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• What Is Root Cause Analysis? | Definition & Examples

What Is Root Cause Analysis? | Definition & Examples

Published on January 6, 2023 by Tegan George . Revised on November 17, 2023.

Root cause analysis is a problem-solving approach that uses the analogy of roots and blooms to model cause-and-effect relationships. Rather than focusing on what’s above the surface, root cause analysis troubleshoots solutions to problems by analyzing what is causing them. Note Similarly to exploratory research , it’s important to remember that root cause analysis does not provide solutions to problems. Rather, it’s one method within a larger problem-solving landscape.

Root cause analysis is a form of quality management, often used in organizational management, quality control, and in healthcare fields like nursing. Root cause analysis can be a helpful study tool for students, too, when used for brainstorming or memorization exercises.

Table of contents

Root cause analysis template, the “5 whys” of root cause analysis, advantages and disadvantages of root cause analysis, other interesting articles, frequently asked questions.

It’s easy to draw root cause analysis charts by hand, on a whiteboard or a big piece of paper. Many people use fishbone diagrams as well, or you can download our template below.

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One of the most common ways to conduct root cause analysis is using the “5 Whys” method, popular in lean management. The 5 Whys are an interconnected method of analysis: after defining your problem, you ask “why?”  and answer as concisely as possible. The first “why” often leads to the second, which leads to the third, etc.

In short, you continue to ask “why” until the answer provided is no longer a contributor to the broader issue, but a possible solution to that issue. In other words, as you strategize, you’ll sense it’s time to stop when a provided answer has the potential to stop the whole problem from occurring, rather than only one aspect of that problem. This often takes 3-5 “whys” but can definitely stretch out for longer.

You can use this template to map out your whys.

Root cause analysis is a great way to organize your thoughts, but its simplicity leads to a few downsides.

• Great brainstorming tool for individual or group projects.
• Can help identify causal relationships and clarify relationships between variables .
• “5 whys” system can help simplify complex issues and drive possible solutions.

Disadvantages

• Can be overly simplistic, not leaving much room for nuance or variations.
• Path dependence can occur if the wrong question is asked, leading to incorrect conclusions.
• Cannot provide answers, only suggestions, so best used in the exploratory research phase .

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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There are several common tools used for root cause analysis , the most popular of which include fishbone diagrams , scatterplots, and the “5 whys.”

A fishbone diagram is a method that can be used to conduct root cause analysis.

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Problem solving through values: A challenge for thinking and capability development

• • This paper introduces the 4W framework of consistent problem solving through values.
• • The 4W suggests when, how and why the explication of values helps to solve a problem.
• • The 4W is significant to teach students to cope with problems having crucial consequences.
• • The paper considers challenges using such framework of thinking in different fields of education.

The paper aims to introduce the conceptual framework of problem solving through values. The framework consists of problem analysis, selection of value(s) as a background for the solution, the search for alternative ways of the solution, and the rationale for the solution. This framework reveals when, how, and why is important to think about values when solving problems. A consistent process fosters cohesive and creative value-based thinking during problem solving rather than teaching specific values. Therefore, the framework discloses the possibility for enabling the development of value-grounded problem solving capability.The application of this framework highlights the importance of responsibility for the chosen values that are the basis for the alternatives which determine actions. The 4W framework is meaningful for the people’s lives and their professional work. It is particularly important in the process of future professionals’ education. Critical issues concerning the development of problem solving through values are discussed when considering and examining options for the implementation of the 4W framework in educational institutions.

1. Introduction

The core competencies necessary for future professionals include problem solving based on complexity and collaborative approaches ( OECD, 2018 ). Currently, the emphasis is put on the development of technical, technological skills as well as system thinking and other cognitive abilities (e.g., Barber, 2018 ; Blanco, Schirmbeck, & Costa, 2018 ). Hence, education prepares learners with high qualifications yet lacking in moral values ( Nadda, 2017 ). Educational researchers (e.g., Barnett, 2007 ; Harland & Pickering, 2010 ) stress that such skills and abilities ( the how? ), as well as knowledge ( the what? ), are insufficient to educate a person for society and the world. The philosophy of education underlines both the epistemological and ontological dimensions of learning. Barnett (2007) points out that the ontological dimension has to be above the epistemological one. The ontological dimension encompasses the issues related to values that education should foster ( Harland & Pickering, 2010 ). In addition, values are closely related to the enablement of learners in educational environments ( Jucevičienė et al., 2010 ). For these reasons, ‘ the why ?’ based on values is required in the learning process. The question arises as to what values and how it makes sense to educate them. Value-based education seeks to address these issues and concentrates on values transfer due to their integration into the curriculum. Yazdani and Akbarilakeh (2017) discussed that value-based education could only convey factual knowledge of values and ethics. However, such education does not guarantee the internalization of values. Nevertheless, value-based education indicates problem solving as one of the possibilities to develop values.

Values guide and affect personal behavior encompassing the ethical aspects of solutions ( Roccas, Sagiv, & Navon, 2017 ; Schwartz, 1992 , 2012 ; Verplanken & Holland, 2002 ). Therefore, they represent the essential foundation for solving a problem. Growing evidence indicates the creative potential of values ( Dollinger, Burke, & Gump, 2007 ; Kasof, Chen, Himsel, & Greenberger, 2007 ; Lebedeva et al., 2019) and emphasizes their significance for problem solving. Meanwhile, research in problem solving pays little attention to values. Most of the problem solving models (e.g., Newell & Simon, 1972 ; Jonassen, 1997 ) utilize a rational economic approach. Principally, the research on the mechanisms of problem solving have been conducted under laboratory conditions performing simple tasks ( Csapó & Funke, 2017 ). Moreover, some of the decision-making models share the same steps as problem solving (c.f., Donovan, Guss, & Naslund, 2015 ). This explains why these terms are sometimes used interchangeably ( Huitt, 1992 ). Indeed, decision-making is a part of problem solving, which emerges while choosing between alternatives. Yet, values, moral, and ethical issues are more common in decision-making research (e.g., Keeney, 1994 ; Verplanken & Holland, 2002 ; Hall & Davis, 2007 ; Sheehan & Schmidt, 2015 ). Though, research by Shepherd, Patzelt, and Baron (2013) , Baron, Zhao, and Miao (2015) has affirmed that contemporary business decision makers rather often leave aside ethical issues and moral values. Thus, ‘ethical disengagement fallacy’ ( Sternberg, 2017, p.7 ) occurs as people think that ethics is more relevant to others. In the face of such disengagement, ethical issues lose their prominence.

The analysis of the literature revealed a wide field of problem solving research presenting a range of more theoretical insights rather empirical evidence. Despite this, to date, a comprehensive model that reveals how to solve problems emphasizing thinking about values is lacking. This underlines the relevance of the chosen topic, i.e. a challenge for thinking and for the development of capabilities addressing problems through values. To address this gap, the following issues need to be investigated: When, how, and why a problem solver should take into account values during problem solving? What challenges may occur for using such framework of thinking in different fields of education? Aiming this, the authors of the paper substantiated the conceptual framework of problem solving grounded in consistent thinking about values. The substantiation consists of several parts. First, different approaches to solving problems were examined. Second, searching to reveal the possibilities of values integration into problem solving, value-based approaches significant for problem solving were critically analyzed. Third, drawing on the effect of values when solving a problem and their creative potential, the authors of this paper claim that the identification of values and their choice for a solution need to be specified in the process of problem solving. As a synthesis of conclusions coming from the literature review and conceptual extensions regarding values, the authors of the paper created the coherent framework of problem solving through values (so called 4W).

The novelty of the 4W framework is exposed by several contributions. First, the clear design of overall problem solving process with attention on integrated thinking about values is used. Unlike in most models of problem solving, the first stage encompass the identification of a problem, an analysis of a context and the perspectives that influence the whole process, i.e. ‘What?’. The stage ‘What is the basis for a solution?’ focus on values identification and their choice. The stage ‘Ways how?’ encourages to create alternatives considering values. The stage ‘Why?’ represent justification of a chosen alternative according particular issues. Above-mentioned stages including specific steps are not found in any other model of problem solving. Second, even two key stages nurture thinking about values. The specificity of the 4W framework allows expecting its successful practical application. It may help to solve a problem more informed revealing when and how the explication of values helps to reach the desired value-based solution. The particular significance is that the 4W framework can be used to develop capabilities to solve problems through values. The challenges to use the 4W framework in education are discussed.

2. Methodology

To create the 4W framework, the integrative literature review was chosen. According to Snyder (2019) , this review is ‘useful when the purpose of the review is not to cover all articles ever published on the topic but rather to combine perspectives to create new theoretical models’ (p.334). The scope of this review focused on research disclosing problem solving process that paid attention on values. The following databases were used for relevant information search: EBSCO/Hostdatabases (ERIC, Education Source), Emerald, Google Scholar. The first step of this search was conducted using integrated keywords problem solving model , problem solving process, problem solving steps . These keywords were combined with the Boolean operator AND with the second keywords values approach, value-based . The inclusion criteria were used to identify research that: presents theoretical backgrounds and/or empirical evidences; performed within the last 5 years; within an educational context; availability of full text. The sources appropriate for this review was very limited in scope (N = 2).

We implemented the second search only with the same set of the integrated keywords. The inclusion criteria were the same except the date; this criterion was extended up to 10 years. This search presented 85 different sources. After reading the summaries, introductions and conclusions of the sources found, the sources that do not explicitly provide the process/models/steps of problem solving for teaching/learning purposes and eliminates values were excluded. Aiming to see a more accurate picture of the chosen topic, we selected secondary sources from these initial sources.

Several important issues were determined as well. First, most researchers ground their studies on existing problem solving models, however, not based on values. Second, some of them conducted empirical research in order to identify the process of studies participants’ problem solving. Therefore, we included sources without date restrictions trying to identify the principal sources that reveal the process/models/steps of problem solving. Third, decision-making is a part of problem solving process. Accordingly, we performed a search with the additional keywords decision-making AND values approach, value-based decision-making . We used such inclusion criteria: presents theoretical background and/or empirical evidence; no date restriction; within an educational context; availability of full text. These all searches resulted in a total of 16 (9 theoretical and 7 empirical) sources for inclusion. They were the main sources that contributed most fruitfully for the background. We used other sources for the justification the wholeness of the 4W framework. We present the principal results of the conducted literature review in the part ‘The background of the conceptual framework’.

3. The background of the conceptual framework

3.1. different approaches of how to solve a problem.

Researchers from different fields focus on problem solving. As a result, there still seems to be a lack of a conventional definition of problem solving. Regardless of some differences, there is an agreement that problem solving is a cognitive process and one of the meaningful and significant ways of learning ( Funke, 2014 ; Jonassen, 1997 ; Mayer & Wittrock, 2006 ). Differing in approaches to solving a problem, researchers ( Collins, Sibthorp, & Gookin, 2016 ; Jonassen, 1997 ; Litzinger et al., 2010 ; Mayer & Wittrock, 2006 ; O’Loughlin & McFadzean, 1999 ; ect.) present a variety of models that differ in the number of distinct steps. What is similar in these models is that they stress the procedural process of problem solving with the focus on the development of specific skills and competences.

For the sake of this paper, we have focused on those models of problem solving that clarify the process and draw attention to values, specifically, on Huitt (1992) , Basadur, Ellspermann, and Evans (1994) , and Morton (1997) . Integrating the creative approach to problem solving, Newell and Simon (1972) presents six phases: phase 1 - identifying the problem, phase 2 - understanding the problem, phase 3 - posing solutions, phase 4 - choosing solutions, phase 5 - implementing solutions, and phase 6 - final analysis. The weakness of this model is that these phases do not necessarily follow one another, and several can coincide. However, coping with simultaneously occurring phases could be a challenge, especially if these are, for instance, phases five and six. Certainly, it may be necessary to return to the previous phases for further analysis. According to Basadur et al. (1994) , problem solving consists of problem generation, problem formulation, problem solving, and solution implementation stages. Huitt (1992) distinguishes four stages in problem solving: input, processing, output, and review. Both Huitt (1992) and Basadur et al. (1994) four-stage models emphasize a sequential process of problem solving. Thus, problem solving includes four stages that are used in education. For example, problem-based learning employs such stages as introduction of the problem, problem analysis and learning issues, discovery and reporting, solution presentation and evaluation ( Chua, Tan, & Liu, 2016 ). Even PISA 2012 framework for problem solving composes four stages: exploring and understanding, representing and formulating, planning and executing, monitoring and reflecting ( OECD, 2013 ).

Drawing on various approaches to problem solving, it is possible to notice that although each stage is named differently, it is possible to reveal some general steps. These steps reflect the essential idea of problem solving: a search for the solution from the initial state to the desirable state. The identification of a problem and its contextual elements, the generation of alternatives to a problem solution, the evaluation of these alternatives according to specific criteria, the choice of an alternative for a solution, the implementation, and monitoring of the solution are the main proceeding steps in problem solving.

3.2. Value-based approaches relevant for problem solving

Huitt (1992) suggests that important values are among the criteria for the evaluation of alternatives and the effectiveness of a chosen solution. Basadur et al. (1994) point out to visible values in the problem formulation. Morton (1997) underlines that interests, investigation, prevention, and values of all types, which may influence the process, inspire every phase of problem solving. However, the aforementioned authors do not go deeper and do not seek to disclose the significance of values for problem solving.

Decision-making research shows more possibilities for problem solving and values integration. Sheehan and Schmidt (2015) model of ethical decision-making includes moral sensitivity, moral judgment, moral motivation, and moral action where values are presented in the component of moral motivation. Another useful approach concerned with values comes from decision-making in management. It is the concept of Value-Focused Thinking (VFT) proposed by Keeney (1994) . The author argues that the goals often are merely means of achieving results in traditional models of problem solving. Such models frequently do not help to identify logical links between the problem solving goals, values, and alternatives. Thus, according to Keeney (1994) , the decision-making starts with values as they are stated in the goals and objectives of decision-makers. VFT emphasizes the core values of decision-makers that are in a specific context as well as how to find a way to achieve them by using means-ends analysis. The weakness of VFT is its restriction to this means-ends analysis. According to Shin, Jonassen, and McGee (2003) , in searching for a solution, such analysis is weak as the problem solver focuses simply on removing inadequacies between the current state and the goal state. The strengths of this approach underline that values are included in the decision before alternatives are created. Besides, values help to find creative and meaningful alternatives and to assess them. Further, they include the forthcoming consequences of the decision. As VFT emphasizes the significant function of values and clarifies the possibilities of their integration into problem solving, we adapt this approach in the current paper.

3.3. The effect of values when solving a problem

In a broader sense, values provide a direction to a person’s life. Whereas the importance of values is relatively stable over time and across situations, Roccas et al. (2017) argue that values differ in their importance to a person. Verplanken and Holland (2002) investigated the relationship between values and choices or behavior. The research revealed that the activation of a value and the centrality of a value to the self, are the essential elements for value-guided behavior. The activation of values could happen in such cases: when values are the primary focus of attention; if the situation or the information a person is confronted with implies values; when the self is activated. The centrality of a particular value is ‘the degree to which an individual has incorporated this value as part of the self’ ( Verplanken & Holland, 2002, p.436 ). Thus, the perceived importance of values and attention to them determine value-guided behavior.

According to Argandoña (2003) , values can change due to external (changing values in the people around, in society, changes in situations, etc.) and internal (internalization by learning) factors affecting the person. The research by Hall and Davis (2007) indicates that the decision-makers’ applied value profile temporarily changed as they analyzed the issue from multiple perspectives and revealed the existence of a broader set of values. The study by Kirkman (2017) reveal that participants noticed the relevance of moral values to situations they encountered in various contexts.

Values are tightly related to personal integrity and identity and guide an individual’s perception, judgment, and behavior ( Halstead, 1996 ; Schwartz, 1992 ). Sheehan and Schmidt (2015) found that values influenced ethical decision-making of accounting study programme students when they uncovered their own values and grounded in them their individual codes of conduct for future jobs. Hence, the effect of values discloses by observing the problem solver’s decision-making. The latter observations could explain the abundance of ethics-laden research in decision-making rather than in problem solving.

Contemporary researchers emphasize the creative potential of values. Dollinger et al. (2007) , Kasof et al. (2007) , Lebedeva, Schwartz, Plucker, & Van De Vijver, 2019 present to some extent similar findings as they all used Schwartz Value Survey (respectively: Schwartz, 1992 ; ( Schwartz, 1994 ), Schwartz, 2012 ). These studies disclosed that such values as self-direction, stimulation and universalism foster creativity. Kasof et al. (2007) focused their research on identified motivation. Stressing that identified motivation is the only fully autonomous type of external motivation, authors define it as ‘the desire to commence an activity as a means to some end that one greatly values’ (p.106). While identified motivation toward specific values (italic in original) fosters the search for outcomes that express those specific values, this research demonstrated that it could also inhibit creative behavior. Thus, inhibition is necessary, especially in the case where reckless creativity could have painful consequences, for example, when an architect creates a beautiful staircase without a handrail. Consequently, creativity needs to be balanced.

Ultimately, values affect human beings’ lives as they express the motivational goals ( Schwartz, 1992 ). These motivational goals are the comprehensive criteria for a person’s choices when solving problems. Whereas some problem solving models only mention values as possible evaluation criteria, but they do not give any significant suggestions when and how the problem solver could think about the values coming to the understanding that his/her values direct the decision how to solve the problem. The authors of this paper claim that the identification of personal values and their choice for a solution need to be specified in the process of problem solving. This position is clearly reflected in humanistic philosophy and psychology ( Maslow, 2011 ; Rogers, 1995 ) that emphasize personal responsibility for discovering personal values through critical questioning, honest self-esteem, self-discovery, and open-mindedness in the constant pursuit of the truth in the path of individual life. However, fundamental (of humankind) and societal values should be taken into account. McLaughlin (1997) argues that a clear boundary between societal and personal values is difficult to set as they are intertwined due to their existence in complex cultural, social, and political contexts at a particular time. A person is related to time and context when choosing values. As a result, a person assumes existing values as implicit knowledge without as much as a consideration. This is particularly evident in the current consumer society.

Moreover, McLaughlin (1997) stresses that if a particular action should be tolerated and legitimated by society, it does not mean that this action is ultimately morally acceptable in all respects. Education has possibilities to reveal this. One such possibility is to turn to the capability approach ( Sen, 1990 ), which emphasizes what people are effectively able to do and to be. Capability, according to Sen (1990) , reflects a person’s freedom to choose between various ways of living, i.e., the focus is on the development of a person’s capability to choose the life he/she has a reason to value. According to Webster (2017) , ‘in order for people to value certain aspects of life, they need to appreciate the reasons and purposes – the whys – for certain valuing’ (italic in original; p.75). As values reflect and foster these whys, education should supplement the development of capability with attention to values ( Saito, 2003 ). In order to attain this possibility, a person has to be aware of and be able to understand two facets of values. Argandoña (2003) defines them as rationality and virtuality . Rationality refers to values as the ideal of conduct and involves the development of a person’s understanding of what values and why he/she should choose them when solving a problem. Virtuality approaches values as virtues and includes learning to enable a person to live according to his/her values. However, according to McLaughlin (1997) , some people may have specific values that are deep or self-evidently essential. These values are based on fundamental beliefs about the nature and purpose of the human being. Other values can be more or less superficial as they are based on giving priority to one or the other. Thus, virtuality highlights the depth of life harmonized to fundamentally rather than superficially laden values. These approaches inform the rationale for the framework of problem solving through values.

4. The 4W framework of problem solving through values

Similar to the above-presented stages of the problem solving processes, the introduced framework by the authors of this paper revisits them (see Fig. 1 ). The framework is titled 4W as its four stages respond to such questions: Analyzing the Problem: W hat ? → Choice of the value(s): W hat is the background for the solution? → Search for the alternative w ays of the solution: How ? → The rationale for problem solution: W hy is this alternative significant ? The stages of this framework cover seven steps that reveal the logical sequence of problem solving through values.

The 4 W framework: problem solving through values.

Though systematic problem solving models are criticized for being linear and inflexible (e.g., Treffinger & Isaksen, 2005 ), the authors of this paper assume a structural view of the problem solving process due to several reasons. First, the framework enables problem solvers to understand the thorough process of problem solving through values. Second, this framework reveals the depth of each stage and step. Third, problem solving through values encourages tackling problems that have crucial consequences. Only by understanding and mastering the coherence of how problems those require a value-based approach need to be addressed, a problem solver will be able to cope with them in the future. Finally, this framework aims at helping to recognize, to underline personal values, to solve problems through thinking about values, and to take responsibility for choices, even value-based. The feedback supports a direct interrelation between stages. It shapes a dynamic process of problem solving through values.

The first stage of problem solving through values - ‘ The analysis of the problem: What? ’- consists of three steps (see Fig. 1 ). The first step is ‘ Recognizing the problematic situation and naming the problem ’. This step is performed in the following sequence. First, the problem solver should perceive the problematic situation he/she faces in order to understand it. Dostál (2015) argues that the problematic situation has the potential to become the problem necessary to be addressed. Although each problem is limited by its context, not every problematic situation turns into a problem. This is related to the problem solver’s capability and the perception of reality: a person may not ‘see’ the problem if his/her capability to perceive it is not developed ( Dorst, 2006 ; Dostál, 2015 ). Second, after the problem solver recognizes the existence of the problematic situation, the problem solver has to identify the presence or absence of the problem itself, i.e. to name the problem. This is especially important in the case of the ill-structured problems since they cannot be directly visible to the problem solver ( Jonassen, 1997 ). Consequently, this step allows to determine whether the problem solver developed or has acquired the capability to perceive the problematic situation and the problem (naming the problem).

The second step is ‘ Analysing the context of the problem as a reason for its rise ’. At this step, the problem solver aims to analyse the context of the problem. The latter is one of the external issues, and it determines the solution ( Jonassen, 2011 ). However, if more attention is paid to the solution of the problem, it diverts attention from the context ( Fields, 2006 ). The problem solver has to take into account both the conveyed and implied contextual elements in the problematic situation ( Dostál, 2015 ). In other words, the problem solver has to examine it through his/her ‘contextual lenses’ ( Hester & MacG, 2017 , p.208). Thus, during this step the problem solver needs to identify the elements that shape the problem - reasons and circumstances that cause the problem, the factors that can be changed, and stakeholders that are involved in the problematic situation. Whereas the elements of the context mentioned above are within the problematic situation, the problem solver can control many of them. Such control can provide unique ways for a solution.

