problem solving process engagement

The Social Work “Helping Process”- Engagement, Assessment, Planning, Intervention, Evaluation, and Termination ASWB

Meagan Mitchell
March 19, 2022

The Social Work Helping Process on the ASWB Exam

This process will be extremely helpful for you to know and understand for the LCSW and LMSW exams, especially for questions that end in FIRST and NEXT.

What is The Helping Process?

Intervention
Termination

This six-step process is very helpful to have a mastery of when you are taking your exam.

1) Engagement : You cannot build a therapeutic relationship without engaging the client. Also includes intake, confidentiality, consent, and explaining the risks of treatment. Additionally, you’ll be setting boundaries during this session.

2) Assessment : Assessment is when you are determining the client’s presenting problem. This step is used to collect information that will be helpful in treatment. This may include a biopsychosocial assessment. We need to determine strengths and weaknesses so we can identify areas that will be worked on in treatment.

Example: In this phase, you may determine anxiety is the main problem you will work on treatment.

3) Planning : The planning stage refers to planning for treatment. This includes setting goals and objectives. Treatment modality may also be determined in this stage. In this stage, you are making an action plan for the next steps in treatment.

Example: You have determined that you will address the client’s anxiety through 12 weeks of CBT. The goal will be to utilize coping strategies to decrease negative thinking in 3 out of 5 situations.

4) Intervention : This is where the “bulk” of the clinical work will be done. This is when you are actively working with the client. In this stage, you may be utilizing a variety of techniques to work towards established goals. You may also give clients activities to work on outside of the session. Depending on the type of work you are doing, this phase may be long or short-term in nature.

Example: During the intervention phase you will be utilizing CBT. This may include working on distorted thoughts, practicing muscle relaxation, and mindfulness.

Learn more about different intervention methods in this post: Mastering Clinical Social Work Interventions For The ASWB Exam

5) Evaluation : This is where you are looking at the client’s progress. Are they making progress towards established goals? Are there areas that need to be changed? Is the treatment modality working? This is an ongoing process that should be used to determine if changes need to be made in the treatment process.

Example: Is my client making progress towards a decrease in negative thinking? Are the CBT techniques and strategies helping the client cope with anxiety?

6) Termination : Termination is signaled by the close or ending of the therapeutic relationship. Ideally, termination occurs once the client and therapist agree that the treatment goals have been met and services are no longer needed. However, a client can terminate at any time. Termination should be discussed early and clients should have time to prepare for the end of treatment.

Learn more about the Termination stage in this post: Termination Of Clients On The ASWB Exam

** For some people termination is signaled by joy and pride. For others, it is more challenging and may be signaled by fear and anxiety. Support your client’s through the termination phase.

Want to learn more about the different stages of the helping process? Watch the video below!

Learn more about my course here: https://agentsofchangeprep.com/

A little bit about me: I am a Licensed Clinical Social Worker and I have been providing individualized and group test prep for the ASWB for over three years. From all of this experience helping others pass their exams, I have created a course to help you prepare for and pass the ASWB exam!

Find more from Agents of Change here:

► Facebook Group: https://www.facebook.com/groups/aswbtestprep

► Podcast: https://anchor.fm/agents-of-change-sw

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Disclaimer: This video content has been made available for informational and educational purposes only. This content is not intended to be a substitute for professional medical or clinical advice, diagnosis, or treatment.

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40 problem-solving techniques and processes

All teams and organizations encounter challenges. Approaching those challenges without a structured problem solving process can end up making things worse.

Proven problem solving techniques such as those outlined below can guide your group through a process of identifying problems and challenges , ideating on possible solutions , and then evaluating and implementing the most suitable .

In this post, you'll find problem-solving tools you can use to develop effective solutions. You'll also find some tips for facilitating the problem solving process and solving complex problems.

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What is problem solving?

Problem solving is a process of finding and implementing a solution to a challenge or obstacle. In most contexts, this means going through a problem solving process that begins with identifying the issue, exploring its root causes, ideating and refining possible solutions before implementing and measuring the impact of that solution.

For simple or small problems, it can be tempting to skip straight to implementing what you believe is the right solution. The danger with this approach is that without exploring the true causes of the issue, it might just occur again or your chosen solution may cause other issues.

Particularly in the world of work, good problem solving means using data to back up each step of the process, bringing in new perspectives and effectively measuring the impact of your solution.

Effective problem solving can help ensure that your team or organization is well positioned to overcome challenges, be resilient to change and create innovation. In my experience, problem solving is a combination of skillset, mindset and process, and it’s especially vital for leaders to cultivate this skill.

A group of people looking at a poster with notes on it

What is the seven step problem solving process?

A problem solving process is a step-by-step framework from going from discovering a problem all the way through to implementing a solution.

With practice, this framework can become intuitive, and innovative companies tend to have a consistent and ongoing ability to discover and tackle challenges when they come up.

You might see everything from a four step problem solving process through to seven steps. While all these processes cover roughly the same ground, I’ve found a seven step problem solving process is helpful for making all key steps legible.

We’ll outline that process here and then follow with techniques you can use to explore and work on that step of the problem solving process with a group.

The seven-step problem solving process is:

1. Problem identification

The first stage of any problem solving process is to identify the problem(s) you need to solve. This often looks like using group discussions and activities to help a group surface and effectively articulate the challenges they’re facing and wish to resolve.

Be sure to align with your team on the exact definition and nature of the problem you’re solving. An effective process is one where everyone is pulling in the same direction – ensure clarity and alignment now to help avoid misunderstandings later.

2. Problem analysis and refinement

The process of problem analysis means ensuring that the problem you are seeking to solve is the right problem . Choosing the right problem to solve means you are on the right path to creating the right solution.

At this stage, you may look deeper at the problem you identified to try and discover the root cause at the level of people or process. You may also spend some time sourcing data, consulting relevant parties and creating and refining a problem statement.

Problem refinement means adjusting scope or focus of the problem you will be aiming to solve based on what comes up during your analysis. As you analyze data sources, you might discover that the root cause means you need to adjust your problem statement. Alternatively, you might find that your original problem statement is too big to be meaningful approached within your current project.

Remember that the goal of any problem refinement is to help set the stage for effective solution development and deployment. Set the right focus and get buy-in from your team here and you’ll be well positioned to move forward with confidence.

3. Solution generation

Once your group has nailed down the particulars of the problem you wish to solve, you want to encourage a free flow of ideas connecting to solving that problem. This can take the form of problem solving games that encourage creative thinking or techniquess designed to produce working prototypes of possible solutions.

The key to ensuring the success of this stage of the problem solving process is to encourage quick, creative thinking and create an open space where all ideas are considered. The best solutions can often come from unlikely places and by using problem solving techniques that celebrate invention, you might come up with solution gold.

4. Solution development

No solution is perfect right out of the gate. It’s important to discuss and develop the solutions your group has come up with over the course of following the previous problem solving steps in order to arrive at the best possible solution. Problem solving games used in this stage involve lots of critical thinking, measuring potential effort and impact, and looking at possible solutions analytically.

During this stage, you will often ask your team to iterate and improve upon your front-running solutions and develop them further. Remember that problem solving strategies always benefit from a multitude of voices and opinions, and not to let ego get involved when it comes to choosing which solutions to develop and take further.

Finding the best solution is the goal of all problem solving workshops and here is the place to ensure that your solution is well thought out, sufficiently robust and fit for purpose.

5. Decision making and planning

Nearly there! Once you’ve got a set of possible, you’ll need to make a decision on which to implement. This can be a consensus-based group decision or it might be for a leader or major stakeholder to decide. You’ll find a set of effective decision making methods below.

Once your group has reached consensus and selected a solution, there are some additional actions that also need to be decided upon. You’ll want to work on allocating ownership of the project, figure out who will do what, how the success of the solution will be measured and decide the next course of action.

Set clear accountabilities, actions, timeframes, and follow-ups for your chosen solution. Make these decisions and set clear next-steps in the problem solving workshop so that everyone is aligned and you can move forward effectively as a group.

Ensuring that you plan for the roll-out of a solution is one of the most important problem solving steps. Without adequate planning or oversight, it can prove impossible to measure success or iterate further if the problem was not solved.

6. Solution implementation

This is what we were waiting for! All problem solving processes have the end goal of implementing an effective and impactful solution that your group has confidence in.

Project management and communication skills are key here – your solution may need to adjust when out in the wild or you might discover new challenges along the way. For some solutions, you might also implement a test with a small group and monitor results before rolling it out to an entire company.

You should have a clear owner for your solution who will oversee the plans you made together and help ensure they’re put into place. This person will often coordinate the implementation team and set-up processes to measure the efficacy of your solution too.

7. Solution evaluation

So you and your team developed a great solution to a problem and have a gut feeling it’s been solved. Work done, right? Wrong. All problem solving strategies benefit from evaluation, consideration, and feedback.

You might find that the solution does not work for everyone, might create new problems, or is potentially so successful that you will want to roll it out to larger teams or as part of other initiatives.

None of that is possible without taking the time to evaluate the success of the solution you developed in your problem solving model and adjust if necessary.

Remember that the problem solving process is often iterative and it can be common to not solve complex issues on the first try. Even when this is the case, you and your team will have generated learning that will be important for future problem solving workshops or in other parts of the organization.

It’s also worth underlining how important record keeping is throughout the problem solving process. If a solution didn’t work, you need to have the data and records to see why that was the case. If you go back to the drawing board, notes from the previous workshop can help save time.

What does an effective problem solving process look like?

Every effective problem solving process begins with an agenda . In our experience, a well-structured problem solving workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

The format of a workshop ensures that you can get buy-in from your group, encourage free-thinking and solution exploration before making a decision on what to implement following the session.

This Design Sprint 2.0 template is an effective problem solving process from top agency AJ&Smart. It’s a great format for the entire problem solving process, with four-days of workshops designed to surface issues, explore solutions and even test a solution.

Check it for an example of how you might structure and run a problem solving process and feel free to copy and adjust it your needs!

For a shorter process you can run in a single afternoon, this remote problem solving agenda will guide you effectively in just a couple of hours.

Whatever the length of your workshop, by using SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Complete problem-solving methods

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

The Six Thinking Hats #creative thinking #meeting facilitation #problem solving #issue resolution #idea generation #conflict resolution The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.

Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on.

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages.

Lightning Decision Jam (LDJ) #action #decision making #problem solving #issue analysis #innovation #design #remote-friendly It doesn’t matter where you work and what your job role is, if you work with other people together as a team, you will always encounter the same challenges: Unclear goals and miscommunication that cause busy work and overtime Unstructured meetings that leave attendants tired, confused and without clear outcomes. Frustration builds up because internal challenges to productivity are not addressed Sudden changes in priorities lead to a loss of focus and momentum Muddled compromise takes the place of clear decision- making, leaving everybody to come up with their own interpretation. In short, a lack of structure leads to a waste of time and effort, projects that drag on for too long and frustrated, burnt out teams. AJ&Smart has worked with some of the most innovative, productive companies in the world. What sets their teams apart from others is not better tools, bigger talent or more beautiful offices. The secret sauce to becoming a more productive, more creative and happier team is simple: Replace all open discussion or brainstorming with a structured process that leads to more ideas, clearer decisions and better outcomes. When a good process provides guardrails and a clear path to follow, it becomes easier to come up with ideas, make decisions and solve problems. This is why AJ&Smart created Lightning Decision Jam (LDJ). It’s a simple and short, but powerful group exercise that can be run either in-person, in the same room, or remotely with distributed teams.

Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design.

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition #problem solving #idea generation #creativity #online #remote-friendly A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

The 5 Whys

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges.

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results.

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys #hyperisland #innovation This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold.

World Cafe #hyperisland #innovation #issue analysis World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD) #idea generation #liberating structures #action #issue analysis #remote-friendly DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology #action plan #idea generation #problem solving #issue analysis #large group #online #remote-friendly Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around.

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish.

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis #problem solving ##root cause analysis #decision making #online facilitation A process to help identify and understand the origins of problems, issues or observations.

Problem Tree

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them.

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree #define intentions #create #design #issue analysis A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward.

SWOT analysis #gamestorming #problem solving #action #meeting facilitation The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results.

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause.

Agreement-Certainty Matrix #issue analysis #liberating structures #problem solving You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic . A problem is simple when it can be solved reliably with practices that are easy to duplicate. It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably. A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail. Chaotic is when the context is too turbulent to identify a path forward. A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.” The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process.

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID #gamestorming #project planning #issue analysis #problem solving When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!

Speed Boat #gamestorming #problem solving #action Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How #idea generation #issue analysis #problem solving #online #creative thinking #remote-friendly A questioning method for generating, explaining, investigating ideas.

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed.

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It! #gamestorming #problem solving #action Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills.

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO!

LEGO Challenge #hyperisland #team A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems.

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken.

