• Oct 13, 2019

10 Steps to Problem Solving for Engineers

Updated: Dec 6, 2020

With the official launch of the engineering book 10+1 Steps to Problem Solving: An Engineer's Guide it may be interesting to know that formalization of the concept began in episode 2 of the Engineering IRL Podcast back in July 2018.

As noted in the book remnants of the steps had existed throughout my career and in this episode I actually recorded the episode off the top of my head.

My goal was to help engineers build a practical approach to problem solving.

Have a listen.

Who can advise on the best approach to problem solving other than the professional problem solvers - Yes. I'm talking about being an Engineer.

There are 2 main trains of thought with Engineering work for non-engineers and that's trying to change the world with leading edge tech and innovations, or plain old boring math nerd type things.

Whilst, somewhat the case what this means is most content I read around Tech and Engineering are either super technical and (excruciatingly) detailed. OR really riff raff at the high level reveling at the possibilities of changing the world as we know it. And so what we end up with is a base (engineer only details) and the topping (media innovation coverage) but what about the meat? The contents?

There's a lot of beauty and interesting things there too. And what's the centrepiece? The common ground between all engineers? Problem solving.

The number one thing an Engineer does is problem solving. Now you may say, "hey, that's the same as my profession" - well this would be true for virtually every single profession on earth. This is not saying there isn't problem solving required in other professions. Some problems require very basic problem solving techniques such is used in every day life, but sometimes problems get more complicated, maybe they involve other parties, maybe its a specific quirk of the system in a specific scenario. One thing you learn in engineering is that not all problems are equal. These are

 The stages of problem solving like a pro:

Is the problem identified (no, really, are you actually asking the right question?)

Have you applied related troubleshooting step to above problem?

Have you applied basic troubleshooting steps (i.e. check if its plugged in, turned it on and off again, checked your basics)

Tried step 2 again? (Desperation seeps in, but check your bases)

Asked a colleague or someone else that may have dealt with your problem? (50/50 at this point)

Asked DR. Google (This is still ok)

Deployed RTFM protocol (Read the F***ing Manual - Engineers are notorious for not doing this)

Repeated tests, changing slight things, checking relation to time, or number of people, or location or environment (we are getting DEEP now)

Go to the bottom level, in networking this is packet sniffers to inspect packets, in systems this is taking systems apart and testing in isolation, in software this is checking if 1 equals 1, you are trying to prove basic human facts that everyone knows. If 1 is not equal to 1, you're in deep trouble.At this point you are at rebuild from scratch, re install, start again as your answer (extremely expensive, very rare)

And there you have it! Those are your levels of problem solving. As you go through each step, the more expensive the problem is. -- BUT WAIT. I picked something up along the way and this is where I typically thrive. Somewhere between problem solving step 8 and 10. 

engineer problem solving method

The secret step

My recommendation at this point is to try tests that are seemingly unrelated to anything to do with the problem at all.Pull a random cable, test with a random system off/on, try it at a specific time of the day, try it specifically after restarting or replugging something in. Now, not completely random but within some sort of scope. These test are the ones that when someone is having a problem when you suggest they say "that shouldn't fix the problem, that shouldn't be related" and they are absolutely correct.But here's the thing -- at this stage they have already tried everything that SHOULD fix the problem. Now it's time for the hail mary's, the long shots, the clutching at straws. This method works wonders for many reasons. 1. You really are trying to try "anything" at this point.

2. Most of the time we may think we have problem solving step number 1 covered, but we really don't.

3. Triggering correlations.

This is important.

Triggering correlations

In a later post I will cover correlation vs causation, but for now understand that sometimes all you want to do is throw in new inputs to the system or problem you are solving in order to get clues or re identify problems or give new ways to approach earlier problem solving steps. There you have it. Problem solve like a ninja. Approach that extremely experienced and smart person what their problem and as they describe all the things they've tried, throw in a random thing they haven't tried. And when they say, well that shouldn't fix it, you ask them, well if you've exhausted everything that should  have worked, this is the time to try things that shouldn't. Either they will think of more tests they haven't considered so as to avoid doing your preposterous idea OR they try it and get a new clue to their problem. Heck, at worst they confirm that they do know SOMETHING about the system.

Go out and problem solve ! As always, thanks for reading and good luck with all of your side hustles.

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Engineering Problem Solving ¶

Some problems are so complex that you have to be highly intelligent and well-informed just to be undecided about them. —Laurence J. Peter

Steps in solving ‘real world’ engineering problems ¶

The following are the steps as enumerated in your textbook:

Collaboratively define the problem

List possible solutions

Evaluate and rank the possible solutions

Develop a detailed plan for the most attractive solution(s)

Re-evaluate the plan to check desirability

Implement the plan

Check the results

A critical part of the analysis process is the ‘last’ step: checking and verifying the results.

Depending on the circumstances, errors in an analysis, procedure, or implementation can have significant, adverse consequences (NASA Mars orbiter crash, Bhopal chemical leak tragedy, Hubble telescope vision issue, Y2K fiasco, BP oil rig blowout, …).

In a practical sense, these checks must be part of a comprehensive risk management strategy.

My experience with problem solving in industry was pretty close to this, though encumbered by numerous business practices (e.g., ‘go/no-go’ tollgates, complex approval processes and procedures).

In addition, solving problems in the ‘real world’ requires a multidisciplinary effort, involving people with various expertise: engineering, manufacturing, supply chain, legal, marketing, product service and warranty, …

Exercise: Problem solving

Step 3 above refers to ranking of alternatives.

Think of an existing product of interest.

What do you think was ranked highest when the product was developed?

Consider what would have happened if a different ranking was used. What would have changed about the product?

Brainstorm ideas with the students around you.

Defining problems collaboratively ¶

Especially in light of global engineering , we need to consider different perspectives as we define our problem. Let’s break the procedure down into steps:

Identify each perspective that is involved in the decision you face. Remember that problems often mean different things in different perspectives. Relevant differences might include national expectations, organizational positions, disciplines, career trajectories, etc. Consider using the mnemonic device “Location, Knowledge, and Desire.”

Location : Who is defining the problem? Where are they located or how are they positioned? How do they get in their positions? Do you know anything about the history of their positions, and what led to the particular configuration of positions you have today on the job? Where are the key boundaries among different types of groups, and where are the alliances?

Knowledge : What forms of knowledge do the representatives of each perspective have? How do they understand the problem at hand? What are their assumptions? From what sources did they gain their knowledge? How did their knowledge evolve?

Desire : What do the proponents of each perspective want? What are their objectives? How do these desires develop? Where are they trying to go? Learn what you can about the history of the issue at hand. Who might have gained or lost ground in previous encounters? How does each perspective view itself at present in relation to those it envisions as relevant to its future?

As formal problem definitions emerge, ask “Whose definition is this?” Remember that “defining the problem clearly” may very well assert one perspective at the expense of others. Once we think about problem solving in relation to people, we can begin to see that the very act of drawing a boundary around a problem has non-technical, or political dimensions, depending on who controls the definition, because someone gains a little power and someone loses a little power.

Map what alternative problem definitions mean to different participants. More than likely you will best understand problem definitions that fit your perspective. But ask “Does it fit other perspectives as well?” Look at those who hold Perspective A. Does your definition fit their location, their knowledge, and their desires? Now turn to those who hold Perspective B. Does your definition fit their location, knowledge, and desires? Completing this step is difficult because it requires stepping outside of one’s own perspective and attempting to understand the problem in terms of different perspectives.

To the extent you encounter disagreement or conclude that the achievement of it is insufficient, begin asking yourself the following: How might I adapt my problem definition to take account of other perspectives out there? Is there some way of accommodating myself to other perspectives rather than just demanding that the others simply recognize the inherent value and rationality of mine? Is there room for compromise among contrasting perspectives?

How ‘good’ a solution do you need ¶

There is also an important aspect of real-world problem solving that is rarely articulated and that is the idea that the ‘quality’ of the analysis and the resources expended should be dependent on the context.

This is difficult to assess without some experience in the particular environment.

How ‘Good’ a Solution Do You Need?

Some rough examples:

10 second answer (answering a question at a meeting in front of your manager or vice president)

10 minute answer (answering a quick question from a colleague)

10 hour answer (answering a request from an important customer)

10 day answer (assembling information as part of a trouble-shooting team)

10 month answer (putting together a comprehensive portfolio of information as part of the design for a new $200,000,000 chemical plant)

Steps in solving well-defined engineering process problems, including textbook problems ¶

Essential steps:

Carefully read the problem statement (perhaps repeatedly) until you understand exactly the scenario and what is being asked.

Translate elements of the word problem to symbols. Also, look for key words that may convey additional information, e.g., ‘steady state’, ‘constant density’, ‘isothermal’. Make note of this additional information on your work page.

Draw a diagram. This can generally be a simple block diagram showing all the input, output, and connecting streams.

Write all known quantities (flow rates, densities, etc.) from step 2 in the appropriate locations on, or near, the diagram. If symbols are used to designate known quantities, include those symbols.

Identify and assign symbols to all unknown quantities and write them in the appropriate locations on, or near, the diagram.

Construct the relevant equation(s). These could be material balances, energy balances, rate equations, etc.

Write down all equations in their general forms. Don’t simplify anything yet.

Discard terms that are equal to zero (or are assumed negligible) for your specific problem and write the simplified equations.

Replace remaining terms with more convenient forms (because of the given information or selected symbols).

Construct equations to express other known relationships between variables, e.g., relationships between stoichiometric coefficients, the sum of species mass fractions must be one.

Whenever possible, solve the equations for the unknown(s) algebraically .

Convert the units of your variables as needed to have a consistent set across your equations.

Substitute these values into the equation(s) from step 7 to get numerical results.

Check your answer.

Does it make sense?

Are the units of the answer correct?

Is the answer consistent with other information you have?

Exercise: Checking results

How do you know your answer is right and that your analysis is correct?

This may be relatively easy for a homework problem, but what about your analysis for an ill-defined ‘real-world’ problem?

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An Inquiry-Based Introduction to Engineering pp 71–78 Cite as

Engineering Problem-Solving

  • Michelle Blum 2  
  • First Online: 21 September 2022

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You are becoming an engineer to become a problem solver. That is why employers will hire you. Since problem-solving is an essential portion of the engineering profession, it is necessary to learn approaches that will lead to an acceptable resolution. In real-life, the problems engineers solve can vary from simple single solution problems to complex opened ended ones. Whether simple or complex, problem-solving involves knowledge, experience, and creativity. In college, you will learn prescribed processes you can follow to improve your problem-solving abilities. Also, you will be required to solve an immense amount of practice and homework problems to give you experience in problem-solving. This chapter introduces problem analysis, organization, and presentation in the context of the problems you will solve throughout your undergraduate education.

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Mark Thomas Holtzapple, W. Dan Reece (2000), Foundations of Engineering, McGraw-Hill, New York, New York, ISBN:978-0-07-029706-7.

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Aide, A.R., Jenison R.D., Mickelson, S.K., Northup, L.L., Engineering Fundamentals and Problem Solving, McGraw-Hill, New York, NY, ISBN: 978-0-07-338591-4.

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End of Chapter Problems

1.1 ibl questions.

IBL1: Using standard problem-solving technique, answer the following questions

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, draw the vector representation of your path (hint: use a compass legend to help create your coordinate system)

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate the velocity you ran in the north direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate the velocity you ran in the east direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate how far you ran in the north direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, explain how to calculate how far you ran in the east direction.

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, how far north have you traveled in 5 min?

If you run in a straight line at a velocity of 10 mph in a direction of 35 degree North of East, how far east have you traveled in 5 min?

What type of problem did you solve?

IBL2: For the following scenarios, explain what type of problem it is that needs to be solved.

Scientists hypothesize that PFAS chemicals in lawn care products are leading to an increase in toxic algae blooms in lakes during summer weather.

An engineer notices that a manufacturing machine motor hums every time the fluorescent floor lights are turned on.

The U.N. warns that food production must be increased by 60% by 2050 to keep up with population growth demand.

Engineers are working to identify and create viable alternative energy sources to combat climate change.

1.2 Practice Problems

Make sure all problems are written up using appropriate problem-solving technique and presentation.

The principle of conservation of energy states that the sum of the kinetic energy and potential energy of the initial and final states of an object is the same. If an engineering student was riding in a 200 kg roller coaster car that started from rest at 10 m above the ground, what is the velocity of the car when it drops to 2.5 m above the ground?

Archimedes’ principle states that the total mass of a floating object equals the mass of the fluid displaced by the object. A 45 cm cylindrical buoy is floating vertically in the water. If the water density is 1.00 g/cm 3 and the buoy plastic has a density of 0.92 g/cm 3 determine the length of the buoy that is not submerged underwater.

A student throws their textbook off a bridge that is 30 ft high. How long would it take before the book hits the ground?

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3 What is Problem Solving?

Chapter table of contents, what is problem solving.

  • What Does Problem Solving Look Like?

Developing Problem Solving Processes

Summary of strategies, problem solving:  an important job skill.

engineer problem solving method

The ability to solve problems is a basic life skill and is essential to our day-to-day lives, at home, at school, and at work. We solve problems every day without really thinking about how we solve them. For example: it’s raining and you need to go to the store. What do you do? There are lots of possible solutions. Take your umbrella and walk. If you don’t want to get wet, you can drive, or take the bus. You might decide to call a friend for a ride, or you might decide to go to the store another day. There is no right way to solve this problem and different people will solve it differently.

Problem solving is the process of identifying a problem, developing possible solution paths, and taking the appropriate course of action.

Why is problem solving important? Good problem solving skills empower you not only in your personal life but are critical in your professional life. In the current fast-changing global economy, employers often identify everyday problem solving as crucial to the success of their organizations. For employees, problem solving can be used to develop practical and creative solutions, and to show independence and initiative to employers.

what does problem solving look like?

engineer problem solving method

The ability to solve problems is a skill at which you can improve.  So how exactly do you practice problem solving? Learning about different problem solving strategies and when to use them will give you a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. There are two basic types of strategies: algorithmic and heuristic.

Algorithmic strategies are traditional step-by-step guides to solving problems. They are great for solving math problems (in algebra: multiply and divide, then add or subtract) or for helping us remember the correct order of things (a mnemonic such as “Spring Forward, Fall Back” to remember which way the clock changes for daylight saving time, or “Righty Tighty, Lefty Loosey” to remember what direction to turn bolts and screws). Algorithms are best when there is a single path to the correct solution.

But what do you do when there is no single solution for your problem? Heuristic methods are general guides used to identify possible solutions. A popular one that is easy to remember is IDEAL [Bransford & Stein [1] ] :

IDEAL is just one problem solving strategy. Building a toolbox of problem solving strategies will improve your problem solving skills. With practice, you will be able to recognize and use multiple strategies to solve complex problems.

What is the best way to get a peanut out of a tube that cannot be moved? Watch a chimpanzee solve this problem in the video below [Geert Stienissen [2] ].

Problem solving is a process that uses steps to solve problems. But what does that really mean? Let's break it down and start building our toolbox of problem solving strategies.

What is the first step of solving any problem? The first step is to recognize that there is a problem and identify the right cause of the problem. This may sound obvious, but similar problems can arise from different events, and the real issue may not always be apparent. To really solve the problem, it's important to find out what started it all. This is called identifying the root cause .

Example: You and your classmates have been working long hours on a project in the school's workshop. The next afternoon, you try to use your student ID card to access the workshop, but discover that your magnetic strip has been demagnetized. Since the card was a couple of years old, you chalk it up to wear and tear and get a new ID card. Later that same week you learn that several of your classmates had the same problem! After a little investigation, you discover that a strong magnet was stored underneath a workbench in the workshop. The magnet was the root cause of the demagnetized student ID cards.