Although the problem solver tries to predict the undesirable results, some criteria remain underestimated. For that reason, it is necessary to highlight values underlying the various possible goals during the analysis ( Fields, 2006 ). According to Hester and MacG (2017) , values express one of the main features of the context and direct the attention of the problem solver to a given problematic situation. Hence, the problem solver should explore the value-based positions that emerge in the context of the problem.

The analysis of these contextual elements focus not only on a specific problematic situation but also on the problem that has emerged. This requires setting boundaries of attention for an in-depth understanding ( Fields, 2006 ; Hester & MacG, 2017 ). Such understanding influences several actions: (a) the recognition of inappropriate aspects of the problematic situation; (b) the emergence of paths in which identified aspects are expected to change. These actions ensure consistency and safeguard against distractions. Thus, the problem solver can now recognize and identify the factors that influence the problem although they are outside of the problematic situation. However, the problem solver possesses no control over them. With the help of such context analysis, the problem solver constructs a thorough understanding of the problem. Moreover, the problem solver becomes ready to look at the problem from different perspectives.

The third step is ‘ Perspectives emerging in the problem ’. Ims and Zsolnai (2009) argue that problem solving usually contains a ‘problematic search’. Such a search is a pragmatic activity as the problem itself induces it. Thus, the problem solver searches for a superficial solution. As a result, the focus is on control over the problem rather than a deeper understanding of the problem itself. The analysis of the problem, especially including value-based approaches, reveals the necessity to consider the problem from a variety of perspectives. Mitroff (2000) builds on Linstone (1989) ideas and claims that a sound foundation of both naming and solving any problem lays in such perspectives: the technical/scientific, the interpersonal/social, the existential, and the systemic (see Table 1 ).

The main characteristics of four perspectives for problem solving

Whereas all problems have significant aspects of each perspective, disregarding one or another may lead to the wrong way of solving the problem. While analysing all four perspectives is essential, this does not mean that they all are equally important. Therefore, it is necessary to justify why one or another perspective is more relevant and significant in a particular case. Such analysis, according to Linstone (1989) , ‘forces us to distinguish how we are looking from what we are looking at’ (p.312; italic in original). Hence, the problem solver broadens the understanding of various perspectives and develops the capability to see the bigger picture ( Hall & Davis, 2007 ).

The problem solver aims to identify and describe four perspectives that have emerged in the problem during this step. In order to identify perspectives, the problem solver search answers to the following questions. First, regarding the technical/scientific perspective: What technical/scientific reasons are brought out in the problem? How and to what extent do they influence a problem and its context? Second, regarding the interpersonal/social perspective: What is the impact of the problem on stakeholders? How does it influence their attitudes, living conditions, interests, needs? Third, regarding the existential perspective: How does the problem affect human feelings, experiences, perception, and/or discovery of meaning? Fourth, regarding the systemic perspective: What is the effect of the problem on the person → community → society → the world? Based on the analysis of this step, the problem solver obtains a comprehensive picture of the problem. The next stage is to choose the value(s) that will address the problem.

The second stage - ‘ The choice of value(s): What is the background for the solution?’ - includes the fourth and the fifth steps. The fourth step is ‘ The identification of value(s) as a base for the solution ’. During this step, the problem solver should activate his/her value(s) making it (them) explicit. In order to do this, the problem solver proceeds several sub-steps. First, the problem solver reflects taking into account the analysis done in previous steps. He/she raises up questions revealing values that lay in the background of this analysis: What values does this analyzed context allow me to notice? What values do different perspectives of the problem ‘offer’? Such questioning is important as values are deeply hidden ( Verplanken & Holland, 2002 ) and they form a bias, which restricts the development of the capability to see from various points of view ( Hall & Paradice, 2007 ). In the 4W framework, this bias is relatively eliminated due to the analysis of the context and exploration of the perspectives of a problem. As a result, the problem solver discovers distinct value-based positions and gets an opportunity to identify the ‘value uncaptured’ ( Yang, Evans, Vladimirova, & Rana, 2017, p.1796 ) within the problem analyzed. The problem solver observes that some values exist in the context (the second step) and the disclosed perspectives (the third step). Some of the identified values do not affect the current situation as they are not required, or their potential is not exploited. Thus, looking through various value-based lenses, the problem solver can identify and discover a congruence between the opportunities offered by the values in the problem’s context, disclosed perspectives and his/her value(s). Consequently, the problem solver decides what values he/she chooses as a basis for the desired solution. Since problems usually call for a list of values, it is important to find out their order of priority. Thus, the last sub-step requires the problem solver to choose between fundamentally and superficially laden values.

In some cases, the problem solver identifies that a set of values (more than one value) can lead to the desired solution. If a person chooses this multiple value-based position, two options emerge. The first option is concerned with the analysis of each value-based position separately (from the fifth to the seventh step). In the second option, a person has to uncover which of his/her chosen values are fundamentally laden and which are superficially chosen, considering the desired outcome in the current situation. Such clarification could act as a strategy where the path for the desired solution is possible going from superficially chosen value(s) to fundamentally laden one. When a basis for the solution is established, the problem solver formulates the goal for the desired solution.

The fifth step is ‘ The formulation of the goal for the solution ’. Problem solving highlights essential points that reveal the structure of a person’s goals; thus, a goal is the core element of problem solving ( Funke, 2014 ). Meantime, values reflect the motivational content of the goals ( Schwartz, 1992 ). The attention on the chosen value not only activates it, but also motivates the problem solver. The motivation directs the formulation of the goal. In such a way, values explicitly become a basis of the goal for the solution. Thus, this step involves the problem solver in formulating the goal for the solution as the desired outcome.

The way how to take into account value(s) when formulating the goal is the integration of value(s) chosen by the problem solver in the formulation of the goal ( Keeney, 1994 ). For this purpose the conjunction of a context for a solution (it is analyzed during the second step) and a direction of preference (the chosen value reveals it) serves for the formulation of the goal (that represents the desired solution). In other words, a value should be directly included into the formulation of the goal. The goal could lose value, if value is not included into the goal formulation and remains only in the context of the goal. Let’s take the actual example concerning COVID-19 situation. Naturally, many countries governments’ preference represents such value as human life (‘it is important of every individual’s life’). Thus, most likely the particular country government’s goal of solving the COVID situation could be to save the lifes of the country people. The named problem is a complex where the goal of its solution is also complex, although it sounds simple. However, if the goal as desired outcome is formulated without the chosen value, this value remains in the context and its meaning becomes tacit. In the case of above presented example - the goal could be formulated ‘to provide hospitals with the necessary equipment and facilities’. Such goal has the value ‘human’s life’ in the context, but eliminates the complexity of the problem that leads to a partial solution of the problem. Thus, this step from the problem solver requires caution when formulating the goal as the desired outcome. For this reason, maintaining value is very important when formulating the goal’s text. To avoid the loss of values and maintain their proposed direction, is necessary to take into account values again when creating alternatives.

The third stage - ‘ Search for the alternative ways for a solution: How? ’ - encompasses the sixth step, which is called ‘ Creation of value-based alternatives ’. Frequently problem solver invokes a traditional view of problem identification, generation of alternatives, and selection of criteria for evaluating findings. Keeney (1994) ; Ims and Zsolnai (2009) criticize this rational approach as it supports a search for a partial solution where an active search for alternatives is neglected. Moreover, a problematic situation, according to Perkins (2009) , can create the illusion of a fully framed problem with some apparent weighting and some variations of choices. In this case, essential and distinct alternatives to the solution frequently become unnoticeable. Therefore, Perkins (2009) suggest to replace the focus on the attempts to comprehend the problem itself. Thinking through the ‘value lenses’ offers such opportunities. The deep understanding of the problem leads to the search for the alternative ways of a solution.

Thus, the aim of this step is for the problem solver to reveal the possible alternative ways for searching a desired solution. Most people think they know how to create alternatives, but often without delving into the situation. First of all, the problem solver based on the reflection of (but not limited to) the analysis of the context and the perspectives of the problem generates a range of alternatives. Some of these alternatives represent anchored thinking as he/she accepts the assumptions implicit in generated alternatives and with too little focus on values.

The chosen value with the formulated goal indicates direction and encourages a broader and more creative search for a solution. Hence, the problem solver should consider some of the initial alternatives that could best support the achievement of the desired solution. Values are the principles for evaluating the desirability of any alternative or outcome ( Keeney, 1994 ). Thus, planned actions should reveal the desirable mode of conduct. After such consideration, he/she should draw up a plan setting out the actions required to implement each of considered alternatives.

Lastly, after a thorough examination of each considered alternative and a plan of its implementation, the problem solver chooses one of them. If the problem solver does not see an appropriate alternative, he/she develops new alternatives. However, the problem solver may notice (and usually does) that more than one alternative can help him/her to achieve the desired solution. In this case, he/she indicates which alternative is the main one and has to be implemented in the first place, and what other alternatives and in what sequence will contribute in searching for the desired solution.

The fourth stage - ‘ The rationale for the solution: Why ’ - leads to the seventh step: ‘ The justification of the chosen alternative ’. Keeney (1994) emphasizes the compatibility of alternatives in question with the values that guide the action. This underlines the importance of justifying the choices a person makes where the focus is on taking responsibility. According to Zsolnai (2008) , responsibility means a choice, i.e., the perceived responsibility essentially determines its choice. Responsible justification allows for discovering optimal balance when choosing between distinct value-based alternatives. It also refers to the alternative solution that best reflects responsibility in a particular value context, choice, and implementation.

At this stage, the problem solver revisits the chosen solution and revises it. The problem solver justifies his/her choice based on the following questions: Why did you choose this? Why is this alternative significant looking from the technical/scientific, the interpersonal/social, the existential, and the systemic perspectives? Could you take full responsibility for the implementation of this alternative? Why? How clearly do envisaged actions reflect the goal of the desired solution? Whatever interests and for what reasons do this alternative satisfies in principle? What else do you see in the chosen alternative?

As mentioned above, each person gives priority to one aspect or another. The problem solver has to provide solid arguments for the justification of the chosen alternative. The quality of arguments, according to Jonassen (2011) , should be judged based on the quality of the evidence supporting the chosen alternative and opposing arguments that can reject solutions. Besides, the pursuit of value-based goals reflects the interests of the individual or collective interests. Therefore, it becomes critical for the problem solver to justify the level of responsibility he/she takes in assessing the chosen alternative. Such a complex evaluation of the chosen alternative ensures the acceptance of an integral rather than unilateral solution, as ‘recognizing that, in the end, people benefit most when they act for the common good’ ( Sternberg, 2012, p.46 ).

5. Discussion

The constant emphasis on thinking about values as explicit reasoning in the 4W framework (especially from the choice of the value(s) to the rationale for problem solution) reflects the pursuit of virtues. Virtues form the features of the character that are related to the choice ( Argandoña, 2003 ; McLaughlin, 2005 ). Hence, the problem solver develops value-grounded problem solving capability as the virtuality instead of employing rationality for problem solving.

Argandoña (2003) suggests that, in order to make a sound valuation process of any action, extrinsic, transcendent, and intrinsic types of motives need to be considered. They cover the respective types of values. The 4W framework meets these requirements. An extrinsic motive as ‘attaining the anticipated or expected satisfaction’ ( Argandoña, 2003, p.17 ) is reflected in the formulation of the goal of the solution, the creation of alternatives and especially in the justification of the chosen alternative way when the problem solver revisits the external effect of his/her possible action. Transcendent motive as ‘generating certain effects in others’ ( Argandoña, 2003, p.17 ) is revealed within the analysis of the context, perspectives, and creating alternatives. When the learner considers the creation of alternatives and revisits the chosen alternative, he/she pays more attention to these motives. Two types of motives mentioned so far are closely related to an intrinsic motive that emphasizes learning development within the problem solver. These motives confirm that problem solving is, in fact, lifelong learning. In light of these findings, the 4W framework is concerned with some features of value internalization as it is ‘a psychological outcome of conscious mind reasoning about values’ ( Yazdani & Akbarilakeh, 2017, p.1 ).

The 4W framework is complicated enough in terms of learning. One issue is concerned with the educational environments ( Jucevičienė, 2008 ) required to enable the 4W framework. First, the learning paradigm, rather than direct instruction, lies at the foundation of such environments. Second, such educational environments include the following dimensions: (1) educational goal; (2) learning capacity of the learners; (3) educational content relevant to the educational goal: ways and means of communicating educational content as information presented in advance (they may be real, people among them, as well as virtual); (5) methods and means of developing educational content in the process of learners’ performance; (6) physical environment relevant to the educational goal and conditions of its implementation as well as different items in the environment; (7) individuals involved in the implementation of the educational goal.

Another issue is related to exercising this framework in practice. Despite being aware of the 4W framework, a person may still not want to practice problem solving through values, since most of the solutions are going to be complicated, or may even be painful. One idea worth looking into is to reveal the extent to which problem solving through values can become a habit of mind. Profound focus on personal values, context analysis, and highlighting various perspectives can involve changes in the problem solver’s habit of mind. The constant practice of problem solving through values could first become ‘the epistemic habit of mind’ ( Mezirow, 2009, p.93 ), which means a personal way of knowing things and how to use that knowledge. This echoes Kirkman (2017) findings. The developed capability to notice moral values in situations that students encountered changed some students’ habit of mind as ‘for having “ruined” things by making it impossible not to attend to values in such situations!’ (the feedback from one student; Kirkman, 2017, p.12 ). However, this is not enough, as only those problems that require a value-based approach are addressed. Inevitably, the problem solver eventually encounters the challenges of nurturing ‘the moral-ethical habit of mind’ ( Mezirow, 2009, p.93 ). In pursuance to develop such habits of mind, the curriculum should include the necessity of the practising of the 4W framework.

Thinking based on values when solving problems enables the problem solver to engage in thoughtful reflection in contrast to pragmatic and superficial thinking supported by the consumer society. Reflection begins from the first stage of the 4W framework. As personal values are the basis for the desired solution, the problem solver is also involved in self-reflection. The conscious and continuous reflection on himself/herself and the problematic situation reinforce each step of the 4W framework. Moreover, the fourth stage (‘The rationale for the solution: Why’) involves the problem solver in critical reflection as it concerned with justification of ‘the why , the reasons for and the consequences of what we do’ (italic, bold in original; Mezirow, 1990, p.8 ). Exercising the 4W framework in practice could foster reflective practice. Empirical evidence shows that reflective practice directly impacts knowledge, skills and may lead to changes in personal belief systems and world views ( Slade, Burnham, Catalana, & Waters, 2019 ). Thus, with the help of reflective practice it is possible to identify in more detail how and to what extent the 4W framework has been mastered, what knowledge gained, capabilities developed, how point of views changed, and what influence the change process.

Critical issues related to the development of problem solving through values need to be distinguished when considering and examining options for the implementation of the 4W framework at educational institutions. First, the question to what extent can the 4W framework be incorporated into various subjects needs to be answered. Researchers could focus on applying the 4W framework to specific subjects in the humanities and social sciences. The case is with STEM subjects. Though value issues of sustainable development and ecology are of great importance, in reality STEM teaching is often restricted to the development of knowledge and skills, leaving aside the thinking about values. The special task of the researchers is to help practitioners to apply the 4W framework in STEM subjects. Considering this, researchers could employ the concept of ‘dialogic space’ ( Wegerif, 2011, p.3 ) which places particular importance of dialogue in the process of education emphasizing both the voices of teachers and students, and materials. In addition, the dimensions of educational environments could be useful aligning the 4W framework with STEM subjects. As STEM teaching is more based on solving various special tasks and/or integrating problem-based learning, the 4W framework could be a meaningful tool through which content is mastered, skills are developed, knowledge is acquired by solving pre-prepared specific tasks. In this case, the 4W framework could act as a mean addressing values in STEM teaching.

Second is the question of how to enable the process of problem solving through values. In the current paper, the concept of enabling is understood as an integral component of the empowerment. Juceviciene et al. (2010) specify that at least two perspectives can be employed to explain empowerment : a) through the power of legitimacy (according to Freire, 1996 ); and b) through the perspective of conditions for the acquisition of the required knowledge, capabilities, and competence, i.e., enabling. In this paper the 4W framework does not entail the issue of legitimacy. This issue may occur, for example, when a teacher in economics is expected to provide students with subject knowledge only, rather than adding tasks that involve problem solving through values. Yet, the issue of legitimacy is often implicit. A widespread phenomenon exists that teaching is limited to certain periods that do not have enough time for problem solving through values. The issue of legitimacy as an organizational task that supports/or not the implementation of the 4W framework in any curriculum is a question that calls for further discussion.

Third (if not the first), the issue of an educator’s competence to apply such a framework needs to be addressed. In order for a teacher to be a successful enabler, he/she should have the necessary competence. This is related to the specific pedagogical knowledge and skills, which are highly dependent on the peculiarities of the subject being taught. Nowadays actualities are encouraging to pay attention to STEM subjects and their teacher training. For researchers and teacher training institutions, who will be interested in implementing the 4W framework in STEM subjects, it would be useful to draw attention to ‘a material-dialogic approach to pedagogy’ ( Hetherington & Wegerif, 2018, p.27 ). This approach creates the conditions for a deep learning of STEM subjects revealing additional opportunities for problem solving through values in teaching. Highlighting these opportunities is a task for further research.

In contrast to traditional problem solving models, the 4W framework is more concerned with educational purposes. The prescriptive approach to teaching ( Thorne, 1994 ) is applied to the 4W framework. This approach focuses on providing guidelines that enable students to make sound decisions by making explicit value judgements. The limitation is that the 4W framework is focused on thinking but not executing. It does not include the fifth stage, which would focus on the execution of the decision how to solve the problem. This stage may contain some deviation from the predefined process of the solution of the problem.

6. Conclusions

The current paper focuses on revealing the essence of the 4W framework, which is based on enabling the problem solver to draw attention to when, how, and why it is essential to think about values during the problem solving process from the perspective of it’s design. Accordingly, the 4W framework advocates the coherent approach when solving a problem by using a creative potential of values.

The 4W framework allows the problem solver to look through the lens of his/her values twice. The first time, while formulating the problem solving goal as the desired outcome. The second time is when the problem solver looks deeper into his/her values while exploring alternative ways to solve problems. The problem solver is encouraged to reason about, find, accept, reject, compare values, and become responsible for the consequences of the choices grounded on his/her values. Thus, the problem solver could benefit from the 4W framework especially when dealing with issues having crucial consequences.

An educational approach reveals that the 4W framework could enable the development of value-grounded problem solving capability. As problem solving encourages the development of higher-order thinking skills, the consistent inclusion of values enriches them.

The 4W framework requires the educational environments for its enablement. The enablement process of problem solving through values could be based on the perspective of conditions for the acquisition of the required knowledge and capability. Continuous practice of this framework not only encourages reflection, but can also contribute to the creation of the epistemic habit of mind. Applying the 4W framework to specific subjects in the humanities and social sciences might face less challenge than STEM ones. The issue of an educator’s competence to apply such a framework is highly important. The discussed issues present significant challenges for researchers and educators. Caring that the curriculum of different courses should foresee problem solving through values, both practicing and empirical research are necessary.

Declaration of interests

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Both authors have approved the final article.