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What? #issue analysis #innovation #liberating structures You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

Journalists

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists #vision #big picture #issue analysis #remote-friendly This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for brainstorming solutions

Now you have the context and background of the problem you are trying to solving, now comes the time to start ideating and thinking about how you’ll solve the issue.

Here, you’ll want to encourage creative, free thinking and speed. Get as many ideas out as possible and explore different perspectives so you have the raw material for the next step.

Looking at a problem from a new angle can be one of the most effective ways of creating an effective solution. TRIZ is a problem-solving tool that asks the group to consider what they must not do in order to solve a challenge.

By reversing the discussion, new topics and taboo subjects often emerge, allowing the group to think more deeply and create ideas that confront the status quo in a safe and meaningful way. If you’re working on a problem that you’ve tried to solve before, TRIZ is a great problem-solving method to help your team get unblocked.

Making Space with TRIZ #issue analysis #liberating structures #issue resolution You can clear space for innovation by helping a group let go of what it knows (but rarely admits) limits its success and by inviting creative destruction. TRIZ makes it possible to challenge sacred cows safely and encourages heretical thinking. The question “What must we stop doing to make progress on our deepest purpose?” induces seriously fun yet very courageous conversations. Since laughter often erupts, issues that are otherwise taboo get a chance to be aired and confronted. With creative destruction come opportunities for renewal as local action and innovation rush in to fill the vacuum. Whoosh!

Mindspin

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly.

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation.

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex.

MindSpin #teampedia #idea generation #problem solving #action A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed.

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable.

The Creativity Dice #creativity #problem solving #thiagi #issue analysis Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

Idea and Concept Development

Brainstorming without structure can quickly become chaotic or frustrating. In a problem-solving context, having an ideation framework to follow can help ensure your team is both creative and disciplined.

In this method, you’ll find an idea generation process that encourages your group to brainstorm effectively before developing their ideas and begin clustering them together. By using concepts such as Yes and…, more is more and postponing judgement, you can create the ideal conditions for brainstorming with ease.

Idea & Concept Development #hyperisland #innovation #idea generation Ideation and Concept Development is a process for groups to work creatively and collaboratively to generate creative ideas. It’s a general approach that can be adapted and customized to suit many different scenarios. It includes basic principles for idea generation and several steps for groups to work with. It also includes steps for idea selection and development.

Problem-solving techniques for developing and refining solutions

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to develop and refine your ideas in order to bring them closer to a solution that actually solves the problem.

Use these problem-solving techniques when you want to help your team think through their ideas and refine them as part of your problem solving process.

Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result.

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution.

Improved Solutions #creativity #thiagi #problem solving #action #team You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged.

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch #design sprint #innovation #idea generation #remote-friendly The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper, Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

Ensuring that everyone in a group is able to contribute to a discussion is vital during any problem solving process. Not only does this ensure all bases are covered, but its then easier to get buy-in and accountability when people have been able to contribute to the process.

1-2-4-All is a tried and tested facilitation technique where participants are asked to first brainstorm on a topic on their own. Next, they discuss and share ideas in a pair before moving into a small group. Those groups are then asked to present the best idea from their discussion to the rest of the team.

This method can be used in many different contexts effectively, though I find it particularly shines in the idea development stage of the process. Giving each participant time to concretize their ideas and develop them in progressively larger groups can create a great space for both innovation and psychological safety.

1-2-4-All #idea generation #liberating structures #issue analysis With this facilitation technique you can immediately include everyone regardless of how large the group is. You can generate better ideas and more of them faster than ever before. You can tap the know-how and imagination that is distributed widely in places not known in advance. Open, generative conversation unfolds. Ideas and solutions are sifted in rapid fashion. Most importantly, participants own the ideas, so follow-up and implementation is simplified. No buy-in strategies needed! Simple and elegant!

15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change.

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change.

15% Solutions #action #liberating structures #remote-friendly You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference. 15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change. With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

Problem-solving techniques for making decisions and planning

After your group is happy with the possible solutions you’ve developed, now comes the time to choose which to implement. There’s more than one way to make a decision and the best option is often dependant on the needs and set-up of your group.

Sometimes, it’s the case that you’ll want to vote as a group on what is likely to be the most impactful solution. Other times, it might be down to a decision maker or major stakeholder to make the final decision. Whatever your process, here’s some techniques you can use to help you make a decision during your problem solving process.

How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process.

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud.

How-Now-Wow Matrix #gamestorming #idea generation #remote-friendly When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice.

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them.

Impact and Effort Matrix #gamestorming #decision making #action #remote-friendly In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action?

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus.

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively.

Dotmocracy #action #decision making #group prioritization #hyperisland #remote-friendly Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

Straddling the gap between decision making and planning, MoSCoW is a simple and effective method that allows a group team to easily prioritize a set of possible options.

Use this method in a problem solving process by collecting and summarizing all your possible solutions and then categorize them into 4 sections: “Must have”, “Should have”, “Could have”, or “Would like but won‘t get”.

This method is particularly useful when its less about choosing one possible solution and more about prioritorizing which to do first and which may not fit in the scope of your project. In my experience, complex challenges often require multiple small fixes, and this method can be a great way to move from a pile of things you’d all like to do to a structured plan.

MoSCoW #define intentions #create #design #action #remote-friendly MoSCoW is a method that allows the team to prioritize the different features that they will work on. Features are then categorized into “Must have”, “Should have”, “Could have”, or “Would like but won‘t get”. To be used at the beginning of a timeslot (for example during Sprint planning) and when planning is needed.

When it comes to managing the rollout of a solution, clarity and accountability are key factors in ensuring the success of the project. The RAACI chart is a simple but effective model for setting roles and responsibilities as part of a planning session.

Start by listing each person involved in the project and put them into the following groups in order to make it clear who is responsible for what during the rollout of your solution.

Responsibility (Which person and/or team will be taking action?)
Authority (At what “point” must the responsible person check in before going further?)
Accountability (Who must the responsible person check in with?)
Consultation (Who must be consulted by the responsible person before decisions are made?)
Information (Who must be informed of decisions, once made?)

Ensure this information is easily accessible and use it to inform who does what and who is looped into discussions and kept up to date.

RAACI #roles and responsibility #teamwork #project management Clarifying roles and responsibilities, levels of autonomy/latitude in decision making, and levels of engagement among diverse stakeholders.

Problem-solving warm-up activities

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process. Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute.

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out #team #opening #closing #hyperisland #remote-friendly Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

Doodling Together

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start.

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems.

Doodling Together #collaboration #creativity #teamwork #fun #team #visual methods #energiser #icebreaker #remote-friendly Create wild, weird and often funny postcards together & establish a group’s creative confidence.

Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team!

Show and Tell #gamestorming #action #opening #meeting facilitation Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible.

Constellations #trust #connection #opening #coaching #patterns #system Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic.

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree #thiagi #opening #perspectives #remote-friendly With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Closing activities for a problem-solving process

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round.

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them.

One breath feedback #closing #feedback #action This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

Who What When Matrix

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward.

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved.

Who/What/When Matrix #gamestorming #action #project planning With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out!

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised.

Response Cards #debriefing #closing #structured sharing #questions and answers #thiagi #action It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Tips for effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.

Try different approaches

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Create psychologically safe spaces for discussion

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner.

It can be tough for people to stand up and contribute if the problems or challenges are emotive or personal in nature. Try and create a psychologically safe space for these kinds of discussions and where possible, create regular opportunities for challenges to be brought up organically.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

Save time and effort creating an effective problem solving process

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks to build your agenda. When you make changes or update your agenda, your session timing adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore how to use SessionLab to design effective problem solving workshops or watch this five minute video to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you!

James Smart is Head of Content at SessionLab. He’s also a creative facilitator who has run workshops and designed courses for establishments like the National Centre for Writing, UK. He especially enjoys working with young people and empowering others in their creative practice.

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

Your list of techniques for problem solving can be helpfully extended by adding TRIZ to the list of techniques. TRIZ has 40 problem solving techniques derived from methods inventros and patent holders used to get new patents. About 10-12 are general approaches. many organization sponsor classes in TRIZ that are used to solve business problems or general organiztational problems. You can take a look at TRIZ and dwonload a free internet booklet to see if you feel it shound be included per your selection process.

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Master the 7-Step Problem-Solving Process for Better Decision-Making

Discover the powerful 7-Step Problem-Solving Process to make better decisions and achieve better outcomes. Master the art of problem-solving in this comprehensive guide. Download the Free PowerPoint and PDF Template.

StrategyPunk

Introduction.

The 7-Step Problem-Solving Process involves steps that guide you through the problem-solving process. The first step is to define the problem, followed by disaggregating the problem into smaller, more manageable parts. Next, you prioritize the features and create a work plan to address each. Then, you analyze each piece, synthesize the information, and communicate your findings to others.

In this article, we'll explore each step of the 7-Step Problem-Solving Process in detail so you can start mastering this valuable skill. At the end of the blog post, you can download the process's free PowerPoint and PDF templates .

Step 1: Define the Problem

One way to define the problem is to ask the right questions. Questions like "What is the problem?" and "What are the causes of the problem?" can help. Gathering data and information about the issue to assist in the definition process is also essential.

Step 2: Disaggregate

After defining the problem, the next step in the 7-step problem-solving process is to disaggregate the problem into smaller, more manageable parts. Disaggregation helps break down the problem into smaller pieces that can be analyzed individually. This step is crucial in understanding the root cause of the problem and identifying the most effective solutions.

Disaggregation helps in breaking down complex problems into smaller, more manageable parts. It helps understand the relationships between different factors contributing to the problem and identify the most critical factors that must be addressed. By disaggregating the problem, decision-makers can focus on the most vital areas, leading to more effective solutions.

Step 3: Prioritize

Once the issues have been prioritized, developing a plan of action to address them is essential. This involves identifying the resources required, setting timelines, and assigning responsibilities.

Step 4: Workplan

The work plan should include a list of tasks, deadlines, and responsibilities for each team member involved in the problem-solving process. Assigning tasks based on each team member's strengths and expertise ensures the work is completed efficiently and effectively.

Developing a work plan is a critical step in the problem-solving process. It provides a clear roadmap for solving the problem and ensures everyone involved is aligned and working towards the same goal.

Step 5: Analysis

Pareto analysis is another method that can be used during the analysis phase. This method involves identifying the 20% of causes responsible for 80% of the problems. By focusing on these critical causes, organizations can make significant improvements.

Step 6: Synthesize

Once the analysis phase is complete, it is time to synthesize the information gathered to arrive at a solution. During this step, the focus is on identifying the most viable solution that addresses the problem. This involves examining and combining the analysis results for a clear and concise conclusion.

During the synthesis phase, it is vital to remain open-minded and consider all potential solutions. Involving all stakeholders in the decision-making process is essential to ensure everyone's perspectives are considered.

Step 7: Communicate

In addition to the report, a presentation explaining the findings is essential. The presentation should be tailored to the audience and highlight the report's key points. Visual aids such as tables, graphs, and charts can make the presentation more engaging.

The 7-step problem-solving process is a powerful tool for helping individuals and organizations make better decisions. By following these steps, individuals can identify the root cause of a problem, prioritize potential solutions, and develop a clear plan of action. This process can be applied to various scenarios, from personal challenges to complex business problems.

By mastering the 7-step problem-solving process, individuals can become more effective decision-makers and problem-solvers. This process can help individuals and organizations save time and resources while improving outcomes. With practice, individuals can develop the skills to apply this process to a wide range of scenarios and make better decisions in all areas of life.

7-Step Problem-Solving Process PPT Template

Free powerpoint and pdf template, executive summary: the 7-step problem-solving process.

Mastering this process can improve decision-making and problem-solving capabilities, save time and resources, and improve outcomes in personal and professional contexts.

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7-step problem-solving process powerpoint template, multi-chapter growth strategy framework (free template), lidl swot analysis: free ppt template and in-depth insights.

Discover Lidl's strengths, weaknesses, opportunities, and threats with our free PowerPoint template. This in-depth SWOT analysis provides valuable insights to help you understand Lidl's market position and strategic direction.

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Pestle analysis: decoding reddit's landscape (free ppt).

48, 52 AQ: Please confirm the page numbers.

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How to master the seven-step problem-solving process

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

Podcast transcript

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

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

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

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

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

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

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

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

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

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

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

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Simon London: So this is a concise problem statement.

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

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

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

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Both: Yeah.

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

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

Simon London: Right. Right.

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

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

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

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

Simon London: Would you agree with that?

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

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

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

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

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

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

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

Charles Conn: Yeah.

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

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

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

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

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

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

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

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

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

Hugo Sarrazin: Every step of the process.

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

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

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

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

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

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

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

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

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

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

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

Hugo Sarrazin: Absolutely.

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

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

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

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

Hugo Sarrazin: It was a pleasure. Thank you.

