creative problem solving scholarly articles

ORIGINAL RESEARCH article

Fostering creativity and critical thinking in college: a cross-cultural investigation.

• 1 Department of Psychology, Pace University, New York, NY, United States
• 2 Developmental and Educational Research Center for Children's Creativity, Faculty of Education, Beijing Normal University, Beijing, China

Enhancing creativity and critical thinking have garnered the attention of educators and researchers for decades. They have been highlighted as essential skills for the 21st century. A total of 103 United States students (53 female, 24 male, two non-binary, and 24 non-reporting) and 166 Chinese students (128 female, 30 male, one non-binary, and seven non-reporting) completed an online survey. The survey includes the STEAM-related creative problem solving, Sternberg scientific reasoning tasks, psychological critical thinking (PCT) exam, California critical thinking (CCT) skills test, and college experience survey, as well as a demographic questionnaire. A confirmatory factor analysis (CFA) yields a two-factor model for all creativity and critical thinking measurements. Yet, the two latent factors are strongly associated with each other ( r =0.84). Moreover, Chinese students outperform American students in measures of critical thinking, whereas Americans outperform Chinese students in measures of creativity. Lastly, the results also demonstrate that having some college research experience (such as taking research method courses) could positively influence both United States and Chinese students’ creativity and critical thinking skills. Implications are discussed.

Introduction

Creativity and critical thinking have been recognized as essential skills in the 21st century ( National Education Association, 2012 ). Many researchers and educators have focused on these two skills, including acquisition, enhancement, and performance. In addition, numerous studies have been devoted to understanding the conceptual complexities involved in creativity and critical thinking. Although similar to each other, creativity and critical thinking are distinctive by definition, each with a different emphasis.

The concept of creativity has evolved over the years. It was almost exclusively conceptualized as divergent thinking when Guilford (1956 , 1986) proposed divergent thinking as a part of intelligence. Earlier measures of creativity took the approach of divergent thinking, measuring creative potential ( Wallach and Kogan, 1965 ; Torrance, 1966 , 1988 ; Runco and Albert, 1986 ; Kim, 2005 ). In 1990s, many creativity scholars challenged the validity of tests of divergent thinking, and suggested that divergent thinking only captures the trivial sense of creativity, and proposed to use the product-oriented method to measure creativity ( Csikszentmihalyi, 1988 ; Amabile, 1996 ; Sternberg and Lubart, 1999 ). A system model of creativity, which recognizes the important roles individual, field, and domain have played, was used as a framework to conceptualize creativity. A widely accepted definition for creativity is a person’s ability to generate an idea or product that is deemed as both novel and appropriate by experts in a field of human activities ( Scott and Bruce, 1994 ; Amabile, 1996 ; Csikszentmihalyi, 1999 ; Sternberg and Lubart, 1999 ; Hunter et al., 2007 ). Corazza and Lubart (2021) recently proposed a dynamic definition of creativity, in which creativity is defined as a context-embedded phenomenon that is tightly related to the cultural and social environment. Based on this new definition, measures of creativity should be context-specific and culturally relevant, especially when it is examined cross-culturally.

Similarly, the conceptualization of critical thinking has also evolved over the years. Earlier definitions emphasized the broad multidimensional aspects of critical thinking, including at least three aspects: attitude, knowledge, and skills ( Glaser, 1941 ). The definition has been evolved to include specific components for each aspect ( Watson and Glaser, 1980 ). For example, critical thinking is recognized as the ability to use cognitive skills or strategies to increase the probability of a desirable outcome ( Halpern, 1999 ). More specifically, cognitive skills such as evaluation, problem-solving, reflective thinking, logical reasoning, and probability thinking are recognized as parts of critical thinking skills in research and assessments ( Ennis, 1987 , Scriven and Paul, 1987 , Halpern, 1999 ). Moving into the 21st century, metacognition and self-regulatory skills have also become essential components for critical thinking in addition to the cognitive skills recognized by earlier scholars ( Korn, 2014 , Paul and Elder, 2019 ).

Similar to the concept of creativity, critical thinking is also viewed as multidimensional and domain specific ( Bensley and Murtagh, 2012 ). For example, critical thinking in psychology, also referred to as psychological critical thinking (PCT), is defined as one’s ability to evaluate claims in a way that explicitly incorporates basic principles of psychological science ( Lawson, 1999 ). As one of the important hub sciences, psychology is often regarded as a foundational course for scientific training in American higher education ( Boyack et al., 2005 ). In psychological discourse, critical thinking is often defined in tandem with scientific thinking, which places significance on hypothesis-testing and problem-solving in order to reduce bias and erroneous beliefs ( Halpern, 1984 ; American Psychological Association, 2016 ; Lamont, 2020 ; Sternberg and Halpern, 2020 ). Based on this definition, measures of critical thinking should assess cognitive skills (i.e., evaluation, logical reasoning) and ability to utilize scientific methods for problem-solving.