• Argandoña A. Fostering values in organizations. Journal of Business Ethics. 2003; 45 (1–2):15–28. https://link.springer.com/content/pdf/10.1023/A:1024164210743.pdf [ Google Scholar ]
• Barber S. A truly “Transformative” MBA: Executive education for the fourth industrial revolution. Journal of Pedagogic Development. 2018; 8 (2):44–55. [ Google Scholar ]
• Barnett R. McGraw-Hill Education; UK): 2007. Will to learn: Being a student in an age of uncertainty. [ Google Scholar ]
• Baron R.A., Zhao H., Miao Q. Personal motives, moral disengagement, and unethical decisions by entrepreneurs: Cognitive mechanisms on the “slippery slope” Journal of Business Ethics. 2015; 128 (1):107–118. doi: 10.1007/s10551-014-2078-y. [ CrossRef ] [ Google Scholar ]
• Basadur M., Ellspermann S.J., Evans G.W. A new methodology for formulating ill-structured problems. Omega. 1994; 22 (6):627–645. doi: 10.1016/0305-0483(94)90053-1. [ CrossRef ] [ Google Scholar ]
• Blanco E., Schirmbeck F., Costa C. International Conference on Remote Engineering and Virtual Instrumentation . Springer; Cham: 2018. Vocational Education for the Industrial Revolution; pp. 649–658. [ Google Scholar ]
• Chua B.L., Tan O.S., Liu W.C. Journey into the problem-solving process: Cognitive functions in a PBL environment. Innovations in Education and Teaching International. 2016; 53 (2):191–202. doi: 10.1080/14703297.2014.961502. [ CrossRef ] [ Google Scholar ]
• Collins R.H., Sibthorp J., Gookin J. Developing ill-structured problem-solving skills through wilderness education. Journal of Experiential Education. 2016; 39 (2):179–195. doi: 10.1177/1053825916639611. [ CrossRef ] [ Google Scholar ]
• Csapó B., Funke J., editors. The nature of problem solving: Using research to inspire 21st century learning. OECD Publishing; 2017. The development and assessment of problem solving in 21st-century schools. (Chapter 1). [ CrossRef ] [ Google Scholar ]
• Dollinger S.J., Burke P.A., Gump N.W. Creativity and values. Creativity Research Journal. 2007; 19 (2-3):91–103. doi: 10.1080/10400410701395028. [ CrossRef ] [ Google Scholar ]
• Donovan S.J., Guss C.D., Naslund D. Improving dynamic decision making through training and self-reflection. Judgment and Decision Making. 2015; 10 (4):284–295. http://digitalcommons.unf.edu/apsy_facpub/2 [ Google Scholar ]
• Dorst K. Design problems and design paradoxes. Design Issues. 2006; 22 (3):4–17. doi: 10.1162/desi.2006.22.3.4. [ CrossRef ] [ Google Scholar ]
• Dostál J. Theory of problem solving. Procedia-Social and Behavioral Sciences. 2015; 174 :2798–2805. doi: 10.1016/j.sbspro.2015.01.970. [ CrossRef ] [ Google Scholar ]
• Fields A.M. Ill-structured problems and the reference consultation: The librarian’s role in developing student expertise. Reference Services Review. 2006; 34 (3):405–420. doi: 10.1108/00907320610701554. [ CrossRef ] [ Google Scholar ]
• Freire P. Continuum; New York: 1996. Pedagogy of the oppressed (revised) [ Google Scholar ]
• Funke J. Problem solving: What are the important questions?. Proceedings of the 36th Annual Conference of the Cognitive Science Society; Austin, TX: Cognitive Science Society; 2014. pp. 493–498. [ Google Scholar ]
• Hall D.J., Davis R.A. Engaging multiple perspectives: A value-based decision-making model. Decision Support Systems. 2007; 43 (4):1588–1604. doi: 10.1016/j.dss.2006.03.004. [ CrossRef ] [ Google Scholar ]
• Hall D.J., Paradice D. Investigating value-based decision bias and mediation: do you do as you think? Communications of the ACM. 2007; 50 (4):81–85. [ Google Scholar ]
• Halstead J.M. Values and values education in schools. In: Halstead J.M., Taylor M.J., editors. Values in education and education in values. The Falmer Press; London: 1996. pp. 3–14. [ Google Scholar ]
• Harland T., Pickering N. Routledge; 2010. Values in higher education teaching. [ Google Scholar ]
• Hester P.T., MacG K. Springer; New York: 2017. Systemic decision making: Fundamentals for addressing problems and messes. [ Google Scholar ]
• Hetherington L., Wegerif R. Developing a material-dialogic approach to pedagogy to guide science teacher education. Journal of Education for Teaching. 2018; 44 (1):27–43. doi: 10.1080/02607476.2018.1422611. [ CrossRef ] [ Google Scholar ]
• Huitt W. Problem solving and decision making: Consideration of individual differences using the Myers-Briggs type indicator. Journal of Psychological Type. 1992; 24 (1):33–44. [ Google Scholar ]
• Ims K.J., Zsolnai L. The future international manager. Palgrave Macmillan; London: 2009. Holistic problem solving; pp. 116–129. [ Google Scholar ]
• Jonassen D. Supporting problem solving in PBL. Interdisciplinary Journal of Problem-based Learning. 2011; 5 (2):95–119. doi: 10.7771/1541-5015.1256. [ CrossRef ] [ Google Scholar ]
• Jonassen D.H. Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology Research and Development. 1997; 45 (1):65–94. doi: 10.1007/BF02299613. [ CrossRef ] [ Google Scholar ]
• Jucevičienė P. Educational and learning environments as a factor for socioeducational empowering of innovation. Socialiniai mokslai. 2008; 1 :58–70. [ Google Scholar ]
• Jucevičienė P., Gudaitytė D., Karenauskaitė V., Lipinskienė D., Stanikūnienė B., Tautkevičienė G. Technologija; Kaunas: 2010. Universiteto edukacinė galia: Atsakas XXI amžiaus iššūkiams [The educational power of university: the response to the challenges of the 21st century] [ Google Scholar ]
• Kasof J., Chen C., Himsel A., Greenberger E. Values and creativity. Creativity Research Journal. 2007; 19 (2–3):105–122. doi: 10.1080/10400410701397164. [ CrossRef ] [ Google Scholar ]
• Keeney R.L. Creativity in decision making with value-focused thinking. MIT Sloan Management Review. 1994; 35 (4):33–41. [ Google Scholar ]
• Kirkman R. Problem-based learning in engineering ethics courses. Interdisciplinary Journal of Problem-based Learning. 2017; 11 (1) doi: 10.7771/1541-5015.1610. [ CrossRef ] [ Google Scholar ]
• Lebedeva N., Schwartz S., Plucker J., Van De Vijver F. Domains of everyday creativity and personal values. Frontiers in Psychology. 2019; 9 :1–16. doi: 10.3389/fpsyg.2018.02681. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Linstone H.A. Multiple perspectives: Concept, applications, and user guidelines. Systems Practice. 1989; 2 (3):307–331. [ Google Scholar ]
• Litzinger T.A., Meter P.V., Firetto C.M., Passmore L.J., Masters C.B., Turns S.R.…Zappe S.E. A cognitive study of problem solving in statics. Journal of Engineering Education. 2010; 99 (4):337–353. [ Google Scholar ]
• Maslow A.H. Vaga; Vilnius: 2011. Būties psichologija. [Psychology of Being] [ Google Scholar ]
• Mayer R., Wittrock M. Problem solving. In: Alexander P., Winne P., editors. Handbook of educational psychology. Psychology Press; New York, NY: 2006. pp. 287–303. [ Google Scholar ]
• McLaughlin T. The educative importance of ethos. British Journal of Educational Studies. 2005; 53 (3):306–325. doi: 10.1111/j.1467-8527.2005.00297.x. [ CrossRef ] [ Google Scholar ]
• McLaughlin T.H. Technologija; Kaunas: 1997. Šiuolaikinė ugdymo filosofija: demokratiškumas, vertybės, įvairovė [Contemporary philosophy of education: democracy, values, diversity] [ Google Scholar ]
• Mezirow J. Jossey-Bass Publishers; San Francisco: 1990. Fostering critical reflection in adulthood; pp. 1–12. https://my.liberatedleaders.com.au/wp-content/uploads/2017/02/How-Critical-Reflection-triggers-Transformative-Learning-Mezirow.pdf [ Google Scholar ]
• Mezirow J. Contemporary theories of learning. Routledge; 2009. An overview on transformative learning; pp. 90–105. (Chapter 6) [ Google Scholar ]
• Mitroff I. Šviesa; Kaunas: 2000. Kaip neklysti šiais beprotiškais laikais: ar mokame spręsti esmines problemas. [How not to get lost in these crazy times: do we know how to solve essential problems] [ Google Scholar ]
• Morton L. Teaching creative problem solving: A paradigmatic approach. Cal. WL Rev. 1997; 34 :375. [ Google Scholar ]
• Nadda P. Need for value based education. International Education and Research Journal. 2017; 3 (2) http://ierj.in/journal/index.php/ierj/article/view/690/659 [ Google Scholar ]
• Newell A., Simon H.A. Prentice-Hall; Englewood Cliffs, NJ: 1972. Human problem solving. [ Google Scholar ]
• OECD . PISA, OECD Publishing; Paris: 2013. PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy . https://www.oecd.org/pisa/pisaproducts/PISA%202012%20framework%20e-book_final.pdf [ Google Scholar ]
• OECD . PISA, OECD Publishing; 2018. PISA 2015 results in focus . https://www.oecd.org/pisa/pisa-2015-results-in-focus.pdf [ Google Scholar ]
• O’Loughlin A., McFadzean E. Toward a holistic theory of strategic problem solving. Team Performance Management: An International Journal. 1999; 5 (3):103–120. [ Google Scholar ]
• Perkins D.N. Decision making and its development. In: Callan E., Grotzer T., Kagan J., Nisbett R.E., Perkins D.N., Shulman L.S., editors. Education and a civil society: Teaching evidence-based decision making. American Academy of Arts and Sciences; Cambridge, MA: 2009. pp. 1–28. (Chapter 1) [ Google Scholar ]
• Roccas S., Sagiv L., Navon M. Values and behavior. Cham: Springer; 2017. Methodological issues in studying personal values; pp. 15–50. [ Google Scholar ]
• Rogers C.R. Houghton Mifflin Harcourt; Boston: 1995. On becoming a person: A therapist’s view of psychotherapy. [ Google Scholar ]
• Saito M. Amartya Sen’s capability approach to education: A critical exploration. Journal of Philosophy of Education. 2003; 37 (1):17–33. doi: 10.1111/1467-9752.3701002. [ CrossRef ] [ Google Scholar ]
• Schwartz S.H. Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries. In: Zanna M.P., editor. Vol. 25. Academic Press; 1992. pp. 1–65. (Advances in experimental social psychology). [ Google Scholar ]
• Schwartz S.H. Are there universal aspects in the structure and contents of human values? Journal of social issues. 1994; 50 (4):19–45. [ Google Scholar ]
• Schwartz S.H. An overview of the Schwartz theory of basic values. Online Readings in Psychology and Culture. 2012; 2 (1):1–20. doi: 10.9707/2307-0919.1116. [ CrossRef ] [ Google Scholar ]
• Sen A. Development as capability expansion. The community development reader. 1990:41–58. http://www.masterhdfs.org/masterHDFS/wp-content/uploads/2014/05/Sen-development.pdf [ Google Scholar ]
• Sheehan N.T., Schmidt J.A. Preparing accounting students for ethical decision making: Developing individual codes of conduct based on personal values. Journal of Accounting Education. 2015; 33 (3):183–197. doi: 10.1016/j.jaccedu.2015.06.001. [ CrossRef ] [ Google Scholar ]
• Shepherd D.A., Patzelt H., Baron R.A. “I care about nature, but…”: Disengaging values in assessing opportunities that cause harm. The Academy of Management Journal. 2013; 56 (5):1251–1273. doi: 10.5465/amj.2011.0776. [ CrossRef ] [ Google Scholar ]
• Shin N., Jonassen D.H., McGee S. Predictors of well‐structured and ill‐structured problem solving in an astronomy simulation. Journal of Research in Science Teaching. 2003; 40 (1):6–33. doi: 10.1002/tea.10058. [ CrossRef ] [ Google Scholar ]
• Slade M.L., Burnham T.J., Catalana S.M., Waters T. The impact of reflective practice on teacher candidates’ learning. International Journal for the Scholarship of Teaching and Learning. 2019; 13 (2):15. doi: 10.20429/ijsotl.2019.130215. [ CrossRef ] [ Google Scholar ]
• Snyder H. Literature review as a research methodology: An overview and guidelines. Journal of Business Research. 2019; 104 :333–339. doi: 10.1016/j.jbusres.2019.07.039. [ CrossRef ] [ Google Scholar ]
• Sternberg R. Teaching for ethical reasoning. International Journal of Educational Psychology. 2012; 1 (1):35–50. doi: 10.4471/ijep.2012.03. [ CrossRef ] [ Google Scholar ]
• Sternberg R. Speculations on the role of successful intelligence in solving contemporary world problems. Journal of Intelligence. 2017; 6 (1):4. doi: 10.3390/jintelligence6010004. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thorne D.M. Environmental ethics in international business education: Descriptive and prescriptive dimensions. Journal of Teaching in International Business. 1994; 5 (1–2):109–122. doi: 10.1300/J066v05n01_08. [ CrossRef ] [ Google Scholar ]
• Treffinger D.J., Isaksen S.G. Creative problem solving: The history, development, and implications for gifted education and talent development. The Gifted Child Quarterly. 2005; 49 (4):342–353. doi: 10.1177/001698620504900407. [ CrossRef ] [ Google Scholar ]
• Verplanken B., Holland R.W. Motivated decision making: Effects of activation and self-centrality of values on choices and behavior. Journal of Personality and Social Psychology. 2002; 82 (3):434–447. doi: 10.1037/0022-3514.82.3.434. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Webster R.S. Re-enchanting education and spiritual wellbeing. Routledge; 2017. Being spiritually educated; pp. 73–85. [ Google Scholar ]
• Wegerif R. Towards a dialogic theory of how children learn to think. Thinking Skills and Creativity. 2011; 6 (3):179–190. doi: 10.1016/j.tsc.2011.08.002. [ CrossRef ] [ Google Scholar ]
• Yang M., Evans S., Vladimirova D., Rana P. Value uncaptured perspective for sustainable business model innovation. Journal of Cleaner Production. 2017; 140 :1794–1804. doi: 10.1016/j.jclepro.2016.07.102. [ CrossRef ] [ Google Scholar ]
• Yazdani S., Akbarilakeh M. The model of value-based curriculum for medicine and surgery education in Iran. Journal of Minimally Invasive Surgical Sciences. 2017; 6 (3) doi: 10.5812/minsurgery.14053. [ CrossRef ] [ Google Scholar ]
• Zsolnai L. Transaction Publishers; New Brunswick and London: 2008. Responsible decision making. [ Google Scholar ]

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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Critical Thinking and Problem-Solving Skills

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Critical thinking is a type of systematic thinking that is used to solve problems using logic. The first step is to gather the information needed to help you solve your problem. You start by analyzing and evaluating sources for authority to give you the best shot at finding something truthful and unbiased. Watch Out For information overload. Access to information is easier than ever these days, and it is easy to get overwhelmed by it all. As you conduct your research, keep your information organized by filtering, synthesizing, and distilling it. And keep your effort timeboxed. Start broad enough to obtain a wide berth, like a fisherman casting a large net into an ocean. This helps find multiple points of view. But don’t spend more time than necessary. Practicing collecting what is sufficient to answer your questions.

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Restructuring processes and Aha! experiences in insight problem solving

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Insightful solution processes represent cases of problem solving in which the emergence of a new interpretation allows for an abrupt shift from bewilderment to clarity. One approach to researching insight problem solving emphasizes cognitive restructuring of the problem representation as a defining feature of the insightful solution process. By contrast, another approach emphasizes phenomenological Aha! experiences. In this Review, we summarize both approaches, considering the restructuring processes involved in finding a solution and the Aha! experiences that might accompany solutions. We then consider the extent to which Aha! experiences co-occur with restructuring, and the critical observation that sometimes they do not. We conclude by proposing avenues for future research that combine the methodologies used to study restructuring and Aha! experiences to better understand the cognitive and phenomenological underpinnings of insight problem solving and the connections between them.

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Duncker, K. On problem-solving. Psychol. Monogr. 58 , 270 (1945).

Article   Google Scholar  

Ellen, P. Direction, past experience, and hints in creative problem solving: reply to Weisberg and Alba. J. Exp. Psychol. 111 , 316–325 (1982).

Katona, G. Organizing and Memorizing: Studies in the Psychology of Learning and Teaching (Columbia Univ. Press, 1940).

Köhler, W. An aspect of Gestalt psychology. Ped. Sem. J. Genet. Psychol. 32 , 691–723 (1925).

Google Scholar  

Luchins, A. S. Mechanization in problem solving: the effect of Einstellung . Psychol. Monogr. 54 , i–95 (1945).

Maier, N. R. F. Reasoning in humans. I. On direction. J. Comp. Psychol. 10 , 115–143 (1930).

Maier, N. R. F. Reasoning in humans. II. The solution of a problem and its appearance in consciousness. J. Comp. Psychol. 12 , 181–194 (1931).

Wertheimer, M. Productive Thinking Enlarged edn (Harper and Brothers, 1945/1959).

Ash, I. K., Cushen, P. J., & Wiley, J. Obstacles in investigating the role of restructuring in insightful problem solving. J. Probl. Solving https://doi.org/10.7771/1932-6246.1056 (2009).

Ohlsson, S. in Advances in the Psychology of Thinking (eds Keane, M. T. & Gilhooly, K. J.) 1–44 (Harvester Wheatsheaf, 1992).

Ohlsson, S. Deep Learning: How the Mind Overrides Experience (Cambridge Univ. Press, 2011).

Bühler, K. Tatsachen und Probleme zu einer Psychologie der Denkvorgänge. II. Über Gedankenzusammenhänge. Arch. Gesamte Psychol. 12 , 1–23 (1908).

Bowden, E. M. The effect of reportable and unreportable hints on anagram solution and the Aha! Experience. Conscious. Cogn. 6 , 545–573 (1997).

Article   PubMed   Google Scholar  

Bowden, E. M., Jung-Beeman, M., Fleck, J. I. & Kounios, J. New approaches to demystifying insight. Trends Cogn. Sci. 9 , 322–328 (2005).

Danek, A. H. in Routledge International Handbook of Creative Cognition (eds Ball, L. J. & Vallée-Tourangeau, F.) (Routledge, 2023).

Kaplan, C. A. & Simon, H. A. In search of insight. Cogn. Psychol. 22 , 374–419 (1990).

Kounios, J. & Beeman, M. The cognitive neuroscience of insight. Annu. Rev. Psychol. 65 , 71–93 (2014).

Smith, R. W. & Kounios, J. Sudden insight: all-or-none processing revealed by speed–accuracy decomposition. J. Exp. Psychol. Learn. Mem. Cogn. 22 , 1443–1462 (1996).

Gilhooly, K. J. Incubation and intuition in creative problem solving. Front. Psychol. 7 , 1076 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Metcalfe, J. Premonitions of insight predict impending error. J. Exp. Psychol. Learn. Mem. Cogn. 12 , 623–634 (1986).

Metcalfe, J. & Wiebe, D. Intuition in insight and noninsight problem solving. Mem. Cogn. 15 , 238–246 (1987).

Newell, A., & Simon, H. A. Human Problem Solving (Prentice-Hall, 1972).

Davidson, J. E. in The Nature of Insight (eds Sternberg, R. J. & Davidson, J. E.) 125–155 (MIT Press, 1995).

MacGregor, J. N., Ormerod, T. C. & Chronicle, E. P. Information processing and insight: a process model of performance on the nine-dot and related problems. J. Exp. Psychol. Learn. Mem. Cogn. 27 , 176–201 (2001).

Weisberg, R. W. in The Nature of Insight (Sternberg, R. J. & Davidson, J. E.) 157–196 (MIT Press, 1995).

Ash, I. K. & Wiley, J. The nature of restructuring in insight: an individual-differences approach. Psychon. Bull. Rev. 13 , 66–73 (2006).

Cushen, P. J. & Wiley, J. Cues to solution, restructuring patterns, and reports of insight in creative problem solving. Consc. Cogn. 21 , 1166–1175 (2012).

Fedor, A., Szathmáry, E. & Öllinger, M. Problem solving stages in the five square problem. Front. Psychol. 6 , 1050 (2015).

Knoblich, G., Ohlsson, S., Haider, H. & Rhenius, D. Constraint relaxation and chunk decomposition in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 25 , 1534–1555 (1999).

Ormerod, T. C., MacGregor, J. N. & Chronicle, E. P. Dynamics and constraints in insight problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 28 , 791–799 (2002).

Danek, A. H., Fraps, T., von Müller, A., Grothe, B. & Öllinger, M. Working wonders? Investigating insight with magic tricks. Cognition 130 , 174–185 (2014).

Hedne, M. R., Norman, E. & Metcalfe, J. Intuitive feelings of warmth and confidence in insight and noninsight problem solving of magic tricks. Front. Psychol. 7 , 1314 (2016).

Thomas, C., Didierjean, A. & Kuhn, G. It is magic! How impossible solutions prevent the discovery of obvious ones. Q. J. Exp. Psychol. 71 , 2481–2487 (2018).

Danek, A. H., Wiley, J. & Öllinger, M. Solving classical insight problems without Aha! experience: 9 dot, 8 coin, and matchstick arithmetic problems. J. Probl. Solving https://doi.org/10.7771/1932-6246.1183 (2016).

Gilhooly, K. J. & Murphy, P. Differentiating insight from non-insight problems. Think. Reason. 11 , 279–302 (2005).

Öllinger, M., Jones, G. & Knoblich, G. The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem. Psychol. Res. 78 , 266–275 (2014).

Webb, M. E., Little, D. R. & Cropper, S. J. Insight is not in the problem: investigating insight in problem solving across task types. Front. Psychol. 7 , 1424 (2016).

Dygert, S. K. & Jarosz, A. F. Individual differences in creative cognition. J. Exp. Psychol. Gen. 149 , 1249–1274 (2020).

MacGregor, J. N. & Cunningham, J. B. Rebus puzzles as insight problems. Behav. Res. Meth. 40 , 263–268 (2008).

Salvi, C., Costantini, G., Bricolo, E., Perugini, M. & Beeman, M. Validation of Italian rebus puzzles and compound remote associate problems. Behav. Res. Meth. 48 , 664–685 (2016).

Smith, S. M. & Blankenship, S. E. Incubation effects. Bull. Psychon. Soc. 27 , 311–314 (1989).

Threadgold, E., Marsh, J. E. & Ball, L. J. Normative data for 84 UK English rebus puzzles. Front. Psychol. 9 , 2513 (2018).

Mednick, S. The associative basis of the creative process. Psychol. Rev. 69 , 220–232 (1962).

Bowden, E. M. & Beeman, M. J. Getting the right idea: semantic activation in the right hemisphere may help solve insight problems. Psychol. Sci. 9 , 435–440 (1998).

Bowers, K. S., Regehr, G., Balthazard, C. & Parker, K. Intuition in the context of discovery. Cogn. Psychol. 22 , 72–110 (1990).

Schooler, J. W. & Melcher, J. in The Creative Cognition Approach (Smith, S. M., Ward, T. B. & Finke, R. A.) 97–143 (MIT Press, 1995).

Smith, S. M. & Blankenship, S. E. Incubation and the persistence of fixation in problem solving. Am. J. Psychol. 104 , 61–87 (1991).

Wiley, J. Expertise as mental set: the effects of domain knowledge in creative problem solving. Mem. Cogn 26 , 716–730 (1998).

Beeftink, F., Van Eerde, W. & Rutte, C. G. The effect of interruptions and breaks on insight and impasses: do you need a break right now? Creativ. Res. J. 20 , 358–364 (2008).

Friedlander, K. J. & Fine, P. A. “The penny drops”: investigating insight through the medium of cryptic crosswords. Front. Psychol. 9 , 904 (2018).

Bowden, E. M. & Jung-Beeman, M. Normative data for 144 compound remote associate problems. Behav. Res. Meth. Instrum. Comput. 35 , 634–639 (2003).

Cranford, E. A. & Moss, J. Is insight always the same? A protocol analysis of insight in compound remote associate problems. J. Probl. Solving 4 , https://doi.org/10.7771/1932-6246.1129 (2012).

Öllinger, M. & von Müller, A. Search and coherence-building in intuition and insight problem solving. Front. Psychol. 8 , 827 (2017).

Zander, T., Öllinger, M. & Volz, K. G. Intuition and insight: two processes that build on each other or fundamentally differ? Front. Psychol. 7 , 1395 (2016).

Ellis, J. J., Glaholt, M. G. & Reingold, E. M. Eye movements reveal solution knowledge prior to insight. Consc. Cogn. 20 , 768–776 (2011).

Novick, L. R. & Sherman, S. J. On the nature of insight solutions: evidence from skill differences in anagram solution. Q. J. Exp. Psychol. A 56 , 351–382 (2003).