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

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

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Strategy to beat the odds

Five routes to more innovative problem solving

Miles Anthony Smith
Sep 12, 2022
12 min read

The Ultimate Problem-Solving Process Guide: 31 Steps and Resources

Updated: Jan 24, 2023

GOT CHALLENGES WITH YOUR PROBLEM-SOLVING PROCESS? ARE YOU FRUSTRATED?

prob·lem-solv·ing noun -the process of finding solutions to difficult or complex issues. It sounds so simple, doesn’t it? But in reality problem-solving is hard. It's almost always more complex than it seems. That's why problem-solving can be so frustrating sometimes. You can feel like you’re spinning your wheels, arguing in circles, or just failing to find answers that actually work. And when you've got a group working on a problem, it can get even muddier …differences of opinions, viewpoints colored by different backgrounds, history, life experiences, you name it. We’re all looking at life and work from different angles, and that often means disagreement. Sometimes sharp disagreement. That human element, figuring out how to take ourselves out of the equation and make solid, fact-based decisions , is precisely why there’s been so much written on problem-solving. Which creates its own set of problems. Whose method is best? How can you possibly sift through them all? Are we to have one person complete the entire problem-solving process by themselves or rely on a larger team to find answers to our most vexing challenges in the workplace ? Today, we’re going to make sense of it all. We’ll take a close look at nine top problem-solving methods. Then we’ll grab the best elements of all of them to give you a process that will have your team solving problems faster, with better results , and maybe with less sharp disagreement. Ready to dive in? Let’s go!

9 PROFITABLE PROBLEM-SOLVING TECHNIQUES AND METHODS

While there are loads of methods to choose from, we are going to focus on nine of the more common ones. You can use some of these problem-solving techniques reactively to solve a known issue or proactively to find more efficient or effective ways of performing tasks. If you want to explore other methods, check out this resource here . A helpful bit of advice here is to reassure people that you aren’t here to identify the person that caused the problem . You’re working to surface the issue, solve it and make sure it doesn’t happen again, regardless of the person working on the process. It can’t be understated how important it is to continually reassure people of this so that you get unfiltered access to information. Without this, people will often hide things to protect themselves . After all, nobody wants to look bad, do they? With that said, let’s get started...

1. CREATIVE PROBLEM SOLVING (CPS)

Alex Osborn coined the term “Creative Problem Solving” in the 1940s with this simple four-step process:

Clarify : Explore the vision, gather data, and formulate questions.

Ideate : This stage should use brainstorming to generate divergent thinking and ideas rather than the random ideas normally associated with brainstorming.

Develop : Formulate solutions as part of an overall plan.

Implement : Put the plan into practice and communicate it to all parties.

2. APPRECIATIVE INQUIRY

Source: http://www.davidcooperrider.com/ai-process/ This method seeks, first and foremost, to identify the strengths in people and organizations and play to that “positive core” rather than focus our energies on improving weaknesses . It starts with an “affirmative topic,” followed by the “positive core (strengths).” Then this method delves into the following stages:

Discovery (fact-finding)

Dream (visioning the future)

Design (strategic purpose)

Destiny (continuous improvement)

3. “FIVE WHYS” METHOD

This method simply suggests that we ask “Why” at least five times during our review of the problem and in search of a fix. This helps us dig deeper to find the the true reason for the problem, or the root cause. Now, this doesn’t mean we just keeping asking the same question five times. Once we get an answer to our first “why”, we ask why to that answer until we get to five “whys”.

Using the “five whys” is part of the “Analyze” phase of Six Sigma but can be used with or without the full Six Sigma process.

Review this simple Wikipedia example of the 5 Whys in action:

The vehicle will not start. (the problem)

Why? - The battery is dead. (First why)

Why? - The alternator is not functioning. (Second why)

Why? - The alternator belt has broken. (Third why)

Why? - The alternator belt was well beyond its useful service life and not replaced. (Fourth why)

Why? - The vehicle was not maintained according to the recommended service schedule. (Fifth why, a root cause)

4. LEAN SIX SIGMA (DMAIC METHOD)

Define, Measure, Analyze, Design, Verify

While many people have at least heard of Lean or Six Sigma, do we know what it is? Like many problem-solving processes, it has five main steps to follow.

Define : Clearly laying out the problem and soliciting feedback from those who are customers of the process is necessary to starting off on the right foot.

Measure : Quantifying the current state of the problem is a key to measuring how well the fix performed once it was implemented.

Analyze : Finding out the root cause of the problem (see number 5 “Root Cause Analysis” below) is one of the hardest and least explored steps of Six Sigma.

Improve : Crafting, executing, and testing the solution for measureable improvement is key. What doesn’t get implemented and measured really won’t make a difference.

Control : Sustaining the fix through a monitoring plan will ensure things continue to stay on track rather than being a short-lived solution.

5. ROOT CAUSE ANALYSIS

Compared to other methods, you’ll more often find this technique in a reactive problem-solving mode, but it is helpful nonetheless. Put simply, it requires a persistent approach to finding the highest-level cause, since most reasons you’ll uncover for a problem don’t tell the whole story.

Most of the time, there are many factors that contributed to an issue. The main reason is often shrouded in either intentional or unintentional secrecy. Taking the time to drill down to the root of the issue is key to truly solving the problem.

6. DEMING-SHEWHART CYCLE: PLAN-DO-CHECK-ACT (PDCA)

Named for W. Edwards Deming and Walter A. Shewhart, this model follows a four-step process:

Plan: Establish goals and objectives at the outset to gain agreement. It’s best to start on a small scale in order to test results and get a quick win.

Do: This step is all about the implementation and execution of the solution.

Check: Study and compare actual to expected results. Chart this data to identify trends.

Act/Adjust: If the check phase showed different results, then adjust accordingly. If worse than expected, then try another fix. If the same or better than expected, then use that as the new baseline for future improvements.

7. 8D PROBLEM-SOLVING

While this is named “8D” for eight disciplines, there are actually nine , because the first is listed as step zero. Each of the disciplines represents a phase of this process. Its aim is to implement a quick fix in the short term while working on a more permanent solution with no recurring issues.

Prepare and Plan : Collecting initial information from the team and preparing your approach to the process is a necessary first step.

Form a Team : Select a cross-functional team of people, one leader to run meetings and the process, and one champion/sponsor who will be the final decision-maker.

Describe the Problem : Using inductive and deductive reasoning approaches, lay out the precise issue to be corrected.

Interim Containment Action : Determine if an interim solution needs to be implemented or if it can wait until the final fix is firmed up. If necessary, the interim action is usually removed once the permanent solution is ready for implementation.

Root Cause Analysis and Escape Point : Finding the root of the issue and where in the process it could’ve been found but was not will help identify where and why the issue happened.

Permanent Corrective Action : Incorporating key criteria into the solution, including requirements and wants, will help ensure buy-in from the team and your champion.

Implement and Validate the Permanent Corrective Action : Measuring results from the fix implemented validates it or sends the team back to the drawing board to identity a more robust solution.

Prevent Recurrence : Updating work procedure documents and regular communication about the changes are important to keep old habits in check.

Closure and Team Celebration : Taking time to praise the team for their efforts in resolving the problem acknowledges the part each person played and offers a way to move forward.

8. ARMY PROBLEM SOLVING PROCESS

The US Army has been solving problems for more than a couple of centuries , so why not take a look at the problem-solving process they’ve refined over many years? They recommend this five step process:

Identify the Problem : Take time to understand the situation and define a scope and limitations before moving forward.

Gather Information : Uncover facts, assumptions, and opinions about the problem, and challenge them to get to the truth.

Develop Screening and Evaluation Criteria :

Five screening items should be questioned. Is it feasible, acceptable, distinguishable, and complete?

Evaluation criteria should have these 5 elements: short title, definition, unit of measure, benchmark, and formula.

Generate, Analyze, and Compare Possible Solutions : Most fixes are analyzed, but do you compare yours to one another as a final vetting method?

Choose a Solution and Implement : Put the fix into practice and follow up to ensure it is being followed consistently and having the desired effect.

9. HURSON'S PRODUCTIVE THINKING MODEL

Tim Hurson introduced this model in 2007 with his book, Think Better. It consists of the following six actions.

Ask "What is going on?" : Define the impact of the problem and the aim of its solution.

Ask "What is success?" : Spell out the expected outcome, what should not be in fix, values to be considered, and how things will be evaluated.

Ask "What is the question?" : Tailor questions to the problem type. Valuable resources can be wasted asking questions that aren’t truly relevant to the issue.

Generate answers : Prioritize answers that are the most relevant to solutions, without excluding any suggestion to present to the decision-makers.

Forge the solution : Refine the raw list of prioritized fixes, looking for ways to combine them for a more powerful solution or eliminate fixes that don’t fit the evaluation criteria.

Align resources: Identify resources, team, and stakeholders needed to implement and maintain the solution.

STEAL THIS THOROUGH 8-STEP PROBLEM-SOLVING PROCESS

Little Girl Reaching For Strawberries On The Counter

Now that we’ve reviewed a number of problem-solving methods, we’ve compiled the various steps into a straightforward, yet in-depth, s tep-by-step process to use the best of all methods.

1. DIG DEEP: IDENTIFY, DEFINE, AND CLARIFY THE ISSUE

“Elementary, my dear Watson,” you might say.

This is true, but we often forget the fundamentals before trying to solve a problem. So take some time to gain understanding of critical stakeholder’s viewpoints to clarify the problem and cement consensus behind what the issue really is.

Sometimes it feels like you’re on the same page, but minor misunderstandings mean you’re not really in full agreement.. It’s better to take the time to drill down on an issue before you get too far into solving a problem that may not be the exact problem . Which leads us to…

2. DIG DEEPER: ROOT CAUSE ANALYSIS

This part of the process involves identifying these three items :

What happened?

Why did it happen?

What process do we need to employ to significantly reduce the chances of it happening again ?

You’ll usually need to sort through a series of situations to find the primary cause. So be careful not to stop at the first cause you uncover . Dig further into the situation to expose the root of the issue. We don’t want to install a solution that only fixes a surface-level issue and not the root. T here are typically three types of causes :

Physical: Perhaps a part failed due to poor design or manufacturing.

Human error: A person either did something wrong or didn’t do what needed to be done.

Organizational: This one is mostly about a system, process, or policy that contributed to the error .

When searching for the root cause, it is important to ensure people that you aren’t there to assign blame to a person but rather identify the problem so a fix can prevent future issues.

3. PRODUCE A VARIETY OF SOLUTION OPTIONS

So far, you’ve approached the problem as a data scientist, searching for clues to the real issue. Now, it’s important to keep your eyes and ears open, in case you run across a fix suggested by one of those involved in the process failure. Because they are closest to the problem, they will often have an idea of how to fix things. In other cases, they may be too close, and unable to see how the process could change.

The bottom line is to solicit solution ideas from a variety of sources , both close to and far away from the process you’re trying to improve.

You just never know where the top fix might come from!

4. FULLY EVALUATE AND SELECT PLANNED FIX(ES)

"Time To Evaluate" Written on a Notepad with Pink Glasses & Pen

Evaluating solutions to a defined problem can be tricky since each one will have cost, political, or other factors associated with it. Running each fix through a filter of cost and impact is a vital step toward identifying a solid solution and hopefully settling on the one with the highest impact and low or acceptable cost.

Categorizing each solution in one of these four categoriescan help teams sift through them:

High Cost/Low Impact: Implement these last, if at all, since t hey are expensive and won’t move the needle much .

Low Cost/Low Impact: These are cheap, but you won’t get much impact.

High Cost/High Impact: These can be used but should be second to the next category.

Low Cost/High Impact: Getting a solid “bang for your buck” is what these fixes are all about. Start with these first .

5. DOCUMENT THE FINAL SOLUTION AND WHAT SUCCESS LOOKS LIKE

Formalize a document that all interested parties (front-line staff, supervisors, leadership, etc.) agree to follow. This will go a long way towards making sure everyone fully understands what the new process looks like, as well as what success will look like .

While it might seem tedious, try to be overly descriptive in the explanation of the solution and how success will be achieved. This is usually necessary to gain full buy-in and commitment to continually following the solution. We often assume certain things that others may not know unless we are more explicit with our communications.

6. SUCCESSFULLY SELL AND EXECUTE THE FIX

Arriving at this stage in the process only to forget to consistently apply the solution would be a waste of time, yet many organizations fall down in the execution phase . Part of making sure that doesn’t happen is to communicate the fix and ask for questions multiple times until all parties have a solid grasp on what is now required of them.

One often-overlooked element of this is the politics involved in gaining approval for your solution. Knowing and anticipating objections of those in senior or key leadership positions is central to gaining buy-in before fix implementation.

7. RINSE AND REPEAT: EVALUATE, MONITOR, AND FOLLOW UP

Next, doing check-ins with the new process will ensure that the solution is working (or identity if further reforms are necessary) . You’ll also see if the measure of predefined success has been attained (or is making progress in that regard).

Without regularly monitoring the fix, you can only gauge the success or failure of the solution by speculation and hearsay. And without hard data to review, most people will tell their own version of the story.

