 ). These options are then examined through a process of conscious evaluations (Dewey 1933 ) to determine their suitability for advancing the problem towards the final goal. In very simple terms, problem solving by design is the process of deducing the solution from that which is already known.

Mayer ( 1982 ), Schoenfeld ( 1982 ), and Silver ( 1982 ) state that prior knowledge is a key element in the problem solving process. Prior knowledge influences the problem solver’s understanding of the problem as well as the choice of strategies that will be called upon in trying to solve the problem. In fact, prior knowledge and prior experiences is all that a solver has to draw on when first attacking a problem. As a result, all problem solving heuristics incorporate this resource of past experiences and prior knowledge into their initial attack on a problem. Some heuristics refine these ideas, and some heuristics extend them (c.f. Kilpatrick 1985 ; Bruder 2000 ). Of the heuristics that refine, none is more influential than the one created by George Pólya (1887–1985).

1.2.5 George Pólya: How to Solve It

In his book How to Solve It (1949) Pólya lays out a problem solving heuristic that relies heavily on a repertoire of past experience. He summarizes the four-step process of his heuristic as follows:

Understanding the Problem

First. You have to understand the problem.

What is the unknown? What are the data? What is the condition?

Is it possible to satisfy the condition? Is the condition sufficient to determine the unknown? Or is it insufficient? Or redundant? Or contradictory?

Draw a figure. Introduce suitable notation.

Separate the various parts of the condition. Can you write them down?

Devising a Plan

Second. Find the connection between the data and the unknown. You may be obliged to consider auxiliary problems if an immediate connection cannot be found. You should obtain eventually a plan of the solution.

Have you seen it before? Or have you seen the same problem in a slightly different form?

Do you know a related problem? Do you know a theorem that could be useful?

Look at the unknown! And try to think of a familiar problem having the same or a similar unknown.

Here is a problem related to yours and solved before. Could you use it? Could you use its result? Could you use its method? Should you introduce some auxiliary element in order to make its use possible?

Could you restate the problem? Could you restate it still differently? Go back to definitions.

If you cannot solve the proposed problem try to solve first some related problem. Could you imagine a more accessible related problem? A more general problem? A more special problem? An analogous problem? Could you solve a part of the problem? Keep only a part of the condition, drop the other part; how far is the unknown then determined, how can it vary? Could you derive something useful from the data? Could you think of other data appropriate to determine the unknown? Could you change the unknown or data, or both if necessary, so that the new unknown and the new data are nearer to each other?

Did you use all the data? Did you use the whole condition? Have you taken into account all essential notions involved in the problem?

Carrying Out the Plan

Third. Carry out your plan.

Carrying out your plan of the solution, check each step. Can you see clearly that the step is correct? Can you prove that it is correct?

Looking Back

Fourth. Examine the solution obtained.

Can you check the result? Can you check the argument?

Can you derive the solution differently? Can you see it at a glance?

Can you use the result, or the method, for some other problem?

The emphasis on auxiliary problems, related problems, and analogous problems that are, in themselves, also familiar problems is an explicit manifestation of relying on a repertoire of past experience. This use of familiar problems also requires an ability to deduce from these related problems a recognizable and relevant attribute that will transfer to the problem at hand. The mechanism that allows for this transfer of knowledge between analogous problems is known as analogical reasoning (English 1997 , 1998 ; Novick 1988 , 1990 , 1995 ; Novick and Holyoak 1991 ) and has been shown to be an effective, but not always accessible, thinking strategy.

Step four in Pólya’s heuristic, looking back, is also a manifestation of utilizing prior knowledge to solve problems, albeit an implicit one. Looking back makes connections “in memory to previously acquired knowledge [..] and further establishes knowledge in long-term memory that may be elaborated in later problem-solving encounters” (Silver 1982 , p. 20). That is, looking back is a forward-looking investment into future problem solving encounters, it sets up connections that may later be needed.

Pólya’s heuristic is a refinement on the principles of problem solving by design. It not only makes explicit the focus on past experiences and prior knowledge, but also presents these ideas in a very succinct, digestible, and teachable manner. This heuristic has become a popular, if not the most popular, mechanism by which problem solving is taught and learned.

1.2.6 Alan Schoenfeld: Mathematical Problem Solving

The work of Alan Schoenfeld is also a refinement on the principles of problem solving by design. However, unlike Pólya ( 1949 ) who refined these principles at a theoretical level, Schoenfeld has refined them at a practical and empirical level. In addition to studying taught problem solving strategies he has also managed to identify and classify a variety of strategies, mostly ineffectual, that students invoke naturally (Schoenfeld 1985 , 1992 ). In so doing, he has created a better understanding of how students solve problems, as well as a better understanding of how problems should be solved and how problem solving should be taught.

For Schoenfeld, the problem solving process is ultimately a dialogue between the problem solver’s prior knowledge, his attempts, and his thoughts along the way (Schoenfeld 1982 ). As such, the solution path of a problem is an emerging and contextually dependent process. This is a departure from the predefined and contextually independent processes of Pólya’s ( 1949 ) heuristics. This can be seen in Schoenfeld’s ( 1982 ) description of a good problem solver.