In addition to the evolution of the definitions of critical thinking and creativity, research into these two concepts has led to the development of various measurements. For both concepts, there have been numerous measurements that have been studied, utilized, and improved.

The complexities associated with creativity (i.e., context-relevant and domain-specificity) pose a major issue for its measurement. Many different types of creativity measures have been developed in the past. Measures using a divergent thinking approach, such as the Torrance Tests of Creative Thinking ( Torrance, 1974 ) and Alternate Uses Test ( Guilford et al., 1960 ), a product-oriented approach, a third person nomination approach, as well as a self-report approach measuring personality ( Gough, 1979 ), creative behavior ( Hocevar and Michael, 1979 ; Rodriguez-Boerwinkle et al., 2021 ), and creative achievement ( Carson et al., 2005 ; Diedrich et al., 2018 ).

Both the divergent thinking and the product-oriented approaches have been widely used in the creativity literature to objectively measure creativity. The tasks of both approaches are generally heuristic, meaning that no correct answer is expected and the process does not need to be rational. When scoring divergent thinking, the number of responses (i.e., fluency) and the rareness of the response (i.e., originality) were used to represent creativity. When scoring products using the product-orientated approach, a group of experts provides their subjective ratings on various dimensions such as originality, appropriateness, and aesthetically appealing to these products using their subjective criteria. When there is a consensus among the experts, average ratings of these expert scores are used to represent the creativity of the products. This approach is also named as Consensual Assessment Technique (CAT; Amabile, 1982 , 1996 ). Some scholars viewed the CAT approach as focusing on the convergent aspect of creativity ( Lubart et al., 2013 ). Recognizing the importance of divergent and convergent thinking in conceptualizing creativity, Lubart et al. (2013) have suggested including divergent thinking and product-oriented approach (i.e., CAT) to objective measures of creativity ( Barbot et al., 2011 ).

Similar to measures of creativity, measurements of critical thinking are also multilevel and multi-approach. In an article reviewing the construction of critical thinking in psychological studies, Lamont (2020) argues that critical thinking became a scientific object when psychologists attempted to measure it. Different from measures of creativity, where the tasks are heuristic in nature, measures of critical thinking require participants to engage in logical thinking. Therefore, the nature of critical thinking tasks is more algorithmic.

The interest in the study of critical thinking is evident in the increased efforts in the past decades to measure such a complex, multidimensional skill. Watson-Glaser Tests for Critical Thinking ( Watson and Glaser, 1938 ) is widely recognized as the first official measure of critical thinking. Since then, numerous measurements of critical thinking have been developed to evaluate both overall and domain-specific critical thinking, such as the PCT Exam ( Lawson, 1999 ; See Mueller et al., 2020 for list of assessments). A few of the most commonly used contemporary measures of critical thinking include the Watson-Glaser Test for Critical Thinking Appraisals ( Watson and Glaser, 1980 ), Cornell Critical Thinking Test ( Ennis et al., 1985 ), and California Critical Thinking (CCT) Skills Test ( Facione and Facione, 1994 ). As the best established and widely used standardized critical thinking measures, these tests have been validated in various studies and have been used as a criterion for meta-analyses ( Niu et al., 2013 ; Ross et al., 2013 ).

There have also been concerns regarding the usage of these standardized measures of critical thinking on its own due to its emphasis on measuring general cognitive abilities of participants, while negating the domain-specific aspect of critical thinking ( Lamont, 2020 ). The issues associated with standardized measures are not unique to standardized critical thinking measures, as same types of criticisms have been raised for standardized college admissions measures such as the Graduate Record Exam (GRE). To develop an assessment that encompasses a broader range of student abilities that is more aligned to scientific disciplines, Sternberg and Sternberg (2017) developed a scientific inquiry and reasoning measure. This measure is aimed to assess participants’ ability to utilize scientific methods and to think scientifically in order to investigate a topic or solve a problem ( Sternberg and Sternberg, 2017 ). The strength of this measure is that it assesses students’ abilities (i.e., ability to think critically) that are domain-specific and relevant to the sciences. Considering the multidimensional aspect of critical thinking, a combination of a standardized critical thinking measure, an assessment measuring cognitive abilities involved in critical thinking; and a measure that assesses domain-specific critical thinking, would provide a comprehensive evaluation of critical thinking.

The Relationship Between Creativity and Critical Thinking

Most of the studies thus far referenced have investigated creativity and critical thinking separately; however, the discussion on the relationship between creativity and critical thinking spans decades of research ( Barron and Harrington, 1981 ; Glassner and Schwartz, 2007 ; Wechsler et al., 2018 ; Akpur, 2020 ). Some earlier studies on the relationship between divergent thinking and critical thinking have observed a moderate correlation ( r =0.23, p <0.05) between the two ( Gibson et al., 1968 ). Using measures of creative personality, Gadzella and Penland (1995) also found a moderate correlation ( r =0.36, p <0.05) between creative personality and critical thinking.