Rees, H. J. & Israel, H. E. An investigation of the establishment and operation of mental sets. Psychol. Monogr. 46 , 1–26 (1935).

Tempel, T. & Frings, C. Directed forgetting in problem solving. Acta Psychol. 201 , 102955 (2019).

Jacoby, L. L. On interpreting the effects of repetition: solving a problem versus remembering a solution. J. Verb. Learn. Verb. Behav. 17 , 649–667 (1978).

Koppel, R. H. & Storm, B. C. Unblocking memory through directed forgetting. J. Cogn. Psychol. 24 , 901–907 (2012).

Smith, S. M. & Beda, Z. Old problems in new contexts: the context-dependent fixation hypothesis. J. Exp. Psychol. Gen. 149 , 192–197 (2020).

Auble, P. M., Franks, J. J. & Soraci, S. A. Effort toward comprehension: elaboration or “Aha”? Mem. Cogn. 7 , 426–434 (1979).

Bar-Hillel, M. Stumpers: an annotated compendium. Think. Reason. 27 , 536–566 (2021).

Bowden, E. M. Accessing relevant information during problem solving: time constraints on search in the problem space. Mem. Cogn. 13 , 280–286 (1985).

Dow, G. T. & Mayer, R. E. Teaching students to solve insight problems: evidence for domain specificity in creativity training. Creativ. Res. J. 16 , 389–398 (2004).

Durso, F. T., Rea, C. B. & Dayton, T. Graph-theoretic confirmation of restructuring during insight. Psychol. Sci. 5 , 94–98 (1994).

Lockhart, R. S., Lamon, M. & Gick, M. L. Conceptual transfer in simple insight problems. Mem. Cogn. 16 , 36–44 (1988).

Patrick, J. & Ahmed, A. Facilitating representation change in insight problems through training. J. Exp. Psychol. Learn. Mem. Cogn. 40 , 532–543 (2014).

Bar-Hillel, M., Noah, T. & Frederick, S. Solving stumpers, CRT and CRAT: are the abilities related? Judgm. Decis. Mak. 14 , 620–623 (2019).

Webb, M. E., Little, D. R. & Cropper, S. J. Once more with feeling: normative data for the Aha experience in insight and noninsight problems. Behav. Res. Meth. 50 , 2035–2056 (2018).

Beilock, S. L. & DeCaro, M. S. From poor performance to success under stress: working memory, strategy selection, and mathematical problem solving under pressure. J. Exp. Psychol. Learn. Mem. Cogn. 33 , 983–998 (2007).

DeCaro, M. S., Van Stockum, C. A. & Wieth, M. B. When higher working memory capacity hinders insight. J. Exp. Psychol. Learn. Mem. Cogn. 42 , 39–49 (2016).

Van Stockum Jr, C. A. & DeCaro, M. S. When working memory mechanisms compete: predicting cognitive flexibility versus mental set. Cognition 201 , 104313 (2020).

Koppel, R., George, T. & Wiley, J. in The Emergence of Insight (Cambridge Press, 2024).

Bilalić, M., McLeod, P. & Gobet, F. The mechanism of the Einstellung (set) effect: a pervasive source of cognitive bias. Curr. Dir. Psychol. Sci. 19 , 111–115 (2010).

Sheridan, H. & Reingold, E. M. The mechanisms and boundary conditions of the Einstellung effect in chess: evidence from eye movements. PLoS One 8 , e75796 (2013).

Ellis, J. J. & Reingold, E. M. The Einstellung effect in anagram problem solving: evidence from eye movements. Front. Psychol. 5 , 679 (2014).

Beda, Z. & Smith, S. M. Chasing red herrings: memory of distractors causes fixation in creative problem solving. Mem. Cogn. 46 , 671–684 (2018).

Howe, M. L. & Garner, S. R. Can false memories prime alternative solutions to ambiguous problems? Memory 26 , 96–105 (2018).

Smith, S. M. in The Nature of Insight (Sternberg, R. J. & Davidson, J. E.) 229–251 (MIT Press, 1995).

Kohn, N. & Smith, S. M. Partly versus completely out of your mind: effects of incubation and distraction on resolving fixation. J. Creativ. Behav. 43 , 102–118 (2009).

Koppel, R. H. & Storm, B. C. Escaping mental fixation: incubation and inhibition in creative problem solving. Memory 22 , 340–348 (2014).

May, C. P. Synchrony effects in cognition: the costs and a benefit. Psychon. Bull. Rev. 6 , 142–147 (1999).

Penaloza, A. A. & Calvillo, D. P. Incubation provides relief from artificial fixation in problem solving. Creativ. Res. J. 24 , 338–344 (2012).

Storm, B. C. & Angello, G. Overcoming fixation: creative problem solving and retrieval-induced forgetting. Psychol. Sci. 21 , 1263–1265 (2010).

Storm, B. C. & Hickman, M. L. Mental fixation and metacognitive predictions of insight in creative problem solving. Q. J. Exp. Psychol. 68 , 802–813 (2015).

Vul, E. & Pashler, H. Incubation benefits only after people have been misdirected. Mem. Cogn. 35 , 701–710 (2007).

Ricks, T. R., Turley-Ames, K. J. & Wiley, J. Effects of working memory capacity on mental set due to domain knowledge. Mem. Cogn. 35 , 1456–1462 (2007).

Dodds, R. A., Ward, T. B. & Smith, S. M. in Creativity Research Handbook (ed. Runco, M. A.) 291–322 (Hampton Press, 2012).

Sio, U. N. & Ormerod, T. C. Does incubation enhance problem solving? A meta-analytic review. Psychol. Bull. 135 , 94–120 (2009).

Caravona, L. & Macchi, L. Different incubation tasks in insight problem solving: evidence for unconscious analytic thought. Think. Reason. 29 , 559–593 (2023).

Sanders, K. E. & Beeman, M. Sleep and incubation: using problem reactivation during sleep to study forgetting fixation and unconscious processing during sleep incubation. J. Cogn. Psychol. 33 , 738–756 (2021).

Cai, D. J., Mednick, S. A., Harrison, E. M., Kanady, J. C. & Mednick, S. C. REM, not incubation, improves creativity by priming associative networks. Proc. Natl Acad. Sci. USA 106 , 10130–10134 (2009).

Stickgold, R. & Walker, M. P. Sleep-dependent memory triage: evolving generalization through selective processing. Nat. Neurosci. 16 , 139–145 (2013).

Segal, E. Incubation in insight problem solving. Creativ. Res. J. 16 , 141–148 (2004).

George, T. & Wiley, J. Fixation, flexibility, and forgetting during alternate uses tasks. Psychol. Aesth. Creativ. Arts 13 , 305–313 (2019).

Lu, J. G., Akinola, M. & Mason, M. F. “Switching on” creativity: task switching can increase creativity by reducing cognitive fixation. Organ. Behav. Hum. Decis. Process. 139 , 63–75 (2017).

Smith, S. M., Gerkens, D. R. & Angello, G. Alternating incubation effects in the generation of category exemplars. J. Creativ. Behav. 51 , 95–106 (2017).

Ansburg, P. I. & Hill, K. Creative and analytic thinkers differ in their use of attentional resources. Pers. Individ. Differ. 34 , 1141–1152 (2003).

Kim, S., Hasher, L. & Zacks, R. T. Aging and a benefit of distractibility. Psychon. Bull. Rev. 14 , 301–305 (2007).

Reverberi, C., Toraldo, A., D’Agostini, S. & Skrap, M. Better without (lateral) frontal cortex? Insight problems solved by frontal patients. Brain 128 , 2882–2890 (2005).

Wiley, J. & Jarosz, A. How working memory capacity affects problem solving. Psychol. Learn. Motiv. 56 , 185–227 (2012).

Zmigrod, S., Zmigrod, L. & Hommel, B. The relevance of the irrelevant: attentional distractor-response binding predicts performance in the remote associates task. Psychol. Aesth. Creativ. Arts 13 , 15–23 (2019).

Ninomiya, Y. et al. Effect of cognitive load and working memory capacity on the efficiency of discovering better alternatives: a survival analysis. Mem. Cogn . https://doi.org/10.3758/s13421-023-01448-w (2023).

Tan, T., Zou, H., Chen, C. & Luo, J. Mind wandering and the incubation effect in insight problem solving. Creativ. Res. J. 27 , 375–382 (2015).

Yang, T. & Wu, G. Spontaneous or deliberate: the dual influence of mind wandering on creative incubation. J. Creativ. Behav. 56 , 584–600 (2022).

Zedelius, C. M. & Schooler, J. W. Mind wandering “Ahas” versus mindful reasoning: alternative routes to creative solutions. Front. Psychol. 6 , 834 (2015).

Benedek, M., Panzierer, L., Jauk, E. & Neubauer, A. C. Creativity on tap? Effects of alcohol intoxication on creative cognition. Consc. Cogn. 56 , 128–134 (2017).

Jarosz, A. F., Colflesh, G. J. & Wiley, J. Uncorking the muse: alcohol intoxication facilitates creative problem solving. Consc. Cogn. 21 , 487–493 (2012).

Wieth, M. B. & Zacks, R. T. Time of day effects on problem solving: when the non-optimal is optimal. Think. Reason. 17 , 387–401 (2011).

Bolte, A., Goschke, T. & Kuhl, J. Emotion and intuition: effects of positive and negative mood on implicit judgments of semantic coherence. Psychol. Sci. 14 , 416–421 (2003).

Rowe, G., Hirsh, J. B. & Anderson, A. K. Positive affect increases the breadth of attentional selection. Proc. Natl Acad. Sci. USA 104 , 383–388 (2007).

Aiello, D. A., Jarosz, A. F., Cushen, P. J. & Wiley, J. Firing the executive: when an analytic approach to problem solving helps and hurts. J. Probl. Solving https://doi.org/10.7771/1932-6246.1128 (2012).

Ellis, D. M., Robison, M. K. & Brewer, G. A. The cognitive underpinnings of multiply-constrained problem solving. J. Intell. 9 , 7 (2021).

Chein, J. M., Weisberg, R. W., Streeter, N. L. & Kwok, S. Working memory and insight in the nine-dot problem. Mem. Cogn. 38 , 883–892 (2010).

Kershaw, T. C. & Ohlsson, S. Multiple causes of difficulty in insight: the case of the nine-dot problem. J. Exp. Psychol. Learn. Mem. Cogn. 30 , 3–13 (2004).

Lung, C. T. & Dominowski, R. L. Effects of strategy instructions and practice on nine-dot problem solving. J. Exp. Psychol. Learn. Mem. Cogn. 11 , 804–811 (1985).

Öllinger, M., Jones, G., Faber, A. H. & Knoblich, G. Cognitive mechanisms of insight: the role of heuristics and representational change in solving the eight-coin problem. J. Exp. Psychol. Learn. Mem. Cogn. 39 , 931–939 (2013).

Thomas, L. E. & Lleras, A. Swinging into thought: directed movement guides insight in problem solving. Psychon Bull. Rev. 16 , 719–723 (2009).

Hattori, M., Sloman, S. A. & Orita, R. Effects of subliminal hints on insight problem solving. Psychon. Bull. Rev. 20 , 790–797 (2013).

Moss, J., Kotovsky, K. & Cagan, J. The influence of open goals on the acquisition of problem-relevant information. J. Exp. Psychol. Learn. Mem. Cogn. 33 , 876–891 (2007).

Moss, J., Kotovsky, K. & Cagan, J. The effect of incidental hints when problems are suspended before, during, or after an impasse. J. Exp. Psychol. Learn. Mem. Cogn. 37 , 140–148 (2011).

Pétervári, J. & Danek, A. H. Problem solving of magic tricks: guiding to and through an impasse with solution cues. Think. Reason. 26 , 502–533 (2020).

Seifert, C. M., Meyer, D. E., Davidson, N., Patalano, A. L., & Yaniv, I. in The Nature of Insight (Sternberg, R. J. & Davidson, J. E.) 65–124 (MIT Press, 1995).

Gick, M. L. & McGarry, S. J. Learning from mistakes: inducing analogous solution failures to a source problem produces later successes in analogical transfer. J. Exp. Psychol. Learn. Mem. Cogn. 18 , 623–639 (1992).

Ash, I. K., Jee, B. D. & Wiley, J. Investigating insight as sudden learning. J. Probl. Solving https://doi.org/10.7771/1932-6246.1123 (2012).

Ansburg, P. I. & Dominowski, R. I. Promoting insightful problem solving. J. Creat. Behav. 34 , 30–60 (2000).

Bianchi, I., Branchini, E., Burro, R., Capitani, E. & Savardi, U. Overtly prompting people to “think in opposites” supports insight problem solving. Think. Reason. 26 , 31–67 (2020).

Chrysikou, E. G. When shoes become hammers: goal-derived categorization training enhances problem-solving performance. J. Exp. Psychol. Learn. Mem. Cogn. 32 , 935–942 (2006).

Blech, C., Gaschler, R. & Bilalić, M. Why do people fail to see simple solutions? Using think-aloud protocols to uncover the mechanism behind the Einstellung (mental set) effect. Think. Reason. 26 , 552–580 (2020).

Fleck, J. I. & Weisberg, R. W. The use of verbal protocols as data: an analysis of insight in the candle problem. Mem. Cogn. 32 , 990–1006 (2004).

Fleck, J. I. & Weisberg, R. W. Insight versus analysis: evidence for diverse methods in problem solving. J. Cogn. Psychol. 25 , 436–463 (2013).

Tidikis, V. & Ash, I. K. Working in dyads and alone: examining process variables in solving insight problems. Creativ. Res. J. 25 , 189–198 (2013).

Gupta, N., Jang, Y., Mednick, S. C. & Huber, D. E. The road not taken: creative solutions require avoidance of high-frequency responses. Psychol. Sci. 23 , 288–294 (2012).

Bilalić, M., Graf, M., Vaci, N. & Danek, A. H. The temporal dynamics of insight problem solving—restructuring might not always be sudden. Think. Reason. 27 , 1–37 (2021).

Knoblich, G., Ohlsson, S. & Raney, G. E. An eye movement study of insight problem solving. Mem. Cogn. 29 , 1000–1009 (2001).

Nathan, M. J., Schenck, K. E., Vinsonhaler, R., Michaelis, J. E., Swart, M. I. & Walkington, C. Embodied geometric reasoning: dynamic gestures during intuition, insight, and proof. J. Educ. Psychol. 113 , 929–948 (2020).

Vallée-Tourangeau, F., Ross, W., Ruffatto Rech, R. & Vallée-Tourangeau, G. Insight as discovery. J. Cogn. Psychol. 33 , 718–737 (2021).

Loesche, F., Goslin, J. & Bugmann, G. Paving the way to Eureka—introducing “Dira” as an experimental paradigm to observe the process of creative problem solving. Front. Psychol. 9 , 1773 (2018).

Ash, I. K. & Wiley, J. Hindsight bias in insight and mathematical problem solving: evidence of different reconstruction mechanisms for metacognitive versus situational judgments. Mem. Cogn. 36 , 822–837 (2008).

Danek, A. H. & Wiley, J. What causes the insight memory advantage? Cognition 205 , 104411 (2020).

Danek, A. H., Williams, J. & Wiley, J. Closing the gap: connecting sudden representational change to the subjective Aha! experience in insightful problem solving. Psychol. Res. 84 , 111–119 (2020).

Bowden, E. M. & Grunewald, K. in Insight: On the Origins of New Ideas F. (ed. Vallée-Tourangeau, F.) 28–50 (Routledge, 2018).

Gick, M. L., & Lockhart, R. S. in The Nature of Insight (Sternberg, R. J. & Davidson, J. E.) 197–228 (MIT Press, 1995).

Bilalić, M., Graf, M., Vaci, N., & Danek, A. H. When the solution is on the doorstep: better solving performance, but diminished Aha! experience for chess experts on the mutilated checkerboard problem. Cogn. Sci. 43 , e12771 (2019).

Laukkonen, R. E., Ingledew, D. J., Grimmer, H. J., Schooler, J. W. & Tangen, J. M. Getting a grip on insight: real-time and embodied Aha experiences predict correct solutions. Cogn. Emot. 35 , 918–935 (2021).

Ross, W. & Vallée-Tourangeau, F. Insight with stumpers: normative solution data for 25 stumpers and a fresh perspective on the accuracy effect. Think. Skills Creat. 46 , 101114 (2022).

Köhler, W. The Task of Gestalt Psychology (Princeton Univ. Press, 1969).

Ovington, L. A., Saliba, A. J., Moran, C. C., Goldring, J. & MacDonald, J. B. Do people really have insights in the shower? The when, where and who of the Aha! moment. J. Creativ. Behav. 52 , 21–34 (2018).

Gable, S. L., Hopper, E. A. & Schooler, J. W. When the muses strike: creative ideas of physicists and writers routinely occur during mind wandering. Psychol. Sci. 30 , 396–404 (2019).

Jung-Beeman, M. et al. Neural activity when people solve verbal problems with insight. PLoS Biol. 2 , 500–510 (2004).

Danek, A. H. & Wiley, J. What about false insights? Deconstructing the Aha! experience along its multiple dimensions for correct and incorrect solutions separately. Front. Psychol. 7 , 2077 (2017).

Danek, A. H., Fraps, T., von Müller, A., Grothe, B., & Öllinger, M. It’s a kind of magic — what self-reports can reveal about the phenomenology of insight problem solving. Front. Psychol.   5 , 1408 (2014).

Thagard, P. & Stewart, T. C. The Aha! experience: creativity through emergent binding in neural networks. Cogn. Sci. 35 , 1–33 (2011).

Gruber, H. E. in The Nature of Insight (Sternberg, R. J. & Davidson, J. E.) 397–431 (MIT Press, 1995).

Skaar, Ø. O. & Reber, R. Motivation through insight: the phenomenological correlates of insight and spatial ability tasks. J. Cogn. Psychol. 33 , 631–643 (2021).

Spiridonov, V., Loginov, N. & Ardislamov, V. Dissociation between the subjective experience of insight and performance in the CRA paradigm. J. Cogn. Psychol. 33 , 685–699 (2021).

Stuyck, H., Aben, B., Cleeremans, A. & Van den Bussche, E. The Aha! moment: is insight a different form of problem solving? Consc. Cogn. 90 , 103055 (2021).

Webb, M. E., Cropper, S. J. & Little, D. R. Unusual uses and experiences are good for feeling insightful, but not for problem solving: contributions of schizotypy, divergent thinking, and fluid reasoning, to insight moments. J. Cogn. Psychol. 33 , 770–792 (2021).

Kizilirmak, J. M., Serger, V., Kehl, J., Öllinger, M., Folta-Schoofs, K. & Richardson-Klavehn, A. Feelings-of-warmth increase more abruptly for verbal riddles solved with in contrast to without Aha! experience. Front. Psychol. 9 , 1404 (2018).

Becker, M., Kühn, S. & Sommer, T. Verbal insight revisited—dissociable neurocognitive processes underlying solutions accompanied by an Aha! experience with and without prior restructuring. J. Cogn. Psychol. 33 , 659–684 (2021).

Topolinski, S. & Reber, R. Gaining insight into the “Aha” experience. Curr. Dir. Psychol. Sci. 19 , 402–405 (2010).

Salvi, C., Simoncini, C., Grafman, J. & Beeman, M. Oculometric signature of switch into awareness? Pupil size predicts sudden insight whereas microsaccades predict problem-solving via analysis. NeuroImage 217 , 116933 (2020).

Danek, A. H. & Salvi, C. Moment of truth: why Aha! experiences are correct. J. Creativ. Behav. 54 , 484–486 (2020).

Dietrich, A. & Haider, H. Human creativity, evolutionary algorithms, and predictive representations: the mechanics of thought trials. Psychon. Bull. Rev. 22 , 897–915 (2015).

Salvi, C., Bricolo, E., Kounios, J., Bowden, E. M. & Beeman, M. Insight solutions are correct more often than analytic solutions. Think. Reason. 22 , 1–18 (2016).

Webb, M. E., Laukkonen, R. E., Cropper, S. J. & Little, D. R. Commentary: moment of (perceived) truth: exploring accuracy of Aha! experiences. J. Creativ. Behav. 55 , 289–293 (2021).

Shen, W., Tong, Y., Yuan, Y., Zhan, H., Liu, C., Luo, J. & Cai, H. Feeling the insight: uncovering somatic markers of the “Aha” experience. Appl. Psychophysiol. Biofeedback 43 , 13–21 (2018).

Stuyck, H., Cleeremans, A. & Van den Bussche, E. Aha! under pressure: the Aha! experience is not constrained by cognitive load. Cognition 219 , 104946 (2022).

Strickland, T., Wiley, J. & Ohlsson, S. Hints and the Aha-accuracy effect in insight problem solving. Proc. Annu. Meet. Cogn. Sci. Soc. 44 , 3209–3215 (2022).

Kizilirmak, J. M., Gallisch, N., Schott, B. H. & Folta-Schoofs, K. Insight is not always the same: differences between true, false, and induced insights in the matchstick arithmetic task. J. Cogn. Psychol. 33 , 700–717 (2021).

Webb, M. E., Cropper, S. J. & Little, D. R. “Aha!” is stronger when preceded by a “huh?”: presentation of a solution affects ratings of Aha experience conditional on accuracy. Think. Reason. 25 , 324–364 (2019).

Kizilirmak, J. M., Galvao Gomes da Silva, J., Imamoglu, F. & Richardson-Klavehn, A. Generation and the subjective feeling of “Aha!” are independently related to learning from insight. Psychol. Res. 80 , 1059–1074 (2016).

Kounios, J., Fleck, J. I., Green, D. L., Payne, L., Stevenson, J. L., Bowden, E. M. & Jung-Beeman, M. The origins of insight in resting-state brain activity. Neuropsychologia 46 , 281–291 (2008).

Sadler-Smith, E. Wallas’ four-stage model of the creative process: more than meets the eye? Creativ. Res. J. 27 , 342–352 (2015).

Wallas, G. The Art of Thought (Harcourt and Brace, 1926).

Chronicle, E. P., MacGregor, J. N. & Ormerod, T. C. What makes an insight problem? The roles of heuristics, goal conception, and solution recoding in knowledge-lean problems. J. Exp. Psychol. Learn. Mem. Cogn. 30 , 14–27 (2004).