To examine what accounts for expertise in problem solving, you would have to give the expert a problem for which he does not have access to a solution schema. His behavior in such circumstances is radically different from what you would see when he works on routine or familiar “non-routine” problems. On the surface his performance is no longer proficient; it may even seem clumsy. Without access to a solution schema, he has no clear indication of how to start. He may not fully understand the problem, and may simply “explore it for a while until he feels comfortable with it. He will probably try to “match” it to familiar problems, in the hope it can be transformed into a (nearly) schema-driven solution. He will bring up a variety of plausible things: related facts, related problems, tentative approaches, etc. All of these will have to be juggled and balanced. He may make an attempt solving it in a particular way, and then back off. He may try two or three things for a couple of minutes and then decide which to pursue. In the midst of pursuing one direction he may go back and say “that’s harder than it should be” and try something else. Or, after the comment, he may continue in the same direction. With luck, after some aborted attempts, he will solve the problem. (p. 32-33)

Aside from demonstrating the emergent nature of the problem solving process, this passage also brings forth two consequences of Schoenfeld’s work. The first of these is the existence of problems for which the solver does not have “access to a solution schema”. Unlike Pólya ( 1949 ), who’s heuristic is a ‘one size fits all (problems)’ heuristic, Schoenfeld acknowledges that problem solving heuristics are, in fact, personal entities that are dependent on the solver’s prior knowledge as well as their understanding of the problem at hand. Hence, the problems that a person can solve through his or her personal heuristic are finite and limited.

The second consequence that emerges from the above passage is that if a person lacks the solution schema to solve a given problem s/he may still solve the problem with the help of luck . This is an acknowledgement, if only indirectly so, of the difference between problem solving in an intentional and mechanical fashion verses problem solving in a more creative fashion, which is neither intentional nor mechanical (Pehkonen 1997 ).

1.2.7 David Perkins: Breakthrough Thinking

As mentioned, many consider a problem that can be solved by intentional and mechanical means to not be worthy of the title ‘problem’. As such, a repertoire of past experiences sufficient for dealing with such a ‘problem’ would disqualify it from the ranks of ‘problems’ and relegate it to that of ‘exercises’. For a problem to be classified as a ‘problem’, then, it must be ‘problematic’. Although such an argument is circular it is also effective in expressing the ontology of mathematical ‘problems’.

Perkins ( 2000 ) also requires problems to be problematic. His book Archimedes’ Bathtub: The Art and Logic of Breakthrough Thinking (2000) deals with situations in which the solver has gotten stuck and no amount of intentional or mechanical adherence to the principles of past experience and prior knowledge is going to get them unstuck. That is, he deals with problems that, by definition, cannot be solved through a process of design [or through the heuristics proposed by Pólya ( 1949 ) and Schoenfeld ( 1985 )]. Instead, the solver must rely on the extra-logical process of what Perkins ( 2000 ) calls breakthrough thinking .

Perkins ( 2000 ) begins by distinguishing between reasonable and unreasonable problems. Although both are solvable, only reasonable problems are solvable through reasoning. Unreasonable problems require a breakthrough in order to solve them. The problem, however, is itself inert. It is neither reasonable nor unreasonable. That quality is brought to the problem by the solver. That is, if a student cannot solve a problem by direct effort then that problem is deemed to be unreasonable for that student. Perkins ( 2000 ) also acknowledges that what is an unreasonable problem for one person is a perfectly reasonable problem for another person; reasonableness is dependent on the person.

This is not to say that, once found, the solution cannot be seen as accessible through reason. During the actual process of solving, however, direct and deductive reasoning does not work. Perkins ( 2000 ) uses several classic examples to demonstrate this, the most famous being the problem of connecting nine dots in a 3 × 3 array with four straight lines without removing pencil from paper, the solution to which is presented in Fig.  1 .

Nine dots—four lines problem and solution

To solve this problem, Perkins ( 2000 ) claims that the solver must recognize that the constraint of staying within the square created by the 3 × 3 array is a self-imposed constraint. He further claims that until this is recognized no amount of reasoning is going to solve the problem. That is, at this point in the problem solving process the problem is unreasonable. However, once this self-imposed constraint is recognized the problem, and the solution, are perfectly reasonable. Thus, the solution of an, initially, unreasonable problem is reasonable.

The problem solving heuristic that Perkins ( 2000 ) has constructed to deal with solvable, but unreasonable, problems revolves around the idea of breakthrough thinking and what he calls breakthrough problems . A breakthrough problem is a solvable problem in which the solver has gotten stuck and will require an AHA! to get unstuck and solve the problem. Perkins ( 2000 ) poses that there are only four types of solvable unreasonable problems, which he has named wilderness of possibilities , the clueless plateau , narrow canyon of exploration , and oasis of false promise . The names for the first three of these types of problems are related to the Klondike gold rush in Alaska, a time and place in which gold was found more by luck than by direct and systematic searching.