Recent studies have further supported the positive correlation between critical thinking and creativity. For example, using the creative thinking disposition scale to measure creativity, Akpur (2020) found a moderate correlation between the two among college students ( r =0.27, p <0.05). Similarly, using the critical thinking disposition scale to measure critical thinking and scientific creativity scale and creative self-efficacy scale to measure creativity, Qiang et al. (2020) studied the relationship between critical thinking and creativity to a large sample of high school students ( n =1,153). They found that the relationship between the two varied depending on the type of measurement of creativity. More specifically, the correlation between critical thinking disposition and creative self-efficacy was r =0.045 ( p <0.001), whereas the correlation between critical thinking disposition and scientific creativity was r =0.15 ( p <0.01).

Recognizing the moderate relationship between the two, researchers have also aimed to study the independence of creativity and critical thinking. Some studies have found evidence that these constructs are relatively autonomous. The results of Wechsler et al. (2018) study, which aimed to investigate whether creativity and critical thinking are independent or complementary processes, found a relative autonomy of creativity and critical thinking and found that the variables were only moderately correlated. The researchers in this study suggest that a model that differentiated the two latent variables associated with creativity and critical thinking dimensions was the most appropriate method of analysis ( Wechsler et al., 2018 ). Evidence to suggest that creativity and critical thinking are fairly independent processes was also found in study of Ling and Loh (2020) . The results of their research, which examined the relationship of creativity and critical thinking to pattern recognition, revealed that creativity is a weak predictor of pattern recognition. In contrast, critical thinking is a good predictor ( Ling and Loh, 2020 ).

It is worth noting that a possible explanation for the inconsistencies in these studies’ results is the variance in the definition and the measures used to evaluate creativity and critical thinking. Based on the current literature on the relationship between creativity and critical thinking, we believe that more investigation was needed to further clarify the relationship between creativity and critical thinking which became a catalyst for the current study.

Cross-Cultural Differences in Creativity and Critical Thinking Performance

Results from various cross-cultural studies suggest that there are differences in creativity and critical thinking skills among cultures. A common belief is that individuals from Western cultures are believed to be more critical and creative compared to non-Westerners, whereas individuals from non-Western cultures are believed to be better at critical thinking related tasks compared to Westerners ( Ng, 2001 ; Wong and Niu, 2013 ; Lee et al., 2015 ). For example, Wong and Niu (2013) found a persistent cultural stereotype regarding creativity and critical thinking skills that exist cross-culturally. In their study, both Chinese and Americans believed that Chinese perform better in deductive reasoning (a skill comparable to critical thinking) and that Americans perform better on creativity. This stereotype belief was found to be incredibly persistent as participants did not change their opinions even when presented with data that contradicted their beliefs.

Interestingly, research does suggest that such a stereotype might be based on scientific evidence ( Niu et al., 2007 ; Wong and Niu, 2013 ). In the same study, it was revealed that Chinese did in fact perform better than Americans in deductive reasoning, and Americans performed better in creativity tests ( Wong and Niu, 2013 ). Similarly, Lee et al. (2015) found that compared to American students, Korean students believed that they are more prone to use receptive learning abilities (remembering and reproducing what is taught) instead of critical and creative learning abilities.

Cultural Influence on Critical Thinking

Other studies investigating the cultural influence on critical thinking have had more nuanced findings. Manalo et al. (2013) study of university students from New Zealand and Japan found that culture-related factors (self-construal, regulatory mode, and self-efficacy) do influence students’ critical thinking use. Still, the differences in those factors do not necessarily equate to differences in critical thinking. Their results found that students from Western and Asian cultural environments did not have significant differences in their reported use of critical thinking. The researchers in this study suggest that perhaps the skills and values nurtured in the educational environment have a more significant influence on students’ use of critical thinking ( Manalo et al., 2013 ).

Another study found that New Zealand European students performed better on objective measures of critical thinking than Chinese students. Still, such differences could be explained by the student’s English proficiency and not dialectical thinking style. It was also revealed in this study that Chinese students tended to rely more on dialectical thinking to solve critical thinking problems compared to the New Zealand European students ( Lun et al., 2010 ). Other research on the cultural differences in thinking styles revealed that Westerners are more likely to use formal logical rules in reasoning. In contrast, Asians are more likely to use intuitive experience-based sense when solving critical thinking problems ( Nisbett et al., 2001 ).