Grimmer, H., Laukkonen, R., Tangen, J. & von Hippel, W. Eliciting false insights with semantic priming. Psychon. Bull. Rev. 29 , 954–970 (2022).

Laukkonen, R. E., Kaveladze, B. T., Tangen, J. M. & Schooler, J. W. The dark side of Eureka: artificially induced Aha moments make facts feel true. Cognition 196 , 104122 (2020).

Laukkonen, R. E., Kaveladze, B. T., Protzko, J., Tangen, J. M., von Hippel, W. & Schooler, J. W. Irrelevant insights make worldviews ring true. Sci. Rep. 12 , 2075 (2022).

Metcalfe, J., Schwartz, B. L., & Bloom, P. A. The tip-of-the-tongue state and curiosity. Cogn. Res. Princ. Implic.   2 , 31 (2017).  

Bianchi, I., Branchini, E., Canestrari, C. & Burro, R. On pleasures of the mind related to humour and insight problem solving: an investigation of people’s awareness of what they like and why. J. Cogn. Psychol. 34 , 778–795 (2022).

Kozbelt, A. & Nishioka, K. Humor comprehension, humor production, and insight: an exploratory study. Int. J. Humor Res. 23 , 375–401 (2010).

Chesebrough, C., Chrysikou, E. G., Holyoak, K. J., Zhang, F. & Kounios, J. Conceptual change induced by analogical reasoning sparks aha moments. Creativ. Res. J. 35 , 1–23 (2010).

George T., & Wiley, J. in Insight: On the Origin of New Ideas (ed. Vallée-Tourangeau, F.) 143–168 (Routledge, 2018).

Lv, K. The involvement of working memory and inhibition functions in the different phases of insight problem solving. Mem. Cogn. 43 , 709–722 (2015).

Weisberg, R. W. Toward an integrated theory of insight in problem solving. Think. Reason. 21 , 5–39 (2015).

Slamecka, N. J. & Graf, P. The generation effect: delineation of a phenomenon. J. Exp. Psychol. Hum. Learn. Mem. 4 , 592–604 (1978).

Buyer, L. S. & Dominowski, R. L. Retention of solutions: it is better to give than to receive. Am. J. Psychol. 102 , 353–363 (1989).

Kizilirmak, J. M., Wiegmann, B. & Richardson-Klavehn, A. Problem solving as an encoding task: a special case of the generation effect. J. Probl. Solving 9 , 59–76 (2016).

Patalano, A. L. & Seifert, C. M. Memory for impasses during problem solving. Mem. Cogn. 22 , 234–242 (1994).

Dominowski, R. L. & Buyer, L. S. Retention of problem solutions: the re-solution effect. Am. J. Psychol. 113 , 249–274 (2000).

Schwartz, D. & Martin, T. Inventing to prepare for future learning: the hidden efficiency of encouraging original student production in statistics instruction. Cogn. Instr. 22 , 129–184 (2004).

Jarosz, A. F., Goldenberg, O. & Wiley, J. Learning by invention: small group discussion activities that support learning in statistics. Discourse Process. 54 , 285–302 (2017).

Sinha, T. & Kapur, M. When problem solving followed by instruction works: evidence for productive failure. Rev. Educ. Res. 91 , 761–798 (2021).

Danek, A. H., Fraps, T., von Müller, A., Grothe, B. & Öllinger, M. Aha! experiences leave a mark: facilitated recall of insight solutions. Psychol. Res. 77 , 659–669 (2013).

Liljedahl, P. G. Mathematical discovery and affect: the effect of Aha! experiences on undergraduate mathematics students. Int. J. Math. Educ. Sci. Technol. 36 , 219–234 (2005).

Metcalfe, J., Schwartz, B. L. & Eich, T. S. Epistemic curiosity and the region of proximal learning. Curr. Opin. Behav. Sci. 35 , 40–47 (2020).

Van de Cruys, S., Damiano, C., Boddez, Y., Król, M., Goetschalckx, L. & Wagemans, J. Visual affects: linking curiosity, Aha-Erlebnis, and memory through information gain. Cognition 212 , 104698 (2021).

Kizilirmak, J. M., Thuerich, H., Folta-Schoofs, K., Schott, B. H. & Richardson-Klavehn, A. Neural correlates of learning from induced insight: a case for reward-based episodic encoding. Front. Psychol. 7 , 1693 (2016). (2016).

Oh, Y., Chesebrough, C., Erickson, B., Zhang, F. & Kounios, J. An insight-related neural reward signal. NeuroImage 214 , 116757 (2020).

Salvi, C., Leiker, E. K., Baricca, B., Molinari, M. A., Eleopra, R., Nichelli, P. F., Grafman, J. & Dunsmoor, J. E. The effect of dopaminergic replacement therapy on creative thinking and insight problem-solving in Parkinson’s disease patients. Front. Psychol. 12 , 646448 (2021).

Tik, M., Sladky, R., Luft, C. D. B., Willinger, D., Hoffmann, A., Banissy, M. J. & Windischberger, C. Ultra‐high‐field fMRI insights on insight: neural correlates of the Aha!‐moment. Hum. Brain Mapp. 39 , 3241–3252 (2018).

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Acknowledgements

The authors thank I. K. Ash, P. J. Cushen, T. George, A. F. Jarosz, T. S. Miller and S. Ohlsson for discussion on these topics.

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Wiley, J., Danek, A.H. Restructuring processes and Aha! experiences in insight problem solving. Nat Rev Psychol 3 , 42–55 (2024). https://doi.org/10.1038/s44159-023-00257-x

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To Solve a Tough Problem, Reframe It

• Julia Binder
• Michael D. Watkins

Research shows that companies devote too little effort to examining problems before trying to solve them. By jumping immediately into problem-solving, teams limit their ability to design innovative solutions.

The authors recommend that companies spend more time up front on problem-framing, a process for understanding and defining a problem. Exploring different frames is like looking at a scene through various camera lenses while adjusting your angle, aperture, and focus. A wide-angle lens gives you a very different photo from that taken with a telephoto lens, and shifting your angle and depth of focus yields distinct images. Effective problem-framing is similar: Looking at a problem from a variety of perspectives helps you uncover new insights and generate fresh ideas.

This article introduces a five-phase approach to problem-framing: In the expand phase, the team identifies all aspects of a problem; in examine, it dives into root causes; in empathize, it considers key stakeholders’ perspectives; in elevate, it puts the problem into a broader context; and in envision, it creates a road map toward the desired outcome.

Five steps to ensure that you don’t jump to solutions

Idea in Brief

The problem.

Research shows that most companies devote too little effort to examining problems from all angles before trying to solve them. That limits their ability to come up with innovative ways to address them.

The Solution

Companies need a structured approach for understanding and defining complex problems to uncover new insights and generate fresh ideas.

The Approach

This article introduces a five-phase approach to problem-framing: In the expand phase, the team identifies all aspects of a problem; in examine, it dives into root causes; in empathize, it considers key stakeholders’ perspectives; in elevate, it puts the problem into a broader context; and in envision, it creates a road map toward the desired outcome.

When business leaders confront complex problems, there’s a powerful impulse to dive right into “solving” mode: You gather a team and then identify potential solutions. That’s fine for challenges you’ve faced before or when proven methods yield good results. But what happens when a new type of problem arises or aspects of a familiar one shift substantially? Or if you’re not exactly sure what the problem is?

Research conducted by us and others shows that leaders and their teams devote too little effort to examining and defining problems before trying to solve them. A study by Paul Nutt of Ohio State University, for example, looked at 350 decision-making processes at medium to large companies and found that more than half failed to achieve desired results, often because perceived time pressure caused people to pay insufficient attention to examining problems from all angles and exploring their complexities. By jumping immediately into problem-solving, teams limit their ability to design innovative and durable solutions.

When we work with organizations and teams, we encourage them to spend more time up front on problem-framing, a process for understanding and defining a problem. Exploring frames is like looking at a scene through various camera lenses while adjusting your angle, aperture, and focus. A wide-angle lens will give you a very different photo from that taken with a telephoto lens, and shifting your angle and depth of focus yields distinct images. Effective problem-framing is similar: Looking at a problem from a variety of perspectives lets you uncover new insights and generate fresh ideas.

As with all essential processes, it helps to have a methodology and a road map. This article introduces the E5 approach to problem-framing—expand, examine, empathize, elevate, and envision—and offers tools that enable leaders to fully explore the problem space.

Phase 1: Expand

In the first phase, set aside preconceptions and open your mind. We recommend using a tool called frame-storming, which encourages a comprehensive exploration of an issue and its nuances. It is a neglected precursor to brainstorming, which typically focuses on generating many different answers for an already framed challenge. Frame-storming helps teams identify assumptions and blind spots, mitigating the risk of pursuing inadequate or biased solutions. The goal is to spark innovation and creativity as people dig into—or as Tina Seelig from Stanford puts it, “fall in love with”—the problem.

Begin by assembling a diverse team, encompassing a variety of types of expertise and perspectives. Involving outsiders can be helpful, since they’re often coming to the issue cold. A good way to prompt the team to consider alternative scenarios is by asking “What if…?” and “How might we…?” questions. For example, ask your team, “What if we had access to unlimited resources to tackle this issue?” or “How might better collaboration between departments or teams help us tackle this issue?” The primary objective is to generate many alternative problem frames, allowing for a more holistic understanding of the issue. Within an open, nonjudgmental atmosphere, you deliberately challenge established thinking—what we call “breaking” the frame.

It may be easy to eliminate some possibilities, and that’s exactly what you should do. Rather than make assumptions, generate alternative hypotheses and then test them.

Consider the problem-framing process at a company we’ll call Omega Soundscapes, a midsize producer of high-end headphones. (Omega is a composite of several firms we’ve worked with.) Omega’s sales had declined substantially over the past two quarters, and the leadership team’s initial diagnosis, or reference frame, was that recent price hikes to its flagship product made it too expensive for its target market. Before acting on this assumption, the team convened knowledgeable representatives from sales, marketing, R&D, customer service, and external consultants to do some frame-storming. Team members were asked:

• What if we lowered the price of our flagship product? How would that impact sales and profitability?
• How might we identify customers in new target markets who could afford our headphones at the current price?
• What if we offered financing or a subscription-based model for our headphones? How would that change perceptions of affordability?
• How might we optimize our supply chain and production processes to reduce manufacturing costs without compromising quality?

In playing out each of those scenarios, the Omega team generated several problem frames:

• The target market’s preferences have evolved.
• New competitors have entered the market.
• Product quality has decreased.
• Something has damaged perceptions of the brand.
• Something has changed in the priorities of our key distributors.

Each of the frames presented a unique angle from which to approach the problem of declining sales, setting the stage for the development of diverse potential solutions. At this stage, it may be relatively easy to eliminate some possibilities, and that’s exactly what you should do. Rather than make assumptions, generate alternative hypotheses and then test them.

See more HBR charts in Data & Visuals

Phase 2: Examine

If the expand phase is about identifying all the facets of a problem, this one is about diving deep to identify root causes. The team investigates the issue thoroughly, peeling back the layers to understand underlying drivers and systemic contributors.

A useful tool for doing this is the iceberg model, which guides the team through layers of causation: surface-level events, the behavioral patterns that drive them, underlying systematic structures, and established mental models. As you probe ever deeper and document your findings, you begin to home in on the problem’s root causes. As is the case in the expand phase, open discussions and collaborative research are crucial for achieving a comprehensive analysis.

Let’s return to our Omega Soundscapes example and use the iceberg model to delve into the issues surrounding the two quarters of declining sales. Starting with the first layer beneath the surface, the behavioral pattern, the team diligently analyzed customer feedback. It discovered a significant drop in brand loyalty. This finding validated the problem frame of a “shifting brand perception,” prompting further investigation into what might have been causing it.

Phase 3: Empathize

In this phase, the focus is on the stakeholders—employees, customers, clients, investors, supply chain partners, and other parties—who are most central to and affected by the problem under investigation. The core objective is to understand how they perceive the issue: what they think and feel, how they’re acting, and what they want.

First list all the people who are directly or indirectly relevant to the problem. It may be helpful to create a visual representation of the network of relationships in the ecosystem. Prioritize the stakeholders according to their level of influence on and interest in the problem, and focus on understanding the roles, demographics, behavior patterns, motivations, and goals of the most important ones.

Now create empathy maps for those critical stakeholders. Make a template divided into four sections: Say, Think, Feel, and Do. Conduct interviews or surveys to gather authentic data. How do various users explain the problem? How do they think about the issue, and how do their beliefs inform that thinking? What emotions are they feeling and expressing? How are they behaving? Populate each section of the map with notes based on your observations and interactions. Finally, analyze the completed empathy maps. Look for pain points, inconsistencies, and patterns in stakeholder perspectives.

Returning to the Omega case study, the team identified its ecosystem of stakeholders: customers (both current and potential); retail partners and distributors; the R&D, marketing, and sales teams; suppliers of headphone components; investors and shareholders; and new and existing competitors. They narrowed the list to a few key stakeholders related to the declining-sales problem: customers, retail partners, and investors/shareholders; Omega created empathy maps for representatives from each.

Here’s what the empathy maps showed about what the stakeholders were saying, thinking, feeling, and doing:

Sarah, the customer, complained on social media about the high price of her favorite headphones. Dave, the retailer, expressed concerns about unsold inventory and the challenge of convincing customers to buy the expensive headphones. Alex, the shareholder, brought up Omega’s declining financial performance during its annual investor day.

Sarah thought that Omega was losing touch with its loyal customer base. Dave was considering whether to continue carrying Omega’s products in his store or explore other brands. Alex was contemplating diversifying his portfolio into other consumer-tech companies.

As a longtime supporter of the brand, Sarah felt frustrated and slightly betrayed. Dave was feeling anxious about the drop in sales and the impact on his store’s profitability. Alex was unhappy with the declining stock value.

Sarah was looking for alternatives to the headphones, even though she loves the product’s quality. Dave was scheduling a call with Omega to negotiate pricing and terms. Alex was planning to attend Omega’s next shareholder meeting to find out more information from the leadership team.

When Omega leaders analyzed the data in the maps, they realized that pricing wasn’t the only reason for declining sales. A more profound issue was customers’ dissatisfaction with the perceived price-to-quality ratio, especially when compared with competitors’ offerings. That insight prompted the team to consider enhancing the headphones with additional features, offering more-affordable alternatives, and possibly switching to a service model.

Phase 4: Elevate

This phase involves exploring how the problem connects to broader organizational issues. It’s like zooming out on a map to understand where a city lies in relation to the whole country or continent. This bird’s-eye view reveals interconnected issues and their implications.

For this analysis, we recommend the four-frame model developed by Lee Bolman and Terrence Deal, which offers distinct lenses through which to view the problem at a higher level. The structural frame helps you explore formal structures (such as hierarchy and reporting relationships); processes (such as workflow); and systems, rules, and policies. This frame examines efficiency, coordination, and alignment of activities.

The human resources frame focuses on people, relationships, and social dynamics. This includes teamwork, leadership, employee motivation, engagement, professional development, and personal growth. In this frame, the organization is seen as a community or a family that recognizes that talent is its most valuable asset. The political frame delves into power dynamics, competing interests, conflicts, coalitions, and negotiations. From this perspective, organizations are arenas where various stakeholders vie for resources and engage in political struggles to influence decisions. It helps you see how power is distributed, used, and contested.

The symbolic frame highlights the importance of symbols, rituals, stories, and shared values in shaping group identity and culture. In it, organizations are depicted as theaters through which its members make meaning.

Using this model, the Omega team generated the following insights in the four frames:

Structural.

A deeper look into the company’s structure revealed siloing and a lack of coordination between the R&D and marketing departments, which had led to misaligned messaging to customers. It also highlighted a lack of collaboration between the two functions and pointed to the need to communicate with the target market about the product’s features and benefits in a coherent and compelling way.

Human resources.

This frame revealed that the declining sales and price hikes had ramped up pressure on the sales team, damaging morale. The demotivated team was struggling to effectively promote the product, making it harder to recover from declining sales. Omega realized it was lacking adequate support, training, and incentives for the team.

The key insight from this frame was that the finance team’s reluctance to approve promotions in the sales group to maintain margins was exacerbating the morale problem. Omega understood that investing in sales leadership development while still generating profits was crucial for long-term success and that frank discussions about the issue were needed.

This frame highlighted an important misalignment in perception: The company believed that its headphones were of “top quality,” while customers reported in surveys that they were “overpriced.” This divergence raised alarm that branding, marketing, and pricing strategies, which were all predicated on the central corporate value of superior quality, were no longer resonating with customers. Omega realized that it had been paying too little attention to quality assurance and functionality.

Phase 5: Envision

In this phase, you transition from framing the problem to actively imagining and designing solutions. This involves synthesizing the insights gained from earlier phases and crafting a shared vision of the desired future state.

Here we recommend using a technique known as backcasting. First, clearly define your desired goal. For example, a team struggling with missed deadlines and declining productivity might aim to achieve on-time completion rates of 98% for its projects and increase its volume of projects by 5% over the next year. Next, reverse engineer the path to achieving your goal. Outline key milestones required over both the short term and the long term. For each one, pinpoint specific interventions, strategies, and initiatives that will propel you closer to your goal. These may encompass changes in processes, policies, technologies, and behaviors. Synthesize the activities into a sequenced, chronological, prioritized road map or action plan, and allocate the resources, including time, budget, and personnel, necessary to implement your plan. Finally, monitor progress toward your goal and be prepared to adjust the plan in response to outcomes, feedback, or changing circumstances. This approach ensures that the team’s efforts in implementing the insights from the previous phases are strategically and purposefully directed toward a concrete destination.

Applying the Approach

Albert Einstein once said, “If I had one hour to solve a problem, I would spend 55 minutes thinking about the problem and five minutes thinking about the solution.” That philosophy underpins our E5 framework, which provides a structured approach for conscientiously engaging with complex problems before leaping to solutions.

As teams use the methodology, they must understand that problem-framing in today’s intricate business landscape is rarely a linear process. While we’re attempting to provide a structured path, we also recognize the dynamic nature of problems and the need for adaptability. Invariably, as teams begin to implement solutions, new facets of a problem may come to light, unforeseen challenges may arise, or external circumstances may evolve. Your team should be ready to loop back to previous phases—for instance, revisiting the expand phase to reassess the problem’s frame, delving deeper into an overlooked root cause in another examine phase, or gathering fresh insights from stakeholders in a new empathize phase. Ultimately, the E5 framework is intended to foster a culture of continuous improvement and innovation.

• JB Julia Binder is the director of the Center for Sustainable and Inclusive Business and a professor of sustainable innovation at IMD.
• Michael D. Watkins is a professor of leadership and organizational change at IMD , a cofounder of Genesis Advisers , and the author of The Six Disciplines of Strategic Thinking .

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The Art of Effective Problem Solving: A Step-by-Step Guide

Author: Daniel Croft

Daniel Croft is an experienced continuous improvement manager with a Lean Six Sigma Black Belt and a Bachelor's degree in Business Management. With more than ten years of experience applying his skills across various industries, Daniel specializes in optimizing processes and improving efficiency. His approach combines practical experience with a deep understanding of business fundamentals to drive meaningful change.

Whether we realise it or not, problem solving skills are an important part of our daily lives. From resolving a minor annoyance at home to tackling complex business challenges at work, our ability to solve problems has a significant impact on our success and happiness. However, not everyone is naturally gifted at problem-solving, and even those who are can always improve their skills. In this blog post, we will go over the art of effective problem-solving step by step.

You will learn how to define a problem, gather information, assess alternatives, and implement a solution, all while honing your critical thinking and creative problem-solving skills. Whether you’re a seasoned problem solver or just getting started, this guide will arm you with the knowledge and tools you need to face any challenge with confidence. So let’s get started!

Problem Solving Methodologies

Individuals and organisations can use a variety of problem-solving methodologies to address complex challenges. 8D and A3 problem solving techniques are two popular methodologies in the Lean Six Sigma framework.

Methodology of 8D (Eight Discipline) Problem Solving:

The 8D problem solving methodology is a systematic, team-based approach to problem solving. It is a method that guides a team through eight distinct steps to solve a problem in a systematic and comprehensive manner.

The 8D process consists of the following steps:

• Form a team: Assemble a group of people who have the necessary expertise to work on the problem.
• Define the issue: Clearly identify and define the problem, including the root cause and the customer impact.
• Create a temporary containment plan: Put in place a plan to lessen the impact of the problem until a permanent solution can be found.
• Identify the root cause: To identify the underlying causes of the problem, use root cause analysis techniques such as Fishbone diagrams and Pareto charts.
• Create and test long-term corrective actions: Create and test a long-term solution to eliminate the root cause of the problem.
• Implement and validate the permanent solution: Implement and validate the permanent solution’s effectiveness.
• Prevent recurrence: Put in place measures to keep the problem from recurring.
• Recognize and reward the team: Recognize and reward the team for its efforts.

Download the 8D Problem Solving Template

A3 Problem Solving Method:

The A3 problem solving technique is a visual, team-based problem-solving approach that is frequently used in Lean Six Sigma projects. The A3 report is a one-page document that clearly and concisely outlines the problem, root cause analysis, and proposed solution.

The A3 problem-solving procedure consists of the following steps:

• Determine the issue: Define the issue clearly, including its impact on the customer.
• Perform root cause analysis: Identify the underlying causes of the problem using root cause analysis techniques.
• Create and implement a solution: Create and implement a solution that addresses the problem’s root cause.
• Monitor and improve the solution: Keep an eye on the solution’s effectiveness and make any necessary changes.

Subsequently, in the Lean Six Sigma framework, the 8D and A3 problem solving methodologies are two popular approaches to problem solving. Both methodologies provide a structured, team-based problem-solving approach that guides individuals through a comprehensive and systematic process of identifying, analysing, and resolving problems in an effective and efficient manner.

Step 1 – Define the Problem

The definition of the problem is the first step in effective problem solving. This may appear to be a simple task, but it is actually quite difficult. This is because problems are frequently complex and multi-layered, making it easy to confuse symptoms with the underlying cause. To avoid this pitfall, it is critical to thoroughly understand the problem.

To begin, ask yourself some clarifying questions:

• What exactly is the issue?
• What are the problem’s symptoms or consequences?
• Who or what is impacted by the issue?
• When and where does the issue arise?