8. COLLABORATIVE CONTINGENCIES, ITERATION, AND COURSE CORRECTION

Going into any problem-solving process, we should take note that we will not be done once the solution is implemented (or even if it seems to be working better at the moment). Any part of any process will always be subject to the need for future iterations and course corrections . To think otherwise would be either foolish or naive.

There might need to be slight, moderate, or wholesale changes to the solution previously implemented as new information is gained, new technologies are discovered, etc.

14 FRUITFUL RESOURCES AND EXERCISES FOR YOUR PROBLEM-SOLVING JOURNEY

Resources | People Working Together At A Large Table With Laptops, Tablets & Paperwork Everywhere

Want to test your problem-solving skills?

Take a look at these twenty case study scenario exercises to see how well you can come up with solutions to these problems.

Still have a desire to discover more about solving problems?

Check out these 14 articles and books...

1. THE LEAN SIX SIGMA POCKET TOOLBOOK: A QUICK REFERENCE GUIDE TO NEARLY 100 TOOLS FOR IMPROVING QUALITY AND SPEED

This book is like a Bible for Lean Six Sigma , all in a pocket-sized package.

2. SOME SAGE PROBLEM SOLVING ADVICE

Hands Holding Up a Comment Bubble That Says "Advice"

The American Society for Quality has a short article on how it’s important to focus on the problem before searching for a solution.

3. THE SECRET TO BETTER PROBLEM SOLVING: HARVARD BUSINESS REVIEW

Wondering if you are solving the right problems? Check out this Harvard Business Review article.

4. PROBLEM SOLVING 101 : A SIMPLE BOOK FOR SMART PEOPLE

Looking for a fun and easy problem-solving book that was written by a McKinsey consultant? Take a look!

5. THE BASICS OF CREATIVE PROBLEM SOLVING – CPS

A Drawn Lightbulb Where The Lightbulb is a Crumbled Piece Of Yellow Paper

If you want a deeper dive into the seven steps of Creative Problem Solving , see this article.

6. APPRECIATIVE INQUIRY : A POSITIVE REVOLUTION IN CHANGE

Appreciative Inquiry has been proven effective in organizations ranging from Roadway Express and British Airways to the United Nations and the United States Navy. Review this book to join the positive revolution.

7. PROBLEM SOLVING: NINE CASE STUDIES AND LESSONS LEARNED

The Seattle Police Department has put together nine case studies that you can practice solving . While they are about police work, they have practical application in the sleuthing of work-related problems.

8. ROOT CAUSE ANALYSIS : THE CORE OF PROBLEM SOLVING AND CORRECTIVE ACTION

Need a resource to delve further into Root Cause Analysis? Look no further than this book for answers to your most vexing questions .

9. SOLVING BUSINESS PROBLEMS : THE CASE OF POOR FRANK

Business Team Looking At Multi-Colored Sticky Notes On A Wall

This solid case study illustrates the complexities of solving problems in business.

10. THE 8-DISCIPLINES PROBLEM SOLVING METHODOLOGY

Learn all about the “8Ds” with this concise primer.

11. THE PROBLEM-SOLVING PROCESS THAT PREVENTS GROUPTHINK HBR

Need to reduce groupthink in your organization’s problem-solving process ? Check out this article from the Harvard Business Review.

12. THINK BETTER : AN INNOVATOR'S GUIDE TO PRODUCTIVE THINKING

Tim Hurson details his own Productive Thinking Model at great length in this book from the author.

13. 5 STEPS TO SOLVING THE PROBLEMS WITH YOUR PROBLEM SOLVING INC MAGAZINE

This simple five-step process will help you break down the problem, analyze it, prioritize solutions, and sell them internally.

14. CRITICAL THINKING : A BEGINNER'S GUIDE TO CRITICAL THINKING, BETTER DECISION MAKING, AND PROBLEM SOLVING!

LOOKING FOR ASSISTANCE WITH YOUR PROBLEM-SOLVING PROCESS?

There's a lot to take in here, but following some of these methods are sure to improve your problem-solving process. However, if you really want to take problem-solving to the next level, InitiativeOne can come alongside your team to help you solve problems much faster than you ever have before.

There are several parts to this leadership transformation process provided by InitiativeOne, including a personal profile assessment, cognitive learning, group sessions with real-world challenges, personal discovery, and a toolkit to empower leaders to perform at their best.

There are really only two things stopping good teams from being great. One is how they make decisions and two is how they solve problems. Contact us today to grow your team’s leadership performance by making decisions and solving problems more swiftly than ever before!

Featured Post

An engagement-aware predictive model to evaluate problem-solving performance from the study of adult skills' (PIAAC 2012) process data

Jinnie Shin ORCID: orcid.org/0000-0002-1012-0220 1 ,
Bowen Wang 1 ,
Wallace N. Pinto Junior 1 &
Mark J. Gierl 2

Large-scale Assessments in Education volume 12 , Article number: 6 ( 2024 ) Cite this article

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The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee’s performance in sequential, interactive problem-solving tasks from a large-scale assessment of adults' educational competencies. The current methods disambiguate problem-solving behaviors using network analysis to inform the examinee's performance in a series of problem-solving tasks. The unique contribution of this framework lies in the introduction of an “effort-aware” system. The system considers the information regarding the examinee’s task-engagement level to accurately predict their task performance. The study demonstrates the potential to introduce a high-performing deep learning model to learning analytics and examinee performance modeling in a large-scale problem-solving task environment collected from the OECD Programme for the International Assessment of Adult Competencies (PIAAC 2012) test in multiple countries, including the United States, South Korea, and the United Kingdom. Our findings indicated a close relationship between the examinee's engagement level and their problem-solving skills as well as the importance of modeling them together to have a better measure of students’ problem-solving performance.

Large-scale digital assessment in an interactive online environment is designed to evaluate examinees’ thinking and problem-solving skills (Van Laar et al., 2017 ). An increasing number of large-scale assessments, such as the Programme for International Assessment of Adult Competencies (PIAAC), the Programme for International Student Assessment (PISA), and the Trends in International Mathematics and Science Study (TIMSS), have recently introduced more innovative test solutions with novel item formats to assess problem-solving or collaborative problem-solving performance (e.g., Barber et al., 2015 ; Mullis et al., 2021 ). For example, PIAAC is an international assessment which was the first fully computer-based large-scale assessment in education and the first to provide public anonymized log file information widely. Footnote 1 PIAAC’s problem-solving assessment in a technology-rich environment (PS-TRE hereafter) is designed to assess the adult examinee’s ability to use “digital technology, communication tools and networks to acquire and evaluate information, communicate with others and perform practical tasks” (Rouet et al., 2009 , p.9; (OECD), 2012 , p. 47). In this test, examinees are provided with varying types of problem-solving tasks that embed authentic real-life scenarios.

These non-traditional, interactive, digital problem-solving items encourage examinees to demonstrate their authentic skill sets using their responses and the traces of activities associated with solving the task (Jiang et al., 2021 ). The traces stored as metadata of examinees’ interactions are the process log data or click-stream information. The process log data provides insights into the examinee’s behavior that are not easily disambiguated with the response data alone, especially in many non-traditional and interactive large-scale assessments. The process log information uncovers more individualized and diagnostic evidence about the examinees’ latent abilities (Goldhammer et al., 2014 ; He & von Davier, 2015 ; Scherer et al., 2015 ; Wang et al., 2021 ) which enhances the reliability and validity evidence of the assessments (Kroehne & Goldhammer, 2018 ; Ramalingam & Adams, 2018 ), and identifies the examinees who are depicting anomalous behaviors (Lundgren & Eklöf, 2020 ). For instance, Jiang et al., ( 2021 ) demonstrated how the process data gathered specifically from students’ drag-and-drop actions in a large-scale digitally-based assessment environment could infer examinees’ varying levels of cognitive and metacognitive processes, such as their problem-solving strategies.

Incorporating the process information in a large-scale assessment to achieve such goals requires several methodological and empirical considerations. First, the complex micro-level evidence from the examinees (i.e., process log data) needs to be analyzed to extract explainable and interpretable patterns that inform the examinee’s latent abilities (e.g., problem-solving strategies, Polyak et al., 2017 ; von Davier, 2017 ). Second, the examinees’ demonstration of knowledge and skills need to be modeled in the sequences of task levels to provide more generalizable implications compared to the item-level results (Ai et al., 2019 ; Jiang et al., 2020 ; Liu et al., 2019a , 2019b ; Wang et al., 2017 ). Third, careful consideration is required to evaluate the effect of students’ sentiments or affect that may influence their performance, such as their task-disengagement behaviors (Wise, 2020 ).

With the recent wide introduction of machine learning and deep learning approaches in large-scale assessments and learning analytics, increasing attempts are being made to more effectively and efficiently analyze the process data from large-scale assessments. Hence, in this study, we propose a novel analytic framework where the examinee's complex and long traces of process log data are used to understand the problem-solving skills and performance. The present study is rooted in the fields of learning analytics and psychometrics. We combined multiple advanced computational methods, including social network analysis and deep neural network models. Our framework also models the examinee’s task-engagement status for a more accurate representation of the performance and skill demonstration in the series of interactive tasks. One research question is addressed to guide the study: Does modeling the engagement levels with problem-solving skills improve the prediction performance of the LSTM model for items solved on a large-scale assessment?

To describe how our research questions were addressed using the PIAAC’s PS-TRE assessment, the subsequent sections focus on three primary topics. First, we present the construct measured by the PS-TRE test and its three core dimensions, thereby providing contextual information on the types of tasks our research aims to investigate and evaluate. Second, we offer an overview of the literature, concentrating on methodologies introduced to understand the PS-TRE construct, with a specific focus on recent studies that have utilized process data to model the tasks associated with this construct. Lastly, we provide an overview of how test engagement is currently modeled in various large-scale assessment settings, underscoring the importance of capturing test engagement in the PS-TRE.

Problem-solving tasks in PIAAC PS-TRE

The PIAAC’s problem-solving assessment in a technology-rich environment (PS-TRE) is designed to assess the adult examinee’s ability to use “digital technology, communication tools and networks to acquire and evaluate information, communicate with others and perform practical tasks” (OECD, 2012 ). Problem-solving usually means that people cannot solve problems through routine activities, which needs a complex hierarchy of cognitive skills and processes. Technology-rich environment indicates that some technologies (e.g., spreadsheets, Internet search, websites, email, social media, or their combination) are required to solve the task in assessment (Vanek, 2017 ).

The three core dimensions of PS-TRE include task/problem statements, technologies, and cognitive dimensions. These dimensions are closely connected because examinees rely on their choice of technologies to solve the problems, which requires their cognitive skills to successfully use the selected technology to solve the problem or accomplish the task. The examinees are provided with varying types of problem-solving tasks that embed authentic real-life scenarios based on intertwined dimensions of PS-TRE. A problem-solving task can be provided by connecting any domains in each core conceptual dimension as described in Appendix 1 .

The interactions between these three core components create complex problem-solving tasks. Examinees are required to use a sequence of actions to correctly address these tasks, resulting in a substantial collection of process logs and clickstream information. The following section will explore the modeling of this extensive data, aiming to extract meaningful insights into the problem-solving strategies used by examinees during the assessment.

Modeling problem-solving strategies in PS-TRE with process data

Increasingly, studies have been conducted to introduce various computational and artificial intelligence-powered methods to effectively understand examinees’ responses as well as the complex interaction process log information gathered in PIAAC’S PS-TRE. These studies often adopted clustering analysis (He, Liao, & Jiao, 2019 ), pattern mining analysis (Liao et al., 2019 ), graph modeling approaches (Ulitzsch et al., 2021 ), and clickstream analysis with supervised learning models (Ulitzsch et al., 2022 ). For instance, He et al., ( 2019a , b ) adopted the K-means algorithm (Lloyd, 1982 ) to cluster the behavioral patterns from one representative PS-TRE item based on features extracted from process data (i.e., unigrams by n-grams model, total response time, and the length of action sequences) to explore the relationship between behavioral patterns and proficiency estimates covaried by employment-based background. That is, more actions and longer response time tended to generate higher PS-TRE scores when getting incorrect answers. Their findings indicated process data tends to be more informative when items are not answered correctly.

To further investigate the impact of employment-based background, Liao et al., ( 2019 ) mapped the employment-based variables with action sequences in process data using the regression analysis and chi-square feature selection mode. They found that groups with different levels of employment-based background variables tended to generate distinctive characteristics regarding the action sequences to solve problems. However, it should be noted that the previous approaches (e.g., He et al., 2019a , b ; Liao et al., 2019 ) only analyzed item-level timing data instead of time consumed between actions (i.e., action-level time), so the more detailed underlying cognitive processes due to time-stamped action sequences might be neglected.