These studies suggest that culture can be used as a broad taxonomy to explain differences in critical thinking use. Still, one must consider the educational environment and thinking styles when studying the nature of the observed discrepancies. For instance, cultural differences in thinking style, in particular, might explain why Westerners perform better on some critical thinking measures, whereas Easterners perform better on others.

Cultural Influence on Creative Performance

Historically, creativity studies have suggested that individuals from non-Western cultures are not as creative as Westerners ( Torrance, 1974 ; Jellen and Urban, 1989 ; Niu and Sternberg, 2001 ; Tang et al., 2015 ). For example, in one study, Americans generated more aesthetically pleasing artworks (as judged by both American and Chinese judges) than Chinese ( Niu and Sternberg, 2001 ). However, recent creativity research has suggested that cross-cultural differences are primarily attributable to the definition of creativity rather than the level of creativity between cultures. As aforementioned, creativity is defined as an idea or product that is both novel and appropriate. Many cross-cultural studies have found that Westerners have a preference and perform better in the novelty aspect, and Easterners have a preference and perform better in the appropriateness aspect. In cross-cultural studies, Rockstuhl and Ng (2008) found that Israelis tend to generate more original ideas than their Singaporean counterparts. In contrast, Singaporeans tend to produce more appropriate ideas. Bechtoldt et al. (2012) found in their study that Koreans generated more useful ideas, whereas Dutch students developed more original ideas. Liou and Lan (2018) found Taiwanese tend to create and select more useful ideas, whereas Americans tend to generate and choose more novel ideas. The differences in creativity preference and performance found in these studies suggest that cultural influence is a prominent factor in creativity.

In summary, cross-cultural studies have supported the notion that culture influences both creativity and critical thinking. This cultural influence seems relatively unambiguous in creativity as it has been found in multiple studies that cultural background can explain differences in performance and preference to the dual features of creativity. Critical thinking has also been influenced by culture, albeit in an opaquer nature in comparison to creativity. Critical thinking is ubiquitous in all cultures, but the conception of critical thinking and the methods used to think critically (i.e., thinking styles) are influenced by cultural factors.

Influence of College Experience on Creativity and Critical Thinking

Given its significance as a core academic ability, the hypothesis of many colleges and universities emphasize that students will gain critical thinking skills as the result of their education. Fortunately, studies have shown that these efforts have had some promising outcomes. Around 92% of students in multi-institution research reported gains in critical thinking. Only 8.9% of students believed that their critical thinking had not changed or had grown weaker ( Tsui, 1998 ). A more recent meta-analysis by Huber and Kuncel (2016) found that students make substantial gains in critical thinking during college. In addition, the efforts to enhance necessary thinking skills have led to the development of various skill-specific courses. Mill et al. (1994) found that among three groups of undergraduate students, a group that received tutorial sessions and took research methodology and statistics performed significantly better on scientific reasoning and critical thinking abilities tests than control groups. Penningroth et al. (2007) found that students who took a class in which they were required to engage in active learning and critical evaluation of claims by applying scientific concepts, had greater improvement in psychological critical thinking than students in the comparison groups. There have also been studies in which students’ scientific inquiry and critical thinking skills have improved by taking a course designed with specific science thinking and reasoning modules ( Stevens and Witkow, 2014 ; Stevens et al., 2016 ).

Using a Survey of Undergraduate Research Experience (SURE), Lopatto (2004 , 2008) found that research experience can help students gain various learning skills such as ability to integrate theory and practice, ability to analyze data, skill in the interpretation of results, and understanding how scientists work on problem. All of these learning skills correspond to at least one of the dimensions mentioned earlier in the definition of critical thinking (i.e., evaluation, analytical thinking, and problem solving through). Thus, results of SURE provide evidence that critical thinking can be enhanced through research experience ( Lopatto, 2004 , 2008 ).

In comparison to critical thinking, only a few studies have examined the interaction between creativity and college experience. Previous research on STEM provides some evidence to suggest that STEM education can promote the learner’s creativity ( Land, 2013 , Guo and Woulfin, 2016 , Kuo et al., 2018 ). Notably, study of Kuo et al. (2018) suggest that project-based learning in STEM has the merits of improving one’s creativity. They found that the STEM Interdisciplinary Project-Based Learning (IPBL) course is a practical approach to improve college student’s creativity ( Kuo et al., 2018 ). College research experience in particular, has been reported as important or very important by faculty and students for learning how to approach problems creatively ( Zydney et al., 2002 ).

Although specific college courses aimed to enhance creativity have been scarce, some training programs have been developed specifically to improve creativity. Scott et al. (2004) conducted a quantitative review of various creativity training and found that divergent thinking, creative problem solving, and creativity performance can be enhanced through skill-specific training programs. Embodied creativity training programs, consisting of creativity fitness exercises and intensive workshops, have also been effective in enhancing participants’ creative production and improving their creative self-efficacy ( Byrge and Tang, 2015 ).