The best way to identify the root cause of the problem is to ask questions and gather information. If you have a vague problem, investigating facts is more productive than guessing a solution. Ask yourself questions about the problem. What do you know about the problem? What do you not know? When was the last time it worked correctly? What has changed since then? Can you diagram the process into separate steps? Where in the process is the problem occurring? Be curious, ask questions, gather facts, and make logical deductions rather than assumptions.

When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. Although, each path in this exercise ended with a description of a problem solving tool for your toolbox, the first step is always to identify the problem and define the context in which it happened.

There are several strategies that can be used to identify the root cause of a problem. Root cause analysis (RCA) is a method of problem solving that helps people answer the question of why the problem occurred. RCA uses a specific set of steps, with associated tools like the “5 Why Analysis" or the “Cause and Effect Diagram,” to identify the origin of the problem, so that you can:

Once the underlying cause is identified and the scope of the issue defined, the next step is to explore possible strategies to fix the problem.

If you are not sure how to fix the problem, it is okay to ask for help. Problem solving is a process and a skill that is learned with practice. It is important to remember that everyone makes mistakes and that no one knows everything. Life is about learning. It is okay to ask for help when you don’t have the answer. When you collaborate to solve problems you improve workplace communication and accelerates finding solutions as similar problems arise.

One tool that can be useful for generating possible solutions is brainstorming . Brainstorming is a technique designed to generate a large number of ideas for the solution to a problem. The goal is to come up with as many ideas as you can, in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually.

Depending on your path through the exercise, you may have discovered that a couple of your coworkers had experienced similar problems. This should have been an indicator that there was a larger problem that needed to be addressed.

In any workplace, communication of problems and issues (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they be addressed in an efficient and timely manner.  Because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money; effective communication is an important tool..

One strategy for improving communication is the huddle . Just like football players on the field, a huddle is a short meeting with everyone standing in a circle.   It's always important that team members are aware of how their work impacts one another.  A daily team huddle is a great way to ensure that as well as making team members aware of changes to the schedule or any problems or safety issues that have been identified. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.

"Never try to solve all the problems at once — make them line up for you one-by-one.” — Richard Sloma

Problem solving improves efficiency and communication on the shop floor. It increases a company's efficiency and profitability, so it's one of the top skills employers look for when hiring new employees.  Employers consider professional skills, such as problem solving, as critical to their business’s success.

The 2011 survey, "Boiling Point? The skills gap in U.S. manufacturing [3] ," polled over a thousand manufacturing executives who reported that the number one skill deficiency among their current employees is problem solving, which makes it difficult for their companies to adapt to the changing needs of the industry.

  • Bransford, J. & Stein, B.S. (). The Ideal Problem Solver: A Guide For Improving Thinking, Learning, And Creativity . New York, NY: W.H. Freeman. ↵
  • National Geographic. [Geert Stienissen]. (2010, August 19). Insight learning: Chimpanzee Problem Solving [Video file]. Retrieved from http://www.youtube.com/watch?v=fPz6uvIbWZE ↵
  • Report: Boiling Point: The Skills Gap in U.S. Manufacturing Deloitte / The Manufacturing Institute, October 2011. Retrieved from http://www.themanufacturinginstitute.org/Hidden/2011-Skills-Gap-Report/2011-Skills-Gap-Report.aspx ↵

Introduction to Industrial Engineering Copyright © 2020 by Bonnie Boardman is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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curriculum for educators everywhere!

Find more at TeachEngineering.org .

Engineering Design Process

The engineering design process emphasizes open-ended problem solving and encourages students to learn from failure . This process nurtures students’ abilities to create innovative solutions to challenges in any subject!

engineer problem solving method

The engineering design process is a series of steps that guides engineering teams as we solve problems. The design process is iterative , meaning that we repeat the steps as many times as needed, making improvements along the way as we learn from failure and uncover new design possibilities to arrive at great solutions.

Overarching themes of the engineering design process are teamwork and design . Strengthen your students’ understanding of open-ended design as you encourage them to work together to brainstorm new ideas, apply science and math concepts, test prototypes and analyze data—and aim for creativity and practicality in their solutions. Project-based learning engages learners of all ages—and fosters STEM literacy.

Browse all K-12 engineering design process curriculum

Ask: identify the need & constraints.

Engineers ask critical questions about what they want to create, whether it be a skyscraper, amusement park ride, bicycle or smartphone. These questions include: What is the problem to solve? What do we want to design? Who is it for? What do we want to accomplish? What are the project requirements? What are the limitations? What is our goal?

Research the Problem

This includes talking to people from many different backgrounds and specialties to assist with researching what products or solutions already exist, or what technologies might be adaptable to your needs.

Imagine: Develop Possible Solutions

You work with a team to brainstorm ideas and develop as many solutions as possible. This is the time to encourage wild ideas and defer judgment! Build on the ideas of others! Stay focused on topic, and have one conversation at a time! Remember: good design is all about teamwork! Help students understand the brainstorming guidelines by using the TE handout and two sizes of classroom posters .

Plan: Select a Promising Solution

For many teams this is the hardest step! Revisit the needs, constraints and research from the earlier steps, compare your best ideas, select one solution and make a plan to move forward with it.

Create: Build a Prototype

Building a prototype makes your ideas real! These early versions of the design solution help your team verify whether the design meets the original challenge objectives. Push yourself for creativity, imagination and excellence in design.

Test and Evaluate Prototype

Does it work? Does it solve the need? Communicate the results and get feedback. Analyze and talk about what works, what doesn't and what could be improved.

Improve: Redesign as Needed

Discuss how you could improve your solution. Make revisions. Draw new designs. Iterate your design to make your product the best it can be. And now, REPEAT!

Check out our high school engineering design unit

engineer problem solving method

Engineering-Design Aligned Curricula

engineer problem solving method

The TeachEngineering hands-on activities featured here, by grade band, exemplify the engineering design process.

preview of 'Bacteria! It’s Everywhere! ' Activity

Students investigate what causes them to become sick during the school year. They use the engineering design process to test the classroom lab spaces for bacteria. After their tests, they develop ideas to control the spread of germs within the classroom.

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A unique activity for young learners that combines engineering and biology, students design an optimal environment for red wiggler worms in a compost bin.

preview of 'Stop Freewheeling Using Friction! ' Maker Challenge

In this maker challenge, students use the engineering design process to design a covering for a portable wheelchair ramp for their school. The design must be easy to use, and allows people to move up the ramp easily and go down slowly.

preview of 'Inundation Inspiration' Maker Challenge

Students employ the engineering design process to create a device that uses water-absorbing crystals for use during a flood or storm surge. They use (or build) a toy house, follow the engineering design process to build their device, and subject the house to tests that mimic a heavy flood or rising ...

preview of 'Silkworm Strength! ' Maker Challenge

Students use the engineering design process to design a bridge out of silkworm cocoons that can hold at least 50 grams. Students can use other materials to supplement the silk bridge, but have a $10 budget.

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In this multi-day activity, students explore environments, ecosystems, energy flow and organism interactions by creating a scale model biodome, following the steps of the engineering design process.

preview of 'Exploring Variables While Testing & Improving Mint-Mobiles (for Elementary School)' Activity

Build a model race car out of lifesaver candies, popsicle sticks, straws, and other fun materials! Have students learn about independent, dependent, and control variables, and find out who can make the fastest car given their new knowledge.

preview of 'Operation Build a Bridge and Get Over It ' Activity

Design and construct a bridge for a local city that will have a high strength-to-weight ratio and resist collapse. Have students use their understanding of the engineering design process—and a lot of wooden craft sticks—to achieve their goals.

preview of 'Design and Build a Rube Goldberg ' Activity

In this two-part activity, students design and build Rube Goldberg machines. This open-ended challenge employs the engineering design process and may have a pre-determined purpose, such as rolling a marble into a cup from a distance, or let students decide the purposes.

preview of 'Water Bottle Rockets' Activity

Students are challenged to design and build rockets from two-liter plastic soda bottles that travel as far and straight as possible or stay aloft as long as possible. Guided by the steps of the engineering design process, students first watch a video that shows rocket launch failures and then partic...

preview of 'Creative Crash Test Cars' Maker Challenge

Students explore how mass affects momentum in head-on collisions and experience the engineering design process as if they are engineers working on the next big safety feature for passenger cars. They design, create and redesign impact-resistant passenger vehicle compartments for small-size model car...

preview of 'Trebuchet Design & Build Challenge ' Activity

Students work as teams of engineers to design and build their own trebuchets. They research how to build and test their trebuchets, evaluate their results, and present their results and design process to the class.

Grades 9-12

preview of 'Out-of-the Box: A Furniture Design + Engineering Challenge ' Maker Challenge

Student teams follow the steps of the engineering design process as they design and build architecturally inspired cardboard furniture. Given a list of constraints, including limited fabrication materials and tools, groups research architectural styles, brainstorm ideas, make small-scale quick proto...

preview of 'Balloons' Activity

Students follow the steps of the engineering design process as they design and construct balloons for aerial surveillance. Applying their newfound knowledge, the young engineers build and test balloons that fly carrying small flip cameras that capture aerial images of their school.

preview of 'Inquiry and Engineering: Gliders' Activity

Student teams design, build and test small-sized gliders to maximize flight distance and an aerodynamic ratio, applying their knowledge of fluid dynamics to its role in flight. Students experience the entire engineering design process, from brainstorming to CAD (or by hand) drafting, including resea...

preview of 'Bio-Engineering: Making and Testing Model Proteins ' Activity

Students learn about human proteins, how their shapes are related to their functions and how DNA protein mutations result in diseases. Then, in a hypothetical engineering scenario, they use common classroom supplies to design and build their own structural, transport and defense protein models to he...

preview of 'Android App Development' Activity

Students develop an app for an Android device that utilizes its built-in internal sensors, specifically the accelerometer. The goal of this activity is to teach programming design and skills using MIT's App Inventor software (free to download from the Internet) as the vehicle for learning.

Welcome to TeachEngineering’s Engineering Design Process curricula for Grade K-2 Educators!

preview of 'Be “Cool” with Popsicle Engineering' Activity

Create popsicles using the engineering design process! In this activity, students work to solve the problems of a local popsicle shop while learning how scientific and engineering concepts play a part in behind-the-scenes design.

preview of 'Design a Better Bandage' Maker Challenge

In this maker challenge, students follow the engineering design process and use water-absorbing crystals to create a bandage that can be used in a traumatic situation, like a car accident or hiking accident.

Maker Challenge

preview of 'Engineering an Animal’s Survival ' Activity

Students perform research and design prosthetic prototypes for an animal to use for its survival. They research a set of pre-chosen animals and their habitats. They then create habitats for their animals to live and model 3D prosthetics for the animals to use with modeling clay.

preview of 'Invent a Backscratcher from Everyday Materials' Activity

Given scrap cardboard, paper towel tubes, scissors, and glue, how could a student invent their own backscratcher? Engage in the process of how real engineers design products to meet a desired function.

preview of 'Keeping Damp in a Drought ' Maker Challenge

Students design a way for mint plants to keep a constant moisture level for 72 hours. The mint plants must be kept moist since they are young and just starting to establish growth.

preview of 'Naturally Organized ' Activity

Design a customized table top supply organizer inspired by the natural home of a ladybug—or any other insect of a student's choosing—to hold all of their classroom supplies! By the end of this activity, students will understand the properties of biomimicry and the engineering design process.

Welcome to TeachEngineering’s Engineering Design Process curricula for Grade 3-5 Educators!

preview of 'Biodomes are Engineered Ecosystems: A Mini World' Lesson

As students learn about the creation of biodomes, they are introduced to the steps of the engineering design process, including guidelines for brainstorming. They learn how engineers are involved in the design and construction of biodomes and use brainstorming to come up with ideas for possible biod...

preview of 'Biohazard Protection Design Project: Suit Up!' Activity

Students learn about providing healthcare in a global setting and the importance of wearing protective equipment when treating patients with infectious diseases like Ebola. They learn about biohazard suits, heat transfer through conduction and convection and the engineering design cycle. Student tea...

preview of 'Build a Toy Workshop' Activity

Working as if they are engineers who work for (the hypothetical) Build-a-Toy Workshop company, students apply their imaginations and the engineering design process to design and build prototype toys with moving parts. They set up electric circuits using batteries, wire and motors. They create plans ...

preview of 'Clean Enough to Drink: Making Devices to Filter Dirty Water' Activity

Whether on Earth or in space, life-threatening illnesses may occur if the water we drink is of poor quality. It’s up to your students to design and build a filtration system for the International Space Station so they can guarantee astronauts get the safe and clean water they need.

preview of 'Constraints: Pop Rockets on a Shoestring Budget' Activity

Your students have been hired to build a pop rocket, but on a tight budget. Engineering design usually has some constraints and you won’t always have access to the materials you think you might need. But through brainstorming and trial and error, a viable rocket launch is definitely possible!

preview of 'Construct and Test Roofs for Different Climates' Activity

In this activity, students design and build model houses, then test them against various climate elements, and then re-design and improve them. Using books, websites and photos, students learn about the different types of roofs found on various houses in different environments throughout the world....

preview of 'Cutting Through Soil' Activity

Students pretend they are agricultural engineers during the colonial period and design a miniature plow that cuts through a "field" of soil. They are introduced to the engineering design process and learn of several famous historical figures who contributed to plow design.

preview of 'Design and Fly a Kite' Activity

Students learn how to use wind energy to combat gravity and create lift by creating their own tetrahedral kites capable of flying. They explore different tetrahedron kite designs, learning that the geometry of the tetrahedron shape lends itself well to kites and wings because of its advantageous str...

preview of 'Design Criteria-to-Working Model: Engineer a Sneaker' Activity

Students learn the basics of engineering sneakers and shoes. They are challenged to decide on specific design requirements, such as heavy traction or extra cushioning, and then use different materials to create working prototype shoes that meet the design criteria. Includes worksheets.

preview of 'Design Your Own Snazzy Sneakers' Maker Challenge

For this maker challenge, students decide on specific design requirements (such as good traction or deep cushioning), sketch their plans, and then use a variety of materials to build prototype shoes that meet the design criteria.

preview of 'Engineering a Habitat’s Humidity ' Activity

Students design a temporary habitat for a future classroom pet—a hingeback tortoise. The students investigate hingeback tortoise habitat features as well as the design features of such a habitat. Each group communicates and presents this information to the rest of the class after they research, brai...

preview of 'Engineering a Mountain Rescue Litter	' Activity

When a person gets injured in the wilderness and needs medical attention, rescuers might use a device called a mountain rescue litter specifically designed for difficult evacuations. Design and build a small-sized prototype to save some (potatoes’) lives!

preview of 'Engineering Derby: Tool Ingenuity' Activity

Student teams are challenged to navigate a table tennis ball through a timed obstacle course using only the provided unconventional “tools.” Teams act as engineers by working through the steps of the engineering design process to complete the overall task with each group member responsible to accomp...

preview of 'Engineering in the World of Dr. Seuss' Activity

Students explore the engineering design process within the context of Dr. Seuss’s book, Bartholomew and the Oobleck. Students study a sample of aloe vera gel (the oobleck) in lab groups. After analyzing the substance, they use the engineering design process to develop and test other substances to ma...

preview of 'Gone with the Wind Energy: Design-Build-Test Mini Sail Cars! ' Activity

Students explore the use of wind power in the design, construction and testing of "sail cars," which, in this case, are little wheeled carts with masts and sails that are powered by the moving air generated from a box fan. The scientific method is reviewed and reinforced with the use of controls and...

preview of 'Hare and Snail Challenges' Activity

Students engage in the second design challenge of the unit, which is an extension of the maze challenge they solved in the first lesson/activity of this unit. Students extend the ideas learned in the maze challenge with a focus more on the robot design. Specifically, students learn how to design the...