Ulitzsch et al., ( 2021 ) proposed a two-step approach to analyze complete information contained in time-stamped action sequences for a deeper investigation of the behavioral processes underlying task completion. The researchers integrated tools from clickstream analyses and graph-modeled data clustering with psychometrics so that they can combine action sequences and action-level times into one analysis framework. In another study, enriching generic features extracted from sequence data by clickstream analysis, Ulitzsch et al., ( 2022 ) extracted features from time-stamped early action sequences (i.e., early-window clickstream data) and an extreme gradient boosting (XGBoost) classifier was used (Chen & Guestrin, 2016 ). Within the procedure, early-window datasets were created to train the model by getting rid of all afterward time-stamped actions (i.e., occurred after a given number of actions or a given amount of time from the sequences) thereby allowing the features taken from clickstreams to focus on the occurrence, frequency, and sequentiality of actions by adding features based on the amount of time consumed to carry out certain actions. Based on the clickstream analysis with a supervised learning model, Ulitzsch et al., ( 2022 ) investigated the early predictability of success or failure on problem-solving tasks before examinees complete the tasks and deepen the understanding of the trajectories of behavioral patterns in PS-TRE.

These studies demonstrated excellent potential to leverage our understanding with interpretable results to study different facets of students’ knowledge and abilities. However, no study, to our knowledge, was introduced in the sequence of task-level, with the potential to consider the examinees’ engagement status in the analysis of problem-solving knowledge modeling. Therefore, in the subsequent section, we will introduce how test-taking engagement has been defined in previous literature, along with the methodologies explored to investigate such constructs. Consequently, we will highlight the benefits and advantages of employing test-taking engagement as a simultaneous measure to effectively evaluate students' performance.

Engagement in knowledge modeling with problem-solving performance in large-scale assessment

Test-taking engagement is used to describe if the test taker remains engaged throughout a test, which is an underlying assumption of using all psychometrics models in practice (Wise, 2015 , 2017 ). The term test-taking engagement also refers to the test-taking effort. Test disengagement was defined as providing or omitting responses to items with no adequate effort (Kuang & Sahin, 2023 ), indicated by rapid-guessing behavior (Schnipke, 1996 ; Wise & Kong, 2005 ) and item-skipping behavior. A lack of test-taking engagement is a major threat to the validity of test score interpretation even in good test design (Wise & DeMars, 2006 ), especially in low-stakes assessments such as PIAAC (Goldhammer et al., 2016 ).

Modeling test-taking engagement in problem-solving tasks resolves the potential validity threat (e.g., construct-irrelevant variance) that can confound the examinees’ performance results (Braun et al., 2011 ; Goldhammer et al., 2016 ; Keslair, 2018 ; Wise, 2020 ). Information gathered from the examinee’s response data in the task was commonly used to model their task-engagement level. Various item response theory (IRT)-based models incorporate students’ engagement to predict people’s latent traits (Deribo et al., 2021 ; Liu et al., 2019a , b ; Wise & DeMars, 2006 ). For instance, Wise and DeMars, ( 2006 ) introduced the effort-moderate IRT (EM-IRT) model, where disengaged responses are treated as missing data and fit the engaged responses to a unidimensional IRT model. Response time was used to identify students’ engagement in the EM-IRT model. More recently, studies explored the use of data gathered from the interactions, such as process log data, to evaluate examinees’ test-taking effort and motivation (Lundgren & Eklöf, 2020 ; 2021).

The combination of response time and response behaviors was used as an “enhanced” method to detect examinees’ disengagement (Sahin & Colvin, 2020 ). Within this approach, the response behaviors (e.g., keypresses, clicks, and clicking interactive tools) were collected from the process data (Kuang & Sahin, 2023 ). Sahin and Colvin, ( 2020 ) set up the threshold for the maximum number of response behaviors that suggest no or minimum engagement. However, they did not use statistical models to analyze the response behaviors from process data. A small number of studies have demonstrated the capacity to model the examinee’s engagement and problem-solving performance from process data or a sequence of tasks (as well as at an individual task level). Since test engagement can be treated as a latent trait under response behaviors and deep learning approaches have the advantage of modeling process data or a sequence of tasks to capture examinees' response behaviors, it is worth investigating how to apply deep learning approaches (such as Long Short-Term Memory Networks) to detect test engagement.

Long short-term memory networks

Our study implements one of the variational models of recurrent neural network (RNN) models to effectively and accurately track students’ problem-solving performance from a sequence of PS-TRE tasks. Unlike traditional neural network models, the RNN models introduce a simple loop structure in the hidden layer to consider a sequence or a history of input. In our study, we use one of the special variations of the RNN models, which is the Long Short-Term Memory (LSTM) network. The LSTM model consists of units called memory blocks. Each memory block consists of multiple gates—input, forget, and output gates—that control the flow of information.

Figure 1 provides an overview of an example LSTM memory cell structure. In our study, we use the memory cell to input, modify, extract, and communicate the deterministic information about examinee’s problem-solving strategies and performance on a sequence of tasks, where $t$ represents the task that the examinee is interacting with. Specifically, input data is determined based on $n$ batch size with $d$ features and $h$ number of hidden layers, $x(t) \in { R}^{n\times d}$ , and the hidden state of the previous task $h(t-1) \in { R}^{n\times h}$ , indicating the final input data as ${X}^{T}=[h(t-1), x(t)]$ . This input data is first provided to the forget gate $f(t) \in {R}^{n\times h}$ , input gate $i(t) \in { R}^{n\times h}$ , and an output gate $o(t) \in {R}^{n\times h}$ . The forget gate governs the degree to which the information from the previous tasks is omitted from the cell state, the input gate governs how much new information about the examinee’s problem-solving skills are inferred from the current task, and the output gate produces the output that will be communicated to the next cell state for the task, $t+1$ .

A Conceptual Representation of an LSTM Memory Cell

The interim values after entering the gates are computed as below, where ${w}_{xi}$ , ${w}_{xf}, {w}_{xo} \in { R}^{d\times h}$ and ${w}_{hi}$ , ${w}_{hf}, {w}_{ho} \in { R}^{h\times h}$ represent weights of each gate, and ${b}_{i}$ , ${b}_{f}, {b}_{o} \in { R}^{1\times h}$ represent bias of each gate, respectively. The input node $\widetilde{c}(t) \in {R}^{n\times h}$ is also computed similarity with the other gates, where the activation function of $tanh(x) = (e^x -- e^{ - x} ) / (e^x + e^{ - x} )$ replaces the sigmoid function in the other three gates.

The memory cell outputs the internal state and the hidden state $h(t) \in [-\mathrm{1,1}]$ . The hidden state at task $t$ will concern the input, forget, and output gates by deciding the impact of the current memory to the next memory cell. The hidden state that is close to the value of 0 will minimize the current impact to the next cell while the value close to 1 will impact the internal state value of the next cell with no restriction. The memory cell updates the internal state $c(t)$ in the task $t$ by gathering the information from the forget, input, and the previous cell state as follows:

Attention mechanism

The simple LSTM model can be limited in detecting which element provides the important aspect of information to determine examinees’ problem-solving performance while accounting for their engagement level. Hence, we introduce an attention layer to explicitly model this information. Let $H \in {R}^{d\times t}$ represents the hidden layers derived from the memory cell of each problem-solving task $t$ with an LSTM model with $d$ hidden layers. The attention layer we use in the current finding is the global attention layer. The global attention layer represents the latent information extracted from the sequences of output from the encoder (i.e., input data is encoded using LSTM) in order to help decoders (i.e., output data is generated using LSTM) utilize global evidence related to examinees’ problem-solving skills to output correct predictions. The dot-product attention computes the element-wise multiplication between the hidden states of encoder and decoder of task t, ${h}_{t}$ and ${s}_{t}$ with the attention weight $W=\{{w}_{1}, {w}_{2}, ..., {w}_{n}\},$ where the attention $\alpha$ is captured as follows:

Then, the final weighted representation of the hidden state is derived by combining the dot-product attention ( $\alpha$ ) and the hidden layer ( $H$ ) as $r=H{\alpha }^{T}$ . Using this information, we can represent the students’ problem-solving performance as a combination of projection parameters ${W}_{p}$ and ${W}_{r}$ , are ${h}^{*}=sigmoid({W}_{p}r+{W}_{x}{h}_{n})$ . The parameters ${W}_{p}$ and ${W}_{x}$ are learned during training (Rocktaschel et al., 2015 ). In our study, we use these projection parameters to visualize whether the attention layer is accurately capturing the examinee's problem-solving performance and engagement across a sequence of problem-solving tasks. The final univariate/multivariate outcome(s) (performance and engagement) of this process will be computed using ${h}^{*}$ , as $y=softmax({W}_{s}{h}^{*}+{b}_{s}),$ where ${W}_{s}$ represents the output layer weights and ${b}_{s}$ represents the output layer bias. This way we will be able to produce whether the student was engaged (= 0), not engaged (= 1), as well as the score category that the students acquired from the task as the final outcome of our model (see Fig. 2 ).

A Conceptual Representation of the Attention Layer in LSTM

Using Long Short-Term Memory (LSTM) models to evaluate students' engagement and performance from process log data in the PS-TRE is a particularly effective approach due to several key advantages of LSTMs. These neural networks are uniquely suited for handling sequential data, a core aspect of process log data, where the order and timing of actions are critical indicators of student engagement and performance. This allows us to evaluate students’ performance and engagement effectively across multiple items and tasks, moving beyond analyzing the examinee's performance at an individual item level (e.g., Shin et al., 2022 ; Tang et al., 2016 ). LSTMs excel in capturing not just immediate dependencies but also long-term patterns in sequences, which is crucial in the PS-TRE context where early actions can influence later ones, or patterns of engagement may change over time. This indicates possibilities of capturing the information and storing the information from the examinee’s process data at the very first task or the item they engage with, and utilizing their information to infer and predict their performance at the very last item they interact with.

The ability of LSTMs to learn complex patterns in sequential data is another significant advantage. They can handle variable-length sequences, a common characteristic in PS-TRE log data, ensuring consistent model performance across different data lengths (Hernández-Blanco et al., 2019 ). This aspect is vital, considering each examinee’s interaction with the assessment varies in length and complexity. One of the standout features of LSTMs is their capacity for automatic feature extraction from raw sequential data. This is particularly beneficial for PS-TRE, where manually identifying relevant features from log data can be challenging. LSTMs can not only understand the context of each action within the broader sequence of events but also use this understanding to predict future behavior. This predictive ability is not only useful for analyzing past and present actions but also offers potential applications in real-time scenarios, such as adaptive testing or personalized learning interventions. Furthermore, LSTMs are robust to noise and irregularities in data, which are common in log files due to varied user behaviors and system inconsistencies (e.g., Fei & Yeung, 2015 ). Their capability to generalize from training data to unseen test data is vital for deploying models in different assessment environments.

Hence, the LSTM's proficiency in processing sequential data, its capability to detect and learn relevant features, and its robustness against data irregularities make it an appropriate choice for modeling the dynamics of student engagement and performance in PS-TRE. By leveraging the rich, time-ordered data in process logs, LSTMs provide deep insights crucial for educational assessments and learning analytics.

Data and materials

We used the data collected from the first round of the OECD PIAAC Main Study, which was conducted from August 2011 to November 2012, involved 24 countries/economies, and was the first computer-based large-scale assessment to provide public anonymized log file data. Footnote 2 Our investigation focused on the cognitive domain of PS-TRE. A total of 14 tasks were dichotomously or polytomously scored (five 3-point, one 2-point, and 8 dichotomously scored items) (OECD, 2016 ). We analyzed the data collected from the United States (4131 units Footnote 3 ), South Korea (7024 units), and the United Kingdom (7250 units). The log file of the PS-TRE tasks contained various information including the environment from which the event was issued (within the stimulus, outside of the stimulus), the event type, timestamps, and a detailed description of the event. In this proposed study, we experimented with the items included in one booklet (PS1) to demonstrate the prediction capacity of our proposed analytics framework (see Table 1 ).

Binary task engagement level

The method of T-disengagement (Goldhammer et al., 2016 ) was used to label test takers’ engagement by response time as part of the training set. The term “T-disengagement” (OECD, 2019 ) describes a situation where examinees spend less time on a PIAAC task than a task-specific threshold. The approach to computing this item-specific threshold is based on the relationship between the probability of giving correct answers and the time spent on the item (Goldhammer et al., 2016 ). The underlying idea of this approach is that disengaged examinees tend to be less accurate than engaged examinees (Wise, 2017 ). The computation procedure first determined the time threshold t, it is necessary to compute the probability of getting a task correct on time t. The observations with a time on task between t and t + 10(s) are used. Then, the probability of correctness is modeled as a linear function of time if the number of the observations is enough (e.g., > 200). Last, the task-specific time threshold is determined as the smallest t for which the estimated probability of correctness is higher than 10%. The T-disengagement value was used in our study to create an engagement indicator, labeling test-takers' engagement based on response time as part of the training set. If an examinee spends less time on a task than the task-specific threshold, they are labeled as a disengaged examinee. Otherwise, they are considered engaged. Using the threshold calculated for each item in PS-TRE, we generated a binary outcome variable representing each examinee’s engagement status.