Answering these questions will assist you in determining the scope of the problem. However, simply describing the problem is not always sufficient; you must also identify the root cause. The root cause is the underlying cause of the problem and is usually the key to resolving it permanently.

Try asking “why” questions to find the root cause:

• What causes the problem?
• Why does it continue?
• Why does it have the effects that it does?

By repeatedly asking “ why ,” you’ll eventually get to the bottom of the problem. This is an important step in the problem-solving process because it ensures that you’re dealing with the root cause rather than just the symptoms.

Once you have a firm grasp on the issue, it is time to divide it into smaller, more manageable chunks. This makes tackling the problem easier and reduces the risk of becoming overwhelmed. For example, if you’re attempting to solve a complex business problem, you might divide it into smaller components like market research, product development, and sales strategies.

To summarise step 1, defining the problem is an important first step in effective problem-solving. You will be able to identify the root cause and break it down into manageable parts if you take the time to thoroughly understand the problem. This will prepare you for the next step in the problem-solving process, which is gathering information and brainstorming ideas.

Step 2 – Gather Information and Brainstorm Ideas

Gathering information and brainstorming ideas is the next step in effective problem solving. This entails researching the problem and relevant information, collaborating with others, and coming up with a variety of potential solutions. This increases your chances of finding the best solution to the problem.

Begin by researching the problem and relevant information. This could include reading articles, conducting surveys, or consulting with experts. The goal is to collect as much information as possible in order to better understand the problem and possible solutions.

Next, work with others to gather a variety of perspectives. Brainstorming with others can be an excellent way to come up with new and creative ideas. Encourage everyone to share their thoughts and ideas when working in a group, and make an effort to actively listen to what others have to say. Be open to new and unconventional ideas and resist the urge to dismiss them too quickly.

Finally, use brainstorming to generate a wide range of potential solutions. This is the place where you can let your imagination run wild. At this stage, don’t worry about the feasibility or practicality of the solutions; instead, focus on generating as many ideas as possible. Write down everything that comes to mind, no matter how ridiculous or unusual it may appear. This can be done individually or in groups.

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the next step in the problem-solving process, which we’ll go over in greater detail in the following section.

Step 3 – Evaluate Options and Choose the Best Solution

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the third step in effective problem solving, and it entails weighing the advantages and disadvantages of each solution, considering their feasibility and practicability, and selecting the solution that is most likely to solve the problem effectively.

To begin, weigh the advantages and disadvantages of each solution. This will assist you in determining the potential outcomes of each solution and deciding which is the best option. For example, a quick and easy solution may not be the most effective in the long run, whereas a more complex and time-consuming solution may be more effective in solving the problem in the long run.

Consider each solution’s feasibility and practicability. Consider the following:

• Can the solution be implemented within the available resources, time, and budget?
• What are the possible barriers to implementing the solution?
• Is the solution feasible in today’s political, economic, and social environment?

You’ll be able to tell which solutions are likely to succeed and which aren’t by assessing their feasibility and practicability.

Finally, choose the solution that is most likely to effectively solve the problem. This solution should be based on the criteria you’ve established, such as the advantages and disadvantages of each solution, their feasibility and practicability, and your overall goals.

It is critical to remember that there is no one-size-fits-all solution to problems. What is effective for one person or situation may not be effective for another. This is why it is critical to consider a wide range of solutions and evaluate each one based on its ability to effectively solve the problem.

Step 4 – Implement and Monitor the Solution

When you’ve decided on the best solution, it’s time to put it into action. The fourth and final step in effective problem solving is to put the solution into action, monitor its progress, and make any necessary adjustments.

To begin, implement the solution. This may entail delegating tasks, developing a strategy, and allocating resources. Ascertain that everyone involved understands their role and responsibilities in the solution’s implementation.

Next, keep an eye on the solution’s progress. This may entail scheduling regular check-ins, tracking metrics, and soliciting feedback from others. You will be able to identify any potential roadblocks and make any necessary adjustments in a timely manner if you monitor the progress of the solution.

Finally, make any necessary modifications to the solution. This could entail changing the solution, altering the plan of action, or delegating different tasks. Be willing to make changes if they will improve the solution or help it solve the problem more effectively.

It’s important to remember that problem solving is an iterative process, and there may be times when you need to start from scratch. This is especially true if the initial solution does not effectively solve the problem. In these situations, it’s critical to be adaptable and flexible and to keep trying new solutions until you find the one that works best.

To summarise, effective problem solving is a critical skill that can assist individuals and organisations in overcoming challenges and achieving their objectives. Effective problem solving consists of four key steps: defining the problem, generating potential solutions, evaluating alternatives and selecting the best solution, and implementing the solution.

You can increase your chances of success in problem solving by following these steps and considering factors such as the pros and cons of each solution, their feasibility and practicability, and making any necessary adjustments. Furthermore, keep in mind that problem solving is an iterative process, and there may be times when you need to go back to the beginning and restart. Maintain your adaptability and try new solutions until you find the one that works best for you.

• Novick, L.R. and Bassok, M., 2005.  Problem Solving . Cambridge University Press.

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Daniel Croft

Hi im Daniel continuous improvement manager with a Black Belt in Lean Six Sigma and over 10 years of real-world experience across a range sectors, I have a passion for optimizing processes and creating a culture of efficiency. I wanted to create Learn Lean Siigma to be a platform dedicated to Lean Six Sigma and process improvement insights and provide all the guides, tools, techniques and templates I looked for in one place as someone new to the world of Lean Six Sigma and Continuous improvement.

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What is a Research Problem? Characteristics, Types, and Examples

A research problem is a gap in existing knowledge, a contradiction in an established theory, or a real-world challenge that a researcher aims to address in their research. It is at the heart of any scientific inquiry, directing the trajectory of an investigation. The statement of a problem orients the reader to the importance of the topic, sets the problem into a particular context, and defines the relevant parameters, providing the framework for reporting the findings. Therein lies the importance of research problem s.  

The formulation of well-defined research questions is central to addressing a research problem . A research question is a statement made in a question form to provide focus, clarity, and structure to the research endeavor. This helps the researcher design methodologies, collect data, and analyze results in a systematic and coherent manner. A study may have one or more research questions depending on the nature of the study.   

Identifying and addressing a research problem is very important. By starting with a pertinent problem , a scholar can contribute to the accumulation of evidence-based insights, solutions, and scientific progress, thereby advancing the frontier of research. Moreover, the process of formulating research problems and posing pertinent research questions cultivates critical thinking and hones problem-solving skills.   

Table of Contents

What is a Research Problem ?  

Before you conceive of your project, you need to ask yourself “ What is a research problem ?” A research problem definition can be broadly put forward as the primary statement of a knowledge gap or a fundamental challenge in a field, which forms the foundation for research. Conversely, the findings from a research investigation provide solutions to the problem .  

A research problem guides the selection of approaches and methodologies, data collection, and interpretation of results to find answers or solutions. A well-defined problem determines the generation of valuable insights and contributions to the broader intellectual discourse.  

Characteristics of a Research Problem  

Knowing the characteristics of a research problem is instrumental in formulating a research inquiry; take a look at the five key characteristics below:  

Novel : An ideal research problem introduces a fresh perspective, offering something new to the existing body of knowledge. It should contribute original insights and address unresolved matters or essential knowledge.   

Significant : A problem should hold significance in terms of its potential impact on theory, practice, policy, or the understanding of a particular phenomenon. It should be relevant to the field of study, addressing a gap in knowledge, a practical concern, or a theoretical dilemma that holds significance.  

Feasible: A practical research problem allows for the formulation of hypotheses and the design of research methodologies. A feasible research problem is one that can realistically be investigated given the available resources, time, and expertise. It should not be too broad or too narrow to explore effectively, and should be measurable in terms of its variables and outcomes. It should be amenable to investigation through empirical research methods, such as data collection and analysis, to arrive at meaningful conclusions A practical research problem considers budgetary and time constraints, as well as limitations of the problem . These limitations may arise due to constraints in methodology, resources, or the complexity of the problem.  

Clear and specific : A well-defined research problem is clear and specific, leaving no room for ambiguity; it should be easily understandable and precisely articulated. Ensuring specificity in the problem ensures that it is focused, addresses a distinct aspect of the broader topic and is not vague.  

Rooted in evidence: A good research problem leans on trustworthy evidence and data, while dismissing unverifiable information. It must also consider ethical guidelines, ensuring the well-being and rights of any individuals or groups involved in the study.

Types of Research Problems  

Across fields and disciplines, there are different types of research problems . We can broadly categorize them into three types.  

• Theoretical research problems

Theoretical research problems deal with conceptual and intellectual inquiries that may not involve empirical data collection but instead seek to advance our understanding of complex concepts, theories, and phenomena within their respective disciplines. For example, in the social sciences, research problem s may be casuist (relating to the determination of right and wrong in questions of conduct or conscience), difference (comparing or contrasting two or more phenomena), descriptive (aims to describe a situation or state), or relational (investigating characteristics that are related in some way).  

Here are some theoretical research problem examples :   

• Ethical frameworks that can provide coherent justifications for artificial intelligence and machine learning algorithms, especially in contexts involving autonomous decision-making and moral agency.  
• Determining how mathematical models can elucidate the gradual development of complex traits, such as intricate anatomical structures or elaborate behaviors, through successive generations.  
• Applied research problems

Applied or practical research problems focus on addressing real-world challenges and generating practical solutions to improve various aspects of society, technology, health, and the environment.  

Here are some applied research problem examples :   

• Studying the use of precision agriculture techniques to optimize crop yield and minimize resource waste.  
• Designing a more energy-efficient and sustainable transportation system for a city to reduce carbon emissions.  
• Action research problems

Action research problems aim to create positive change within specific contexts by involving stakeholders, implementing interventions, and evaluating outcomes in a collaborative manner.  

Here are some action research problem examples :   

• Partnering with healthcare professionals to identify barriers to patient adherence to medication regimens and devising interventions to address them.  
• Collaborating with a nonprofit organization to evaluate the effectiveness of their programs aimed at providing job training for underserved populations.  

These different types of research problems may give you some ideas when you plan on developing your own.  

How to Define a Research Problem  

You might now ask “ How to define a research problem ?” These are the general steps to follow:   

• Look for a broad problem area: Identify under-explored aspects or areas of concern, or a controversy in your topic of interest. Evaluate the significance of addressing the problem in terms of its potential contribution to the field, practical applications, or theoretical insights.
• Learn more about the problem: Read the literature, starting from historical aspects to the current status and latest updates. Rely on reputable evidence and data. Be sure to consult researchers who work in the relevant field, mentors, and peers. Do not ignore the gray literature on the subject.
• Identify the relevant variables and how they are related: Consider which variables are most important to the study and will help answer the research question. Once this is done, you will need to determine the relationships between these variables and how these relationships affect the research problem . 
• Think of practical aspects : Deliberate on ways that your study can be practical and feasible in terms of time and resources. Discuss practical aspects with researchers in the field and be open to revising the problem based on feedback. Refine the scope of the research problem to make it manageable and specific; consider the resources available, time constraints, and feasibility.
• Formulate the problem statement: Craft a concise problem statement that outlines the specific issue, its relevance, and why it needs further investigation.
• Stick to plans, but be flexible: When defining the problem , plan ahead but adhere to your budget and timeline. At the same time, consider all possibilities and ensure that the problem and question can be modified if needed.

Key Takeaways  

• A research problem concerns an area of interest, a situation necessitating improvement, an obstacle requiring eradication, or a challenge in theory or practical applications.   
• The importance of research problem is that it guides the research and helps advance human understanding and the development of practical solutions.  
• Research problem definition begins with identifying a broad problem area, followed by learning more about the problem, identifying the variables and how they are related, considering practical aspects, and finally developing the problem statement.  
• Different types of research problems include theoretical, applied, and action research problems , and these depend on the discipline and nature of the study.  
• An ideal problem is original, important, feasible, specific, and based on evidence.  

Frequently Asked Questions  

Why is it important to define a research problem?  

Identifying potential issues and gaps as research problems is important for choosing a relevant topic and for determining a well-defined course of one’s research. Pinpointing a problem and formulating research questions can help researchers build their critical thinking, curiosity, and problem-solving abilities.   

How do I identify a research problem?  

Identifying a research problem involves recognizing gaps in existing knowledge, exploring areas of uncertainty, and assessing the significance of addressing these gaps within a specific field of study. This process often involves thorough literature review, discussions with experts, and considering practical implications.  

Can a research problem change during the research process?  

Yes, a research problem can change during the research process. During the course of an investigation a researcher might discover new perspectives, complexities, or insights that prompt a reevaluation of the initial problem. The scope of the problem, unforeseen or unexpected issues, or other limitations might prompt some tweaks. You should be able to adjust the problem to ensure that the study remains relevant and aligned with the evolving understanding of the subject matter.

How does a research problem relate to research questions or hypotheses?  

A research problem sets the stage for the study. Next, research questions refine the direction of investigation by breaking down the broader research problem into manageable components. Research questions are formulated based on the problem , guiding the investigation’s scope and objectives. The hypothesis provides a testable statement to validate or refute within the research process. All three elements are interconnected and work together to guide the research.  

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

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4 Ways to Improve Your Analytical Skills

• 07 Jan 2021

Data is ubiquitous. It’s collected at every purchase made, flight taken, ad clicked, and social media post liked—which means it’s never been more crucial to understand how to analyze it.

“Never before has so much data about so many different things been collected and stored every second of every day,” says Harvard Business School Professor Jan Hammond in the online course Business Analytics .

The volume of data you encounter can be overwhelming and raise several questions: Can I trust the data’s source? Is it structured in a way that makes sense? What story does it tell, and what actions does it prompt?

Data literacy and analytical skills can enable you to answer these questions and not only make sense of raw data, but use it to drive impactful change at your organization.

Here’s a look at what it means to be data literate and four ways to improve your analytical skills.

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What Is Data Literacy?

Data literacy is the ability to analyze, interpret, and question data. A dataset is made up of numerous data points that, when viewed together, tell a story.

Before conducting an analysis, it’s important to ensure your data’s quality and structure is in accordance with your organization’s needs.

“In order to transform data into actionable information, you first need to evaluate its quality,” says Professor Dustin Tingley in the Harvard Online course Data Science Principles . “But evaluating the quality of your data is just the first step. You’ll also need to structure your data. Without structure, it’s nearly impossible to extract any information.”

When you’re able to look at quality data, structure it, and analyze it, trends emerge. The next step is to reflect on your analysis and take action.

Tingley shares several questions to ask yourself once you’ve analyzed your dataset: “Did all the steps I took make sense? If so, how should I respond to my analysis? If not, what should I go back and improve?”

For example, you may track users who click a button to download an e-book from your website.

After ensuring your data’s quality and structuring it in a way that makes sense, you begin your analysis and find that a user’s age is positively correlated with their likelihood to click. What story does this trend tell? What does it say about your users, product offering, and business strategy?

To answer these questions, you need strong analytical skills, which you can develop in several ways.

Related: Business Analytics: What It Is & Why It’s Important

How to Improve Your Analytical Skills

Analysis is an important skill to have in any industry because it enables you to support decisions with data, learn more about your customers, and predict future trends.

Key analytical skills for business include:

• Visualizing data
• Determining the relationship between two or more variables
• Forming and testing hypotheses
• Performing regressions using statistical programs, such as Microsoft Excel
• Deriving actionable conclusions from data analysis

If you want to provide meaningful conclusions and data-based recommendations to your team, here are four ways to bolster your analytical skills.

Related: How to Learn Business Analytics Without A Business Background

1. Consider Opposing Viewpoints

While engaging with opposing viewpoints can help you expand your perspective, combat bias, and show your fellow employees their opinions are valued, it can also be a useful way to practice analytical skills.

When analyzing data, it’s crucial to consider all possible interpretations and avoid getting stuck in one way of thinking.

For instance, revisit the example of tracking users who click a button on your site to download an e-book. The data shows that the user’s age is positively correlated with their likelihood to click the button; as age increases, downloads increase, too. At first glance, you may interpret this trend to mean that a user chooses to download the e-book because of their age.

This conclusion, however, doesn’t take into consideration the vast number of variables that change with age. For instance, perhaps the real reason your older users are more likely to download the e-book is their higher level of responsibility at work, higher average income, or higher likelihood of being parents.

This example illustrates the need to consider multiple interpretations of data, and specifically shows the difference between correlation (the trending of two or more variables in the same direction) and causation (when a trend in one variable causes a trend to occur in one or more other variables).

“Data science is built on a foundation of critical thinking,” Tingley says in Data Science Principles . “From the first step of determining the quality of a data source to determining the accuracy of an algorithm, critical thinking is at the heart of every decision data scientists—and those who work with them—make.”

To practice this skill, challenge yourself to question your assumptions and ask others for their opinions. The more you actively engage with different viewpoints, the less likely you are to get stuck in a one-track mindset when analyzing data.

2. Play Games or Brain Teasers

If you’re looking to sharpen your skills on a daily basis, there are many simple, enjoyable ways to do so.

Games, puzzles, and stories that require visualizing relationships between variables, examining situations from multiple angles, and drawing conclusions from known data points can help you build the skills necessary to analyze data.

Some fun ways to practice analytical thinking include:

• Crossword puzzles
• Mystery novels
• Logic puzzles
• Strategic board games or card games

These options can supplement your analytics coursework and on-the-job experience. Some of them also allow you to spend time with friends or family. Try engaging with one each day to hone your analytical mindset.

Related: 3 Examples of Business Analytics in Action

3. Take an Online Analytics Course

Whether you want to learn the basics, brush up on your skills, or expand your knowledge, taking an analytics course is an effective way to improve. A course can enable you to focus on the content you want to learn, engage with the material presented by a professional in the field, and network and interact with others in the data analytics space.

For a beginner, courses like Harvard Online's Data Science Principles can provide a foundation in the language of data. A more advanced course, like Harvard Online's Data Science for Business , may be a fit if you’re looking to explore specific facets of analytics, such as forecasting and machine learning. If you’re interested in hands-on applications of analytical formulas, a course like HBS Online's Business Analytics could be right for you. The key is to understand what skills you hope to gain, then find a course that best fits your needs.

If you’re balancing a full-time job with your analytics education, an online format may be a good choice . It offers the flexibility to engage with course content whenever and wherever is most convenient for you.

An online course may also present the opportunity to network and build relationships with other professionals devoted to strengthening their analytical skills. A community of like-minded learners can prove to be an invaluable resource as you learn and advance your career.

Related: Is An Online Business Analytics Course Worth It?

4. Engage With Data

Once you have a solid understanding of data science concepts and formulas, the next step is to practice. Like any skill, analytical skills improve the more you use them.

Mock datasets—which you can find online or create yourself—present a low-risk option for putting your skills to the test. Import the data into Microsoft Excel, then explore: make mistakes, try that formula you’re unsure of, and ask big questions of your dataset. By testing out different analyses, you can gain confidence in your knowledge.

Once you’re comfortable, engage with your organization’s data. Because these datasets have inherent meaning to your business's financial health, growth, and strategic direction, analyzing them can produce evidence and insights that support your decisions and drive change at your organization.

Investing in Your Data Literacy

As data continues to be one of businesses’ most valuable resources, taking the time and effort to build and bolster your analytical skill set is vital.

“Much more data are going to be available; we’re only seeing the beginning now,” Hammond says in a previous article . “If you don’t use the data, you’re going to fall behind. People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Are you interested in furthering your data literacy? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.

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A guide to problem-solving techniques, steps, and skills

You might associate problem-solving with the math exercises that a seven-year-old would do at school. But problem-solving isn’t just about math — it’s a crucial skill that helps everyone make better decisions in everyday life or work.

Problem-solving involves finding effective solutions to address complex challenges, in any context they may arise.

Unfortunately, structured and systematic problem-solving methods aren’t commonly taught. Instead, when solving a problem, PMs tend to rely heavily on intuition. While for simple issues this might work well, solving a complex problem with a straightforward solution is often ineffective and can even create more problems.

In this article, you’ll learn a framework for approaching problem-solving, alongside how you can improve your problem-solving skills.

The 7 steps to problem-solving

When it comes to problem-solving there are seven key steps that you should follow: define the problem, disaggregate, prioritize problem branches, create an analysis plan, conduct analysis, synthesis, and communication.

1. Define the problem

Problem-solving begins with a clear understanding of the issue at hand. Without a well-defined problem statement, confusion and misunderstandings can hinder progress. It’s crucial to ensure that the problem statement is outcome-focused, specific, measurable whenever possible, and time-bound.

Additionally, aligning the problem definition with relevant stakeholders and decision-makers is essential to ensure efforts are directed towards addressing the actual problem rather than side issues.

2. Disaggregate

Complex issues often require deeper analysis. Instead of tackling the entire problem at once, the next step is to break it down into smaller, more manageable components.

Various types of logic trees (also known as issue trees or decision trees) can be used to break down the problem. At each stage where new branches are created, it’s important for them to be “MECE” – mutually exclusive and collectively exhaustive. This process of breaking down continues until manageable components are identified, allowing for individual examination.

The decomposition of the problem demands looking at the problem from various perspectives. That is why collaboration within a team often yields more valuable results, as diverse viewpoints lead to a richer pool of ideas and solutions.

3. Prioritize problem branches

The next step involves prioritization. Not all branches of the problem tree have the same impact, so it’s important to understand the significance of each and focus attention on the most impactful areas. Prioritizing helps streamline efforts and minimize the time required to solve the problem.

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4. Create an analysis plan

For prioritized components, you may need to conduct in-depth analysis. Before proceeding, a work plan is created for data gathering and analysis. If work is conducted within a team, having a plan provides guidance on what needs to be achieved, who is responsible for which tasks, and the timelines involved.

5. Conduct analysis

Data gathering and analysis are central to the problem-solving process. It’s a good practice to set time limits for this phase to prevent excessive time spent on perfecting details. You can employ heuristics and rule-of-thumb reasoning to improve efficiency and direct efforts towards the most impactful work.

6. Synthesis

After each individual branch component has been researched, the problem isn’t solved yet. The next step is synthesizing the data logically to address the initial question. The synthesis process and the logical relationship between the individual branch results depend on the logic tree used.

7. Communication

The last step is communicating the story and the solution of the problem to the stakeholders and decision-makers. Clear effective communication is necessary to build trust in the solution and facilitates understanding among all parties involved. It ensures that stakeholders grasp the intricacies of the problem and the proposed solution, leading to informed decision-making.