preview of 'Line-Follower Challenge' Activity

Student groups are challenged to program robots with color sensors to follow a black line. Learning both the logic and skills behind programming robots for this challenge helps students improve their understanding of how robots "think" and widens their appreciation for the complexity involved in pro...

preview of 'Master Driver' Activity

As part of a design challenge, students learn how to use a rotation sensor (located inside the casing of a LEGO® MINDSTORMS ® EV3 motor) to measure how far a robot moves with each rotation. Through experimentation and measurement with the sensor, student pairs determine the relationship between the ...

preview of 'Maze Challenge' Activity

As the first engineering design challenge of the unit, students are introduced to the logic for solving a maze. Student groups apply logic to program LEGO® MINDSTORMS® EV3 robots to navigate through a maze, first with no sensors, and then with sensors.

preview of 'Naked Egg Drop' Activity

Student pairs experience the iterative engineering design process as they design, build, test and improve catching devices to prevent a "naked" egg from breaking when dropped from increasing heights. To support their design work, they learn about materials properties, energy types and conservation o...

preview of 'Problem Solve Your School' Activity

Students apply what they have learned about the engineering design process to a real-life problem that affects them and/or their school. They choose a problem as a group, and then follow the engineering design process to come up with and test their design solution.

preview of 'Race to the Top! Modeling Skyscrapers' Activity

Working individually or in pairs, students compete to design, create, test and redesign free-standing, weight-bearing towers using Kapla® wooden blocks. The challenge is to build the tallest tower while meeting the design criteria and minimizing the amount of material used—all within a time limit.

preview of 'Right on Target: Catapult Game' Activity

Students experience the engineering design process as they design and build accurate and precise catapults using common materials. They use their catapults to participate in a game in which they launch Ping-Pong balls to attempt to hit various targets.

preview of 'Sea Turtle Eggs: Washed to Sea? ' Activity

Students employ the full engineering design process to research and design prototypes that could be used to solve the loss of sea turtle life during a hurricane. Students learn about sea turtle nesting behaviors and environmental impacts of hurricanes. Students work collaboratively to build structur...

preview of 'Simulation in Healthcare' Lesson

Students learn how engineering design is applied to solve healthcare problems by using an engineering tool called simulation. While engineering design is commonly used to study and design everything from bridges, factories, airports to space shuttles, the use of engineering design to study healthcar...

preview of 'Straw Towers to the Moon' Activity

Students learn about civil engineers and work through each step of the engineering design process in two mini-activities that prepare them for a culminating challenge to design and build the tallest straw tower possible, given limited time and resources. In the culminating challenge (tallest straw t...

preview of 'Sumobot Challenge' Activity

Students apply their knowledge of constructing and programming LEGO® MINDSTORMS® robots to create sumobots—strong robots capable of pushing other robots out of a ring. To meet the challenge, groups follow the steps of the engineering design process and consider robot structure, weight and gear ratio...

preview of 'Temperature Tells All! Model House Testing for Clean vs. Warm Air' Activity

Students learn about health risks caused by cooking and heating with inefficient stoves inside homes. They simulate the cook stove scenario and follow the engineering design process steps, including iterative trials, to increase warmth inside a building while reducing air quality problems. A student...

preview of 'The Strongest Strongholds' Activity

Students work together in small groups, while competing with other teams, to explore the engineering design process through a tower building challenge. They are given a set of design constraints and then conduct online research to learn basic tower-building concepts. During a two-day process and usi...

preview of 'Time for Design' Lesson

Students are introduced to the engineering design process, focusing on the concept of brainstorming design alternatives. They learn that engineering is about designing creative ways to improve existing artifacts, technologies or processes, or developing new inventions that benefit society.

preview of 'Ultrasonic Sensor Robot Design Project: Don't Bump into Me!' Activity

Students' understanding of how robotic ultrasonic sensors work is reinforced in a design challenge involving LEGO® MINDSTORMS® EV3 robots and ultrasonic sensors. Student groups program their robots to move freely without bumping into obstacles (toy LEGO people).

preview of 'Wind-Powered Sail Cars' Activity

Student pairs design and construct small, wind-powered sail cars using limited quantities of drinking straws, masking tape, paper and beads. Teams compete to see which sail car travels the farthest when pushed by the wind (simulated by the use of an electric fan). Students learn about wind and kinet...

Welcome to TeachEngineering’s Engineering Design Process curricula for Grade 6-8 Educators!

preview of 'Adding Helpful Carrier Devices to Crutches' Maker Challenge

Student teams are challenged to design assistive devices that modify crutches to help people carry things such as books and school supplies. Given a list of constraints, including a device weight limit and minimum load capacity, groups brainstorm ideas and then make detailed plans for their best sol...

preview of 'Algorithmic Remote Rover Programming: Curiosity Killed the App' Lesson

Students gain experience with the software/system design process, closely related to the engineering design process, to solve a problem. The lesson culminates in a hands-on experience with the design process as students simulate the remote control of a rover.

preview of 'Amusement Park Ride: Ups and Downs in Design' Activity

Students design, build and test looping model roller coasters using foam pipe insulation tubing. They learn about potential and kinetic energy as they test and evaluate designs, addressing the task as if they are engineers. Winning designs have the lowest cost and best aesthetics. Three student work...

preview of 'An Assistive Artistic Device' Activity

Students design and develop a useful assistive device for people challenged by fine motor skill development who cannot grasp and control objects. In the process of designing prototype devices, they learn about the engineering design process and how to use it to solve problems.

preview of 'Automatic Floor Cleaner Computer Program Challenge' Activity

Students learn more about assistive devices, specifically biomedical engineering applied to computer engineering concepts, with an engineering challenge to create an automatic floor cleaner computer program. Following the steps of the design process, they design computer programs and test them by pr...

preview of 'Balsa Towers' Activity

Students groups use balsa wood and glue to build their own towers using some of the techniques they learned from the associated lesson.

preview of 'Boat Design Challenge: Journey to the Egyptian Afterlife' Activity

Student teams are challenged to design models of Egyptian funerary barges for the purpose of transporting mummies through the underworld to the afterlife. Students design and build prototypes using materials and tools like the ancient Egyptians had at their disposal.

preview of 'Bouncy Ball Factory ' Maker Challenge

Students become product engineers in a bouncy ball factory as they design and prototype a polymer bouncy ball that meets specific requirements: must be spherical in shape, cannot disintegrate when thrown on the ground, and must bounce.

preview of 'Broken Bones and Biomedical Materials' Activity

Students are introduced to the concept and steps of the engineering design process and taught how to apply it. In small groups, students learn of their design challenge (improve a cast for a broken arm), brainstorm solutions, are given materials and create prototypes.

preview of 'Chair Design' Activity

Students become familiar with the engineering design process as they design, build, and test chair prototypes.

preview of 'Cleaning the Air ' Activity

In this activity, students undertake a similar engineering challenge as they design and build a filter to remove pepper from an air stream without blocking more than 50% of the air.

preview of 'Clearing a Path to the Heart' Activity

Following the steps of the engineering design process and acting as biomedical engineers, student teams use everyday materials to design and develop devices and approaches to unclog blood vessels. Through this open-ended design project, they learn about the circulatory system, biomedical engineering...

preview of 'Cool Puppy! A Doghouse Design Project' Maker Challenge

Students design and build small doghouses to shelter a (toy) puppy from the heat—and create them within constraints. They apply what they know about light energy and how it travels through various materials, as well as how a material’s color affects its light absorption and reflection. They test the...

preview of 'Cooler Design Challenge' Activity

Students learn about convection, conduction, and radiation in order to solve the challenge of designing and building a small insulated cooler with the goal of keeping an ice cube and a Popsicle from melting. This activity uses the engineering design process to build the cooler as well as to measure ...

preview of 'Design a Carrying Device for People Using Crutches ' Activity

Students are given a biomedical engineering challenge, which they solve while following the steps of the engineering design process. In a design lab environment, student groups design, create and test prototype devices that help people using crutches carry things, such as books and school supplies.

preview of 'Design Air Racer Cars Using Tinkercad ' Activity

Students build an electric racer vehicle using Tinkercad to design blades for their racers. Students print their designs using a MakerBot printer. Students race their vehicles to see which design travels the furthest distance in the least amount of time.

preview of 'Design Your Own Pedometer!' Maker Challenge

Students use the engineering design process to design, create, and test a pedometer that keeps track of the number of steps a person takes. This maker challenge exposes students to basic coding, micro:bit processor applications, and how programming and engineering can be used to solve health problem...

preview of 'Designing Polymers to Clean Water' Activity

Students learn how to engineer a design for a polymer brush—a coating consisting of polymers that represents an antifouling polymer brush coating for a water filtration surface.

preview of 'Do the Robot! Programming a RedBot to Dance' Maker Challenge

Students program the drive motors of a SparkFun RedBot with a multistep control sequence—a “dance.” Doing this is a great introduction to robotics and improves overall technical literacy by helping students understand that we use programs to control the motion and function of robots, and without the...

preview of 'Does It Cut It? Understanding Wind Turbine Blade Performance' Activity

Students gain an understanding of the factors that affect wind turbine operation. Following the steps of the engineering design process, engineering teams use simple materials (cardboard and wooden dowels) to build and test their own turbine blade prototypes with the objective of maximizing electric...

preview of 'E.G. Benedict's Ambulance Patient Safety Challenge ' Activity

Students further their understanding of the engineering design process (EDP) while applying researched information on transportation technology, materials science and bioengineering. Students are given a fictional client statement (engineering challenge) and directed to follow the steps of the EDP t...

preview of 'Engineering in Reverse!' Activity

Students learn about the process of reverse engineering and how this technique is used to improve upon technology. Students analyze push-toys and draw diagrams of the predicted mechanisms inside the toys. Then, they disassemble the toys and draw the actual inner mechanisms.

preview of 'Exploring Variables While Testing & Improving Mint-Mobiles (for Middle School)' Activity

Students design, build, and test model race cars made from simple materials (lifesaver-shaped candies, plastic drinking straws, Popsicle sticks, index cards, tape) as a way to explore independent, dependent and control variables.

preview of 'Fancy Feet! Stress & Strain Forces in Shoe Design' Activity

Students use the engineering design process to solve a real-world problem—shoe engineering! Working in small teams, they design, build and test a pair of wearable platform or high-heeled shoes, taking into consideration the stress and strain forces that it will encounter from the shoe wearer.

preview of 'Follow the Light' Activity

Students' understanding of how robotic color sensors work is reinforced in a design challenge involving LEGO® MINDSTORMS® robots and light sensors. Working in pairs, students program LEGO robots to follow a flashlight as its light beam moves around.

preview of 'Future Hospitals: Robotics and Automated Patient Care Engineering' Activity

Students further their understanding of the engineering design process while combining mechanical engineering and bioengineering to create an automated medical device.

preview of 'Hot Cans and Cold Cans' Activity

Students apply the concepts of conduction, convection and radiation as they work in teams to solve two challenges. One problem requires that they maintain the warm temperature of one soda can filled with water at approximately human body temperature, and the other problem is to cause an identical so...

preview of 'Hydraulic Arm Challenge' Activity

Students design and build a mechanical arm that lifts and moves an empty 12-ounce soda can using hydraulics for power. Small design teams (1-2 students each) design and build a single axis for use in the completed mechanical arm.

preview of 'Just Like Kidneys: Semipermeable Membrane Prototypes' Activity

Using ordinary classroom materials, students act as biomedical engineering teams challenged to design prototype models that demonstrate semipermeability to help medical students learn about kidney dialysis. A model consists of two layers of a medium separated by material acting as the membrane. Grou...

preview of 'Keep Your Cool! Design Your Own Cooler Challenge' Maker Challenge

Students brainstorm, design, and build a cooler and monitor its effectiveness to keep a bottle of ice water cold in comparison to a bottle of ice water left at room temperature. Students engage in design by choosing from a range of materials to build their prototype.

preview of 'Lending a Hand: Teaching Forces through Assistive Device Design ' Activity

Students learn about how biomedical engineers create assistive devices for persons with fine motor skill disabilities. They do this by designing, building and testing their own hand "gripper" prototypes that are able to grasp and lift a 200 ml cup of sand.

preview of 'Mars Rover App Creation' Activity

Based on their experience exploring the Mars rover Curiosity and learning about what engineers must go through to develop a vehicle like Curiosity, students create Android apps that can control LEGO® MINDSTORMS® robots, simulating the difficulties the Curiosity rover could encounter. The activity go...

preview of 'No Valve in Vain' Activity

Acting as biomedical engineers, students design, build, test and redesign prototype heart valves using materials such as waterproof tape, plastic tubing, flexible plastic and foam sheets, clay, wire and pipe cleaners. They test them with flowing water, representing blood moving through the heart.

preview of 'Off-Road Wheelchair Challenge' Activity

Students further their understanding of the engineering design process (EDP) while being introduced to assistive technology devices and biomedical engineering. They are given a fictional client statement and are tasked to follow the steps of the EDP to design and build small-scale, off-road wheelcha...

preview of 'Oil: Clean It Up! ' Maker Challenge

Student groups create and test oil spill cleanup kits that are inexpensive and accessible for homeowners or for big companies to give to individual workers—to aid in home, community or environmental oil spill cleanup process.

preview of 'Paper Drop Design Competition' Activity

Using paper, paper clips and tape, student teams design flying/falling devices to stay in the air as long as possible and land as close as possible to a given target. Student teams use the steps of the engineering design process to guide them through the initial conception, evaluation, testing and r...

preview of 'Protect the Pump: Prototyping Designs for Biomedical Devices' Activity

Students learn how biomedical engineers work with engineers and other professionals to develop dependable medical devices. Student teams brainstorm, sketch, design and create prototypes of suction pump protection devices to keep fluid from backing up and ruining the pump motors.

preview of 'Protect Your Body, Filter Your Water!' Activity

Students experience the steps of the engineering design process as they design solutions for a real-world problem that negatively affects the environment. They use plastic tubing and assorted materials such as activated carbon, cotton balls, felt and cloth to create filters with the capability to re...

preview of 'Sensory Toys Make Sense!' Activity

Students design and create sensory integration toys for young children with developmental disabilities—an engineering challenge that combines the topics of biomedical engineering, engineering design and human senses. Students learn the steps of the engineering design process (EDP) and how to use it ...

preview of 'Sled Hockey Design Challenge' Activity

Students are asked to design a hockey stick for a school’s new sled hockey team. Using the engineering design process, students act as material engineers to create hockey sticks that have different interior structures using multiple materials that can withstand flexure testing.

preview of 'Solar Sails: The Future of Space Travel' Activity

Working as if they were engineers, students design and construct model solar sails made of aluminum foil to move cardboard tube satellites through “space” on a string. Working in teams, they follow the engineering design thinking steps—ask, research, imagine, plan, create, test, improve—to design an...

preview of 'Sounds All Around' Activity

Students follow the steps of the engineering design process to create their own ear trumpet devices (used before modern-day hearing aids), including testing them with a set of reproducible sounds.

preview of 'Spaghetti Soapbox Derby' Activity

Student pairs design, build, and test model vehicles capable of rolling down a ramp and then coasting freely as far as possible. The challenge is to make the vehicles entirely out of dry pasta using only adhesive (such as hot glue) to hold the components together.

preview of 'Super Slinger Engineering Challenge' Activity

Students are challenged to design, build and test small-scale launchers while they learn and follow the steps of the engineering design process. For the challenge, the "slingers" must be able to aim and launch Ping-Pong balls 20 feet into a goal using ordinary building materials such as tape, string...

preview of 'Swiss Alps Emergency Sled Design' Activity

Students act as engineers to solve a hypothetical problem that has occurred in the Swiss Alps due to a natural seismic disaster. Working in groups, they follow the engineering design process steps to create model sleds that meet the requirements to transport materials to people in distress that live...

preview of 'The Artificial Bicep' Activity

Students learn more about how muscles work and how biomedical engineers can help keep the muscular system healthy. Following the engineering design process, they create their own biomedical device to aid in the recovery of a strained bicep.

preview of 'Three-Tower Types Challenge: Tower Investigation and the Egg' Activity

In this activity, student groups design and build three types of towers (guyed or cable-supported, free-standing or self-standing, and monopole), engineering them to meet the requirements that they hold an egg one foot high for 15 seconds.

preview of 'Toxic Island: Designing Devices to Deliver Goods' Maker Challenge

A classic engineering challenge involves designing and building devices that can deliver necessary goods to “Toxic Island.” Working within specific constraints, students design a device that must not touch the water or island, and must deliver supplies accurately and quickly.

preview of 'Using Waits, Loops and Switches' Activity

Students are given a difficult challenge that requires they integrate what they have learned so far in the unit about wait blocks, loops and switches. They incorporate these tools into their programming of the LEGO® MINDSTORMS® robots to perform different tasks depending on input from a sound sensor...

preview of 'Wear’s the Technology?' Activity

Students apply their knowledge of scale and geometry to design wearables that would help people in their daily lives, perhaps for medical reasons or convenience. Like engineers, student teams follow the steps of the design process, to research the wearable technology field (watching online videos an...

preview of 'Wimpy Radar Antenna: Reinforced Tower Test, Analyze & Improve' Activity

Students reinforce an antenna tower made from foam insulation so that it can withstand a 480 N-cm bending moment (torque) and a 280 N-cm twisting moment (torque) with minimal deflection.

preview of 'Wristwatch Design for the Visually Impaired' Activity

Students further their understanding of the engineering design process while combining mechanical engineering and bio-engineering to create assistive devices. During this extended activity (seven class periods), students are given a fictional client statement and required to follow the steps of the ...