Figure 3 provides a conceptual representation of our analytic model. Our analytic framework is based on a specific neural network model called the Long Short-Term Memory networks (LSTM; Hochreiter & Schmidhuber, 1997 ). The LSTM model takes a sequence of actions from the examinees which was captured while they were navigating through each item.

A Conceptual Representation of the Effort-Aware Attention-LSTM Model

The first layer of the model focused on converting the input sequences of actions from the process log data into a directed graph, where a node represents an activity in an item and the edges represent the connectivity between the two actions. The edges are weighted by the total amount of time between the two actions. Then, the overall task-navigating process of the examinees was summarized using network statistics. Network statistics summarize the interactions present in the network. Our analysis adopted five network statistics. This method includes five key network statistics: centralization, density, flow hierarchy, shortest path, and total number of nodes, each contributing to a comprehensive understanding of the interactions within the network. This approach aligns with recent trends in educational data mining, where network analysis is increasingly applied to understand learning processes (Salles et al., 2020 ; Zhu et al., 2016 ).

Converting process log data into a directed graph in the first layer in LSTM for predictive modeling is a strategic decision that offers numerous benefits, particularly in the context of assessing complex sequential data like that found in PS-TRE. This conversion allows for a structured representation of the data, where each node in the graph represents an individual action or activity, and directed edges signify the sequence and transition between these actions. Importantly, by weighting these edges with the time elapsed between actions, the graph effectively captures the temporal dynamics integral to understanding examinee engagement and problem-solving processes.

This graph-based approach significantly enhances the analysis of sequential interactions among different actions (Zhu et al., 2016 ). It provides a more nuanced perspective on how examinees approach and navigate through tasks, revealing patterns and strategies in their problem-solving process. By employing network analysis techniques, such as evaluating centralization, density, flow hierarchy, shortest path, and the total number of nodes, the model can delve deeper into the complexity and efficiency of examinees' approaches. Additionally, the directed graph structure is highly conducive to advanced machine learning techniques, such as those used in LSTM models, facilitating more accurate predictions and classifications based on the patterns identified in the graph (e.g., Zeng et al., 2021 ; Zhang & Guo, 2020 ). Beyond the analytical advantages, this representation also aids in the interpretability and visualization of the data, making it more accessible for educators and researchers to understand and visualize the problem-solving process. Moreover, this method's flexibility and scalability make it adaptable to various assessment scenarios, capable of accommodating different types of actions and interactions (Hanga et al., 2020 ). Overall, this first layer's approach of transforming log data into a directed graph lays a robust foundation for subsequent, in-depth analysis, capitalizing on the strengths of network analysis and machine learning to provide insightful interpretations of examinee behavior.

The encoder and decoder then summarized the network statistics and map them into the prediction outcomes. The encoder summarizes the input and represents it as an interim representation called internal state vectors. The decoder, on the other hand, generates sequences of output using the internal state vectors from the encoder as an input. In our study, we presented two variations of models that differ in the type and the number of outputs associated with the input. The first model (Attention-LSTM) only concerns the association between students’ process activities (log information) and their performance outcome (i.e., categorical scores) in each task. The second model (Effort-Aware LSTM) additionally models the associations between students’ process activities with their task engagement level to reduce any effects stemming from the low-stakes characteristics of the current dataset. In summary, the second model is designed to produce output regarding students’ performance scores simultaneously with their task engagement level for each task.

In order to increase the interpretability of the model decisions (i.e., whether the model is correctly stipulating the information related to students’ latent ability level), we included an attention mechanism. The global attention layer represents the latent information extracted from the sequences of output from the encoder in order to help decoders utilize global evidence related to examinees’ problem-solving skills (Model 1) and problem-solving skills with engagement level (Model 2).

A two-step evaluation process was used. First, the two variations of the model were compared based on the overall and item (or task)-specific performance score prediction accuracies. In the first step of our evaluation process, we compared the two variations of the LSTM model based on their ability to predict overall and item-specific performance scores. To ensure a comprehensive assessment, we employed three evaluation metrics: accuracy, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. These metrics were chosen for their ability to provide a balanced view of the model's predictive performance, considering aspects like the balance between sensitivity and specificity (ROC curve) and the harmonic mean of precision and recall (F1-score). The final evaluation metrics were derived from the average results obtained through threefold cross-validation. This cross-validation approach adds rigor to our evaluation, ensuring that the performance metrics are robust and not overly fitted to a specific partition of the data.

The second step involved conducting a Principal Component Analysis (PCA) on the final attention layer of our engagement-aware model (e.g., Chen et al., 2018 ). Applying PCA to the last attention layer of an LSTM model, which handles complex datasets related to student engagement and performance, offers significant benefits. Firstly, PCA is instrumental in reducing the dimensionality of high-dimensional outputs generated by the attention layer. This reduction is crucial, as it retains essential patterns and variances while uncovering underlying latent associations. The ability of PCA to reveal latent relationships within the attention layer’s output is particularly valuable. It exposes underlying structures that might not be immediately evident, providing deeper insights into how the model processes and combines various aspects of the input data (e.g., Qiao & Li, 2020 ; Zhang et al., 2020 ). Moreover, PCA helps validate the focus of the attention mechanism, ensuring that it aligns with features pertinent to the task. This validation is essential for confirming that the model adheres to theoretical and empirical expectations, ensuring that the predictive model is focusing on and depending on the 'adequate' source of information for the decision-making process (Terrin et al., 2003 ).

Tables 2 and 3 provide the overall performance results of the two variations of the models proposed in this study. The results showed that the Effort-Aware Attention-LSTM model could achieve improved performance in predicting student performance scores in all three-evaluation metrics across all three countries. Our first model (Attention-LSTM) produced f1-scores close to 0.82, ROC of 0.70–0.75, and accuracy of 0.75–0.78 across all three countries. The second model (Engagement-Aware Attention-LSTM) produced f1-scores close to 0.88–0.90, ROC of 0.82–0.84, and accuracy of 0.80–0.88. The prediction performance on the examinee's engagement level produced f1-scores close to f1-scores 0.92–0.94, ROC of 0.86 to 0.88, and accuracy of 0.84–0.87. In summary, an improvement in the problem-solving performance prediction was observed in the second model.

For individual tasks (see Table 3 ), similar patterns were identified across the three countries where engagement-aware models acquired slightly improved performance results compared to the other model. The model results also demonstrated that the engagement-aware model could predict the engagement and disengagement level of the participants across all five tasks with high performance accuracies. Specifically, the improvement in prediction accuracy was the highest in Task 5 where F1-score improved by + 0.21 to + 0.28, and accuracy improved by + 0.20 to + 0.23.

Attention-layer visualization: engagement and performance latent variables

Appendix 2 provides visualizations of the attention layer from the engagement-aware model for each problem-solving task with the U.S. participant data set. The principal component analysis results visualized the potential underlying components that our attention mechanism captured to make correct decisions regarding students’ performance results. The results showed that the interim output of the attention layer could be systematically explained by the two components which aligned with the problem-solving performance skill level with a relatively small variance explained by the second component, engagement level. The two components accounted for 75.5% and 14.4% of the variance in Task 1 attention score, 74.1% and 13.7% of the variance in Task 2 attention score, 56% and 30.7% in Task 3, 75.9% and 13.4% in Task 4, and 80.3% and 9.1% in Task 5.

More specifically, the size of the dots in Appendix 3 represents the students’ performance scores, whereas the bigger dots represent students who scored higher in the task. The red and blue dots each represent students’ engagement and disengagement status (Goldhammer et al., 2016 ). The figures for Tasks 1, 4, and 5 showed clear alignments between the principal component scores and the problem-solving performance and engagement levels. For instance, visualization of the principal component scores for Tasks 1, 4, and 5 indicates a visible alignment between the size of the dot along the continuum of principal component score 1. Moreover, a clear alignment between the color coordination of the dots with principal component score 1, where the higher component score indicated an increased engagement level. However, the alignment between component scores and the performance and engagement level was less clear when visualized in Tasks 2 and 3, where the color coordination of the dots (engagement vs. disengagement) was less distinctive across the component scores.

The Pearson’s correlation coefficients between the principal component scores and the examinee’s performance and the engagement level also revealed similar findings. The primary component score in Task 4 and Task 5 showed moderate to high positive correlations coefficients with the students’ engagement level (0.45–0.53) and the performance level (0.28–0.67). The primary component in Task 1, interestingly, showed moderate negative correlations with the engagement score (-0.57) and a positive correlation with the performance (0.564). We also observed that when the PCA scores aligned well with the engagement and the performance level, that comparably higher contribution to the prediction performance was observed. We discussed this and the implications of these findings further in the next section (Table 4 ).

Conclusions and discussion

The purpose of our study was to describe and demonstrate an analytic framework where the complex and long traces of process log data are used to understand the problem-solving skills and performance based on the examinee’s log data in a problem-solving task in PIAAC 2012. Our engagement aware-LSTM model could outperform the other model in accurately classifying students based on their problem-solving performance.

The current empirical findings situate well in the existing literature by highlighting the importance of behavioral patterns or action sequences that are valuable to capture in modeling the examinee’s problem-solving skills in PIAAC (He et al., 2019a , b ). Some of the widely discussed benefits of incorporating behavioral patterns into problem-solving performance modeling involve the improvement of measurement accuracy (He et al., 2019a , b ; Sireci & Zenisky, 2015 ), the establishment of the evidence to capture other latent or cognitive dimensions, such as engagement (He & von Davier, 2016 ; Zhu et al., 2016 ), and improvement in capturing abnormal behaviors (Hellas et al., 2017 ). Consistent with the previous literature, incorporating sequence-level process log features could successfully be associated with their performance (0.82–0.83 f1-score on average) while modeling students’ engagement levels (0.92–0.97 f1-score on average) simultaneously in our findings. In our study, the low engagement that was captured across the problem-solving tasks could be interpreted as one source of anomalies that were commonly reported in the previous literature concerning formative or low-stakes assessments (Pastor et al., 2019 ; Pools & Monseur, 2021 ).

In addition, the findings from the current study align with previous research results indicating a close relationship between the examinee’s engagement level and their problem-solving skills as well as the importance of modeling them together to have a better measure of students’ problem-solving performance. Previously the connections between problem-solving performance and engagement were studied in relation to the complexity of the testing or assessment environments such as interactive games (Eseryel et al., 2014 ). For instance, Lein et al. ( 2016 ) indicated that engagement is a unique significant predictor that was associated with students’ mathematical problem-solving performance when controlling for students’ prior knowledge. Similarly, ongoing efforts are made in measurement research, where variations of IRT models are introduced to accurately estimate students’ abilities (Nagy & Ulitzsch, 2022 ; Wise & DeMars, 2006 ).

Accordingly, the problem-solving task with the largest performance improvement in measuring students’ problem-solving performance was in Task 1, Task 4, and Task 5, where the correlation coefficients between the performance and the engagement scores were the highest (ρ = 0.480, ρ = 0.412, ρ = 0.373). Conversely, in the tasks that showed a low to the negligible correlation between engagement and performance (2 and 3), the improvement in performance also remained relatively low.

Implication

The results provide practical and methodological implications for test developers and psychometric researchers. Using our approach, students’ problem-solving abilities can be modeled in real-time and predicted to provide more direct and prompt feedback for student performance. Also, the visualization and validation of the interim layer of complex machine learning models provide important evidence and insights to psychometric researchers which allows them to compare the model performance of deep learning models with the traditional psychometric approaches, such as IRT. Last, our engagement-aware model may allow test developers to adopt the system in a low-stakes assessment setting where the accurate evaluation of the student's ability, knowledge, and skills is challenging due to the lack of student motivation or engagement.

Wise and Kong, ( 2005 ) previously outlined large-scale assessment scenarios where the simultaneous measurement of engagement and students’ ability level (e.g., problem-solving performance) may be recommended. First, the use of a low-stakes environment to pilot and validate the large-scale high-stakes exams may entail assessment situations where engagement detection may be necessary. Large-scale assessments, such as PIAAC and PISA commonly adopt such approaches to investigate the psychometric properties of the item prior to being officially introduced in their test booklets. Second, large-scale assessments are increasingly used to make inferences about teacher, school, and district evaluation, which may be deemed by the students to have low to negligible consequences for each participating individual. Not explicitly modeling students’ engagement level during the participation may have significant consequences on validity of the test scores.

In essence, the deep learning methods proposed in this study provide the benefits of a data-informed and machine-learning based approach with an educational and psychometric consideration which could increase the capacity of promptly and accurately deriving decisions about examinee’s performance from the education assessment with an increasingly digitized environment.