The wilderness of possibilities is a term given to a problem that has many tempting directions but few actual solutions. This is akin to a prospector searching for gold in the Klondike. There is a great wilderness in which to search, but very little gold to be found. The clueless plateau is given to problems that present the solver with few, if any, clues as to how to solve it. The narrow canyon of exploration is used to describe a problem that has become constrained in such a way that no solution now exists. The nine-dot problem presented above is such a problem. The imposed constraint that the lines must lie within the square created by the array makes a solution impossible. This is identical to the metaphor of a prospector searching for gold within a canyon where no gold exists. The final type of problem gets its name from the desert. An oasis of false promise is a problem that allows the solver to quickly get a solution that is close to the desired outcome; thereby tempting them to remain fixed on the strategy that they used to get this almost-answer. The problem is, that like the canyon, the solution does not exist at the oasis; the solution strategy that produced an almost-answer is incapable of producing a complete answer. Likewise, a desert oasis is a false promise in that it is only a reprieve from the desolation of the dessert and not a final destination.

Believing that there are only four ways to get stuck, Perkins ( 2000 ) has designed a problem solving heuristic that will “up the chances” of getting unstuck. This heuristic is based on what he refers to as “the logic of lucking out” (p. 44) and is built on the idea of introspection. By first recognizing that they are stuck, and then recognizing that the reason they are stuck can only be attributed to one of four reasons, the solver can access four strategies for getting unstuck, one each for the type of problem they are dealing with. If the reason they are stuck is because they are faced with a wilderness of possibilities they are to begin roaming far, wide, and systematically in the hope of reducing the possible solution space to one that is more manageable. If they find themselves on a clueless plateau they are to begin looking for clues, often in the wording of the problem. When stuck in a narrow canyon of possibilities they need to re-examine the problem and see if they have imposed any constraints. Finally, when in an oasis of false promise they need to re-attack the problem in such a way that they stay away from the oasis.

Of course, there are nuances and details associated with each of these types of problems and the strategies for dealing with them. However, nowhere within these details is there mention of the main difficulty inherent in introspection; that it is much easier for the solver to get stuck than it is for them to recognize that they are stuck. Once recognized, however, the details of Perkins’ ( 2000 ) heuristic offer the solver some ways for recognizing why they are stuck.

1.2.8 John Mason, Leone Burton, and Kaye Stacey: Thinking Mathematically

The work of Mason et al. in their book Thinking Mathematically ( 1982 ) also recognizes the fact that for each individual there exists problems that will not yield to their intentional and mechanical attack. The heuristic that they present for dealing with this has two main processes with a number of smaller phases, rubrics, and states. The main processes are what they refer to as specializing and generalizing. Specializing is the process of getting to know the problem and how it behaves through the examination of special instances of the problem. This process is synonymous with problem solving by design and involves the repeated oscillation between the entry and attack phases of Mason et al. ( 1982 ) heuristic. The entry phase is comprised of ‘getting started’ and ‘getting involved’ with the problem by using what is immediately known about it. Attacking the problem involves conjecturing and testing a number of hypotheses in an attempt to gain greater understanding of the problem and to move towards a solution.

At some point within this process of oscillating between entry and attack the solver will get stuck, which Mason et al. ( 1982 ) refer to as “an honourable and positive state, from which much can be learned” (p. 55). The authors dedicate an entire chapter to this state in which they acknowledge that getting stuck occurs long before an awareness of being stuck develops. They proposes that the first step to dealing with being stuck is the simple act of writing STUCK!

The act of expressing my feelings helps to distance me from my state of being stuck. It frees me from incapacitating emotions and reminds me of actions that I can take. (p. 56)

The next step is to reengage the problem by examining the details of what is known, what is wanted, what can be introduced into the problem, and what has been introduced into the problem (imposed assumptions). This process is engaged in until an AHA!, which advances the problem towards a solution, is encountered. If, at this point, the problem is not completely solved the oscillation is then resumed.

At some point in this process an attack on the problem will yield a solution and generalizing can begin. Generalizing is the process by which the specifics of a solution are examined and questions as to why it worked are investigated. This process is synonymous with the verification and elaboration stages of invention and creativity. Generalization may also include a phase of review that is similar to Pólya’s ( 1949 ) looking back.

1.2.9 Gestalt: The Psychology of Problem Solving

The Gestalt psychology of learning believes that all learning is based on insights (Koestler 1964 ). This psychology emerged as a response to behaviourism, which claimed that all learning was a response to external stimuli. Gestalt psychologists, on the other hand, believed that there was a cognitive process involved in learning as well. With regards to problem solving, the Gestalt school stands firm on the belief that problem solving, like learning, is a product of insight and as such, cannot be taught. In fact, the theory is that not only can problem solving not be taught, but also that attempting to adhere to any sort of heuristic will impede the working out of a correct solution (Krutestkii 1976 ). Thus, there exists no Gestalt problem solving heuristic. Instead, the practice is to focus on the problem and the solution rather than on the process of coming up with a solution. Problems are solved by turning them over and over in the mind until an insight, a viable avenue of attack, presents itself. At the same time, however, there is a great reliance on prior knowledge and past experiences. The Gestalt method of problem solving, then, is at the same time very different and very similar to the process of design.