Both critical thinking and creativity were also found to be important in students’ learning. Using a longitudinal design for one semester to 52 graduate students in biology, Siburian et al. (2019) studied how critical thinking and creative thinking contribute to improving cognitive learning skills. They found that both critical and creative thinking significantly contributes to enhancing cognitive learning skills ( R 2 =0.728). They each contribute separately to the development of cognitive learning skills ( b was 0.123 between critical thinking and cognitive learning and 0.765 between creative thinking and cognitive learning). The results from research on creativity and critical thinking indicate that training and experiences of students in college can enhance both of these skills.

Current Study

Previous literature on creativity and critical thinking suggests that there is a positive correlation between these two skills. Moreover, cultural background influences creativity and critical thinking conception and performance. However, our literature review suggests that there are only a few studies that have investigated creativity and critical thinking simultaneously to examine whether cultural background is a significant influence in performance. In addition, most of the past research on creativity and critical thinking have relied on dispositions or self-reports to measure the two skills and the investigation on the actual performance have been scarce. Lastly, past studies suggest that the acquisition and enhancement of these skills are influenced by various factors. Notably, college experience and skill-specific training have been found to improve both creativity and critical thinking. However, it is not yet clear how college experience aids in fostering creativity and critical thinking and which elements of college education are beneficial for enhancing these two skills. The cultural influence on creativity and critical thinking performance also needs further investigation.

The current study aimed to answer two questions related to this line of thought. How does culture influence creativity and critical thinking performance? How does college experience affect creativity and critical thinking? Based on past findings, we developed three hypotheses. First, we hypothesized that there is a positive association between critical thinking and creativity. Second, we suggest that college students from different countries have different levels of creativity and critical thinking. More specifically, we predicted that United States students would perform better than Chinese students on both creativity and critical thinking. Last, we hypothesized that having college research experience (through courses or research labs) will enhance creativity and critical thinking.

Materials and Methods

Participants.

The study was examined by the Internal Review Board by the host university in the United States and obtained an agreement from a partner university in China to meet the ethical standard of both countries.

Participants include 103 university students from the United States and 166 university students from Mainland China. Among all participants, 181 were female (67.3%), 54 were male (20.1%), non-binary or gender fluid ( n =3, 1.1%), and some did not report their gender ( n =31, 11.5%). The majority of participants majored in social sciences ( n =197, 73.2%). Other disciplines include business and management ( n =38, 14.1%), engineering and IT ( n =20, 7.4%), and sciences ( n =14, 5.2%). A Chi-square analysis was performed to see if the background in major was different between the American and Chinese samples. The results showed that the two samples are comparable in college majors, X 2 (3, 265) =5.50, p =0.138.

The American participants were recruited through campus recruitment flyers and a commercial website called Prolific (online survey distribution website). Ethnicities of the American participants were White ( n =44, 42.7%), Asian ( n =13, 12.6%), Black or African American ( n =11, 10.7%), Hispanic or Latinos ( n =5, 4.9%), and some did not report their ethnicity ( n =30, 29.1%). The Chinese participants were recruited through online recruitment flyers. All Chinese students were of Han ethnicity.

After reviewing and signing an online consent form, both samples completed a Qualtrics survey containing creativity and critical thinking measures.

Measurements

Steam related creative problem solving.

This is a self-designed measurement, examining participant’s divergent and convergent creative thinking in solving STEAM-related real-life problems. It includes three vignettes, each depicting an issue that needs to be resolved. Participants were given a choice to pick two vignettes to which they would like to provide possible solutions for. Participants were asked to provide their answers in two parts. In the first part, participants were asked to provide as many solutions as they can think of for the problem depicted (divergent). In the second part, participants were asked to choose one of the solutions they gave in the first part that they believe is the most creative and elaborate on how they would carry out the solution (convergent).

The responses for the first part of the problem (i.e., divergent) were scored based on fluency (number of solutions given). Each participant received a score on fluency by averaging the number of solutions given across three tasks. In order to score the originality of the second part of the solution (i.e., convergent), we invited four graduate students who studied creativity for at least 1year as expert judges to independently rate the originality of all solutions. The Cronbach’s Alpha of the expert ratings was acceptable for all three vignette solutions (0.809, 0.906, and 0.703). We then averaged the originality scores provided by the four experts to represent the originality of each solution. We then averaged the top three solutions as rated by the experts to represent the student’s performance on originality. In the end, each student received two scores on this task: fluency and originality.