Welcome to TeachEngineering’s Engineering Design Process curricula for Grade 9-12 Educators!

preview of 'A Zombie Got My Leg Challenge: Making Makeshift Legs' Activity

Students experience the engineering design process as they design and construct lower-leg prostheses in response to a hypothetical zombie apocalypse scenario. Building on what they learned and researched in the associated lesson, they design and fabricate a replacement prosthetic limb using given sp...

preview of 'Above-Ground Storage Tank Design Project' Activity

In this culminating activity, student groups act as engineering design teams to derive equations to determine the stability of specific above-ground storage tank scenarios with given tank specifications and liquid contents. With their flotation analyses completed and the stability determined, studen...

preview of 'An Implementation of Steganography' Activity

Students apply the design process to the problem of hiding a message in a digital image using steganographic methods, a PictureEdit Java class, and API (provided as an attachment). They identify the problems and limitations associated with this task, brainstorm solutions, select a solution, and impl...

preview of 'Augmented Reality Programming Challenge' Maker Challenge

Students explore augmented reality programs, including muscle and bone overlays and body tracking recording program, using Unity and Microsoft Visual Studio and develop ways to modify, enhance, and redesign the program to meet a particular real-world need.

preview of 'Boom Construction' Activity

Student teams design their own booms (bridges) and engage in a friendly competition with other teams to test their designs. Each team strives to design a boom that is light, can hold a certain amount of weight, and is affordable to build.

preview of 'Build Your Own Night-Light with Arduino' Maker Challenge

Students use Arduino microcontrollers and light-sensitive resistors (photocells) to sense the ambient light levels in a room and turn LEDs on and off based on those readings. They are challenged to personalize their basic night-lights with the use of more LEDs, if/else statements and voltage divider...

preview of 'Building Arduino Light Sculptures' Maker Challenge

Students gain practice in Arduino fundamentals as they design their own small-sized prototype light sculptures to light up a hypothetical courtyard. They program Arduino microcontrollers to control the lighting behavior of at least three light-emitting diodes (LEDs) to create imaginative light displ...

preview of 'Control a Servo with Your Phone Using Bluetooth!' Maker Challenge

Students learn how to control an Arduino servo wirelessly using a simple phone application, Bluetooth module and an Android phone. This prepares them to wirelessly control their own projects.

preview of 'Convertible Shoes: Function, Fashion and Design' Activity

Student teams design and build shoe prototypes that convert between high heels and athletic shoes. They apply their knowledge about the mechanics of walking and running as well as shoe design (as learned in the associated lesson) to design a multifunctional shoe that is both fashionable and function...

preview of 'Create and Control a Popsicle Stick Finger Robot' Maker Challenge

Students use servos and flex sensors to make simple, one-jointed, finger robots. They use Arduino microcontrollers, create circuits and write code to read finger flexes and send angle info to servos. They explore the constrain, map and smoothing commands. Can teams combine fingers to create an entir...

preview of 'Creating Mini Wastewater Treatment Plants' Activity

Student teams design, construct, test and improve small working models of water treatment plant processes to filter out contaminants and reclaim resources from simulated wastewater. They keep to a materials budget and earn money from reclaimed materials. They conduct before/after water quality tests...

preview of 'Design a Bicycle Helmet' Activity

Students are introduced to the biomechanical characteristics of helmets, and are challenged to incorporate them into designs for helmets used for various applications.

preview of 'Design Your Own Nano-Polymer Smartphone Case' Maker Challenge

Students design and create their own nano-polymer smartphone case. Students choose their design, mix their nano-polymer (based in silicone) with starch and add coloring of their choice. While students think critically about their design, they embed strings in the nano-polymer material to optimize bo...

preview of 'Designing a Robotic Surgical Device' Activity

Student teams create laparoscopic surgical robots designed to reduce the invasiveness of diagnosing endometriosis and investigate how the disease forms and spreads. Using a synthetic abdominal cavity simulator, students test and iterate their remotely controlled, camera-toting prototype devices, whi...

preview of 'Designing an Elliptical Pool Table' Activity

Students learn about the mathematical characteristics and reflective property of ellipses by building their own elliptical-shaped pool tables. After a slide presentation introduction to ellipses, student “engineering teams” follow the steps of the engineering design process to develop prototypes, wh...

preview of 'Does My Model Valve Stack up to the Real Thing?' Activity

Following the steps of the iterative engineering design process, student teams use what they learned in the previous lessons and activity in this unit to research and choose materials for their model heart valves and test those materials to compare their properties to known properties of real heart ...

preview of 'Energy Storage Derby and Proposal' Activity

Students design, build and test small-sized vehicle prototypes that transfer various types of potential energy into motion. To complete the Go Public phase of the legacy cycle, students demonstrate their understanding of how potential energy may be transferred into kinetic energy.

preview of 'Engineering Self-Cleaning Hydrophobic Surfaces ' Maker Challenge

Students explore how to modify surfaces such as wood or cotton fabric at the nanoscale. They create specialized materials with features such as waterproofing and stain resistance. The challenge starts with student teams identifying an intended user and developing scenarios for using their developed ...

preview of 'Exploiting Polarization: Designing More Effective Sunglasses' Activity

Students apply their understanding of light polarization and attenuation to design, fabricate, test and refine their own prototype sunglasses that better reduce glare and lower light intensity compared to available sunglasses, and better protect eyes from UVA and UVB radiation. They meet the project...

preview of 'Exploring Variables While Testing & Improving Mint-Mobiles (for High School) ' Activity

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Students work as materials and chemical engineers to develop a bouncy ball using a select number of materials. They develop a plan of what materials they might need to design their product, and then create, test, and evaluate their bouncy ball.

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Student pairs design, redesign and perform simple experiments to test the differences in thermal conductivity (heat flow) through different media (foil and thin steel). Then students create visual diagrams of their findings that can be understood by anyone with little background on the subject, appl...

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Problem-solving for Engineers: Root Cause Analysis Fundamentals (Virtual Classroom)

Credits: CEUs: 2.30 | PDHs: 23.00

Language: English - US

Learn root cause analysis (RCA) fundamentals, explore RCA tools' purpose and application, and perform RCA on real-world problems to find solutions.

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Jun 10-12th, 2024

This course commences at 9:30 AM and ends at 6PM each day, with breaks scheduled throughout. Interested in taking this course in person?  Please follow this link !

Even with the best quality systems and training, problems can happen. Root cause analysis (RCA) describes a wide range of approaches, tools, and techniques used to uncover causes of problems. For engineers, this could be applied to failure analysis in engineering and maintenance, quality control problems, safety performance, and computer systems or software analysis. The goal of RCA is to identify the origin of a problem using a systematic approach and determine:

  • What happened
  • Why it happened
  • How to reduce the likelihood that it happens again
  • How to launch a solution implementation plan

This three-day course provides a collaborative and dynamic learning environment that affords the participant the ability to perform RCA on real-world problems and overlay solutions to the problems. Each RCA tool is presented in an easy-to-follow structure: a general description of the tool, its purpose and typical applications, the procedure when using it, an example of its use, a checklist to help you make sure it is applied properly, and different forms and templates.

The examples used can be tailored to many different industries and markets, including manufacturing, robotics, bioengineering, energy, and pressure technology. The layout of this course has been designed to help speed participants’ learning through short videos depicting well-known scenarios for analysis in class. Course Materials (included in purchase of course):  Digital course notes via ASME’s Learning Platform 

By participating in this course, you will learn how to successfully:

  • Explain the concept of root cause analysis
  • Describe how to use tools for problem cause brainstorming
  • Ask the right questions; establish triggers that drive you to the RCA process
  • Develop strategies for problem cause data collection and analysis
  • Deploy tools for root cause identification and elimination
  • Perform a cost-benefit analysis
  • Practice ways of implementation solutions

Who should attend? This course is intended for engineers and technical professionals involved in flow of complex processes, materials and equipment, or those who serve in a project or product management function. This  ASME Virtual Classroom  course is held live with an instructor on our online learning platform. A Certificate of Completion will be issued to registrants who successfully attend and complete the course. Can't make one of the scheduled sessions? This course is also available On Demand.

  • Introduction to Root Cause Analysis (RCA)
  • The need and the practice
  • Defining a Problem
  • Strategies to Solve Problems
  • Understanding Causes and Its Levels
  • Finding Root Causes
  • Eliminating Root Causes
  • Proactive Problem Solving
  • Case Studies & Hands-on Activity
  • Defining Root Cause Analysis
  • Conducting Root Cause Analysis
  • Case Study & Group Activity
  • Problem Understanding
  • The Purpose and Applications of Flowcharts
  • Using Flowcharts
  • Using Critical Incidents
  • Using Performance Matrices
  • Problem Cause Brainstorming
  • The Purpose and Application of Brainstorming
  • Brainstorming Recording Templates
  • Problem Cause Data Collection
  • Taking Advantage of Samplings
  • Steps in Using Samplings
  • Taking Advantage of SurveysUsing Check Sheets
  • Problem Cause Data Collection Checklist
  • Understanding Problem Cause Data Analysis
  • The Purpose and Application of Histograms
  • Using and Interpreting Histograms
  • Using Relations Diagram
  • Case Study & Hands-on Activity
  • Fundamentals of Root Cause Identification
  • Using Cause-and-Effect Diagrams
  • Using the Five Whys Method
  • Using the Fault Tree Analysis Technique
  • An Overview of Root Cause Elimination
  • Using DeBono’s Six Hats
  • Overview of Solution Implementation
  • Organizing the Implementation
  • Developing an Implementation Plan
  • Using Tree Diagrams
  • Creating Change Acceptance
  • The Purpose and Application of Force-Field Analysis
  • What to Watch for When Using Tools and Techniques
  • Selecting the Right Tool  
  • Example Cases and Practice

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Jim Willey, P.E.

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Jim Willey, P.E., is currently the Engineering Manager for Chelan PUD in Washington State.

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Engineering LibreTexts

1.4: Problem Solving

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  • Page ID 70205

  • Daniel W. Baker and William Haynes
  • Colorado State University via Engineeringstatics

Key Questions

  • What are some strategies to practice selecting a tool from your problem-solving toolbox?
  • What is the basic problem-solving process for equilibrium?

Statics may be the first course you take where you are required to decide on your own how to approach a problem. Unlike your previous physics courses, you can't just memorize a formula and plug-and-chug to get an answer; there are often multiple ways to solve a problem, not all of them equally easy, so before you begin you need a plan or strategy. This seems to cause a lot of students difficulty.

The ways to think about forces, moments and equilibrium, and the mathematics used to manipulate them are like tools in your toolbox. Solving statics problems requires acquiring, choosing, and using these tools. Some problems can be solved with a single tool, while others require multiple tools. Sometimes one tool is a better choice, sometimes another. You need familiarity and practice to get skilled using your tools. As your skills and understanding improve, it gets easier to recognize the most efficient way to get a job done.

Struggling statics students often say things like:

“I don't know where to start the problem.” “It looks so easy when you do it.” “If I only knew which equation to apply, I could solve the problem.”

These statements indicate that the students think they know how to use their tools, but are skipping the planning step. They jump right to writing equations and solving for things without making much progress towards the answer, or they start solving the problem using a reasonable approach but abandon it in mid-stream to try something else. They get lost, confused and give up.

Choosing a strategy gets easier with experience. Unfortunately, the way you get that experience is to solve problems. It seems like a chicken and egg problem and it is, but there are ways around it. Here are some suggestions which will help you become a better problem-solver.

  • Get fluent with the math skills from algebra and trigonometry.
  • Do lots of problems, starting with simple ones to build your skills.
  • Study worked out solutions, however don't assume that just because you understand how someone else solved a problem that you can do it yourself without help.
  • Solve problems using multiple approaches. Confirm that alternate approaches produce the same results, and try to understand why one method was easier than the other.
  • Draw neat, clear, labeled diagrams.
  • Familiarize yourself with the application, assumptions, and terminology of the methods covered in class and the textbook.
  • When confused, identify what is confusing you and ask questions.

The majority of the topics in this book focus on equilibrium. The remaining topics are either preparing you for solving equilibrium problems or setting you up with skills that you will use in later classes. For equilibrium problems, the problem-solving steps are:

1. Read and understand the problem.

2. Identify what you are asked to find and what is given.

3. Stop, think, and decide on an strategy.

4. Draw a free-body diagram and define variables.

5. Apply the strategy to solve for unknowns and check solutions.

6. a. Write equations of equilibrium based on the free-body diagram.

b. Check if the number of equations equals the number of unknowns. If it doesn’t, you are missing something. You may need additional free-body diagrams or other relationships.

c. Solve for unknowns.

7. Conceptually check solutions.

Using these steps does not guarantee that you will get the right solution, but it will help you be critical and conscious of your chosen strategies. This reflection will help you learn more quickly and increase the odds that you choose the right tool for the job.

Engineering Passion

Tips for Solving Engineering Problems Effectively

engineer problem solving method

Problem solving is the process of determining the best feasible action to take in a given situation. Problem solving is an essential skill for engineers to have. Engineers are problem solvers, as the popular quote says:

“Engineers like to solve problems. If there are no problems handily available, they will create their own problems.” – Scott Adams

Engineers are faced with a range of problems in their everyday life. The nature of problems that engineers must solve differs between and among the various disciplines of engineering. Because of the diversity of problems there is no universal list of procedures that will fit every engineering problem. Engineers use various approaches while solving problems.

Engineering problems must be approached systematically, applying an algorithm, or step-by-step practice by which one arrives at a feasible solution. In this post, we’ve prepared a list of tips for solving engineering problems effectively.

#1 Identify the Problem

Identify the Problem

Evaluating the needs or identifying the problem is a key step in finding a solution for engineering problems. Recognize and describe the problem accurately by exploring it thoroughly. Define what question is to be answered and what outputs or results are to be produced. Also determine the available data and information about the problem in hand.

An improper definition of the problem will cause the engineer to waste time, lengthen the problem solving process and finally arrive at an incorrect solution. It is essential that the stated needs be real needs.