Gestalt psychology has not fared well during the evolution of cognitive psychology. Although it honours the work of the unconscious mind it does so at the expense of practicality. If learning is, indeed, entirely based on insight then there is little point in continuing to study learning. “When one begins by assuming that the most important cognitive phenomena are inaccessible, there really is not much left to talk about” (Schoenfeld 1985 , p. 273). However, of interest here is the Gestalt psychologists’ claim that focus on problem solving methods creates functional fixedness (Ashcraft 1989 ). Mason et al. ( 1982 ), as well as Perkins ( 2000 ) deal with this in their work on getting unstuck.

1.2.10 Final Comments

Mathematics has often been characterized as the most precise of all sciences. Lost in such a misconception is the fact that mathematics often has its roots in the fires of creativity, being born of the extra-logical processes of illumination and intuition. Problem solving heuristics that are based solely on the processes of logical and deductive reasoning distort the true nature of problem solving. Certainly, there are problems in which logical deductive reasoning is sufficient for finding a solution. But these are not true problems. True problems need the extra-logical processes of creativity, insight, and illumination, in order to produce solutions.

Fortunately, as elusive as such processes are, there does exist problem solving heuristics that incorporate them into their strategies. Heuristics such as those by Perkins ( 2000 ) and Mason et al. ( 1982 ) have found a way of combining the intentional and mechanical processes of problem solving by design with the extra-logical processes of creativity, illumination, and the AHA!. Furthermore, they have managed to do so without having to fully comprehend the inner workings of this mysterious process.

1.3 Digital Technologies and Mathematical Problem Solving—Luz Manuel Santos-Trigo

Mathematical problem solving is a field of research that focuses on analysing the extent to which problem solving activities play a crucial role in learners’ understanding and use of mathematical knowledge. Mathematical problems are central in mathematical practice to develop the discipline and to foster students learning (Pólya 1945 ; Halmos 1994 ). Mason and Johnston-Wilder ( 2006 ) pointed out that “The purpose of a task is to initiate mathematically fruitful activity that leads to a transformation in what learners are sensitized to notice and competent to carry out” (p. 25). Tasks are essential for learners to elicit their ideas and to engage them in mathematical thinking. In a problem solving approach, what matters is the learners’ goals and ways to interact with the tasks. That is, even routine tasks can be a departure point for learners to extend initial conditions and transform them into some challenging activities.

Thus, analysing and characterizing ways in which mathematical problems are formulated (Singer et al. 2015 ) and the process involved in pursuing and solving those problems generate important information to frame and structure learning environments to guide and foster learners’ construction of mathematical concepts and problem solving competences (Santos-Trigo 2014 ). Furthermore, mathematicians or discipline practitioners have often been interested in unveiling and sharing their own experience while developing the discipline. As a results, they have provided valuable information to characterize mathematical practices and their relations to what learning processes of the discipline entails. It is recognized that the work of Pólya ( 1945 ) offered not only bases to launch several research programs in problem solving (Schoenfeld 1992 ; Mason et al. 1982 ); but also it became an essential resource for teachers to orient and structure their mathematical lessons (Krulik and Reys 1980 ).

1.3.1 Research Agenda

A salient feature of a problem solving approach to learn mathematics is that teachers and students develop and apply an enquiry or inquisitive method to delve into mathematical concepts and tasks. How are mathematical problems or concepts formulated? What types of problems are important for teachers/learners to discuss and engage in mathematical reasoning? What mathematical processes and ways of reasoning are involved in understanding mathematical concepts and solving problems? What are the features that distinguish an instructional environment that fosters problem-solving activities? How can learners’ problem solving competencies be assessed? How can learners’ problem solving competencies be characterized and explained? How can learners use digital technologies to understand mathematics and to develop problem-solving competencies? What ways of reasoning do learners construct when they use digital technologies in problem solving approaches? These types of questions have been important in the problem solving research agenda and delving into them has led researchers to generate information and results to support and frame curriculum proposals and learning scenarios. The purpose of this section is to present and discuss important themes that emerged in problem solving approaches that rely on the systematic use of several digital technologies.

In the last 40 years, the accumulated knowledge in the problem solving field has shed lights on both a characterization of what mathematical thinking involves and how learners can construct a robust knowledge in problem solving environments (Schoenfeld 1992 ). In this process, the field has contributed to identify what types of transformations traditional learning scenarios might consider when teachers and students incorporate the use of digital technologies in mathematical classrooms. In this context, it is important to briefly review what main themes and developments the field has addressed and achieved during the last 40 years.