Limitations and future research

While our study was carefully constructed and implemented to avoid potential bias, we acknowledge that it is not free from limitations, which can be addressed in future research. First, the use of Principal Component Analysis (PCA) to improve the validity and interpretability of our model provided important benefits. However, it is important to recognize the limitations of PCA, notably its linear nature, which might not capture all non-linear relationships in the data. Also, the interpretation of the principal components, being linear combinations of original features, might not always be straightforward. Despite these limitations, the application of PCA on the last attention layer remains a valuable tool, offering a balanced approach to understanding and interpreting complex models in the context of educational assessments. Hence, we encourage future studies focusing on validating the PCA results to evaluate whether such patterns and relationships can be replicated and revealed when analyzing similar types of process data in large-scale assessment settings.

Availability of data and materials

The PIAAC PS-TRE 2012 Log dataset is available at https://www.oecd.org/skills/piaac/ .

https://www.oecd.org/skills/piaac/ .

https://search.gesis.org/research_data/ZA6712

According to https://search.gesis.org/research_data/ZA6712 .

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Domains of Three Core Conceptual Dimensions. Adapted from "PIAAC Problem Solving in Technology-Rich Environments: A Conceptual Framework", OECD Education Working Papers, No. 36, OECD Publishing, Paris.

Dimension	Domain	Examples
Task	Purpose/context	Personal, Work/occupation, Civic purposes
	Intrinsic complexity	Minimal number of steps required to solve the problem
		Number of options or alternatives at various stages in the problem space
		Diversity of operators required, complexity of computation/transformation
		Likelihood of impasses or unexpected outcomes
		Amount of transformation required to communicate a solution
	Explicitness of problem statement	Ill-defined (implicit, unspecified) vs. well-defined (explicit, described in detail)
Technology	Hardware devices	Desktop or laptop computers, mobile phones, personal assistants, geographical information systems, integrated digital devices
	Software applications	File management, Web browser, Email, Spreadsheet
	Commands, functions	Buttons, Links, Textboxes, Copy/Cut-Paste, Sort, Find
	Representations	Texts, Sound, Numbers, Graphics (fixed or animated), Video
Cognitive dimension	Goal setting and progress monitoring	Identifying one's needs or purposes, given the explicit and implicit constraints of a situation
		Establishing and applying criteria for constraint satisfaction and achievement of a solution
		Monitoring progress
		Detecting and interpreting unexpected events, impasses and breakdowns
	Planning, self-organizing	Setting up adequate plans, procedures, and strategies (operators) and selecting appropriate devices, tools or categories of information
	Acquiring and evaluating information	Orienting and focusing one's attention; selecting information; assessing reliability, relevance, adequacy, comprehensibility; and reasoning about sources and contents
	Making use of information	Organizing information, integrating across potentially inconsistent texts and across formats, making informed decisions
	Making use of information	Transforming information through writing, from text to table, from table to graph, etc., and communicating with relevant parties

Principal Component Analysis Results for the Task-level Attention Scores.

Distribution of the principal component scores extracted from the attention model for Task 1 to Task 5 (green = PC 1, blue = PC 2).

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Shin, J., Wang, B., Pinto Junior, W.N. et al. An engagement-aware predictive model to evaluate problem-solving performance from the study of adult skills' (PIAAC 2012) process data. Large-scale Assess Educ 12 , 6 (2024). https://doi.org/10.1186/s40536-024-00194-y

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Organizing Engagement

Advancing Knowledge of Education Organizing, Engagement, and Equity

Appreciative Inquiry

Appreciative inquiry is an asset-based approach to organizational and social engagement that utilizes questions and dialogue to help participants uncover existing strengths, advantages, or opportunities in their communities, organizations, or teams.

Originally proposed by David Cooperrider and Suresh Srivastva in 1987, Appreciative Inquiry is a theory, methodology, and process of organizational and social change that has given rise over the past few decades to a global network of researchers, practitioners, trainers, and consultants. Appreciative Inquiry — or AI as it is commonly known—grew out of the fields of organizational management, development, and action research, but it has since evolved into a process that is widely used and adapted by engagement professionals and facilitators. Appreciative Inquiry even has its own dedicated international journal called AI Practitioner .

“[A]ction-research has become increasingly rationalized and enculturated to the point where it risks becoming little more than a crude empiricism imprisoned in a deficiency mode of thought. In its conventional form action research has largely failed as an instrument for advancing social knowledge of consequence and has not, therefore, achieved its potential as a vehicle for human development and social-organizational transformation. While the literature consistently signals the worth of action research as a managerial tool for problem-solving (‘first-order’ incremental change), it is conspicuously quiet concerning reports of discontinuous change of the ‘second order’ where organizational paradigms, norms, ideologies, or values are transformed in fundamental ways.” David Cooperrider and Suresh Srivastva, “Appreciative Inquiry in Organizational Life,” Research in Organizational Change and Development (1987)

Appreciative Inquiry is commonly called an “asset-based” or “strengths-based” approach to systems change because it emphasizes positive idea generation over negative problem identification (the later is often framed as a “deficit-based” approach). The model utilizes questions and dialogue to help participants uncover existing assets, strengths, advantages, or opportunities in their communities, organizations, or teams, and then collectively work toward developing and implementing strategies for improvement.

Appreciative Inquiry is grounded in social-constructivist theory, which posits that human development is a fundamentally social process, and that both knowledge (how people come to understand, interpret, and experience the world and others) and organizations (how people organize themselves to achieve goals) are constructed through social and cultural interactions, relationships, and dialogue. In a 2012 overview of the history and foundations of Appreciative Inquiry, Gervase Bushe, a leading researcher in the field, provided the following useful description of social constructivism and its application in Appreciative Inquiry:

In other words, if humans socially construct their perception of the world and others, then certain problems, solutions, ideas, or opportunities will become — depending one’s social or cultural context — either more or less “visible” (and therefore more or less “changeable”). For example, those with social privilege, whether that privilege is due to wealth or membership in a racial majority, may be more likely to perceive social problems as being “caused” by the behaviors of poor people or non-dominant racial groups, rather than being caused by the systems and policies that advantage some (those with wealth and a certain skin color or ethnic background) and disadvantage others.

In this case, if the questions “Whose interests does it serve” and “Is it generative in the service of those interests?” are asked, it may become apparent that framing social problems in terms of negative group behaviors — rather than in terms of systemic structural biases in society that give rise to negative behaviors — serves the interests of those who benefit from that bias, which then perpetuates a worldview that sees inequitable policies as “solutions” to the very problems those inequitable policies created.

Appreciative Inquiry, therefore, could be seen as an attempt to use collective inquiry and dialogue to generate positive ideas that might otherwise be masked by unproductive, though hidden, cultural biases. In this way, positive socially constructed ideas that are revealed and developed through the Appreciative Inquiry process—ideas that might have otherwise remained invisible and unconsidered — become an antidote to negative socially constructed “problems.”

Appreciative Inquiry Model

While Appreciative Inquiry takes many forms, and the approach has been widely adapted for different purposes across the globe, a standard model has emerged in the AI community over the past three decades. The original Appreciative Inquiry framework consisted of four steps—called the 4D Cycle —and five principles, but some practitioners later recognized a fifth step, leading to the creation of a 5D Cycle . For the purposes of comprehensiveness, the 5D Cycle is presented and described here.

The 5D Cycle of Appreciative Inquiry:

1. Definition (Clarifying)

The first step in an Appreciative Inquiry process is defining the central question or topic of the inquiry, dialogue, or engagement process. The definition phase establishes the scope and goals of the inquiry. Importantly, AI emphasizes a positive, solutions-oriented approach to defining the process. While a more traditional “problem-solving” process might begin with collecting data and diagnosing weaknesses, AI begins with positive, asset-based framing questions to determine what’s already working in a community, organization, or team.

According to the Center for Appreciative Inquiry at Champlain College, “The difference is in the questions asked. ‘What can we do to minimize client anger and complaints?’ is an example of an old-style question. In an AI process, we would ask, ‘When have customers been most pleased with our service and what can we learn and apply from those moments of success?’” In the AI community, this step is also sometimes called the “Affirmative Topic.”

2. Discovery (Appreciating)

In the second step of an Appreciative Inquiry process, participants engage in a dialogue designed to surface the most positive features of a community, organization, or team. By beginning with positively framed questions, participants discuss and come to appreciate what’s already working. According to David Cooperrider, one of the co-founders of AI, “This task is accomplished by focusing on peak times of organizational excellence, when people have experienced the organization as most alive and effective. Seeking to understand the unique factors (e.g., leadership, relationships, technologies, core processes, structures, values, learning processes, external relationships, planning methods, and so on) that made the high points possible, people deliberately ‘let go’ of analyses of deficits and systematically seek to isolate and learn from even the smallest wins.”

In some presentations of the model, the Discovery step is divided into two phases: the first phase is to identify and discuss positive, effective, or exceptional moments, events, or periods of success, and the second phase is to look for themes or common elements among those positive moments, events, and successes.

Life Giving Forces: In the parlance of the AI community, these moments and themes are sometimes called “Life Giving Forces” (or LGFs), which the Center for Appreciative Inquiry defines as “elements or experiences within the organization’s past and/or present that represent the organization’s strengths when it is operating at its very best.”

Positive Core: Another common term in the AI community, the “Positive Core” refers to the central assets of community, organization, or team. According to Cooperrider, “AI has demonstrated that human systems grow in the direction of their persistent inquiries, and this propensity is strongest and most sustainable when the means and ends of inquiry are positively correlated. In the AI process, the future is consciously constructed upon the positive core strengths of the organization.”

3. Dream (Envisioning)

In the third step of an Appreciative Inquiry process, participants collaboratively envision a desired future for their community, organization, or team. According to Cooperrider, “One aspect that differentiates AI from other visioning or planning methodologies is that images of the future emerge out of grounded examples from its positive past.” Rather than imagining hypothetical strategies to address past problems, AI asks participants to consciously envision a preferred future that is grounded in past successes but imaginatively and creatively unrestrained.

Provocative Proposition : In the AI community, a “Provocative Proposition” refers to a collectively produced statement or, in some cases, a graphic or illustration that captures the outcome of the dreaming/envisioning process. According to the Center for Appreciative Inquiry, “The provocative proposition bridges the best of ‘what is’ with your/their own speculation or intuition of ‘what might be.’ It is provocative to the extent that it stretches the realm of the status quo, challenges common assumptions or routines, and helps suggest real possibilities that represent desired possibilities for the individual, group, or organization.” In some AI processes, Provocative Propositions are used (or also used) in the Design phase and are sometimes called Possibility Propositions because, as Cooperrider explains, “They bridge ‘the best of what is’ (identified in Discovery ) with ‘what might be’ (imagined in Dream ).”

4. Design (Co-Constructing)

In the fourth step of an Appreciative Inquiry process, participants begin to co-constructively design a new or refashioned community, organization, or team. While participants imagined possibilities in the Dream stage, they start to assemble the practical elements of a plan in the Design stage.

5. Deliver/Destiny (Innovating)

The final step in an Appreciative Inquiry process is the implementation of the collective design. In his original formulations of the model, Cooperrider called this final step Deliver , but later changed it to Destiny because, according to Gervase Busche (2011), “Delivery evoked images of traditional change-management implementation.”

Importantly, the Center for Appreciative Inquiry notes that during this phase communities, organizations, or teams “innovate and improvise ways to create the preferred future by continuously improvising and building AI competencies into the culture,” which includes “noticing and celebrating successes that are moving the system toward the preferred future the organization or group co-created.”

The Principles of Appreciative Inquiry

According to AI Commons, a project of the Center for Appreciative Inquiry, the Principles of Appreciative Inquiry “describe the basic tenets of the underlying AI philosophy” and “serve as the building blocks for all AI work.” While the principles have undergone revision and adaptation over the years, they can be traced back to the original 1987 article on Appreciative Inquiry written by David Cooperrider and Suresh Srivastva.

In a later formulation, Cooperrider and his colleague Diana Whitney (2001) proposed and described the five principles that are now considered standard: Constructionist , Simultaneity , Poetic , Anticipatory , and Positive . The definitions below are taken directly from AI Commons .

The five principles of Appreciative Inquiry:

Constructionist Principle (Words Create Worlds): Reality, as we know it, is a subjective vs. objective state and is socially created through language and conversations.
Simultaneity Principle (Inquiry Creates Change): The moment we ask a question, we begin to create a change. The questions we ask are fateful.
Poetic Principle (We Can Choose What We Study): Teams and organizations, like open books, are endless sources of study and learning. What we choose to study makes a difference. It describes—even creates—the world as we know it.
Anticipatory Principle (Images Inspire Action): Human systems move in the direction of their images of the future. The more positive and hopeful the image of the future, the more positive the present-day action.
Positive Principle (Positive Questions Lead to Positive Change): Momentum for small- or large-scale change requires large amounts of positive affect and social bonding. This momentum is best generated through positive questions that amplify the positive core.

Discussion: Criticisms of Appreciative Inquiry

Several criticisms of the Appreciative Inquiry model have emerged over the years, but the most salient and widely discussed tend to focus on (1) the lack of strong evidence supporting the model’s efficacy and (2) the model’s emphasis on positivity. In addition, the evangelical manner and idolatry of some practitioners in the AI community, as well as the community’s sometimes quasi-mystical language, have made some observers skeptical of both the AI process and the objectivity of the AI community.