As an engineer, you should also be careful not to make the problem pointlessly bound. Placing too many limitations on the problem may make the solution extremely complex and tough or impossible to solve. To put it simply, eliminate the unnecessary details and only keep relevant details and the root problem.

#2 Collect Relevant Information and Data

Collect Relevant Information and Data

After defining the problem, an engineer begins to collect all the relevant information and data needed to solve the problem. The collected data could be physical measurements, maps, outcomes of laboratory experiments, patents, results of conducted surveys, or any number of other types of information. Verify the accuracy of the collected data and information.

As an engineer, you should always try to build on what has already been done before. Don’t reinvent the wheel. Information on related problems that have been solved or unsolved earlier, may help engineers find the optimal solution for a given problem.

#3 Search for Creative Solutions

Search for Creative Solutions

There are a number of methods to help a group or individual to produce original creative ideas. The development of these new ideas may come from creativity, a subconscious effort, or innovation, a conscious effort.

You can try to visualize the problem or make a conceptual model for the given problem. So think of visualizing the given problem and see if that can help you gain more knowledge about the problem.

#4 Develop a Mathematical Model

Develop a Mathematical Model

Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers, and guidance useful for the originating application.

To develop a mathematical model for the problem, determine what basic principles are applicable and then draw sketches or block diagrams to better understand the problem. Then define and introduce the necessary variables so that the problem is stated purely in mathematical terms.

Afterwards, simplify the problem so that you can obtain the required result. Also identify the and justify the assumptions and constraints in the mathematical model.

#5 Use Computational Method

Use Computational Method

You can use a computational method based on the mathematical method you’ve developed for the problem. Derive a set of equations that enable the calculation of the desired parameters and variables as described in your mathematical model. You can also develop an algorithm, or step-by-step procedure of evaluating the equations involved in the solution.

To do so, describe the algorithm in mathematical terms and then execute it as a computer program.

#6 Repeat the Problem Solving Process

Repeat the Problem Solving Process

Not every problem solving is immediately successful. Problems aren’t always solved appropriately the first time. You’ve to rethink and repeat the problem solving process or choose an alternative solution or approach to solving the problem.


Engineers often use the reverse-engineering method to solve problems. For example, by taking things apart to identify a problem, finding a solution and then putting the object back together again. Engineers are creative , they know how things work, and so they constantly analyze things and discover how they work.

Problem-solving skills help you to resolve obstacles in a situation. As stated earlier, problem solving is a skill that an engineer must have and fortunately it’s a skill that can be learned. This skill gives engineers a mechanism for identifying things, figuring out why they are broken and determining a course of action to fix them.

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Engineering Method

The engineering method (also known as engineering design) is a systematic approach used to reach the desired solution to a problem. There are six steps (or phases): idea, concept, planning, design, development, and launch from problem definition to desired result.

Engineering Method. Source: Ronald L. Lasser

The engineering method has six steps (or phases):

  • Development

The development step is often divided to include the iterative cycle of build, test, debug, and redesign. The engineering method by nature is an iterative process.

The idea phase usually begins with a problem. The problem statement is typically only vaguely defined and requires research into its viability and its feasibility. Viability suggests that there is significant value (or demand in the case of product development) in pursing the solution. Feasibility serves as a check on whether the idea can be realized. Feasibility may be high, medium, or low: where high feasibility means that people, technology, and time resources are readily available or known; medium is that resources may not be available directly, but can be found; and low means the resources may be rare or do not exist. The most critical part of the idea phase is to define the problem, validate its value, and identify the customer who desires its solution.

The concept phase is about generating numerous models (mathematical, physical, simulation, simple drawings or sketches), all of which should convey that the solution meets the customer’s expectations or requirements. The numerous concepts are generated using brainstorming techniques, which are review sessions in which elements of one concept are recombined with elements from other in an effort to find a single concept that fits best. Typical design judgment and compromise are required to merge concepts. The concept phase ends with a selection of a single concept.

3. Planning

The planning phase is about defining the implementation plan: identifying the people, tasks, task durations, task dependencies, task interconnections, and budget required to get the project done. Many tools are used to convey this information to team members and other stakeholders including Gantt and Pert charts, resource loading spreadsheets, sketches, drawings, proof-of-concept models to validate that the project can be successfully completed.

One critical tool of the planning phase is the system engineering diagram. This diagram shows the solution as an interconnection of smaller and less complicated sub-systems. A system engineering diagram establishes all the inputs and outputs for each module, as well as the way in which the module transforms the inputs into outputs.

The design phase is where “the rubber meets the road.” Details are specified; specifications are established. Some call this phase “design planning” and the development phase “detailed design.” But no matter what it is called, the purpose of this phase is to translate the customer requirements and systems engineering model into engineering specifications that an engineer (designer) can work with to design and build a working prototype. Specifications are detailed using a number with associated units, e.g., 4 volts, or 3.82 inches, or 58 Hz, or a completion time of 22 days.

5. Development

The purpose of development is to generate the engineering documentation: schematics, drawings, source code, and other design information into a working prototype that demonstrates the solution to the problem. The solution may be a tangible working prototype or an intangible working simulation. Of course, nothing works the first time, so this part of the process tends to be more iterative than the other phases. Specifically, it consists of the iterative cycle: design, test, debug, and redesign. If the project had earlier delays or is not on the planned schedule for other reasons, then this time may be the most frantic since the customer deadline may be closely looming.

While testing and debug are often consider a separate phase, most times they occur side-by-side with development as a design morphs from a concept to an artifact. The latter is recommended, reserving time at the end of development for a final test to confirm the desired result meets customer expectation and designer’s intent. Testing is the verification and validation phase where the concept meets both the anticipated design specifications and the customer’s requirements of the solution. Testing is achieved through experiments—an information-gathering method where dissimilarity and difference are assessed with respect to the design’s present and compared to desired state for the design. The purpose of an experiment is to determine whether test results agree or conflict with the a priori stated behavior. A sufficient numbers of successful testing verifications and validations are necessary to generate acceptable results and to reduce any risk that the desired behavior is present and functions as expected. If the test observations and results do not agree, then a debug process is necessary to identify the root causes and begin corrective action to resolve the discrepancies.

Launch includes the release of the engineering design and documentation package to manufacturing facilities for production. At this point, all qualification testing is complete, and the working prototype has demonstrated functionality.

Cited References

  • Ertas, A., & Jones, J. C. (1996). The Engineering design process (2nd ed.). New York: John Wiley & Sons. OCLC WorldCat Permalink: http://www.worldcat.org/oclc/807148675
  • Ullman, D. G. (2009). The Mechanical Design Process (4th ed.). New York, N.Y.: McGraw Hill. OCLC WorldCat Permalink: http://www.worldcat.org/oclc/244060468
  • Ulrich, K.T., & Eppinger, S. D. (2008). Product Design and Development (4th ed.) New York, N.Y.: McGraw Hill. OCLC WorldCat Permalink: http://www.worldcat.org/oclc/122424997
  • Articles > 1. Design Process > Engineering Method

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  • Electrical and Computer Engineering Design Handbook

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A Detailed Characterization of the Expert Problem-Solving Process in Science and Engineering: Guidance for Teaching and Assessment

  • Argenta M. Price
  • Candice J. Kim
  • Eric W. Burkholder
  • Amy V. Fritz
  • Carl E. Wieman

*Address correspondence to: Argenta M. Price ( E-mail Address: [email protected] ).

Department of Physics, Stanford University, Stanford, CA 94305

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Graduate School of Education, Stanford University, Stanford, CA 94305

School of Medicine, Stanford University, Stanford, CA 94305

Department of Electrical Engineering, Stanford University, Stanford, CA 94305

A primary goal of science and engineering (S&E) education is to produce good problem solvers, but how to best teach and measure the quality of problem solving remains unclear. The process is complex, multifaceted, and not fully characterized. Here, we present a detailed characterization of the S&E problem-solving process as a set of specific interlinked decisions. This framework of decisions is empirically grounded and describes the entire process. To develop this, we interviewed 52 successful scientists and engineers (“experts”) spanning different disciplines, including biology and medicine. They described how they solved a typical but important problem in their work, and we analyzed the interviews in terms of decisions made. Surprisingly, we found that across all experts and fields, the solution process was framed around making a set of just 29 specific decisions. We also found that the process of making those discipline-general decisions (selecting between alternative actions) relied heavily on domain-specific predictive models that embodied the relevant disciplinary knowledge. This set of decisions provides a guide for the detailed measurement and teaching of S&E problem solving. This decision framework also provides a more specific, complete, and empirically based description of the “practices” of science.


Many faculty members with new graduate students and many managers with employees who are recent college graduates have had similar experiences. Their advisees/employees have just completed a program of rigorous course work, often with distinction, but they seem unable to solve the real-world problems they encounter. The supervisor struggles to figure out exactly what the problem is and how they can guide the person in overcoming it. This paper is providing a way to answer those questions in the context of science and engineering (S&E). By characterizing the problem-solving process of experts, this paper investigates the “mastery” performance level and specifies an overarching learning goal for S&E students, which can be taught and measured to improve teaching.

The importance of problem solving as an educational outcome has long been recognized, but too often postsecondary S&E graduates have serious difficulties when confronted with real-world problems ( Quacquarelli Symonds, 2018 ). This reflects two long-standing educational problems with regard to problem solving: how to properly measure it, and how to effectively teach it. We theorize that the root of these difficulties is that good “problem solving” is a complex multifaceted process, and the details of that process have not been sufficiently characterized. Better characterization of the problem-solving process is necessary to allow problem solving, and more particularly, the complex set of skills and knowledge it entails, to be measured and taught more effectively. We sought to create an empirically grounded conceptual framework that would characterize the detailed structure of the full problem-solving process used by skilled practitioners when solving problems as part of their work. We also wanted a framework that would allow use and comparison across S&E disciplines. To create such a framework, we examined the operational decisions (choices among alternatives that result in subsequent actions) that these practitioners make when solving problems in their discipline.

Various aspects of problem solving have been studied across multiple domains, using a variety of methods (e.g., Newell and Simon, 1972 ; Dunbar, 2000 ; National Research Council [NRC], 2012b ; Lintern et al. , 2018 ). These ranged from expert self-reflections (e.g., Polya, 1945 ), to studies on knowledge lean tasks to discover general problem-solving heuristics (e.g., Egan and Greeno, 1974 ), to comparisons of expert and novice performances on simplified problems across a variety of disciplines (e.g., Chase and Simon, 1973 ; Chi et al. , 1981 ; Larkin and Reif, 1979 ; Ericsson et al. , 2006 , 2018 ). These studies revealed important novice–expert differences—notably, that experts are better at identifying important features and have knowledge structures that allow them to reduce demands on working memory. Studies that specifically gave the experts unfamiliar problems in their disciplines also found that, relative to novices, they had more deliberate and reflective strategies, including more extensive planning and managing of their own behavior, and they could use their knowledge base to better define the problem ( Schoenfeld, 1985 ; Wineburg, 1998 ; Singh, 2002 ). While these studies focused on discrete cognitive steps of the individual, an alternative framing of problem solving has been in terms of “ecological psychology” of “situativity,” looking at how the problem solver views and interacts with the environment in terms of affordances and constraints ( Greeno, 1994 ). “Naturalistic decision making” is a related framework that specifically examines how experts make decisions in complex, real-world, settings, with an emphasis on the importance of assessing the situation surrounding the problem at hand ( Klein, 2008 ; Mosier et al. , 2018 ).

While this work on expertise has provided important insights into the problem-solving process, its focus has been limited. Most has focused on looking for cognitive differences between experts and novices using limited and targeted tasks, such as remembering the pieces on a chessboard ( Chase and Simon, 1973 ) or identifying the important concepts represented in an introductory physics textbook problem ( Chi et al. , 1981 ). It did not attempt to explore the full process of solving, particularly for solving the type of complex problem that a scientist or engineer encounters as a member of the workforce (“authentic problems”).

There have also been many theoretical proposals as to expert problem-solving practices, but with little empirical evidence as to their completeness or accuracy (e.g., Polya, 1945 ; Heller and Reif, 1984 ; Organisation for Economic Cooperation and Development [OECD], 2019 ). The work of Dunbar (2000) is a notable exception to the lack of empirical work, as his group did examine how biologists solved problems in their work by analyzing lab meetings held by eight molecular biology research groups. His groundbreaking work focused on creativity and discovery in the research process, and he identified the importance of analogical reasoning and distributed reasoning by scientists in answering research questions and gaining new insights. Kozma et al. (2000) studied professional chemists solving problems, but their work focused only on the use of specialized representations.

The “cognitive systems engineering” approach ( Lintern et al. , 2018 ) takes a more empirically based approach looking at experts solving problems in their work, and as such tends to span aspects of both the purely cognitive and the ecological psychological theories. It uses both observations of experts in authentic work settings and retrospective interviews about how experts carried out particular work tasks. This theoretical framing and the experimental methods are similar to what we use, particularly in the “naturalistic decision making” area of research ( Mosier et al. , 2018 ). That work looks at how critical decisions are made in solving specific problems in their real-world setting. The decision process is studied primarily through retrospective interviews about challenging cases faced by experts. As described below, our methods are adapted from that work ( Crandall et al. , 2006 ), though there are some notable differences in focus and field. A particular difference is that we focused on identifying what are decisions to be made, which are more straight-forward to identify from retrospective interviews than how those decisions are made. We all have the same ultimate goal, however, to improve the training/teaching of the respective expertise.

Problem solving is central to the processes of science, engineering, and medicine, so research and educational standards about scientific thinking and the process and practices of science are also relevant to this discussion. Work by Osborne and colleagues describes six styles of scientific reasoning that can be used to explain how scientists and students approach different problems ( Kind and Osborne, 2016 ). There are also numerous educational standards and frameworks that, based on theory, lay out the skills or practices that science and engineering students are expected to master (e.g., American Association for the Advancement of Science [AAAS], 2011 ; Next Generation Science Standards Lead States, 2013 ; OECD, 2019 ; ABET, 2020 ). More specifically related to the training of problem solving, Priemer et al. (2020) synthesizes literature on problem solving and scientific reasoning to create a “STEM [science, technology, engineering, and mathematics] and computer science framework for problem solving” that lays out steps that could be involved in a students’ problem-solving efforts across STEM fields. These frameworks provide a rich groundwork, but they have several limitations: 1) They are based on theoretical ideas of the practice of science, not empirical evidence, so while each framework contains overlapping elements of the problem-solving process, it is unclear whether they capture the complete process. 2) They are focused on school science, rather than the actual problem solving that practitioners carry out and that students will need to carry out in future STEM careers. 3) They are typically underspecified, so that the steps or practices apply generally, but it is difficult to translate them into measurable learning goals for students to practice. Working to address that, Clemmons et al. (2020) recently sought to operationalize the core competencies from the Vision and Change report ( AAAS, 2011 ), establishing a set of skills that biology students should be able to master.

Our work seeks to augment this prior work by building a conceptual framework that is empirically based, grounded in how scientists and engineers solve problems in practice instead of in school. We base our framework on the decisions that need to be made during problem solving, which makes each item clearly defined for practice and assessment. In our analysis of expert problem solving, we empirically identified the entire problem-solving process. We found this includes deciding when and how to use the steps and skills defined in the work described previously but also includes additional elements. There are also questions in the literature about how generalizable across fields a particular set of practices may be. Here, we present the first empirical examination of the entire problem-solving process, and we compare that process across many different S&E disciplines.