Exploring problem-solving in various contexts

While problem-solving has traditionally been associated with fields like engineering and science, today it has become a fundamental skill for individuals across all professions. In fact, problem-solving consistently ranks as one of the top skills required by employers.

Problem-solving techniques can be applied in diverse contexts:

• Individuals — What career path should I choose? Where should I live? These are examples of simple and common personal challenges that require effective problem-solving skills
• Organizations — Businesses also face many decisions that are not trivial to answer. Should we expand into new markets this year? How can we enhance the quality of our product development? Will our office accommodate the upcoming year’s growth in terms of capacity?
• Societal issues — The biggest world challenges are also complex problems that can be addressed with the same technique. How can we minimize the impact of climate change? How do we fight cancer?

Despite the variation in domains and contexts, the fundamental approach to solving these questions remains the same. It starts with gaining a clear understanding of the problem, followed by decomposition, conducting analysis of the decomposed branches, and synthesizing it into a result that answers the initial problem.

Real-world examples of problem-solving

Let’s now explore some examples where we can apply the problem solving framework.

Problem: In the production of electronic devices, you observe an increasing number of defects. How can you reduce the error rate and improve the quality?

Before delving into analysis, you can deprioritize branches that you already have information for or ones you deem less important. For instance, while transportation delays may occur, the resulting material degradation is likely negligible. For other branches, additional research and data gathering may be necessary.

Once results are obtained, synthesis is crucial to address the core question: How can you decrease the defect rate?

While all factors listed may play a role, their significance varies. Your task is to prioritize effectively. Through data analysis, you may discover that altering the equipment would bring the most substantial positive outcome. However, executing a solution isn’t always straightforward. In prioritizing, you should consider both the potential impact and the level of effort needed for implementation.

By evaluating impact and effort, you can systematically prioritize areas for improvement, focusing on those with high impact and requiring minimal effort to address. This approach ensures efficient allocation of resources towards improvements that offer the greatest return on investment.

Problem : What should be my next job role?

When breaking down this problem, you need to consider various factors that are important for your future happiness in the role. This includes aspects like the company culture, our interest in the work itself, and the lifestyle that you can afford with the role.

However, not all factors carry the same weight for us. To make sense of the results, we can assign a weight factor to each branch. For instance, passion for the job role may have a weight factor of 1, while interest in the industry may have a weight factor of 0.5, because that is less important for you.

By applying these weights to a specific role and summing the values, you can have an estimate of how suitable that role is for you. Moreover, you can compare two roles and make an informed decision based on these weighted indicators.

Key problem-solving skills

This framework provides the foundation and guidance needed to effectively solve problems. However, successfully applying this framework requires the following:

• Creativity — During the decomposition phase, it’s essential to approach the problem from various perspectives and think outside the box to generate innovative ideas for breaking down the problem tree
• Decision-making — Throughout the process, decisions must be made, even when full confidence is lacking. Employing rules of thumb to simplify analysis or selecting one tree cut over another requires decisiveness and comfort with choices made
• Analytical skills — Analytical and research skills are necessary for the phase following decomposition, involving data gathering and analysis on selected tree branches
• Teamwork — Collaboration and teamwork are crucial when working within a team setting. Solving problems effectively often requires collective effort and shared responsibility
• Communication — Clear and structured communication is essential to convey the problem solution to stakeholders and decision-makers and build trust

How to enhance your problem-solving skills

Problem-solving requires practice and a certain mindset. The more you practice, the easier it becomes. Here are some strategies to enhance your skills:

• Practice structured thinking in your daily life — Break down problems or questions into manageable parts. You don’t need to go through the entire problem-solving process and conduct detailed analysis. When conveying a message, simplify the conversation by breaking the message into smaller, more understandable segments
• Regularly challenging yourself with games and puzzles — Solving puzzles, riddles, or strategy games can boost your problem-solving skills and cognitive agility.
• Engage with individuals from diverse backgrounds and viewpoints — Conversing with people who offer different perspectives provides fresh insights and alternative solutions to problems. This boosts creativity and helps in approaching challenges from new angles

Final thoughts

Problem-solving extends far beyond mathematics or scientific fields; it’s a critical skill for making informed decisions in every area of life and work. The seven-step framework presented here provides a systematic approach to problem-solving, relevant across various domains.

Now, consider this: What’s one question currently on your mind? Grab a piece of paper and try to apply the problem-solving framework. You might uncover fresh insights you hadn’t considered before.

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Exploring the Elements: Analysis & Problem Solving

Analysis & problem solving are necessary to navigate everyday life, especially in a world of echo-chambers and #fakenews . through analysis, we seek to fully understand issues and uncover potential solutions. so keep reading for insights into how these skills can be developed…, what is analysis & problem solving and why is it important .

Analysis and Problem Solving is the ability to critically evaluate data and use judgement to work through issues. It involves spotting connections between data. And essentially, involves seeing and actioning solutions effectively.

Firstly, Analysis is about being objective. And looking for evidence to support the conclusions we reach. Ultimately to improve judgement. And good analysis helps us to keep in check important cognitive shortcuts that can often impair our judgements– our biases.

We all have biases. When left unmanaged, biases are very problematic. One common bias, the Halo Effect , leads us to amplify the positive aspects of people. For example, thinking because a person is attractive, they’ll automatically be a good person. By building an analytical mindset, we can manage our biases, make better decisions and effectively solve problems.

The goal is to problem-solve on the basis of objective evidence, not sentiment. Emotions and biases cloud our judgement. So it’s essential to probe the evidence, determine what’s fact from fiction. Good analysis helps us to do this.

When starting out

At the outset of your career, you’re likely to be given tasks or problems to solve. A good determinant of how successful you’ll be in problem solving or delivering tasks is your ability to conduct strong analysis.

Naturally, over time, the problems you’ll deal with will become increasingly complex. So, it’s good to get in the habit of conducting thorough, evidence-based analysis early on.

First, focus on identifying relevant information. Is there data, facts, evidence available to help you analyse? Be careful too, it’s easy to waste time with interesting yet ultimately irrelevant data.

During this time, you’ll be getting to grips with the role, workplace and your colleagues. So you’ll have all sorts of information to handle. This makes it even more important to focus on what really matters to the task at hand.

Ask questions for a better understanding of what you’re trying to solve. Seek others’ views and opinions. This is important for ensuring others trust and engage with you. But your priority should be on building a picture of problems, built of evidence and data.

Test assumptions to decipher and challenge the myths. In a world of fake news, critical thinking is integral to analysis and problem solving. Sometimes can be as simple as reviewing problems again after a break. Ask yourself:  what am I assuming here? What is really going? W hat might I have missed before?

Exercise lateral thinking. Think outside the box and to look at problems from different perspectives. For example, if you’re a product designer it’s effective to interact with product from the perspective of users, suppliers and distributors.

Beware of  overconfidence . Both your own and that of others. Don’t just expect your managers or seniors to be correct, examine the source of data. Also, get a handle on the different types of biases that hinder  analysis . Think about which biases you might be prone to.

Essentially, it’s about having your research hat on. So stay alert and conduct qualitative and quantitative analysis as appropriate. Try testing yourself to build your capacity for spotting trends and patterns in complex problems. Logical or abstract reasoning tests can be a great one to start with.

Analysis and Problem Solving on your way up

So as you gain more experience in dealing with analysis, you’ll become better at problem-solving. Often the more senior your role is means the more responsibility you have, thus more potential problems.

With experience, you’ll start to more routinely tune-in to the workplace. Y ou’ll be aware of issues brewing beneath the surface, like office politics. And you will analyse and navigate underlying issues like this when problem-solving.

By its very nature, you’re dealing with more data, more information, more stakeholders and more pressure. So your ability to analyse despite additional distractions is truly put to the test. However, you may now have the opportunity to delegate tasks.

And if the option of help is there – grab it with both hands! The variety of information you’re dealing with grows with increasing responsibility. It’s easy to think you can continue to effectively analyse as you once did with a more focused workload, but don’t be fooled. We all have limits. We have to prioritise our attention. Pick what and when we analyse.

As you take more of a lead on problems, help others to think critically. Point out the evidence, data or facts underpinning your judgements. And ask them to critically evaluate them too.

More and more, leaders will want to see depth in your analysis and evidence that your ideas are future-proofed. They’ll ask to see the business case for any recommendations you make. So prioritise building strong rationale in business cases and focus on testing assumptions and iterating your solutions. Build prototypes or minimal viable products (MVPs) to truly stress test your ideas or judgements.

Develop a process for problem solving. Try to implement systems to seek, analyse, formulate solutions and evaluate outcomes. Building loops like this can help create habits and make analysis more seamless.

You could use tools to appraise data quickly. Or you can work with specialists in evaluating big data. In the future we’ll be using more tools to analyse complex and diverse data. So get ahead of the curb, by building you capacity for interpreting data now.

Leading in Analysis and Problem Solving

No matter what career stage you’re at, there will be problems to solve. This is especially the case when you’re in a position of leadership. A good leader is both demonstrator and facilitator of strong analysis and problem-solving skills.

At this stage you’ll likely be responsible for the management of a team and the big picture of the organisation.  Ultimately, you’ll need to lead by example and set the tone for your team when problem-solving.

Leaders are expected to be decisive. And good decisions are made by harnessing the power of an analytical mindset to collect and decipher data and information. No knee-jerk reactions, but thoughtful and strategic responses. Here are some pointers on how you can do this:

Use patterns and trends to uncover longer term opportunities and draw potential conclusions. This could relate to commercial thinking when looking for financially beneficial opportunities.

Recognise and respond strategically to the pressures faced by your people. Engage with your team and look at how you can improve processes, wellbeing and overall productivity.

Consistently build and review your awareness of new technologies shaping the way things might be done in the future. Stay in the know and beware of the fads. It’s about choosing what is best to pursue.

Increase your awareness and use of Systems Thinking . Identify the links between different tasks and functions. Then evaluate on the basis of seeing things in a system, rather than treating issues in isolation.

Say what you think. Share your thought-process openly. Be a thought-leader, create an open space for sharing thoughts and your team will contribute. This ultimately increases potential collaboration, empowers the team and teases out team-working issues.

From analysing the situation to solving it

Whatever walk of life or occupation, your chances of success will improve along with your capacity for analysis and problem solving. Whether you’re just starting out or you’ve been around for a while, these skills are needed at every stage.

And analysis is important in our interpersonal interactions, as it is for regular tasks. There are always instances where you might need to read between the lines of what someone is saying. And to identify what’s really going on, beneath the conversation’s surface.

Additionally, the more hands-on experience we have with problems, the better we’ll be at finding solutions. So maybe we should schedule some time for ‘brain-training’ exercises like Sudoku? Although there isn’t any conclusive evidence to suggest games like Sudoku substantially improve our problem-solving abilities, the regular exposure to problems tests us and builds confidence.

So if you’re looking to build up your analysis and problem solving skills, set up a  spotlight  on the  WiseAmigo app . Doing this will help you stay on track with your development, and get inspired along the way.

And once you’ve nailed Analysis and Problem Solving, you’ll be in a better place to think strategically, commercially and manage conflict better too.

“Analysis is the art of creation through destruction.”  

― p.s. baber, cassie draws the universe..

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Analytical Skills Definition

Analytical skills examples, how to show your analytical skills on your resume, how to talk about your analytical skills in an interview, how to improve your analytical skills, analytics at work: the bottom line, what are analytical skills definition and examples.

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Table of Contents

If you’re looking for a job in 2024, chances are you’ll need stellar analytical skills. Analytical skills help you assess information and facts, problem-solve, and implement the best solutions. According to LinkedIn , they’re one of the top 10 most in-demand soft skills of 2024. So, what are some analytical skills examples and how can you improve yours?

Analytical skills are the skills you use to make decisions and find solutions to problems. In the workplace, an analytical person helps the company problem-solve by breaking down information; looking through data and finding patterns, trends, and outliers; brainstorming new ideas; and making decisions on what solutions to implement.

If you’re like me, you might be thinking that analytical skills are usually just for data-heavy or analytical roles. But even as a more creative professional — working on writing and marketing — I’ve learned analytical skills are crucial to essentially any role. For example, I use analytical skills to understand which of my articles are performing well and which ones aren’t to help inform what I’ll write about next. Even though my primary role is to write content, analytical skills are key to prioritizing my work and ensuring what I’m writing is successful. 

Companies hire people to help them solve problems, and analytical skills are what you use to do just that. You can use analytical skills in the workplace:

• In marketing , to review traffic to the website and understand what is (and isn’t) driving people to the site  
• In data analytics , to identify seasonal trends in a company’s sales to understand the best time to launch a campaign
• In finance , to prepare forecasts of the company’s financial performance for the next year
• In user experience (UX) design , to understand current issues with the company’s UX while interviewing a user
• In sales, to create models to track revenue growth
• In software engineering , to see what parts of the software are performing as expected and which ones aren’t and why 
• In human resources, to understand employee performance, turnover, and engagement 
• In law, to comb through legal documents to develop legal arguments and strategies for clients.

Human Resources

Apply analytical skills in HR to analyze compensation data and make recommendations to managers about which employees should receive pay rises or adjustments.

Avg. Time: 3-4 hours

Skills you’ll build: Process mapping, empowering with insights, feedback giving, continuous improvement tools

While analytical skills are a type of soft skill, you may apply hard skills to help you become a better analytical thinker. Analytical skills examples include data analysis, logical thinking, research, creativity, and communication.

>>MORE: Discover the right career for you based on your skills with a career aptitude test .

Data Analytics

Data analytics is a hard skill where you look at data to put numbers behind answers to questions or potential solutions. For example, you might use data analytics to answer what products have had the most success during the summer vs. winter months, or to create charts or graphs that show the company’s recent financial performance. 

You don’t need to be a data analyst to use data analytics in your everyday work; in fact, it’s a valuable asset to your skill set to ensure the impact of your work, no matter what you do. Going back to my example of using data to help me understand article performance, being able to pull this data on my own and synthesize it into results and learnings is crucial for showing whether I’m performing well at work. Anyone can benefit from knowing how to pull and visualize the proof that their work is having an impact!

Examples of data analytics skills include:

• Programming languages (specifically SQL, Python, and R)
• Probability and statistical analysis
• Machine learning
• Microsoft Excel
• Data visualization

Logical Thinking

Logical thinking is when you use reason to analyze a situation and come up with a solution. There are a few different types of logical thinking, including:

• Inference: Assuming an answer based on facts we already know
• Inductive reasoning : Observing a specific pattern, then making a general conclusion
• Deductive reasoning : Observing a general premise, then applying it to a specific situation 

For example, as a writer on a marketing team, I might use logical thinking, and specifically inductive reasoning, by taking action based on a specific trend I notice about my company’s audience. I may notice a specific pattern — for instance, that our audience is clicking on stories that have investment banking skills in them. Then, I could make the general conclusion that our audience values investment banking content. I would then test my hypothesis by writing more content on that topic, and hopefully increase our audience in the process. 

Analytical people seek all the facts and information before coming to a conclusion. A smart researcher knows where to find those facts and who to ask for help to get more information. 

In the workplace, you might apply research skills to discover facts about the company’s history, like conducting a reflective analysis, and showing the company’s progress over the last five years. You could also do more qualitative research , and speak to colleagues in other departments to understand how a problem is affecting their team, or even set up an informational interview with an outside expert to learn from their experience.

Examples of research analytical skills include: 

• Report writing
• Data collection and analysis
• Critical thinking
• User interviews

Communication

Analytical skills aren’t just about facts and figures; they also require creativity to brainstorm solutions and possible answers to problems. Creativity helps analytical people move away from the small points and think big picture. 

In the workplace, you might use creative thinking to organize a brainstorm with team members, or to propose product improvements based on a client survey. You could also use it to present information to stakeholders in a new, exciting way, or to create a new brand design for your company’s website. Creative thinking can be applied to numerous industries, even in more data-heavy or analytical roles.

Examples of analytical creativity skills include:

• Active listening
• Risk-taking
• Storytelling

Introduction to Strategy Consulting

Use creative thinking skills to generate ideas to help a fictional luxury clothing company increase sales revenue.

Avg. Time: 1-2 hours

Skills you’ll build: Critical thinking, creativity, brainstorming

Your analytical thinking won’t have an impact unless you share it with the team; however, not everyone can easily understand data or analytical problem-solving. Communication skills help you translate complex analytical ideas into digestible, actionable takeaways for the rest of your team.

For example, you can use communication skills to explain a data visualization to team members and help them understand company performance, or to present high-level findings from a data exercise or statistical analysis. 

Examples of analytical communication skills include:

• Verbal communication
• Chart, graph, and data presentation
• Public speaking

There are two types of ways to show your analytical skills on your resume: listing your hard skills in a “skills” section or explaining your analytical skills in your “experience” section. 

“For early professionals, definitely showing the tools, the technical skills, and also projects you’ve worked on is important,” Kristen Rice, product manager, website growth at Sprout Social, says. “If you don’t have a particular project in mind or that you can share, showcase ideas that you do have around analytics. If you use a type of code such as SQL, Python, R etc., that is huge because businesses seek to automate analyses a lot quicker and there is an increasing need to connect data that doesn’t always share the same foundation. These different programming languages allow for the ability to do those things.”

For example, if you used your data analytics skills in a finance internship , you could write: 

Used SQL queries to extract data and create reports that helped the team decrease surplus spending by 13% MoM.

Even if you’re talking about soft skills, you should include the impact your skills had. For example, as a writer, I might write something like:

• Performed competitive research analysis to identify three key improvement opportunities for our blog, leading to 10% traffic growth in two months
• Led brainstorming sessions to produce 30 new content ideas each month
• Conducted and shared analysis of top-performing content to inform future content strategy, leading to 20% MoM traffic growth

Log in to download a customizable resume template with examples of how to include analytical skills:

You don’t need to know multiple coding languages or analytics programs to show off your analytical skills. You can also show analytical thinking through how you describe your problem-solving methods and approach at work. 

In the interview , use the STAR method to show how you apply analytical skills and the impact your skills had. Even if you’re talking about soft skills, get specific about programs, tactics, or methodology you use when solving problems. This will give the interviewer a clear picture of how you work and problem-solve.

• What do you first consult when solving a problem? Can you talk about any experience analyzing numerical results, looking at website analytics, etc.?
• What steps do you take to make sense of a problem? 
• Who or what do you consult to help you solve the problem?
• How do you test and iterate your solution?
• How do you reflect on your solution? What steps do you take after?

For example, you might be asked about your decision-making process at work. You can respond with something like:

My decision-making process usually starts with gathering all the information I know about the problem, whether that’s by researching, collaborating with other teams, or performing data analysis. Once I have a better understanding of the problem, I’ll then share this information with my coworkers and ask them to brainstorm with me. After that, I’ll perform a risk analysis of all of the solutions we brainstormed and make a final decision on the best path forward.

>>MORE: Analytical Skills Interview Questions (and Answers)

BCLP Interview Success

Practice answering some of the most common interview questions.

Avg. Time: 4-5 hours

Skills you’ll build: Public speaking, poise, presentation, communication

Even though some technical skills are involved in analytical thinking, much of analytical thinking relies on your soft skills — which means it’s harder to know how to be a better analytical thinker. However, by understanding your current problem-solving process and asking others about theirs, you’ll start to hone your analytical skills.

Document Your Current Skills

It isn’t easy to assess your current skill level if you don’t know how you currently use analytical thinking, even in your everyday life. The next time you approach a problem, even something like figuring out what to wear to dinner with friends, ask yourself:

• What facts am I considering here?
• What research do I do? Do I ask anyone for help, and who?
• How do I brainstorm solutions?
• How do I make my final decision on how to move forward?
• Do I reflect on my decision-making skills after, and if so, how does that affect my future decisions?

To use the dinner example, maybe you consider factors like the weather and the restaurant’s dress code when deciding what to wear. You might look up the weather using an app and research the restaurant online to see what the vibe is. Then, maybe you pull out a few options and try them on to see what you’re comfortable wearing. 

This decision-making process might seem simple, but it’s a true skill! Improving your analytical skills starts with understanding how you uniquely solve problems. 

Network With Other Teams

Learning from people around you can help you identify the problems they’re working on and show you how they may solve problems. You might learn about new resources or tools, or even just methods and tricks they use at work.

“ Network with people in roles that you’re interested in,” Rice recommends. “I’ve connected with people on LinkedIn who are resources for me, internally at my organization I’ve had the opportunity to learn from our data science, data engineering, and business analytics team, and I also try to attend events or webinars that are geared towards analytics to build my knowledge and connections as well.”

Create Opportunities for Yourself

An analytical thinker will take in facts, do their research, brainstorm creative solutions, narrow down to the most logical one, and reflect on their solutions after the decision was made to learn for the next time. There’s no better way to improve your skills than to put yourself into situations where you need to exercise your analytical skills — whether that’s doing something simple like logic puzzles, or even putting yourself in a professional’s shoes and pretending you have to make a big company decision. Practice walking through these steps when you problem-solve and make a decision, whether big or small.

Practice Putting Your Analytical Skills in Context

It can be hard to know what it’s like to use analytical skills in the workplace if you’ve never had a full-time job before. With Forage job simulations, you can get free access to real-world work problems to practice using your analytical skills in a professional context. 

Apply your analytical skills to real-world work situations in whatever industry interests you:

Analytical skills help you dig into problems and come out with facts-based solutions. While some technical skills like data analysis and visualization are elements of analytical skills, there are also soft skills like creativity and communication that are essential to being an effective analytical thinker. 

No matter what kinds of analytical skills you have, show them off on your resume and in the interview by detailing your unique, informative analytical problem-solving process.

Examples of analytical skills include data analytics, research, logical thinking, creativity, and communication. There are hard analytical skills, like data analytics, that help you use numbers to answer business questions, but also soft analytical skills, like creativity, that help you brainstorm potential solutions.

You can demonstrate analytical skills on your resume by either listing out data tools you use in a skills section or by describing scenarios in which you’ve used analytical skills in your experience section. In an interview, be sure to clearly outline what the problem was, who you worked with, any tools you used, and how your analytical skills led to the right solution.