1.3.2 Problem Solving Developments

There are traces of mathematical problems and solutions throughout the history of civilization that explain the humankind interest for identifying and exploring mathematical relations (Kline 1972 ). Pólya ( 1945 ) reflects on his own practice as a mathematician to characterize the process of solving mathematical problems through four main phases: Understanding the problem, devising a plan, carrying out the plan, and looking back. Likewise, Pólya ( 1945 ) presents and discusses the role played by heuristic methods throughout all problem solving phases. Schoenfeld ( 1985 ) presents a problem solving research program based on Pólya’s ( 1945 ) ideas to investigate the extent to which problem solving heuristics help university students to solve mathematical problems and to develop a way of thinking that shows consistently features of mathematical practices. As a result, he explains the learners’ success or failure in problem solving activities can be characterized in terms their mathematical resources and ways to access them, cognitive and metacognitive strategies used to represent and explore mathematical tasks, and systems of beliefs about mathematics and solving problems. In addition, Schoenfeld ( 1992 ) documented that heuristics methods as illustrated in Pólya’s ( 1945 ) book are ample and general and do not include clear information and directions about how learners could assimilate, learn, and use them in their problem solving experiences. He suggested that students need to discuss what it means, for example, to think of and examining special cases (one important heuristic) in finding a closed formula for series or sequences, analysing relationships of roots of polynomials, or focusing on regular polygons or equilateral/right triangles to find general relations about these figures. That is, learners need to work on examples that lead them to recognize that the use of a particular heuristic often involves thinking of different type of cases depending on the domain or content involved. Lester and Kehle ( 2003 ) summarize themes and methodological shifts in problem solving research up to 1995. Themes include what makes a problem difficult for students and what it means to be successful problem solvers; studying and contrasting experts and novices’ problem solving approaches; learners’ metacognitive, beliefs systems and the influence of affective behaviours; and the role of context; and social interactions in problem solving environments. Research methods in problem solving studies have gone from emphasizing quantitative or statistical design to the use of cases studies and ethnographic methods (Krutestkii ( 1976 ). Teaching strategies also evolved from being centred on teachers to the active students’ engagement and collaboration approaches (NCTM 2000 ). Lesh and Zawojewski ( 2007 ) propose to extend problem solving approaches beyond class setting and they introduce the construct “model eliciting activities” to delve into the learners’ ideas and thinking as a way to engage them in the development of problem solving experiences. To this end, learners develop and constantly refine problem-solving competencies as a part of a learning community that promotes and values modelling construction activities. Recently, English and Gainsburg ( 2016 ) have discussed the importance of modeling eliciting activities to prepare and develop students’ problem solving experiences for 21st Century challenges and demands.

Törner et al. ( 2007 ) invited mathematics educators worldwide to elaborate on the influence and developments of problem solving in their countries. Their contributions show a close relationship between countries mathematical education traditions and ways to frame and implement problem solving approaches. In Chinese classrooms, for example, three instructional strategies are used to structure problem solving lessons: one problem multiple solutions , multiple problems one solution , and one problem multiple changes . In the Netherlands, the realistic mathematical approach permeates the students’ development of problem solving competencies; while in France, problem solving activities are structured in terms of two influential frameworks: The theory of didactical situations and anthropological theory of didactics.

In general, problem solving frameworks and instructional approaches came from analysing students’ problem solving experiences that involve or rely mainly on the use of paper and pencil work. Thus, there is a need to re-examined principles and frameworks to explain what learners develop in learning environments that incorporate systematically the coordinated use of digital technologies (Hoyles and Lagrange 2010 ). In this perspective, it becomes important to briefly describe and identify what both multiple purpose and ad hoc technologies can offer to the students in terms of extending learning environments and representing and exploring mathematical tasks. Specifically, a task is used to identify features of mathematical reasoning that emerge through the use digital technologies that include both mathematical action and multiple purpose types of technologies.

1.3.3 Background

Digital technologies are omnipresent and their use permeates and shapes several social and academic events. Mobile devices such as tablets or smart phones are transforming the way people communicate, interact and carry out daily activities. Churchill et al. ( 2016 ) pointed out that mobile technologies provide a set of tools and affordances to structure and support learning environments in which learners continuously interact to construct knowledge and solve problems. The tools include resources or online materials, efficient connectivity to collaborate and discuss problems, ways to represent, explore and store information, and analytical and administration tools to management learning activities. Schmidt and Cohen ( 2013 ) stated that nowadays it is difficult to imagine a life without mobile devices, and communication technologies are playing a crucial role in generating both cultural and technical breakthroughs. In education, the use of mobile artefacts and computers offers learners the possibility of continuing and extending peers and groups’ mathematical discussions beyond formal settings. In this process, learners can also consult online materials and interact with experts, peers or more experienced students while working on mathematical tasks. In addition, dynamic geometry systems (GeoGebra) provide learners a set of affordances to represent and explore dynamically mathematical problems. Leung and Bolite-Frant ( 2015 ) pointed out that tools help activate an interactive environment in which teachers and students’ mathematical experiences get enriched. Thus, the digital age brings new challenges to the mathematics education community related to the changes that technologies produce to curriculum, learning scenarios, and ways to represent, explore mathematical situations. In particular, it is important to characterize the type of reasoning that learners can develop as a result of using digital technologies in their process of learning concepts and solving mathematical problems.