A variety of instructional methods have been used to try and teach science and engineering problem solving, but there has been little evidence of their efficacy at improving problem solving (for a review, see NRC, 2012b ). Research explicitly on teaching problem solving has primarily focused on textbook-type exercises and utilized step-by-step strategies or heuristics. These studies have shown limited success, often getting students to follow specific procedural steps but with little gain in actually solving problems and showing some potential drawbacks ( Heller and Reif, 1984 ; Heller et al. , 1992 ; Huffman, 1997 ; Heckler, 2010 ; Kuo et al. , 2017 ). As discussed later, the framework presented here offers guidance for different and potentially more effective approaches to teaching problem solving.

These challenges can be illustrated by considering three different problems taken from courses in mechanical engineering, physics, and biology, respectively ( Figure 1 ). All of these problems are challenging, requiring considerable knowledge and effort by the student to solve correctly. Problems such as these are routinely used to both assess students’ problem-solving skills, and students are expected to learn such skills by practicing doing such problems. However, it is obvious to any expert in the respective fields, that, while these problems might be complicated and difficult to answer, they are vastly different from solving authentic problems in that field. They all have well-defined answers that can be reached by straightforward solution paths. More specifically, they do not involve needing to use judgment to make any decisions based on limited information (e.g., insufficient to specify a correct decision with certainty). The relevant concepts and information and assumptions are all stated or obvious. The failure of problems like these to capture the complexity of authentic problem solving underlies the failure of efforts to measure and teach problem solving. Recognizing this failure motivated our efforts to more completely characterize the problem-solving process of practicing scientists, engineers, and doctors.

FIGURE 1. Example problems from courses or textbooks in mechanical engineering, physics and biology. Problems from: Mechanical engineering: Wayne State mechanical engineering sample exam problems (Wayne State, n.d.), Physics: A standard physics problem in nearly every advanced quantum mechanics course, Biology: Molecular Biology of the Cell 6th edition, Chapter 7 end of chapter problems ( Alberts et al ., 2014 ).

We are building on the previous work studying expert–novice differences and problem solving but taking a different direction. We sought to create an empirically grounded framework that would characterize the detailed structure of the full problem-solving process by focusing on the operational decisions that skilled practitioners make when successfully solving authentic problems in their scientific, engineering, or medical work. We chose to identify the decisions that S&E practitioners made, because, unlike potentially nebulous skills or general problem-solving steps that might change with the discipline, decisions are sufficiently specified that they can be individually practiced by students and measured by instructors or departments. The authentic problems that we analyzed are typical problems practitioners encounter in “doing” the science or engineering entailed in their jobs. In the language of traditional problem-
solving and expertise research, such authentic problems are “ill-structured” ( Simon, 1973 ) and require “adaptive expertise” ( Hatano and Inagaki, 1986 ) to solve. However, our authentic problems are considerably more complex and unstructured than what is normally considered in those literatures, because not only do they lack a clear solution path, but in many cases, it is not clear a priori that they have any solution at all. Determining that, and whether the problem needs to be redefined to be soluble, is part of the successful expert solution process. Another way in which our set of decisions goes beyond the characterization of what is involved in adaptive expertise is the prominent role of making judgments with limited information.

A common reaction of scientists and engineers to seeing the list of decisions we obtain as our primary result is, “Oh, yes, these are things I always do in solving problems. There is nothing new here.” It is comforting that these decisions all look familiar; that supports their validity. However, what is new is not that experts are making such decisions, but rather that there is a relatively small but complete set of decisions that has now been explicitly identified and that applies so generally.

We have used a much larger and broader sample of experts in this work than used in prior expert–novice studies, and we used a more stringent selection criterion. Previous empirical work has typically involved just a few experts, almost always in a single domain, and included graduate students as “experts” in some cases. Our semistructured interview sample was 31 experienced practitioners from 10 different disciplines of science, engineering, and medicine, with demonstrated competence and accomplishments well beyond those of most graduate students. Also, approximately 25 additional experts from across science, engineering, and medicine served as consultants during the planning and execution of this work.

Our research question was: What are the decisions experts make in solving authentic problems, and to what extent is this set of decisions to be made consistent both within and across disciplines?

Our approach was designed to identify the level of consistency and unique differences across disciplines. Our hypothesis was that there would be a manageable number (20–50) of decisions to be made, with a large amount of overlap of decisions made between experts within each discipline and a substantial but smaller overlap across disciplines. We believed that if we had found that every expert and/or discipline used a large and completely unique set of decisions, it would have been an interesting research result but of little further use. If our hypothesis turned out to be correct, we expected that the set of decisions obtained would have useful applications in guiding teaching and assessment, as they would show how experts in the respective disciplines applied their content knowledge to solve problems and hence provide a model for what to teach. We were not expecting to find the nearly complete degree of overlap in the decisions made across all the experts.

We first conducted 22 relatively unstructured interviews with a range of S&E experts, in which we asked about problem-solving expertise in their fields. From these interviews, we developed an initial list of decisions to be made in S&E problem solving. To refine and validate the list, we then carried out a set of 31 semistructured interviews in which S&E experts chose a specific problem from their work and described the solution process in detail. The semistructured interviews were coded for the decisions represented, either explicitly stated or implied by a choice of action. This provided a framework of decisions that characterize the problem-solving process across S&E disciplines. The research was approved by the Stanford Institutional Review Board (IRB no. 48785), and informed consent was obtained from all the participants.

This work involved interviewing many experts across different fields. We defined experts as practicing scientists, engineers, or physicians with considerable experience working as faculty at highly rated universities or having several years of experience working in moderately high-level technical positions at successful companies. We also included a few longtime postdocs and research staff in biosciences to capture more details of experimental decisions from which faculty members in those fields often were more removed. This definition of expert allows us to identify the practices of skilled professionals; we are not studying what makes only the most exceptional experts unique.

Experts were volunteers recruited through direct contact via the research team's personal and professional networks and referrals from experts in our networks. This recruitment method likely biased our sample toward people who experienced relatively similar training (most were trained in STEM disciplines at U.S. universities within the last 15–50 years). Within this limitation, we attempted to get a large range of experts by field and experience. This included people from 10 different fields (including molecular biology/biochemistry, ecology, and medicine), 11 U.S. universities, and nine different companies or government labs, and the sample was 33% female (though our engineering sample only included one female). The medical experts were volunteers from a select group of medical school faculty chosen to serve as clinical reasoning mentors for medical students at a prestigious university. We only contacted people who met our criteria for being an “expert,” and everyone who volunteered was included in the study. Most of the people who were contacted volunteered, and the only reason given for not volunteering was insufficient time. Other than their disciplinary expertise, there was little to distinguish these experts beyond the fact they were acquaintances with members of the team or acquaintances of acquaintances of team or project advisory board members. The precise number from each field was determined largely by availability of suitable experts.

We defined an “authentic problem” to be one that these experts solve in their actual jobs. Generally, this meant research projects for the science and engineering faculty, design problems for the industry engineers, and patient diagnoses for the medical doctors. Such problems are characterized by complexity, with many factors involved and no obvious solution process, and involve substantial time, effort, and resources. Such problems involve far more complexity and many more decisions, particularly decisions with limited information, than the typical problems used in previous problem-solving research or used with students in instructional settings.

Creating an Initial List of Problem-Solving Decisions

We first interviewed 22 experts ( Table 1 ), most of whom were faculty at a prestigious university, in which we asked them to discuss expertise and problem solving in their fields as it related to their own experiences. This usually resulted in their discussing examples of one or more problems they had solved. Based on the first seven interviews, plus reflections on personal experience from the research team and review of the literature on expert problem solving and teaching of scientific practices ( Ericsson et al. , 2006 ; NRC, 2012a ; Wieman, 2015 ), we created a generic list of decisions that were made in S&E problem solving. In the rest of the unstructured interviews (15), we also provided the experts with our list and asked them to comment on any additions or deletions they would suggest. Faculty who had close supervision of graduate students and industry experts who had extensively supervised inexperienced staff were particularly informative. Their observations of the way inexperienced people could fail made them sensitive to the different elements of expertise and where incorrect decisions could be made. Although we initially expected to find substantial differences across disciplines, from early in the process, we noted a high degree of overlap across the interviews in the decisions that were described.

URM (under-represented minority) included 3 African American and 2 Hispanic/Latinx. One medical faculty member was interviewed twice – in both informal and structure interviews, for a total of 53 interviews with 52 experts.

Refinement and Validation of the List of Decisions

After creating the preliminary list of decisions from the informal interviews, we conducted a separate set of more structured interviews to test and refine the list. Semistructured interviews were conducted with 31 experts from across science, engineering, and medical fields ( Table 1 ). For these interviews, we recruited experts from a range of universities and companies, though the range of institutions is still limited, given the sample size. Interviews were conducted in person or over video chat and were transcribed for analysis. In the semistructured interviews, experts were asked to choose a problem or two from their work that they could recall the details of solving and then describe the process, including all the steps and decisions they made. So that we could get a full picture of the successful problem-solving process, we decided to focus the interviews on problems that they had eventually solved successfully, though their processes inherently involved paths that needed to be revised and reconsidered. Transcripts from interviewees who agreed to have their interview transcript published are available in the supplemental data set.

Our interview protocol (see Supplemental Text) was inspired in part by the critical decision method of cognitive task analysis ( Crandall et al. , 2006 ; Lintern et al. , 2018 ), which was created for research in cognitive systems engineering and naturalistic decision making. There are some notable differences between our work and theirs, both in research goal and method. First, their goal is to improve training in specific fields by focusing on how critical decisions are made in that field during an unusual or important event; the analysis seeks to identify factors involved in making those critical decisions. We are focusing on the overall problem solving and how it compares across many different fields, which quickly led to attention on what decisions are to be made, rather than how a limited set of those decisions are made. We asked experts to describe a specific, but not necessarily unusual, problem in their work, and focused our analysis on identifying all decisions made, not reasons for making them or identifying which were most critical. The specific order of problem-solving steps was also less important to us, in part because it was clear that there was no consistent order that was followed. Second, we are looking at different types of work. Cognitive systems engineering work has primarily focused on performance in professions like firefighters, power plant operators, military technicians, and nurses. These tend to require time-sensitive critical skills that are taught with modest amounts of formal training. We are studying scientists, engineers, and doctors solving problems that require much longer and less time-critical solutions and for which the formal training occupies many years.

Given our different focus, we made several adaptations to eliminate some of the more time-consuming steps from the interview protocol, allowing us to limit the interview time to approximately 1 hour. Both protocols seek to elicit an accurate and complete reporting of the steps taken and decisions made in the process of solving a problem. Our general strategy was: 1) Have the expert explain the problem and talk step by step through the decisions involved in solving it, with relatively few interruptions from the interviewer except to keep the discussion focused on the specific problem and occasionally to ask for clarifications. 2) Ask follow-up questions to probe for more detail about particular steps and aspects of the problem-solving process. 3) Occasionally ask for general thoughts on how a novice's process might differ.

While some have questioned the reliability of information from retrospective interviews ( Nisbett and Wilson, 1977 ), we believe we avoid these concerns, because we are only identifying a decision to be made, which in this case, means identifying a well-defined action that was chosen from alternatives. This is less subjective and much more likely to be accurately recalled than is the rationale behind such a decision. See Ericsson and Simon (1980) . However, the decisions identified may still be somewhat limited—the process of deciding among possible actions might involve additional decisions in the moment, when the solution is still unknown, that we are unable to capture in the retrospective context. For the decisions we can identify, we are able to check their accuracy and completeness by comparing them with the actions taken in the conduct of the research/design. For example, consider this quote from a physician who had to re-evaluate a diagnosis, “And, in my very subjective sense, he seemed like he was being forthcoming and honest. Granted people can fool you, but he seemed like he was being forthcoming. So we had to reevaluate.” The physician then considered alternative diagnoses that could explain a test result that at first had indicated an incorrect diagnosis. While this quote does describe the (retrospective) reasoning behind a decision, we do not need to know whether that reasoning is accurately recalled. We can simply code this as “decision 18, how believable is info?” The physician followed up by considering alternative diagnoses, which in this context was coded as “26, how good is solution?” and “8, potential solutions?” This was followed by the description of the literature and additional tests conducted. These indicated actions taken that confirm the physician made a decision about the reliability of the information given by the patient.

Interview Coding

We coded the semistructured interviews in terms of decisions made, through iterative rounds of coding ( Chi, 1997 ), following a “directed content analysis approach,” which involves coding according to predefined theoretical categories and updating the codes as needed based on the data ( Hsieh and Shannon, 2005 ). Our predefined categories were the list of decisions we had developed during the informal interviews. This approach means that we limited the focus of our qualitative analysis—we were able to test and refine the list of decisions, but we did not seek to identify all possible categories of approach to selecting and solving problems. The goals of each iterative round of coding are described in the next three paragraphs. To code for decisions in general, we matched decisions from the list to statements in each interview, based on the following criteria: 1) there was an explicit statement of a decision or choice made or needing to be made; 2) there was the description of the outcome of a decision, such as listing important features of the problem (that had been decided on) or conclusions arrived at; or 3) there was a statement of actions taken that indicated a decision about the appropriate action had been made, usually from a set of alternatives. Two examples illustrate the types of comments we identified as decisions: A molecular biologist explicitly stated the decisions required to decompose a problem into subproblems (decision 11), “Which cell do we use? The gene. Which gene do we edit? Which part of that gene do we edit? How do we build the enzyme that is going to do the cutting? … And how do we read out that it worked?” An ecologist made a statement that was also coded as a decomposition decision, because it described the action taken: “So I analyze the bird data first on its own, rather than trying to smash all the taxonomic groups together because they seem really apples and oranges. And just did two kinds of analysis, one was just sort of across all of these cases, around the world.” A single statement could be coded as multiple decisions if they were occurring simultaneously in the story being recalled or were intimately interconnected in the context of that interview, as with the ecology quote, in which the last sentence leads into deciding what data analysis is needed. Inherent in nearly every one of these decisions was that there was insufficient information to know the answer with certainty, so judgment was required.

Our primary goal for the first iterative round of coding was to check whether our list was complete by checking for any decisions that were missing, as indicated by either an action taken or a stated decision that was not clearly connected to a decision on our initial list. In this round, we also clarified wording and combined decisions that we were consistently unable to differentiate during the coding. A sample of three interviews (from biology, medicine, and electrical engineering) were first coded independently by four coders (AP, EB, CK, and AF), then discussed. The decision list was modified to add decisions and update wording based on that discussion. Then the interviews were recoded with the new list and rediscussed, leading to more refinements to the list. Two additional interviews (from physics and chemical engineering) were then coded by three coders (AP, EB, and CK) and further similar refinements were made. Throughout the subsequent rounds of coding, we continued to check for missing decisions, but after the additions and adjustments made based on these five interviews, we did not identify any more missing decisions.

In our next round of coding, we focused on condensing overlapping decisions and refining wording to improve the clarity of descriptions as they applied across different disciplinary contexts and to ensure consistent interpretation by different coders. Two or three coders independently coded an additional 11 interviews, iteratively meeting to discuss codes identified in the interviews, refining wording and condensing the list to improve agreement and combine overlapping codes, and then using the updated list to code subsequent interviews. We condensed the list by combining decisions that represented the same cognitive process taking place at different times, that were discipline-specific variations on the same decision, or that were substeps involved in making a larger decision. We noticed that some decisions were frequently co-coded with others, particularly in some disciplines. But if they were identified as distinct a reasonable fraction of the time in any discipline, we listed them as separate. This provided us with a list, condensed from 42 to 29 discrete decisions (plus five additional non-decision themes that were so prevalent that they are important to describe), that gave good consistency between coders.

Finally, we used the resulting codes to tabulate which decisions occurred in each interview, simplifying our coding process to focus on deciding whether or not each decision had occurred, with an example if it did occur to back up the “yes” code, but no longer attempting to capture every time each decision was mentioned. Individual coders identified decisions mentioned in the remaining 15 interviews. Interviews that had been coded with the early versions of the list were also recoded to ensure consistency. Coders flagged any decisions they were unsure about occurring in a particular interview, and two to four coders (AP, EB, CK, and CW) met to discuss those debated codes, with most uncertainties being resolved by explanations from a team member who had more technical expertise in the field of the interview. Minor wording changes were made during this process to ensure that each description of a decision captured all instantiations of the decision across disciplines, but no significant changes to the list were needed or made.