Psychological Critical Thinking Exam

We adopted an updated PCT Exam developed by Lawson et al. (2015) , which made improvements to the original measure ( Lawson, 1999 ). We used PCT to measure the participants’ domain-specific critical thinking: critical thinking involved in the sciences. The initial assessment aimed to examine the critical thinking of psychology majors; however, the updated measure was developed so that it can be used to examine students’ critical thinking in a variety of majors. The split-half reliability of the revised measurement was 0.88, and test-retest reliability was 0.90 ( Lawson et al., 2015 ). Participants were asked to identify issues with a problematic claim made in two short vignettes. For example, one of the questions states:

Over the past few years, Jody has had several dreams that apparently predicted actual events. For example, in one dream, she saw a car accident and later that week she saw a van run into the side of a pickup truck. In another dream, she saw dark black clouds and lightning and 2days later a loud thunderstorm hit her neighborhood. She believes these events are evidence that she has a psychic ability to predict the future through her dreams. Could the event have occurred by chance? State whether or not there is a problem with the person’s conclusions and explain the problem (if there is one).

Responses were scored based on the rubric provided in the original measurement ( Lawson et al., 2015 ). If no problem was identified the participants would receive zero points. If a problem was recognized but misidentified, the participants would receive one point. If the main problem was identified and other less relevant problems were identified, the participants received two points. If participants identified only the main problem, they received three points. Following the rubric, four graduate students independently rated the students’ critical thinking task. The Cronbach’s Alpha of the expert ratings was acceptable for both vignettes (0.773 and 0.712). The average of the four scores given by the experts was used as the final score for the participants.

California Critical Thinking Skills Test

This objective measure of critical thinking was developed by Facione and Facione (1994) . We used CCT to measure a few of the multidimensions of critical thinking such as evaluation, logical reasoning, and probability thinking. Five sample items provided from Insight Assessment were used instead of the standard 40-min long CCT. Participants were presented with everyday scenarios with 4–6 answer choices. Participants were asked to make an accurate and complete interpretation of the question in order to correctly answer the question by choosing the right answer choice (each correct answer was worth one point). This test is commonly used to measure critical thinking, and previous research has reported its reliability as r =0.86 ( Hariri and Bagherinejad, 2012 ).

Sternberg Scientific Inquiry and Reasoning

This measure was developed by Sternberg and Sternberg (2017) as an assessment of scientific reasoning. We used this assessment as a domain-specific assessment to measure participants’ scientific creativity (generating testable hypotheses) and scientific critical thinking involved in generating experiments. For this two-part measure, participants were asked to read two short vignettes. For one of the vignettes, participants were asked to generate as many hypotheses as possible to explain the events described in the vignette. For the other, create an experiment to test the hypothesis mentioned in the vignette.

After carefully reviewing the measurement, we notice that the nature of the tasks in the first part of this measure (hypothesis generation) relied on heuristics, requiring participants to engage in divergent thinking. The number of valid hypotheses provided (i.e., fluency) was used to represent the performance of this task. We, therefore, deem that this part measures creativity. In contrast, the second part of the measure, experiment generation, asked participants to use valid scientific methods to design an experiment following the procedure of critical thinking such as evaluation, problem-solving, and task evaluation. Its scoring also followed algorithms so that a correct answer could be achieved. For the above reasons, we believe hypotheses generation is a measurement of creativity and experiment generation is a measurement for critical thinking.

Based on the recommended scoring manual, one graduate student calculated the fluency score from the hypothesis generation measurement. Four experts read through all students’ responses to the experiment generation. They discussed a rubric on how to score these responses, using a four-point scale, with a “0” representing no response or wrong response, a “1” representing partially correct, a “2” representing correct response. An additional point (the three points) was added if the participant provided multiple design methods. Based on the above rubric, the four experts independently scored this part of the questionnaire. The Cronbach’s Alpha of the four expert ratings was 0.792. The average score of the four judges was used to represent their critical thinking scores on this task.

College Experience Survey

Participants were asked about their past research experience, either specifically in psychology or in general academia. Participants were asked to choose between three choices: no research experience, intermediate research experience (i.e., research work for class, research work for lab), and advanced research experience (i.e., professional research experience, published works).

Demographic and Background Questionnaire

Series of standard demographic questions were asked, including participants’ age, gender, and ethnicity.

We performed a Pearson correlation to examine the relationship between creativity and critical thinking (the two-c), which include performances on three measures on creativity ( creativity originality , creativity fluency , and hypothesis generation ) and three measures on critical thinking ( experiment generation , CCT , and PCT ).

Most of the dependent variables had a significantly positive correlation. The only insignificant correlation was found between Sternberg hypothesis generation and CCT, r (247) =0.024, p =0.708 (see Table 1 ).

Table 1 . Correlation coefficients for study variables.

Confirmatory factor analysis (CFA) was conducted by applying SEM through AMOS 21 software program and the maximum likelihood method. One-factor and two-factor models have been analyzed, respectively (see Figure 1 ).

Figure 1 . The comparison of the two confirmatory factor analysis (CFA) models: one-factor vs. two-factor.