1.3.4 A Focus on Mathematical Tasks

Mathematical tasks are essential elements for engaging learners in mathematical reasoning which involves representing objects, identifying and exploring their properties in order to detect invariants or relationships and ways to support them. Watson and Ohtani ( 2015 ) stated that task design involves discussions about mathematical content and students’ learning (cognitive perspective), about the students’ experiences to understand the nature of mathematical activities; and about the role that tasks played in teaching practices. In this context, tasks are the vehicle to present and discuss theoretical frameworks for supporting the use of digital technology, to analyse the importance of using digital technologies in extending learners’ mathematical discussions beyond formal settings, and to design ways to foster and assess the use of technologies in learners’ problem solving environments. In addition, it is important to discuss contents, concepts, representations and strategies involved in the process of using digital technologies in approaching the tasks. Similarly, it becomes essential to discuss what types of activities students will do to learn and solve the problems in an environment where the use of technologies fosters and values the participation and collaboration of all students. What digital technologies are important to incorporate in problem solving approaches? Dynamic Geometry Systems can be considered as a milestone in the development of digital technologies. Objects or mathematical situations can be represented dynamically through the use of a Dynamic Geometry System and learners or problem solvers can identify and examine mathematical relations that emerge from moving objects within the dynamic model (Moreno-Armella and Santos-Trigo 2016 ).

Leung and Bolite-Frant ( 2015 ) stated that “dynamic geometry software can be used in task design to cover a large epistemic spectrum from drawing precise robust geometrical figures to exploration of new geometric theorems and development of argumentation discourse” (p. 195). As a result, learners not only need to develop skills and strategies to construct dynamic configuration of problems; but also ways of relying on the tool’s affordances (quantifying parameters or objects attributes, generating loci, graphing objects behaviours, using sliders, or dragging particular elements within the configuration) in order to identify and support mathematical relations. What does it mean to represent and explore an object or mathematical situation dynamically?

A simple task that involves a rhombus and its inscribed circle is used to illustrate how a dynamic representation of these objects and embedded elements can lead learners to identify and examine mathematical properties of those objects in the construction of the configuration. To this end, learners are encouraged to pose and pursue questions to explain the behaviours of parameters or attributes of the family of objects that is generated as a result of moving a particular element within the configuration.

1.3.5 A Task: A Dynamic Rhombus

Figure  2 represents a rhombus APDB and its inscribed circle (O is intersection of diagonals AD and BP and the radius of the inscribed circle is the perpendicular segment from any side of the rhombus to point O), vertex P lies on a circle c centred at point A. Circle c is only a heuristic to generate a family of rhombuses. Thus, point P can be moved along circle c to generate a family of rhombuses. Indeed, based on the symmetry of the circle it is sufficient to move P on the semicircle B’CA to draw such a family of rhombuses.

A dynamic construction of a rhombus

1.3.6 Posing Questions

A goal in constructing a dynamic model or configuration of problems is always to identify and explore mathematical properties and relations that might result from moving objects within the model. How do the areas of both the rhombus and the inscribed circle behave when point P is moved along the arc B’CB? At what position of point P does the area of the rhombus or inscribed circle reach the maximum value? The coordinates of points S and Q (Fig.  3 ) are the x -value of point P and as y -value the corresponding area values of rhombus ABDP and the inscribed circle respectively. Figure  2 shows the loci of points S and Q when point P is moved along arc B’CB. Here, finding the locus via the use of GeoGebra is another heuristic to graph the area behaviour without making explicit the algebraic model of the area.

Graphic representation of the area variation of the family of rhombuses and inscribed circles generated when P is moved through arc B’CB

The area graphs provide information to visualize that in that family of generated rhombuses the maximum area value of the inscribed circle and rhombus is reached when the rhombus becomes a square (Fig.  4 ). That is, the controlled movement of particular objects is an important strategy to analyse the area variation of the family of rhombuses and their inscribed circles.

Visualizing the rhombus and the inscribed circle with maximum area

It is important to observe the identification of points P and Q in terms of the position of point P and the corresponding areas and the movement of point P was sufficient to generate both area loci. That is, the graph representation of the areas is achieved without having an explicit algebraic expression of the area variation. Clearly, the graphic representations provide information regarding the increasing or decreasing interval of both areas; it is also important to explore what properties both graphic representations hold. The goal is to argue that the area variation of the rhombus represents an ellipse and the area of the inscribed circle represents a parabola. An initial argument might involve selecting five points on each locus and using the tool to draw the corresponding conic section (Fig.  5 ). In this case, the tool affordances play an important role in generating the graphic representation of the areas’ behaviours and in identifying properties of those representations. In this context, the use of the tool can offer learners the opportunity to problematize (Santos-Trigo 2007 ) a simple mathematical object (rhombus) as a means to search for mathematical relations and ways to support them.

Drawing the conic section that passes through five points

1.3.7 Looking for Different Solutions Methods

Another line of exploration might involve asking for ways to construct a rhombus and its inscribed circle: Suppose that the side of the rhombus and the circle are given, how can you construct the rhombus that has that circle inscribed? Figure  6 shows the given data, segment A 1 B 1 and circle centred at O and radius OD. The initial goal is to draw the circle tangent to the given segment. To this end, segment AB is congruent to segment A 1 B 1 and on this segment a point P is chosen and a perpendicular to segment AB that passes through point P is drawn. Point C is on this perpendicular and the centre of a circle with radius OD and h is the perpendicular to line PC that passes through point C. Angle ACB changes when point P is moved along segment AB and point E and F are the intersection of line h and the circle with centre M the midpoint of AB and radius MA (Fig.  6 ).