Analytical skills can be hard or soft skills. Analytical hard skills are typically data or other tech tools that help you use numbers to answer questions or find solutions. Soft analytical skills are the ones you use when you’re thinking about how to solve a problem and how you figure out what strategic action to take.

Image Credit: olia danilevich / Pexels

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The 5 Steps in Problem Analysis

One technique that is extremely useful to gain a better understanding of the problems before determining a solution is problem analysis .

Problem analysis is the process of understanding real-world problems and user’s needs and proposing solutions to meet those needs. The goal of problem analysis is to gain a better understanding of the problem being solved before developing a solution.

There are five useful steps that can be taken to gain a better understanding of the problem before developing a solution.

• Gain agreement on the problem definition
• Understand the root-causes – the problem behind the problem
• Identify the stakeholders and the users
• Define the solution boundary
• Identify the constraints to be imposed on the solution

Table of Contents

Gain agreement on the problem definition.

The first step is to gain agreement on the definition of the problem to be solved. One of the simplest ways to gain agreement is to simply write the problem down and see whether everyone agrees.

Business Problem Statement Template

A helpful and standardised format to write the problem definition is as follows:

• The problem of – Describe the problem
• Affects – Identify stakeholders affected by the problem
• The results of which – Describe the impact of this problem on stakeholders and business activity
• Benefits of – Indicate the proposed solution and list a few key benefits

Example Business Problem Statement

There are many problems statement examples that can be found in different business domains and during the discovery when the business analyst is conducting analysis. An example business problem statement is as follows:

The problem of  having to manually maintain an accurate single source of truth for finance product data across the business, affects the finance department. The results of which has the impact of not having to have duplicate data, having to do workarounds and difficulty of maintaining finance product data across the business and key channels. A successful solution would  have the benefit of providing a single source of truth for finance product data that can be used across the business and channels and provide an audit trail of changes, stewardship and maintain data standards and best practices.

Understand the Root Causes Problem Behind the Problem

You can use a variety of techniques to gain an understanding of the real problem and its real causes. One such popular technique is root cause analysis, which is a systematic way of uncovering the root or underlying cause of an identified problem or a symptom of a problem.

Root cause analysis helps prevents the development of solutions that are focussed on symptoms alone .

To help identify the root cause, or the problem behind the problem, ask the people directly involved.

The primary goal of the technique is to determine the root cause of a defect or problem by repeating the question “Why?” . Each answer forms the basis of the next question. The “five” in the name derives from an anecdotal observation on the number of iterations needed to resolve the problem .

Identify the Stakeholders and the Users

Effectively solving any complex problem typically involves satisfying the needs of a diverse group of stakeholders. Stakeholders typically have varying perspectives on the problem and various needs that must be addressed by the solution. So, involving stakeholders will help you to determine the root causes to problems.

Define the Solution Boundary

Once the problem statement is agreed to and the users and stakeholders are identified, we can turn our attention of defining a solution that can be deployed to address the problem.

Identify the Constraints  Imposed on Solution

We must consider the constraints that will be imposed on the solution. Each constraint has the potential to severely restrict our ability to deliver a solution as we envision it.

Some example solution constraints and considerations could be:-

• Economic – what financial or budgetary constraints are applicable?
• Environmental – are there environmental or regulatory constraints?
• Technical  – are we restricted in our choice of technologies?
• Political – are there internal or external political issues that affect potential solutions?

Conclusion – Problem Analysis

Try the five useful steps for problem solving when your next trying to gain a better understanding of the problem domain on your business analysis project or need to do problem analysis in software engineering.

The problem statement format can be used in businesses and across industries. 

Jerry Nicholas

Jerry continues to maintain the site to help aspiring and junior business analysts and taps into the network of experienced professionals to accelerate the professional development of all business analysts. He is a Principal Business Analyst who has over twenty years experience gained in a range of client sizes and sectors including investment banking, retail banking, retail, telecoms and public sector. Jerry has mentored and coached business analyst throughout his career. He is a member of British Computer Society (MBCS), International Institute of Business Analysis (IIBA), Business Agility Institute, Project Management Institute (PMI), Disciplined Agile Consortium and Business Architecture Guild. He has contributed and is acknowledged in the book: Choose Your WoW - A Disciplined Agile Delivery Handbook for Optimising Your Way of Working (WoW).

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Effectiveness of the flipped project-based learning model based on moodle lms to improve student communication and problem-solving skills in learning programming.

1. Introduction

Research questions.

• Is there an effect of using the flipped project-based learning (FPBL) model based on the LMS Moodle on communication and problem-solving skills?
• What are the differences between students who follow the FPBL model and those who follow the blended learning model in improving their communication and problem-solving skills?

2. Materials and Methods

2.1. design, 2.2. participants, survey instruments, and data collection.

• Problem solving: The test consisted of 25 multiple-choice questions designed to assess students’ ability to identify, analyze, and solve problems related to web programming. The questions covered programming scenarios commonly encountered in web application development.
• Communication skills: The communication skills questionnaire consisted of 12 questions on a 5-point Likert scale that evaluated students’ ability to convey ideas, collaborate with peers, and articulate web programming solutions. The questionnaire assessed various aspects of communication that are important in web project development.
• Sessions 1–2: Introduction to HTML - Session 1: Introduction to basic HTML tags such as <html>, <head>, and <body>, and the creation of a simple webpage structure. The students built a basic webpage and discussed their results. - Session 2: Development of more complex webpage structures using elements such as <table>, <form>, and <div>. Each group created a more structured layout and received feedback.
• Sessions 3–5: Applying CSS - Session 3: Introduction to basic CSS, including the use of selectors and properties such as color, margin, and padding for styling the webpage. - Session 4: Use of Flexbox and Grid to create responsive webpage layouts. Groups design responsive layouts and discuss their designs. - Session 5: Implementation of animations and media queries to make webpages more dynamic and responsive across different devices.
• Sessions 6–7: Implementing JavaScript - Session 6: Introduction to basic JavaScript concepts, including variables, functions, and event handling to add interactivity to the webpage. - Session 7: Development of advanced functionality using JavaScript, such as DOM manipulation and API integration, culminating in the completion of group projects.
• Problem solving: The post-test for problem-solving skills maintained the same format as the pre-test, consisting of 25 multiple-choice questions. These questions aimed to measure the extent to which students developed their problem-solving skills in the context of web programming after following the FPBL or blended learning model. The post-test results were analyzed to compare the improvement between the experimental and control groups.
• Communication skills: The post-test for communication skills used the same questionnaire as the pre-test, consisting of 12 questions. It aimed to assess the changes that occurred in students’ ability to communicate effectively during web programming projects, in the delivery of ideas, team collaboration, and presentation of project results.

2.3. Hypothesis

2.4. data analysis, 3. results and analysis, 3.1. results, 3.1.1. the application of the flipped project-based learning model leads to a significant improvement in problem-solving and communication skills, 3.1.2. differences in problem-solving abilities and communication skills between the experimental group employing the flipped project-based learning model and the control group using blended learning, 3.1.3. addressing the research questions, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Yong, S.T.; Tiong, K.M. A Blended Learning Approach. Int. J. Inf. Commun. Technol. Educ. 2022 , 18 , 1–16. [ Google Scholar ] [ CrossRef ]
• Fu, Q.; Zhang, L.; Hong, J.-C.; Dong, Y. Development and Verification of the Programming Resilience Scale for University Students. Australas. J. Educ. Technol. 2021 , 37 , 141–155. [ Google Scholar ] [ CrossRef ]
• Pu, Y.-W.; Cheng, P.-H.; Chen, L. Research on Online Programming Educational Tool: Case Study on a Three-in-One Environment. Educ. New Dev. 2022 , 2 , 406–410. [ Google Scholar ] [ CrossRef ]
• Dogara, G.; Bin Saud, M.S.; Bin Kamin, Y.; Bin Abd Hamid, M.Z.; Bin Nordin, M.S. Developing Soft Skills through Project-Based Learning in Technical and Vocational Institutions. Int. J. Recent Technol. Eng. 2019 , 8 , 5872–5877. [ Google Scholar ] [ CrossRef ]
• Johansen, J. Instructional Design Models for Well-Structured and Ill-Structured Problem-Solving Learning Outcomes. Educ. Technol. Res. Dev. 1997 , 45 , 65–94. [ Google Scholar ] [ CrossRef ]
• Koshelieva, O.; Tsyselska, O.; Kravchuk, O.; Mironov, V.; Miatenko, N.; Baida, I. New Information Technologies in Improving the Population’s Information Culture. Earth Sci. Hum. Constr. 2023 , 3 , 40–48. [ Google Scholar ] [ CrossRef ]
• Anderson, Z.D.; Hargiss, C.L.M.; Norland, J.E. Skill Development Using Interdisciplinary Problem Solving in a Natural Resources Capstone Course. Nat. Sci. Educ. 2022 , 51 , e20078. [ Google Scholar ] [ CrossRef ]
• Groeneveld, W.; Luyten, L.; Vennekens, J.; Aerts, K. Exploring the Role of Creativity in Software Engineering. In Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), Madrid, Spain, 25–28 May 2021; pp. 1–9. [ Google Scholar ] [ CrossRef ]
• Keller, S.; Parker, C.M.; Chan, C. Employability Skills: Student Perceptions of an Is Final Year Capstone Subject. Ital. Innov. Teach. Learn. Inf. Comput. Sci. 2011 , 10 , 4–15. [ Google Scholar ] [ CrossRef ]
• Ghany, W.A. Mathematical Communication Skills of Junior High School Students in Solving Straight Line Equation Problems Based on Learning Interest. KnE Soc. Sci. 2024 , 9 , 219–228. [ Google Scholar ] [ CrossRef ]
• Shuherk, C.C.; Glaser, S.R.; Glaser, P.A. BreakThrough Communication in a Hybrid World: Amplifying Interactive, Experiential Learning. Int. J. Adv. Corp. Learn. 2022 , 15 , 65–71. [ Google Scholar ] [ CrossRef ]
• Oktasari, D.; Jumadi, J.; Warsono, W.; Hariadi, M.H.; Syari, E.L. 3D Page-Flipped Worksheet on Impulse-Momentum to Develop Students’ Scientific Communication Skills. J. Pendidik. IPA Indones 2019 , 8 , 211–219. [ Google Scholar ] [ CrossRef ]
• Hicks, N.R. Recreation-Based Programming for Veteran Families: Practitioners’ Perspective. J. Veterans Stud. 2024 , 10 , 225–235. [ Google Scholar ] [ CrossRef ]
• White, V.; Chiswell, M.; Webber, E.; Martin, P.; Piper, A.J. What Impact Does Participation in a Communication Skills Training Program Have on Health Professionals’ Communication Behaviors: Findings From a Qualitative Study. J. Cancer Educ. 2023 , 38 , 1600–1607. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wiraharja, I.P.G.S. Improving English Communication for Specific Purposes Using Role-Play and Learning by Doing Techniques for the Staff at the Samaya Resort Seminyak, Badung. Community Serv. J. 2023 , 6 , 9–15. [ Google Scholar ] [ CrossRef ]
• Bell, S. Project-Based Learning for the 21st Century: Skills for the Future. Clear. House A J. Educ. Strateg. Issues Ideas 2010 , 83 , 39–43. [ Google Scholar ] [ CrossRef ]
• Habibah, I.A.N.; Harti, H.; Sudarwanto, T. Project Based Learning Module Development in Vocational High Schools. EduLine J. Educ. Learn. Innov. 2022 , 2 , 206–213. [ Google Scholar ] [ CrossRef ]
• Maccabe, R.; Fonseca, T.D. ‘Lightbulb’ Moments in Higher Education: Peer-to-Peer Support in Engineering Education. Mentor. Tutoring Partnersh. Learn. 2021 , 29 , 453–470. [ Google Scholar ] [ CrossRef ]
• Levy, K.L. Important Leadership Skills and Benefits of Shared Leadership Training for Chief Residents: A Delphi Analysis. J. Healthc. Leadersh. 2023 , 15 , 221–230. [ Google Scholar ] [ CrossRef ]
• Hasnah, H.P. The Description of Mathematics Problem Solving Skills Reviewed from The Learning Style in Students of Grade VI of SDN 44 Rampoang Palopo. J. Appl. Sci. Eng. Technol. Educ. 2021 , 4 , 1–8. [ Google Scholar ] [ CrossRef ]
• Putri, R.K.; Bukit, N.; Simanjuntak, M.P. The Effect of Project Based Learning Model’s on Critical Thinking Skills, Creative Thinking Skills, Collaboration Skills, & Communication Skills (4C) Physics in Senior High School. In Proceedings of the 6th Annual International Seminar on Transformative Education and Educational Leadership (AISTEEL 2021), Virtual, 21 September 2021. [ Google Scholar ] [ CrossRef ]
• Mustafa, M.; Rahmah, U.; Hanafi, H.; Wahidah, N.I. Analysis Trends of Flipped Classroom Research in Education. In Proceedings of the International Conference Primary Education Research Pivotal Literature and Research UNNES 2018 (IC PEOPLE UNNES 2018), Semarang, Indonesia, 15 November 2018. [ Google Scholar ] [ CrossRef ]
• Tomesko, J.; Cohen, D.; Bridenbaugh, J. Using a Virtual Flipped Classroom Model to Promote Critical Thinking in Online Graduate Courses in the United States: A Case Presentation. J. Educ. Eval. Health Prof. 2022 , 19 , 5. [ Google Scholar ] [ CrossRef ]
• Fakhri, M.M.; Andayani, D.D.; Kaswar, A.B.; Zahra Adistia, A.; Fadhilatunisa, D. Dampak Penerapan Flipped Classroom Terhadap Pengetahuan, Keterampilan, Dan Keterlibatan Mahasiswa Dalam Pembelajaran. J. Ilm. Edutic Pendidik. Inform. 2023 , 9 , 127–138. [ Google Scholar ] [ CrossRef ]
• Aidoo, B.; Macdonald, A.; Vesterinen, V.-M.; Pétursdóttir, S.; Gísladóttir, B. Transforming Teaching with ICT Using the Flipped Classroom Approach: Dealing with COVID-19 Pandemic. Educ. Sci. 2022 , 12 , 421. [ Google Scholar ] [ CrossRef ]
• Taspolat, A.; Özdamlı, F.; Soykan, E. Programming Language Training with the Flipped Classroom Model. Sage Open 2021 , 11 , 215824402110214. [ Google Scholar ] [ CrossRef ]
• Thongkoo, K.; Panjaburee, P.; Daungcharone, K. A Development of Ubiquitous Learning Support System Based on an Enhanced Inquiry-Based Learning Approach. Int. J. Mob. Learn. Organ. 2019 , 13 , 129. [ Google Scholar ] [ CrossRef ]
• Cohen, A.; Reynolds, E.E.; Huang, G.C. Effect of a Flipped Classroom on Knowledge Acquisition and Retention in an Internal Medicine Residency Program. J. Grad. Med. Educ. 2019 , 11 , 92–97. [ Google Scholar ] [ CrossRef ]
• Ahmar, D.S.; Azzajjad, M.F.; Ahmar, A.S. The Effect of Review Video Making on Problem Based Learning to Spatial Ability. JINAV J. Inf. Vis. 2023 , 3 , 104–108. [ Google Scholar ] [ CrossRef ]
• Creswell, J.W.; Plano Clark, V.L. Designing and Conducting Mixed Methods Research , 3rd ed.; SAGE: Los Angeles, CA, USA, 2018; ISBN 978-1-4833-4437-9. [ Google Scholar ]
• Olatide, A. Effect of Polya Problem-Solving Model on Senior Secondary School Students’ Performance in Current Electricity. Eur. J. Sci. Math. Educ. 2015 , 3 , 97–104. [ Google Scholar ]
• Purwanto, A.; Rasyid, Y.; Anwar, M.; Mayuni, I. Moodle-Based Flipped Learning-Model to Increase Basic Translation Skills and 21st Century Skills. Scope J. English Lang. Teach. 2022 , 7 , 1–10. [ Google Scholar ] [ CrossRef ]
• Zhou, L.; Chen, L.; Fan, Q.; Ji, Y. Students’ Perception of Using Digital Badges in Blended Learning Classrooms. Sustainability 2019 , 11 , 2151. [ Google Scholar ] [ CrossRef ]
• Rasmitadila, R.; Widyasari, W.; Humaira, M.A.; Tambunan, A.R.S.; Rachmadtullah, R.; Samsudin, A. Using Blended Learning Approach (BLA) in Inclusive Education Course: A Study Investigating Teacher Students’ Perception. Int. J. Emerg. Technol. Learn. 2020 , 15 , 72–85. [ Google Scholar ] [ CrossRef ]
• Bilbao-Quintana, N.; López-De-la-Serna, A.; Romero-Andonegui, A.; Tejada-Garitano, E. Developing Visible Thinking and Motivation through the Curricular Design of an Escape Room in Higher Education. Rev. Electron. Educ. 2021 , 25 , 493–512. [ Google Scholar ] [ CrossRef ]
• Siburian, J. Implementasi Model Project Based Learning Berbasis Flipped Classroom Terhadap Problem Solving Skills Siswa. J. Pendidik. Biol. 2022 , 13 , 113. [ Google Scholar ] [ CrossRef ]
• Agustiningsih, N.; Effendi-Hasibuan, M.H. The Effectiveness of Modified Flip-Based Argumentation Learning in Improving Students’ Argumentation Skills about Hydrocarbon. J. Pendidik. Kim. 2021 , 13 , 250–260. [ Google Scholar ] [ CrossRef ]
• Fadhilatunisa, D.; Fakhri, M.M.; Rosidah, R. Pengaruh Blended Learning Terhadap Aktivitas Belajar Dan Hasil Belajar Mahasiswa Akuntansi. J. Pendidik. Akunt. Indones. 2020 , 18 , 93–106. [ Google Scholar ] [ CrossRef ]
• Karyadi, P.A.; Paristiowati, M.; Afrizal, A. Analysis the 21st Century Skills of Students in Chemical Equilibrium Learning with Flipped Classroom-Collaborative Problem Solving Model. JTK J. Tadris Kim. 2020 , 5 , 48–60. [ Google Scholar ] [ CrossRef ]
• Paristiowati, M.; Cahyana, U.; Bulan, B.I.S. Implementation of Problem-Based Learning—Flipped Classroom Model in Chemistry and Its Effect on Scientific Literacy. Univers. J. Educ. Res. 2019 , 7 , 56–60. [ Google Scholar ] [ CrossRef ]
• Ramadhani, R.; Fitri, Y. A Project-Based Learning Into Flipped Classroom for EPUB3 Electronic Mathematics Learning Module (EMLM)-Based on Course Design and Implementation. Univers. J. Educ. Res. 2020 , 8 , 3119–3135. [ Google Scholar ] [ CrossRef ]
• Chua, K.J.; Islam, S. The Hybrid Project-Based Learning–Flipped Classroom: A Design Project Module Redesigned to Foster Learning and Engagement. Int. J. Mech. Eng. Educ. 2020 , 49 , 289–315. [ Google Scholar ] [ CrossRef ]
• Priyaadharshini, M.; Sundaram, B. Evaluation of Higher-order Thinking Skills Using Learning Style in an Undergraduate Engineering in Flipped Classroom. Comput. Appl. Eng. Educ. 2018 , 26 , 2237–2254. [ Google Scholar ] [ CrossRef ]
• Zhou, C.; Li, Q.; Li, C.; Wang, Y.; Liu, Y.; Wang, G.; Sun, L. A Comprehensive Survey on Pretrained Foundation Models: A History from Bert to Chatgpt. arXiv 2023 , arXiv:2302.09419. [ Google Scholar ]
• Harmini, T.; Sudibyo, N.A.; Suprihatiningsih, S. The Effect of the Flipped Classroom Learning Model on Students’ Learning Outcome in Multivariable Calculus Course. Alphamath J. Math. Educ. 2022 , 8 , 72. [ Google Scholar ] [ CrossRef ]
• Prasetya, R.E. Strategy for Designing English Language Resource-Based Learning Materials in the Moodle LMS. E-Structural 2022 , 5 , 75–92. [ Google Scholar ] [ CrossRef ]
• Aufi, A.A.; Naidu, V.R. Students’ Perception of the Use of Learning Management System to Facilitate Flipped Learning Experience for English Language Teaching in Private Higher Education Institution in Oman: A Developmental Evaluation. Arab World Engl. J. 2021 , 360–377. [ Google Scholar ] [ CrossRef ]
• Fakhri, M.M.; Fadhilatunisa, D.; Rosidah, R.; Satnur, M.A.; Fajrin, F. Pengaruh Media E-Learning Berbasis LMS Moodle Dan Motivasi Belajar Terhadap Hasil Belajar Mahasiswa Di Masa Pandemi COVID-19. Chem. Educ. Rev. 2022 , 5 , 157. [ Google Scholar ] [ CrossRef ]
• Andika, I.; Rosidah, R.; Sigit Sahalik, R.; Nasrullah, N.; Arif Setiawan, S.; Novita, S. Analisis Penggunaan Chatbot Berbasis AI Pada Model Hybrid Di Jurusan Teknik Informatika Dan Komputer. J. Vocat. Inform. Comput. Educ. 2023 , 1 , 79–92. [ Google Scholar ] [ CrossRef ]

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Ruslan, R.; Lu’mu, L.; Fakhri, M.M.; Ahmar, A.S.; Fadhilatunisa, D. Effectiveness of the Flipped Project-Based Learning Model Based on Moodle LMS to Improve Student Communication and Problem-Solving Skills in Learning Programming. Educ. Sci. 2024 , 14 , 1021. https://doi.org/10.3390/educsci14091021

Ruslan R, Lu’mu L, Fakhri MM, Ahmar AS, Fadhilatunisa D. Effectiveness of the Flipped Project-Based Learning Model Based on Moodle LMS to Improve Student Communication and Problem-Solving Skills in Learning Programming. Education Sciences . 2024; 14(9):1021. https://doi.org/10.3390/educsci14091021

Ruslan, Ruslan, Lu’mu Lu’mu, M. Miftach Fakhri, Ansari Saleh Ahmar, and Della Fadhilatunisa. 2024. "Effectiveness of the Flipped Project-Based Learning Model Based on Moodle LMS to Improve Student Communication and Problem-Solving Skills in Learning Programming" Education Sciences 14, no. 9: 1021. https://doi.org/10.3390/educsci14091021

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