When theoretical, conceptual, or procedural models are applied in community, organization, or team processes, the efficacy of a given model will depend on the quality of implementation, which can encompass a wide range of complex factors that can positively or negatively impact outcomes (e.g.: Was the facilitation strong or weak? Did the facilitators understand the model and did they maintain fidelity to the model? Was a sufficient amount of time allocated for the process? Or was the process rushed? Etc.). Consequently, it is often difficult to determine what may have gone right or wrong with the application of a given model or process.

In a 2005 article, Gervase Bushe and Aniq F. Kassam discuss the results of a “meta-case analysis” of AI applications that found only 35% of the 20 cases studied resulted in “transformational outcomes.” While in all 20 cases the practitioners followed the recommended Appreciative Inquiry principles, methods, and processes, Bushe and Kassam conclude that two qualities of appreciative inquiry are necessary to achieving AI’s transformative potential: “(a) a focus on changing how people think instead of what people do, and (b) a focus on supporting self-organizing change processes that flow from new ideas.”

Perhaps the most conspicuous criticisms of Appreciative Inquiry center on the model’s insistence on positive framing. In “Appreciative Inquiry: Theory and Critique” (2011), and in his 2012 article on the history and foundations of Appreciative Inquiry, Gervase Bushe discusses and responds to the major criticisms that have emerged over the past three decades.

In some cases, critics of AI claim that positive transformational change is unlikely to take hold in a community, organization, or team if problems are ignored, overlooked, and left unaddressed, though proponents of AI would argue that “deficit-based” processes also have their own problems and downsides, including ample evidence that more traditional problem-oriented approaches also routinely fail to result in positive transformational change.

One compelling argument against AI’s emphasis on positivity, however, is that community, organization, or team leaders may use AI’s positive framing to shutting down discussion of problems. In this case, for example, an organization’s directors may have a vested interest in avoiding discussions of problems in the organization because leadership quality may be cited as one of the organization’s biggest problems. As Gervase and others have discussed, AI does not necessarily exclude all forms of negativity, and AI processes can be designed to frame discussions of problems in ways that are “generative” and productive.

Perhaps the most potentially problematic dimension of Appreciative Inquiry’s positive framing is that an AI process may be used in ways that reinforce and perpetuate racial or cultural bias, prejudice, and discrimination. By insisting that an inquiry, dialogue, or engagement process focus exclusively on positive questions, comments, and ideas, for example, the AI process can potentially be used—intentionally or unintentionally—in ways that silence legitimate concerns and criticisms raised by the victims of bias, prejudice, and discrimination.

When applied to equity-based dialogues or engagement processes, AI’s perceived prohibition on negativity raises both serious and well-founded concerns, given that silencing legitimate anger, frustration, and complaints is a ubiquitous feature of inequitable, discriminatory, and oppressive systems. Consequently, engagement professionals and practitioners should be mindful of their cultural biases and motivations when facilitating AI-based processes, especially in diverse communities and workplaces, and they should consider adaptations that do not stifle necessary discussions about uncomfortable or troubling issues such as racial prejudice, gender discrimination, or workplace abuse.

For more detailed discussions of these issues, see “Embracing the Shadow through Appreciative Inquiry,” the November 2012 issue of AI Practitioner: The International Journal of Appreciative Inquiry .

Acknowledgments

Organizing Engagement thanks Larissa Loures for helpfully pointing out and correcting an error in this introduction.

Additional Resources

AI Practitioner: The International Journal of Appreciative Inquiry
Center for Appreciative Inquiry

Bushe, G. R. (2012). Foundations of appreciative inquiry: History, criticism and potential. AI Practitioner , 14(1), 8–20.

Bushe, G. R. (2011). Appreciative inquiry: Theory and critique. In Boje, D., Burnes, B. and Hassard, J. (Eds.). The Routledge Companion To Organizational Change (pp. 87–103). Oxford, UK: Routledge.

Bushe, G. R. & Kassam, A. F. (2005). When is appreciative inquiry transformational? A meta-case analysis. The Journal of Applied Behavioral Science , 41(2), 161–181.

Center for Appreciative Inquiry. Generic process of appreciative inquiry . Retrieved from centerforappreciativeinquiry.net/more-on-ai/the-generic-processes-of-appreciative-inquiry .

Cooperrider, D. L. & Srivastva, S. (1987). Appreciative inquiry in organizational life. In Woodman, R. W. and Pasmore, W.A. (Eds.), Research in Organizational Change and Development, 129–169. Stamford, CT: JAI Press.

Coopperrider, D. L., & Whitney, D. (2001). A positive revolution in change. In D. L. Cooperrider, P. Sorenson, D. Whitney, & T. Yeager (Eds.), Appreciative Inquiry: An Emerging Direction for Organization Development (pp. 9–29). Champaign, IL: Stipes.

David Cooperrider and Associates. What is appreciative inquiry? Retrieved from davidcooperrider.com/ai-process .

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Relational Nutrients

Dr. John Townsend

November 13, 2014.

Leaders must have the skill of having problem solving talks and conducting them successfully. This is not an easy thing to do, but it is important and valuable. In fact, some of the best gains that can happen in an organization can happen at a tough talk, when people are honest, direct and still respectful of each other. Problems are solved, and solutions are followed.

When I train executive and management teams, I set them up with “rules of engagement ” when there is a specific conflict or problem, to keep the process going. I present them, we discuss them and make sure everyone understands and buys in, and things work more seamlessly. Here are some of the basic rules for leadership that will help you:

The leader presents the issue and the desired outcome. This is the leader’s job, and it keeps things focused, for example, “Sales have been slow, and there is conflict over the problem. Some people say it’s the sales department, some say it’s fulfillment and some say it’s marketing. We are going to get input from everyone, find reality, and come up with solutions that work.”
Everyone commits to a solution that is for the good of the company as a whole. Ask the players to agree that, as much as possible, they will stay away from protecting their turf, and engage with thinking that is for the best idea that guards the mission of the company. Simply saying this helps people to be more objective.
Everyone presents their side without interruption. No ifs, ands or but. Everybody gets their opinion out there.
In order to provide a response, the person must first paraphrase the previous person’s comments to that person’s satisfaction. In other words, you don’t go ahead until the person says, “Yes, you understand my point of view.” I can’t emphasize how important this is. Too many meetings are just a sequence of people saying “Well I think X” and “Well, I think Y”, and there is simply no connection between the thoughts. Here’s how it would look: “Sandra, I hear you saying that marketing has been unclear in its strategy, and that has led to the sales slump. Do I get it?” Sandra: “No, that’s not really what I was saying”, and you try again. This tip will save you hours and hours of time!
Someone is a timekeeper. When there is no time structure, meetings can go way too long. Estimate what this decision will take: “I think we should be able to have a solution in 30 minutes, let’s go.” Our brains respond to structure like this.
During the last third of the time, press for solutions. Even if you don’t have 100% of the info and discussion done, as long as most of it is done, start saying, “OK, what is our best solution for this?” Don’t get lost in the paralysis of analysis.

Problem solving talks are a permanent reality for every leader. Learn and execute these skills.

If your meeting gets off track, there are 6 words you can use to redirect the group. The video below reveals these words:

Craig Kautsch “True Grit” podcast

Vulnerability: absolutely risky. and absolutely necessary, nyt: how to set pandemic boundaries for relatives.

Dr. John Townsend On Structure, Connection, & Boundaries In A Pandemic with Jen Hatmaker
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Jan 11, 2021

Ever wonder what the meaning of emotional intelligence really is? My next guest is Dr. John Townsend has written over 30 books on the topic. He has sold over 10 million copies, including the New York Times bestselling Boundaries series. John is a nationally-known...

Vulnerability: Absolutely Risky. And Absolutely Necessary

Nov 3, 2020

You and I need people in our lives who care deeply about us, who are safe, and who want our best. We want them to “be there” with us, to encourage us when we are discouraged, to give us insight on what’s going on, and to call us to the right action steps to make life...

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Aug 26, 2020

Dr. Townsend had the privilege of being interviewed for this informative and timely article by the New York Times. Article Excerpt: Be a conduit, not a lifeline ... "When you feel overwhelmed, Dr. Townsend recommends that you create a list of all of your...

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In this episode of the McKinsey Podcast, Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.. Podcast transcript. Simon London: Hello, and welcome to this episode of the McKinsey Podcast, with me, Simon London.
The Ultimate Problem-Solving Process Guide: 31 Steps & Resources
Then this method delves into the following stages: Discovery (fact-finding) Dream (visioning the future) Design (strategic purpose) Destiny (continuous improvement) 3. "FIVE WHYS" METHOD. This method simply suggests that we ask "Why" at least five times during our review of the problem and in search of a fix.
PDF CLIENT SYSTEM ASSESSMENT TOOLS FOR SOCIAL WORK PRACTICE By ...
1. Compton and Galaway feature Phases of the Problem-Solving Model: Phase I - Contact or Engagement Phase Phase II - Contract Phase, including assessment Phase III - Action Phase, including evaluation (1989, p. 389-391) 2. Johnson features the Stages of the Problem-Solving Process: Stage 1 - Preliminary statement of the problem
PDF Measuring Cognitive Engagement: Instruments and Techniques
strategically across the learning or problem-solving process in a specific task Task level Being strategic or self-regulating Therefore, a review that summarizes the studies that have measured the construct of cognitive engagement is crucial. On the one hand, it will help researchers better understand this divergent research base. On the other
An engagement-aware predictive model to evaluate problem-solving
The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's performance in sequential, interactive problem ...
Problem-Based Learning: An Overview of its Process and Impact on
Problem-based learning (PBL) has been widely adopted in diverse fields and educational contexts to promote critical thinking and problem-solving in authentic learning situations. Its close affiliation with workplace collaboration and interdisciplinary learning contributed to its spread beyond the traditional realm of clinical education 1 to ...
Appreciative Inquiry
Appreciative Inquiry is an asset-based approach to organizational and social engagement that utilizes questions and dialogue to help participants uncover existing strengths, advantages, or opportunities in their communities, organizations, or teams. Originally proposed by David Cooperrider and Suresh Srivastva in 1987, Appreciative Inquiry is a ...
PDF Fostering Student Engagement: Creative Problem-Solving in Small Group
ouched in small group facilitations to support peer learning.IntroductionCreativeProblem-Solving (CPS) is a powerful teaching method that can support a pedagogica. shift in the classroom and foster both student engagement and motivation to learn. Caswell (2006) describe. it as an approach to finding workable answers to problems that exist in ...
PDF Problem Based Learning: A Student-Centered Approach
6). Problem solving can incites for learning. 7). Throughout the learning process, critical reflection happens The main important point of this approach is that students are responsible for their own learning, learn how to use prior knowledge and the way of knowledge acquisition. The PBL approach gives more focus on self and peer
PDF The Social Work Process
e Social Work Process 3S ocial workers traditionally use a series of steps or processes to help clients. resolve their problems. These steps include collecting informa-tion about the client (assessment), making sense out of the information (diagnosis), collaborating with the client to develop a plan to change the problems being experienced (the ...
PDF Creative Problem Solving
CPS is a comprehensive system built on our own natural thinking processes that deliberately ignites creative thinking and produces innovative solutions. Through alternating phases of divergent and convergent thinking, CPS provides a process for managing thinking and action, while avoiding premature or inappropriate judgment. It is built upon a ...
7 Problem-Solving Skills That Can Help You Be a More ...
Although problem-solving is a skill in its own right, a subset of seven skills can help make the process of problem-solving easier. These include analysis, communication, emotional intelligence, resilience, creativity, adaptability, and teamwork. 1. Analysis. As a manager, you'll solve each problem by assessing the situation first.
Rules of Engagement for Problem Solving Conversations
Here are some of the basic rules for leadership that will help you: The leader presents the issue and the desired outcome. This is the leader's job, and it keeps things focused, for example, "Sales have been slow, and there is conflict over the problem. Some people say it's the sales department, some say it's fulfillment and some say it ...
PDF A PROCESS OVERVIEW The Stakeholder Engagement Process
THE STAKEHOLDER ENGAGEMENT PROCESS WHO GET THE SYSTEM IN THE ROOM WHY HELP PEOPLE SEE THE SYSTEM WHAT CO-CREATE SOLUTIONS TOGETHER HOW REDESIGN THE SYSTEM PROBLEM-SOLVING SYSTEMS-BUILDING PILOT TEAMS Relationships: Transforming a system is about transforming relationships. A system is a set of relationships. The "system" is the way we work ...
Badge: SkillsBuild
This credential earner knows best practices essential to resolving client problems through organization, retrieval, and usage of resources and information essential to customer success operations. The individual understands concepts and methods related to Service Level Agreements (SLAs), application of support ticketing systems, Knowledge-Centered Service (KCS) methodology, and application of ...