Drawing segment AB tangent to the given circle

Figure  7 a shows the right triangle AFB as the base to construct the rhombus and the inscribed circle and Fig.  7 b shows the second solution based on triangle AEB.

a Drawing the rhombus and the inscribed circle. b Drawing the second solution

Another approach might involve drawing the given circle centred at the origin and the segment as EF with point E on the y-axis. Line OC is perpendicular to segment EF and the locus of point C when point E moves along the y-axis intersects the given circle (Fig.  8 a, b). Both figures show two solutions to draw the rhombus that circumscribe the given circle.

a and b Another solution that involves finding a locus of point C

In this example, the GeoGebra affordances not only are important to construct a dynamic model of the task; but also offer learners and opportunity to explore relations that emerge from moving objects within the model. As a result, learners can rely on different concepts and strategies to solve the tasks. The idea in presenting this rhombus task is to illustrate that the use of a Dynamic Geometry System provides affordances for learners to construct dynamic representation of mathematical objects or problems, to move elements within the representation to pose questions or conjectures to explain invariants or patterns among involved parameters; to search for arguments to support emerging conjectures, and to develop a proper language to communicate results.

1.3.8 Looking Back

Conceptual frameworks used to explain learners’ construction of mathematical knowledge need to capture or take into account the different ways of reasoning that students might develop as a result of using a set of tools during the learning experiences. Figure  9 show some digital technologies that learners can use for specific purpose at the different stages of problem solving activities.

The coordinated use of digital tools to engage learners in problem solving experiences

The use of a dynamic system (GeoGebra) provides a set of affordances for learners to conceptualize and represent mathematical objects and tasks dynamically. In this process, affordances such as moving objects orderly (dragging), finding loci of objects, quantifying objects attributes (lengths, areas, angles, etc.), using sliders to vary parameters, and examining family of objects became important to look for invariance or objects relationships. Likewise, analysing the parameters or objects behaviours within the configuration might lead learners to identify properties to support emerging mathematical relations. Thus, with the use of the tool, learners might conceptualize mathematical tasks as an opportunity for them to engage in mathematical activities that include constructing dynamic models of tasks, formulating conjectures, and always looking for different arguments to support them. Similarly, learners can use an online platform to share their ideas, problem solutions or questions in a digital wall and others students can also share ideas or solution methods and engaged in mathematical discussions that extend mathematical classroom activities.

1.4 Problem Posing: An Overview for Further Progress—Uldarico Malaspina Jurado

Problem posing and problem solving are two essential aspects of the mathematical activity; however, researchers in mathematics education have not emphasized their attention on problem posing as much as problem solving. In that sense, due to its importance in the development of mathematical thinking in students since the first grades, we agree with Ellerton’s statement ( 2013 ): “for too long, successful problem solving has been lauded as the goal; the time has come for problem posing to be given a prominent but natural place in mathematics curricula and classrooms” (pp. 100–101); and due to its importance in teacher training, with Abu-Elwan’s statement ( 1999 ):

While teacher educators generally recognize that prospective teachers require guidance in mastering the ability to confront and solve problems, what is often overlooked is the critical fact that, as teachers, they must be able to go beyond the role as problem solvers. That is, in order to promote a classroom situation where creative problem solving is the central focus, the practitioner must become skillful in discovering and correctly posing problems that need solutions. (p. 1)

Scientists like Einstein and Infeld ( 1938 ), recognized not only for their notable contributions in the fields they worked, but also for their reflections on the scientific activity, pointed out the importance of problem posing; thus it is worthwhile to highlight their statement once again:

The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skills. To raise new questions, new possibilities, to regard old questions from a new angle, requires creative imagination and marks real advance in science. (p. 92)

Certainly, it is also relevant to remember mathematician Halmos’s statement ( 1980 ): “I do believe that problems are the heart of mathematics, and I hope that as teachers (…) we will train our students to be better problem posers and problem solvers than we are” (p. 524).

An important number of researchers in mathematics education has focused on the importance of problem posing, and we currently have numerous, very important publications that deal with different aspects of problem posing related to the mathematics education of students in all educational levels and to teacher training.

1.4.1 A Retrospective Look

Kilpatrick ( 1987 ) marked a historical milestone in research related to problem posing and points out that “problem formulating should be viewed not only as a goal of instruction but also as a means of instruction” (Kilpatrick 1987 , p. 123); and he also emphasizes that, as part of students’ education, all of them should be given opportunities to live the experience of discovering and posing their own problems. Drawing attention to the few systematic studies on problem posing performed until then, Kilpatrick contributes defining some aspects that required studying and investigating as steps prior to a theoretical building, though he warns, “attempts to teach problem-formulating skills, of course, need not await a theory” (p. 124).