Coding an interview in terms of decisions made and actions taken in the research often required a high level of expertise in the discipline in question. The coder had to be familiar with the conduct of research in the field in order to recognize which actions corresponded to a decision between alternatives, but our team was assembled with this requirement in mind. It included high-level expertise across five different fields of science, engineering, and medicine and substantial familiarity with several other fields.

Supplemental Table S1 shows the final tabulation of decisions identified in each interview. In the tabulation, most decisions were marked as either “yes” or “no” for each interview, though 65 out of 1054 total were marked as “implied,” for one of the following reasons: 1) for 40/65, based on the coder's knowledge of the field, it was clear that a step must have been taken to achieve an outcome or action, even though that decision was not explicitly mentioned (e.g., interviewees describe collecting certain raw data and then coming to a specific conclusion, so they must have decided how to analyze the data, even if they did not mention the analysis explicitly); 2) for 15/65, the interview context was important, in that multiple statements from different parts of the interview taken together were sufficient to conclude that the decision must have happened, though no single statement described that decision explicitly; 3) 10/65 involved a decision that was explicitly discussed as an important step in problem solving, but they did not directly state how it was related to the problem at hand, or it was stated only in response to a direct prompt from the interviewer. The proportion of decisions identified in each interview, broken down by either explicit or explicit + implied, is presented in Supplemental Tables S1 and S2. Table 2 and Figure 2 of the main text show explicit + implied decision numbers.

a See supplementary text and Table S2 for full description and examples of each decision. A set of other non-decision knowledge and skill development themes were also frequently mentioned as important to professional success: Staying up to date in the field (84%), intuition and experience (77%), interpersonal and teamwork (100%), efficiency (32%), and attitude (68%).

b Percentage of interviews in which category or decision was mentioned.

c Numbering is for reference. In practice ordering is fluid – involves extensive iteration with other possible starting points.

d Chosen predictive framework(s) will inform all other decisions.

e Reflection occurs throughout process, and often leads to iteration. Reflection on solution occurs at the end as well.

FIGURE 2. Proportion of decisions coded in interviews by field. This tabulation includes decisions 1–29, not the additional themes. Error bars represent standard deviations. Number of interviews: total = 31; physical science = 9; biological science = 8; engineering = 8; medicine = 6. Compared with the sciences, slightly fewer decisions overall were identified in the coding of engineering and medicine interviews, largely for discipline-specific reasons. See Supplemental Table S2 and associated discussion.

Two of the interviews that had not been discussed during earlier rounds of coding (one physics [AP and EB], one medicine [AP and CK]) were independently coded by two coders to check interrater reliability using the final list of decisions. The goal of our final coding was to tabulate whether or not each expert described making each decision at any point in the problem-solving process, so the level of detail we chose for coding and interrater reliability was whether or not a decision was present in the entire interview. The decisions identified in each interview were compared for the two coders. For both interviews, the raters disagreed on whether or not only one of the 29 decisions occurred. Codes of “implied” were counted as agreement if the other coder selected either “yes” or “implied.” This equates to a percent agreement of 97% for each interview (28 agree/29 total decisions per interview = 97%). As a side note, there was also one disagreement per interview on the coding of the five other themes, but those themes were not a focus of this work nor the interviews.

We identified a total set of 29 decisions to be made (plus five other themes), all of which were identified in a large fraction of the interviews across all disciplines ( Table 2 and Figure 2 ). There was a surprising degree of overlap across the different fields with all the experts mentioning similar decisions to be made. All 29 were evident by the fifth semistructured interview, and on average, each interview revealed 85% of the 29 decisions. Many decisions occurred multiple times in an interview, with the number of times varying widely, depending on the length and complexity of the problem-solving process discussed.

We focused our analysis on what decisions needed to be made, not on the experts’ processes for making those decisions: noting that a choice happened, not how they selected and chose among different alternatives. This is because, while the decisions to be made were the same across disciplines, how the experts made those decisions varied greatly by discipline and individual. The process of making the decisions relied on specialized disciplinary knowledge and experience and may vary depending on demographics or other factors that our study design (both our sample and nature of retrospective interviews) did not allow us to investigate. However, while that knowledge was distinct and specialized, we could tell that it was consistently organized according to a common structure we call a “predictive framework,” as discussed in the “ Predictive Framework ” section below. Also, while every “decision” reflected a step in the problem solving involved in the work, and the expert being interviewed was involved in making or approving the decision, that does not mean the decision process was carried out only by that individual. In many cases, the experts described the decisions made in terms of ideas and results of their teams, and the importance of interpersonal skills and teamwork was an important non-decision theme raised in all interviews.

We were particularly concerned with the correctness and completeness of the set of decisions. Although the correctness was largely established by the statements in the interviews, we also showed the list of decisions to these experts at the end of the interviews as well as to about a dozen other experts. In all cases, they all agreed that these decisions were ones they and others in their field made when solving problems. The completeness of the list of decisions was confirmed by: 1) looking carefully at all specific actions taken in the described problem-solving process and checking that each action matched a corresponding decision from the list; and 2) the high degree of consistency in the set of decisions across all the interviews and disciplines. This implies that it is unlikely that there are important decisions that we are missing, because that would require any such missing decisions to be consistently unspoken by all 31 interviewees as well as consistently unrecognized by us from the actions that were taken in the problem-solving process.

In focusing on experts’ recollections of their successful solving of problems, our study design may have missed decisions that experts only made during failed problem-solving attempts. However, almost all interviews described solution paths that were not smooth and continuous, but rather involved going down numerous dead ends. There were approaches that were tried and failed, data that turned out to be ambiguous and worthless, and so on. Identifying the failed path involved reflection decisions (23–26). Often decision 9 (is problem solvable?) would be mentioned, because it described a path that was determined to be not solvable. For example, a biologist explained, “And then I ended up just switching to a different strain that did it [crawling off the plate] less. Because it was just … hard to really get them to behave themselves. I suppose if I really needed to rely on that very particular one, I probably would have exhausted the possibilities a bit more.” Thus, we expect unsuccessful problem solving would entail a smaller subset of decisions being made, particularly lack of reflection decisions, or poor choices on the decisions, rather than making a different set of decisions.

The set of decisions represent a remarkably consistent structure underlying S&E problem solving. For the purposes of presentation, we have categorized the decisions as shown in Figure 3 , roughly based on the purposes they achieve. However, the process is far less orderly and sequential than implied by this diagram, or in fact any characterization of an orderly “scientific method.” We were struck by how variable the sequence of decisions was in the descriptions provided. For example, experts who described how they began work on a problem sometimes discussed importance and goals (1–3, what is important in field?; opportunity fits solver’s expertise?; and goals, criteria, constraints?), but others mentioned a curious observation (20, any significant anomalies?), important features of their system that led them to questions (4, important features and info?, 6, how to narrow down problem?), or other starting points. We also saw that there were flexible connections between decisions and repeated iterations—jumping back to the same type of decision multiple times in the solution process, often prompted by reflection as new information and insights were developed. The sequence and number of iterations described varied dramatically by interview, and we cannot determine to what extent this was due to legitimate differences in the problem-solving process or to how the expert recalled and chose to describe the process. This lack of a consistent starting point, with jumping and iterating between decisions, has also been identified in the naturalistic decision-making literature ( Mosier et al. , 2018 ). Finally, the experts also often described considering multiple decisions simultaneously. In some interviews, a few decisions were always described together, while in others, they were clearly separate decisions. In summary, while the specific decisions themselves are fully grounded in expert practice, the categories and order shown here are artificial simplifications for presentation purposes.

FIGURE 3. Representation of problem-solving decisions by categories. The black arrows represent a hypothetical but unrealistic order of operations, the blue arrows represent more realistic iteration paths. The decisions are grouped into categories for presentation purposes; numbers indicate the number of decisions in each category. Knowledge and skill development were commonly mentioned themes but are not decisions.

The decisions contained in the seven categories are summarized here. See Supplemental Table S2 for specific examples of each decision across multiple disciplines.

Category A. Selection and Goals of the Problem

This category involves deciding on the importance of the problem, what criteria a solution must meet, and how well it matches the capabilities, resources, and priorities of the expert. As an example, an earth scientist described the goal of her project (decision 3, goals, criteria, constraints?) to map and date the earliest volcanic rocks associated with what is now Yellowstone and explained why the project was a good fit for her group (2, opportunity fits solver’s expertise?) and her decision to pursue the project in light of the significance of this type of eruption in major extinction events (1, what is important in field?). In many cases, decisions related to framing (see category B) were mentioned before decisions in this category or were an integral part of the process for developing goals.

1. What is important in the field?

What are important questions or problems? Where is the field heading? Are there advances in the field that open new possibilities?

2. Opportunity fits solver's expertise?

If and where are there gaps/opportunities to solve in field? Given experts’ unique perspectives and capabilities, are there opportunities particularly accessible to them? (This could involve challenging the status quo, questioning assumptions in the field.)

3. Goals, criteria, constraints?

a. What are the goals, design criteria, or requirements of the problem or its solution?

b. What is the scope of the problem?

c. What constraints are there on the solution?

d. What will be the criteria on which the solution is evaluated?

Category B. Frame Problem

These decisions lead to a more concrete formulation of the solution process and potential solutions. This involves identifying the key features of the problem and deciding on predictive frameworks to use (see “ Predictive Framework ” section below), as well as narrowing down the problem, often forming specific questions or hypotheses. Many of these decisions are guided by past problem solutions with which the expert is familiar and sees as relevant. The framing decisions of a physician can be seen in his discussion of a patient with liver failure who had previously been diagnosed with HIV but had features (4, important features and info?; 5, what predictive framework?) that made the physician question the HIV diagnosis (5, what predictive framework?; 26, how good is solution?). His team then searched for possible diagnoses that could explain liver failure and lead to a false-positive HIV test (7, related problems?; 8, potential solutions?), which led to their hypothesis the patient might have Q fever (6, how to narrow down problem?; 13, what info needed?; 15, specific plan for getting info?). While each individual decision is strongly supported by the data, the categories are groupings for presentation purposes. In particular, framing (category B) and planning (see category C) decisions often blended together in interviews.

a. Which available information is relevant to problem solving and why?

b. (When appropriate) Create/find a suitable abstract representation of core ideas and information Examples: physics, equation representing process involved; chemistry, bond diagrams/potential energy surfaces; biology, diagram of pathway steps.

5. What predictive framework?

Which potential predictive frameworks to use? (Decide among possible predictive frameworks or create framework.) This includes deciding on the appropriate level of mechanism and structure that the framework needs to embody to be most useful for the problem at hand.

6. How to narrow down the problem?

How to narrow down the problem? Often involves formulating specific questions and hypotheses.

7. Related problems?

What are related problems or work seen before, and what aspects of their problem-solving process and solutions might be useful in the present context? (This may involve reviewing literature and/or reflecting on experience.)

8. Potential solutions?

What are potential solutions? (This is based on experience and fitting some criteria for solution they have for a problem having general key features identified.)

9. Is problem solvable?

Is the problem plausibly solvable and is the solution worth pursuing given the difficulties, constraints, risks, and uncertainties?

Category C. Plan the Process for Solving

These decisions establish the specifics needed to solve the problem and include: how to simplify the problem and decompose it into pieces, what specific information is needed, how to obtain that information, and what are the resources needed and priorities? Planning by an ecologist can be seen in her extensive discussion of her process of simplifying (10, approximations/simplifications to make?) a meta-analysis project about changes in migration behavior, which included deciding what types of data she needed (13, what info needed?), planning how to conduct her literature search (15, specific plan for getting info?), difficulties in analyzing the data (12, most difficult/uncertain areas?; 16, which calculations and data analysis?), and deciding to analyze different taxonomic groups separately (11, how to decompose into subproblems?). In general, decomposition often resulted in multiple iterations through the problem-solving decisions, as subsets of decisions need to be made about each decomposed aspect of a problem. Framing (category B) and planning (category C) decisions occupied much of the interviews, indicating their importance.

10. Approximations and simplifications to make?

What approximations or simplifications are appropriate? How to simplify the problem to make it easier to solve? Test possible simplifications/approximations against established criteria.

11. How to decompose into subproblems?

How to decompose the problem into more tractable subproblems? (Subproblems are independently solvable pieces with their own subgoals.)

12. Most difficult or uncertain areas?

a. What are acceptable levels of uncertainty with which to proceed at various stages?

13. What info needed?

a. What will be sufficient to test and distinguish between potential solutions?

14. Priorities?

What to prioritize among many competing considerations? What to do first and how to obtain necessary resources?

Considerations could include: What's most important? Most difficult? Addressing uncertainties? Easiest? Constraints (time, materials, etc.)? Cost? Optimization and trade-offs? Availability of resources? (facilities/materials, funding sources, personnel)

15. Specific plan for getting information?

a. What are the general requirements of a problem-solving approach, and what general approach will they pursue? (These decisions are often made early in the problem-solving process as part of framing.)

b. How to obtain needed information? Then carry out those plans. (This could involve many discipline- and problem-specific investigation possibilities such as: designing and conducting experiments, making observations, talking to experts, consulting the literature, doing calculations, building models, or using simulations.)

c. What are achievable milestones, and what are metrics for evaluating progress?

d. What are possible alternative outcomes and paths that may arise during the problem-solving process, both consistent with predictive framework and not, and what would be paths to follow for the different outcomes?

Category D. Interpret Information and Choose Solution(s)

This category includes deciding how to analyze, organize, and draw conclusions from available information, reacting to unexpected information, and deciding upon a solution. A biologist studying aging in worms described how she analyzed results from her experiments, which included representing her results in survival curves and conducting statistical analyses (16, which calculations and data analysis?; 17, how to represent and organize info?), as well as setting up blind experiments (15, specific plan for getting info?) so that she could make unbiased interpretations (18, how believable is info?) of whether a worm was alive or dead. She also described comparing results with predictions to justify the conclusion that worm aging was related to fertility (19, how does info compare to predictions?; 21, appropriate conclusions?; 22, what is best solution?). Deciding how results compared with expectations based on a predictive framework was a key decision that often preceded several other decisions.

16. Which calculations and data analysis?

What calculations and data analysis are needed? Once determined, these must then be carried out.

17. How to represent and organize information?

What is the best way to represent and organize available information to provide clarity and insights? (Usually this will involve specialized and technical representations related to key features of predictive framework.)

18. How believable is the information?

Is information valid, reliable, and believable (includes recognizing potential biases)?

19. How does information compare to predictions?

As new information comes in, particularly from experiments or calculations, how does it compare with expected results (based on the predictive framework)?

20. Any significant anomalies?

a. Does potential anomaly fit within acceptable range of predictive framework(s) (given limitations of predictive framework and underlying assumptions and approximations)?

b. Is potential anomaly an unusual statistical variation or relevant data? Is it within acceptable levels of uncertainty?

21. Appropriate conclusions?

What are appropriate conclusions based on the data? (This involves making conclusions and deciding if they are justified.)

22. What is the best solution?

a. Which of multiple candidate solutions are consistent with all available information and which can be rejected? (This could be based on comparing data with predicted results.)

b. What refinements need to be made to candidate solutions?