As it is demonstrated in Table 2 , the value ranges of the most addressed fit indices used in the analysis of SEM are presented. Comparing two models, χ 2 /df of the two-factor model is in a good fit, while the index of the one-factor model is in acceptable fit. The comparison of the two models suggest that the two-factor model is a better model than the one-factor model.

Table 2 . Recommended values for evaluation and the obtained values.

Cross-Cultural Differences in Critical Thinking and Creativity

We conducted a 2 (Country: the United States vs. China)×2 (Two-C: Creativity and Critical Thinking) ANOVA to investigate the cultural differences in critical thinking and creativity. We averaged scores of three critical thinking measurement ( experiment generation , PCT , and CCT ) to represent critical thinking and averaged three creativity scores ( creativity originality , creativity fluency , and hypothesis generation ).

This analysis revealed a significant main effect for the type of thinking (i.e., creative vs. critical thinking), F (1,247) =464.77, p <0.01, η p 2 =0.653. Moreover, there was a significant interaction between country (i.e., the United States vs. China) and type of thinking, F (1,247) =62.00, p <0.01, η p 2 =0.201. More specifically, Chinese students ( M =1.32, SD =0.59) outperformed American students ( M =1.02, SD =0.44) on critical thinking. In contrast, American students ( M =2.59, SD =1.07) outperformed Chinese students ( M =2.05, SD =0.83) on creativity.

Influence of Research Experience on Critical Thinking and Creativity

The last hypothesis states that having college research experience (through courses or research lab) would enhance students’ creativity and critical thinking from both countries. We performed a 2 (Two-C: Creativity and Critical Thinking)×2 (Country: the United States vs. China)×3 (Research Experience: Advanced vs. Some vs. No) ANOVA to test this hypothesis. This analysis revealed a significant main effect for research experience, F (2,239) =4.05, p =0.019, η p 2 =0.033. Moreover, there was a significant interaction between country (i.e., the United States vs. China) and research experience, F (2,239) =5.77, p =0.004, η p 2 =0.046. In addition, there was a three-way interaction among country, two-C, and research experience. More specifically, with an increase of research experience for American students, both critical thinking and creativity improved. In contrast, for Chinese students, the impact of research experience was not significant for creativity. However, some research experience positively impacted Chinese students’ critical thinking (see Figure 2 ).

Figure 2 . Estimated marginal means of Two-C for the United States and Chinese samples.

The current study aimed to investigate the relationship between creativity and critical thinking, how culture influences creativity and critical thinking, and how college research experience affects creativity and critical thinking. Our results supported the first hypothesis regarding the positive correlation among all of the dependent variables. The mean correlation between the measures of creativity and critical thinking was 0.230. This result was in line with the findings from previous research ( Gibson et al., 1968 ; Gadzella and Penland, 1995 ; Siburian et al., 2019 ; Akpur, 2020 ; Qiang et al., 2020 ). Moreover, our confirmatory factor analysis yielded similar results as analysis of Wechsler et al. (2018) and Akpur (2020) and provides more evidence of the relative independence between creativity and critical thinking. We found that at the latent variable level, the two skills are highly correlated to each other ( r =0.84). In addition, we found that although the one-factor model was an acceptable fit, a two-factor model was a better fit for analysis. This result suggests that despite the correlation between creativity and critical thinking, the two skills should be studied as separate factors for an appropriate and comprehensive analysis.

The results of this study partially confirmed our second hypothesis and replicated the findings from past studies ( Niu et al., 2007 ; Lun et al., 2010 ; Wong and Niu, 2013 ; Tang et al., 2015 ). As predicted, there was a significant main effect for culture in students’ performance for all six measures in the two-C analysis model. United States students performed better than Chinese students in all three creativity measures, and Chinese students performed better than United States students in all critical thinking measures. Given the diversity in the type of measures used in this study, the results suggest that United States and Chinese students’ performance aligns with the stereotype belief found in study of Wong and Niu (2013) . The findings from the current study suggest that the stereotype belief observed in both United States and Chinese students (United States students generally perform better on creativity tasks, while Chinese students perform typically better on critical thinking tasks) is not entirely unfounded. Furthermore, the clear discrepancy in performance between United States and Chinese students provides more evidence to suggest that creativity and critical thinking are relatively autonomous skills. Although, a high correlation between these two skills was found in our study, the fact that students from two different cultures have two different development trajectories in critical thinking and creativity suggests that these two skills are relatively autonomous.