Kilpatrick refers to the “Source of problems” and points out how virtually all problems students solve have been posed by another person; however, in real life “many problems, if not most, must be created or discovered by the solver, who gives the problem an initial formulation” (p. 124). He also points out that problems are reformulated as they are being solved, and he relates this to investigation, reminding us what Davis ( 1985 ) states that, “what typically happens in a prolonged investigation is that problem formulation and problem solution go hand in hand, each eliciting the other as the investigation progresses” (p. 23). He also relates it to the experiences of software designers, who formulate an appropriate sequence of sub-problems to solve a problem. He poses that a subject to be examined by teachers and researchers “is whether, by drawing students’ attention to the reformulating process and given them practice in it, we can improve their problem solving performance” (p. 130). He also points out that problems may be a mathematical formulation as a result of exploring a situation and, in that sense, “school exercises in constructing mathematical models of a situation presented by the teacher are intended to provide students with experiences in formulating problems.” (p. 131).

Another important section of Kilpatrick’s work ( 1987 ) is Processes of Problem Formulating , in which he considers association, analogy, generalization and contradiction. He believes the use of concept maps to represent concept organization, as cognitive scientists Novak and Gowin suggest, might help to comprehend such concepts, stimulate creative thinking about them, and complement the ideas Brown and Walter ( 1983 ) give for problem posing by association. Further, in the section “Understanding and developing problem formulating abilities”, he poses several questions, which have not been completely answered yet, like “Perhaps the central issue from the point of view of cognitive science is what happens when someone formulates the problem? (…) What is the relation between problem formulating, problem solving and structured knowledge base? How rich a knowledge base is needed for problem formulating? (…) How does experience in problem formulating add to knowledge base? (…) What metacognitive processes are needed for problem formulating?”

It is interesting to realize that some of these questions are among the unanswered questions proposed and analyzed by Cai et al. ( 2015 ) in Chap. 1 of the book Mathematical Problem Posing (Singer et al. 2015 ). It is worth stressing the emphasis on the need to know the cognitive processes in problem posing, an aspect that Kilpatrick had already posed in 1987, as we just saw.

1.4.2 Researches and Didactic Experiences

Currently, there are a great number of publications related to problem posing, many of which are research and didactic experiences that gather the questions posed by Kilpatrick, which we just commented. Others came up naturally as reflections raised in the framework of problem solving, facing the natural requirement of having appropriate problems to use results and suggestions of researches on problem solving, or as a response to a thoughtful attitude not to resign to solving and asking students to solve problems that are always created by others. Why not learn and teach mathematics posing one’s own problems?

1.4.3 New Directions of Research

Singer et al. ( 2013 ) provides a broad view about problem posing that links problem posing experiences to general mathematics education; to the development of abilities, attitudes and creativity; and also to its interrelation with problem solving, and studies on when and how problem-solving sessions should take place. Likewise, it provides information about research done regarding ways to pose new problems and about the need for teachers to develop abilities to handle complex situations in problem posing contexts.

Singer et al. ( 2013 ) identify new directions in problem posing research that go from problem-posing task design to the development of problem-posing frameworks to structure and guide teachers and students’ problem posing experiences. In a chapter of this book, Leikin refers to three different types of problem posing activities, associated with school mathematics research: (a) problem posing through proving; (b) problem posing for investigation; and (c) problem posing through investigation. This classification becomes evident in the problems posed in a course for prospective secondary school mathematics teachers by using a dynamic geometry environment. Prospective teachers posed over 25 new problems, several of which are discussed in the article. The author considers that, by developing this type of problem posing activities, prospective mathematics teachers may pose different problems related to a geometric object, prepare more interesting lessons for their students, and thus gradually develop their mathematical competence and their creativity.

1.4.4 Final Comments

This overview, though incomplete, allows us to see a part of what problem posing experiences involve and the importance of this area in students mathematical learning. An important task is to continue reflecting on the questions posed by Kilpatrick ( 1987 ), as well as on the ones that come up in the different researches aforementioned. To continue progressing in research on problem posing and contribute to a greater consolidation of this research line, it will be really important that all mathematics educators pay more attention to problem posing, seek to integrate approaches and results, and promote joint and interdisciplinary works. As Singer et al. ( 2013 ) say, going back to Kilpatrick’s proposal ( 1987 ),

Problem posing is an old issue. What is new is the awareness that problem posing needs to pervade the education systems around the world, both as a means of instruction (…) and as an object of instruction (…) with important targets in real-life situations. (p. 5)

Although it can be argued that there is a difference between creativity, discovery, and invention (see Liljedahl and Allan 2014 ) for the purposes of this book these will be assumed to be interchangeable.

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Further Reading

Boaler, J. (1997). Experiencing school mathematics: Teaching styles, sex, and setting . Buckingham, PA: Open University Press.

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Liljedahl, P., Santos-Trigo, M., Malaspina, U., Bruder, R. (2016). Problem Solving in Mathematics Education. In: Problem Solving in Mathematics Education. ICME-13 Topical Surveys. Springer, Cham. https://doi.org/10.1007/978-3-319-40730-2_1

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