Category E. Reflect

Reflection decisions occur throughout the process and include deciding whether assumptions are justified, whether additional knowledge or information is needed, how well the solution approach is working, and whether potential and then final solutions are adequate. These decisions match the categories of reflection identified by Salehi (2018) . A mechanical engineer described developing a model (to inform surgical decisions) of which muscles allow the thumb to function in the most useful manner (22, what is best solution?), including reflecting on how well engineering approximations applied in the biological context (23, assumptions and simplifications appropriate?). He also described reflecting on his approach, that is, why he chose to use cadaveric models instead of mathematical models (25, how well is solving approach working?), and the limitations of his findings in that the “best” muscle identified was difficult to access surgically (26, how good is solution?; 27, broader implications?). Reflection decisions are made throughout the problem-solving process, often lead to reconsidering other decisions, and are critical for success.

23. Assumptions and simplifications appropriate?

a. Do the assumptions and simplifications made previously still look appropriate considering new information?

b Does predictive framework need to be modified?

24. Additional knowledge needed?

a. Is solver's relevant knowledge sufficient?

b. Is more information needed and, if so, what?

c. Does some information need to be checked? (Is there a need to repeat experiment or check a different source?)

25. How well is the problem-solving approach working?

How well is the problem-solving approach working, and does it need to be modified? This includes possibly modifying the goals. (One needs to reflect on one's strategy by evaluating progress toward the solution.) and reflecting on one’s strategy by evaluating progress toward the solution.

26. How good is the solution?

a. Decide by exploring possible failure modes and limitations—“try to break” solution.

b. Does it “make sense” and pass discipline-specific tests for solutions of this type of problem?

c. Does it completely meet the goals/criteria?

Category F. Implications and Communication of Results

These are decisions about the broader implications of the work, and how to communicate results most effectively. For example, a theoretical physicist developing a method to calculate the magnetic moment of the muon decided on who would be interested in his work (28, audience for communication?) and what would be the best way to present it (29, best way to present work?). He also discussed the implications of preliminary work on a simplified aspect of the problem (10, approximations and simplifications to make?) in terms of evaluating its impact on the scientific community and deciding on next steps (27, broader implications?; 29, best way to present work?). Many interviewees described that making decisions in this category affected their decisions in other categories.

27. Broader implications?

What are the broader implications of the results, including over what range of contexts does the solution apply? What outstanding problems in the field might it solve? What novel predictions can it enable? How and why might this be seen as interesting to a broader community?

28. Audience for communication?

What is the audience for communication of work, and what are their important characteristics?

29. Best way to present work?

What is the best way to present the work to have it understood, and its correctness and importance appreciated? How to make a compelling story of the work?

Category G. Ongoing Skill and Knowledge Development

Although we focused on decisions in the problem-solving process, the experts volunteered general skills and knowledge they saw as important elements of problem-solving expertise in their fields. These included teamwork and interpersonal skills (strongly emphasized), acquiring experience and intuition, and keeping abreast of new developments in their fields.

30. Stay up to date in field

a. Reviewing literature, which does involve making decisions as to which is important.

b. Learning relevant new knowledge (ideas and technology from literature, conferences, colleagues, etc.)

31. Intuition and experience

Acquiring experience and associated intuition to improve problem solving.

32. Interpersonal, teamwork

Includes navigating collaborations, team management, patient interactions, communication skills, etc., particularly as how these apply in the context of the various types of problem-solving processes.

33. Efficiency

Time management including learning to complete certain common tasks efficiently and accurately.

34. Attitude

Motivation and attitude toward the task. Factors such as interest, perseverance, dealing with stress, and confidence in decisions.

Predictive Framework

How the decisions were made was highly dependent on the discipline and problem. However, there was one element that was fundamental and common across all interviews: the early adoption of a “predictive framework” that the experts used throughout the problem-solving process. We define this framework as “a mental model of key features of the problem and the relationships between the features.” All the predictive frameworks involved some degree of simplification and approximation and an underlying level of mechanism that established the relationships between key features. The frameworks provided a structure of knowledge and facilitated the application of that knowledge to the problem at hand, allowing experts to repeatedly run “mental simulations” to make predictions for dependencies and observables and to interpret new information.

As an example, an ecologist described her predictive framework for migration, which incorporated important features such as environmental conditions and genetic differences between species and the mechanisms by which these interacted to impact the migration patterns for a species. She used this framework to guide her meta-analysis of changes in migration patterns, affecting everything from her choice of data sets to include to her interpretation of why migration patterns changed for different species. In many interviews, the frameworks used evolved as additional information was obtained, with additional features being added or underlying assumptions modified. For some problems, the relevant framework was well established and used with confidence, while for other problems, there was considerable uncertainty as to a suitable framework, so developing and testing the framework was a substantial part of the solution process.

A predictive framework contains the expert knowledge organization that has been observed in previous studies of expertise ( Egan and Greeno, 1974 ) but goes further, as here it serves as an explicit tool that guides most decisions and actions during the solving of complex problems. Mental models and mental simulations that are described in the naturalistic decision-making literature are similar, in that they are used to understand the problem and guide decisions ( Klein, 2008 ; Mosier et al. , 2018 ), but they do not necessarily contain the same level of mechanistic understanding of relationships that underlies the predictive frameworks used in science and engineering problem solving. While the use of predictive frameworks was universal, the individual frameworks themselves explicitly reflected the relevant specialized knowledge, structure, and standards of the discipline, and arguably largely define a discipline ( Wieman, 2019 ).

Discipline-Specific Variation

While the set of decisions to be made was highly consistent across disciplines, there were extensive differences within and across disciplines and work contexts, which reflected the differences in perspectives and experiences. These differences were usually evident in how experts made each of the specific decisions, but not in the choice of which decisions needed to be made. In other words, the solution methods, which included following standard accepted procedures in each field, were very different. For example, planning in some experimental sciences may involve formulating a multiyear construction and data-collection effort, while in medicine it may be deciding on a simple blood test. Some decisions, notably in categories A, D, and F, were less likely to be mentioned in particular disciplines, because of the nature of the problems. Specifically, decisions 1 (what is important in field?), 2 (opportunity fits solver’s expertise?), 27 (broader implications?), 28 (audience for communication?), and 29 (best way to present work?) were dependent on the scope of the problem being described and the expert's specific role in it. These were mentioned less frequently in interviews where the problem was assigned to the expert (most often engineering or industry) or where the importance or audience was implicit (most often in medicine). Decisions 16 (which calculations and data analysis?) and 17 (how to represent and organize info?) were particularly unlikely to be mentioned in medicine, because test results are typically provided to doctors not in the form or raw data, but rather already analyzed by a lab or other medical technology professional, so the doctors we interviewed did not need to make decisions themselves about how to analyze or represent the data. Qualitatively, we also noticed some differences between disciplines in the patterns of connections between decisions. When the problem involved development of a tool or product, most commonly the case in engineering, the interview indicated relatively rapid cycles between goals (3), framing problem/potential solutions (8), and reflection on the potential solution (26), before going through the other decisions. Biology, the experimental science most represented in our interviews, had strong links between planning (15), deciding on appropriate conclusions (21), and reflection on the solution (26). This is likely because the respective problems involved complex systems with many unknowns, so careful planning was unusually important for achieving definitive conclusions. See Supplemental Text and Supplemental Table S2 for additional notes on decisions that were mentioned at lower frequency and decisions that were likely to be interconnected, regardless of field.

This work has created a framework of decisions to characterize problem solving in science and engineering. This framework is empirically based and captures the successful problem-solving process of all experts interviewed. We see that several dozen experts across many different fields all make a common set of decisions when solving authentic problems. There are flexible linkages between decisions that are guided by reflection in a continually evolving process. We have also identified the nature of the “predictive frameworks” that S&E experts consistently use in problem solving. These predictive frameworks reveal how these experts organize their disciplinary knowledge to facilitate making decisions. Many of the decisions we identified are reflected in previous work on expertise and scientific problem solving. This is particularly true for those listed in the planning and interpreting information categories ( Egan and Greeno, 1974 ). The priority experts give to framing and planning decisions over execution compared with novices has been noted repeatedly (e.g., Chi et al. , 1988 ). Expert reflection has been discussed, but less extensively ( Chase and Simon, 1973 ), and elements of the selection and implications and communication categories have been included in policy and standards reports (e.g., AAAS, 2011 ). Thus, our framework of decisions is consistent with previous work on scientific practices and expertise, but it is more complete, specific, empirically based, and generalizable across S&E disciplines.

A limitation of this study is the small number of experts we have in total, from each discipline, and from underrepresented groups (especially lack of female representation in engineering). The lack of randomized selection of participants may also bias the sample toward experts who experienced similar academic training (STEM disciplines at U.S. universities). This means we cannot prove that there are not some experts who follow other paths in problem solving. As with any scientific model, the framework described here should be subjected to further tests and modifications as necessary. However, to our knowledge, this is a far larger sample than used in any previous study of expert problem solving. Although we see a large amount of variation both within and across disciplines in the problem-solving process, this is reflected in how experts make decisions, not in what decisions they make. The very high degree of consistency in the decisions made across the entire sample strongly suggests that we are capturing elements that are common to all experts across science and engineering. A second limitation is that decisions often overlap and co-occur in an interview, so the division between decision items is often somewhat ambiguous and could be defined somewhat differently. As noted, a number of these decisions can be interconnected, and in some fields are nearly always interconnected.

The set of decisions we have observed provides a general framework for characterizing, analyzing, and teaching S&E problem solving. These decisions likely define much of the set of cognitive skills a student needs to practice and master to perform as a skilled practitioner in S&E. This framework of decisions provides a detailed and structured way to approach the teaching and measurement of problem solving at the undergraduate, graduate, and professional training levels. For teaching, we propose using the process of “deliberate practice” ( Ericsson, 2018 ) to help students learn problem solving. Deliberate practice of problem solving would involve effective scaffolding and concentrated practice, with feedback, at making the specific decisions identified here in relevant contexts. In a course, this would likely involve only an appropriately selected set of the decisions, but a good research mentor would ensure that trainees have opportunities to practice and receive feedback on their performance on each of these 29 decisions. Future work is needed to determine whether there are additional decisions that were not identified in experts but are productive components of student problem solving and should also be practiced. Measurements of individual problem-solving expertise based on our decision list and the associated discipline-specific predictive frameworks will allow a detailed measure of an individual's discipline-specific problem-solving strengths and weaknesses relative to an established expert. This can be used to provide targeted feedback to the learner, and when aggregated across students in a program, feedback on the educational quality of the program. We are currently working on the implementation of these ideas in a variety of instructional settings and will report on that work in future publications.

As discussed in the Introduction , typical science and engineering problems fail to engage students in the complete problem-solving process. By considering which of the 29 decisions are required to answer the problem, we can more clearly articulate why. The biology problem, for example, requires students to decide on a predictive framework and access the necessary content knowledge, and they need to decide which information they need to answer the problem. However, other decisions are not required or are already made for them, such as deciding on important features and identifying anomalies. We propose that different problems, designed specifically to require students to make sets of the problem-solving decisions from our framework, will provide more effective tools for measuring, practicing, and ultimately mastering the full S&E problem-solving process.

Our preliminary work with the use of such decision-based problems for assessing problem-solving expertise is showing great promise. For several different disciplines, we have given test subjects a relevant context, requiring content knowledge covered in courses they have taken, and asked them to make decisions from the list presented here. Skilled practitioners in the relevant discipline respond in very consistent ways, while students respond very differently and show large differences that typically correlate with their different educational experiences. What apparently matters is not what content they have seen, but rather what decisions they have had practice making. Our approach was to identify the decisions made by experts, this being the task that educators want students to master. Our data do not exclude the possibility that students engage in and/or should learn other decisions as a productive part of the problem-solving process while they are learning. Future work would seek to identify decisions made at intermediate levels during the development of expertise, to identify potential learning progressions that could be used to teach problem solving more efficiently. What we have seen is consistent with previous work identifying expert–novice differences but provides a much more extensive and detailed picture of a student's strengths and weaknesses and the impacts of particular educational experiences. We have also carried out preliminary development of courses that explicitly involve students making and justifying many of these decisions in relevant contexts, followed by feedback on their decisions. Preliminary results from these courses are also encouraging. Future work will involve the more extensive development and application of decision-based measurement and teaching of problem solving.


We acknowledge the many experts who agreed to be interviewed for this work, M. Flynn for contributions on expertise in mechanical engineering, and Shima Salehi for useful discussions. This work was funded by the Howard Hughes Medical Institute through an HHMI Professor grant to C.E.W.

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engineer problem solving method

Submitted: 2 December 2020 Revised: 11 June 2021 Accepted: 23 June 2021

© 2021 A. M. Price et al. CBE—Life Sciences Education © 2021 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

What Is Problem Solving? How Software Engineers Approach Complex Challenges

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From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving . 

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process. 

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system. 

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity. 

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.


Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.


In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

  • Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
  • Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
  • Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

  • Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
  • Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
  • Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
  • Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software. 

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends. 

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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Aurora beacon-news | problem-solving, critical thinking on display at robotics event at aurora municipal airport.

Students from 9 to 16 years old participated in the Elite Robotics Camp in Aurora which included a competition Friday at the Aurora Municipal Airport in Sugar Grove. (David Sharos / For The Beacon-News)

Robots and the kids that built and operated them took center stage all day Friday at the Aurora Municipal Airport in Sugar Grove as 17 students 9 to 16 years old squared off in a competition during the first-ever Elite Robotics Camp, hosted by the U.S. Engineering League and the Wong Center for Education.

The Friday showcase was the culmination of a week-long camp program that included four days of workshops held at the Hampton Inn in Aurora.

A press release issued by the robotics camp said the 17 students involved spent time with a variety of national champions from multiple countries under Anthony Hsu of OFDL Robotics Lab Taiwan, “one of the world’s most accomplished coaches.”

Susan Mackafey, publicist for the Robotics group, said the event in Aurora came about as a result of the competitions that the Wong group hosts worldwide. William Wong, the founder of the Wong Center for Education, is the national organizer for the World Robot Olympiad, according to a press release.

“There were some students from Ukraine and Kazakhstan wondering if there would be any other kind of competitions as they wanted to hone their skills with one of the experts,” she said. “Will Wong ran with it, and has arranged the camp and the competition going on this Friday.”

Two of the camp members from Ukraine – Margo Proutorbva and Sofia Sova – were sponsored by the Wong Center for Education.

“It’s been an emotional trip for them,” Mackafey said, given the war going on in their homeland. “A lot of the kids are looking to train and do this as their careers and they love to compete. There are various levels of this competition that take place on a global scale.”

Local students were on hand as well, some of whom are being sponsored by the Wong Foundation, sources said.

Wong, of Naperville, was supervising Friday at the airport facility and said he started a robotics program with kids back in 2008.

“STEM has become a lot of the focus,” Wong said. “Even before I started, STEM was a big word. Engineering coding has always been there. It’s just how can we have kids do more of it. What’s happened is there are education companies like LEGO and other companies that have built robots that allow us to teach kids robotics in an easy fashion and we can create real world challenges off those robots so they literally are engineering, building and creating, designing and working with teams to have robots do tasks.”

Other than the collaborative learning, Wong said the biggest takeaways of the program “are problem-solving, figuring out how to make things work, a lot of trial-and-error, analysis and critical thinking.”

“There is teamwork, but the biggest is perseverance and working through the problems,” he said. “If the robot doesn’t work the first time or the second time or the 100th time, they are truly going through the engineering process – building, design and the whole cycle.”

Sofia Sova, left, and Margo Protorbva came from Ukraine to participate in a robotics camp in Aurora that culminated with a competition Friday at the Aurora Municipal Airport in Sugar Grove. (David Sharos / For The Beacon-News)

Margo Proutorbva, 14, spoke about robotics and said through an interpreter she got interested in them two years ago.

“I’ve learned to assemble them,” she said. “The most difficult part of this has been when you assemble a robot with someone else – it’s way easier when you do it on your own. My robot can grab different objects, follow lines and turn in different ways.”

David Sharos is a freelance reporter for The Beacon-News.

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