Lastly, the results also confirmed our third hypothesis, that is, college research experience did have a positive influence on students’ creativity and critical thinking. Compared to students with no research experience, students with some research experience performed significantly better in all measures of creativity and critical thinking. This finding is consistent with the previous literature ( Mill et al., 1994 ; Penningroth et al., 2007 ; Stevens and Witkow, 2014 ; Stevens et al., 2016 ; Kuo et al., 2018 ). The result of our study suggests that college research experience is significant to enhance both creativity and critical thinking. As research experience becomes a more essential component of college education, our results suggest that it not only can add credential for applying to graduate school or help students learn skills specific to research, but also help students enhance both creativity and critical thinking. Furthermore, it is worth noting that this nature held true for both Chinese and American students. To our knowledge, this is a first investigation examining the role of research experience in both creativity and critical thinking cross-culturally.

In addition to the report of our findings, we would like to address some limitations of our study. First, we would like to note that this is a correlational and cross-sectional study. A positive correlation between research experience and the two dependent variables does not necessarily mean causation. Our results indeed indicate a positive correlation between research experience and the two-C variables; however, we are not sure of the nature of this relationship. It is plausible that students with higher creativity and critical thinking skills are more engaged in research as much as it is to argue in favor of a reversed directional relationship. Second, we would like to note the sample bias in our study. Majority of our participants were female, majoring in the social sciences and a relatively high number of participants chose not to report their gender. Third, we would like to note that our study did not measure all creativity and critical thinking dimensions, we discussed in the introduction. Instead, we focused on a few key dimensions of creativity and critical thinking. Our primary focus was on divergent thinking, convergent thinking, and scientific creativity as well as few key dimensions of critical thinking (evaluation, logical reasoning, and probability thinking), scientific critical thinking involved in problem solving and hypothesis testing. Moreover, our results do not show what specific components of research training are beneficial for the enhancement of creativity and critical thinking.

For future research, a longitudinal design involving a field experiment will help investigate how different research training components affect the development of creativity and critical thinking. In addition, a cross-cultural study can further examine how and why the students from different cultures differ from each other in the development of these two potentials. As such, it might shed some light on the role of culture in creativity and critical thinking.

Conclusion and Implication

The result of our study provides few insights to the study of creativity and critical thinking. First, creativity and critical thinking are a different construct yet highly correlated. Second, whereas Americans perform better on creativity measures, Chinese perform better on critical thinking measures. Third, for both American and Chinese students, college research experience is a significant influence on the enhancement of creativity and critical thinking. As research experience becomes more and more essential to college education, its role can not only add professional and postgraduate credentials, but also help students enhance both creativity and critical thinking.

Based on our results, we recommend that research training be prioritized in higher education. Moreover, each culture has strengths to develop one skill over the other, hence, each culture could invest more in developing skills that were found to be weaker in our study. Eastern cultures can encourage more creativity and Western cultures can encourage more critical thinking.

To conclude, we would like to highlight that, although recognized globally as essential skills, methods to foster creativity and critical thinking skills and understanding creativity and critical thinking as a construct requires further research. Interestingly, our study found that experience of research itself can help enhance creativity and critical thinking. Our study also aimed to expand the knowledge of creativity and critical thinking literature through an investigation of the relationship of the two variables and how cultural background influences the performance of these two skills. We hope that our findings can provide insights for researchers and educators to find constructive methods to foster students’ essential 21st century skills, creativity and critical thinking, to ultimately enhance their global competence and life success.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board at Pace University. The participants provided their informed consent online prior to participating in the study.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

This work was supported by the International Joint Research Project of Faculty of Education, Beijing Normal University (ICER201904), and a scholarly research funding by Pace University.

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Keywords: creativity, critical thinking, cross-cultural differences, college, research experience

Citation: Park JH, Niu W, Cheng L and Allen H (2021) Fostering Creativity and Critical Thinking in College: A Cross-Cultural Investigation. Front. Psychol . 12:760351. doi: 10.3389/fpsyg.2021.760351

Received: 18 August 2021; Accepted: 11 October 2021; Published: 11 November 2021.

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*Correspondence: Li Cheng, [email protected]

† These authors have contributed equally to this work and share first authorship

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• Published: 11 January 2023

The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature

• Enwei Xu   ORCID: orcid.org/0000-0001-6424-8169 1 ,
• Wei Wang 1 &
• Qingxia Wang 1  

Humanities and Social Sciences Communications volume  10 , Article number:  16 ( 2023 ) Cite this article

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Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

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Introduction.

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2  ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P  < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2  = 7.95, P  < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2  = 86%, z  = 12.78, P  < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), learning scaffold (chi 2  = 9.03, P  < 0.01), and teaching type (chi 2  = 7.20, P  < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2  = 3.15, P  = 0.21 > 0.05, and chi 2  = 0.08, P  = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2  = 3.15, P  = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P  < 0.01), then higher education (ES = 0.78, P  < 0.01), and middle school (ES = 0.73, P  < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2  = 7.20, P  < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P  < 0.01), integrated courses (ES = 0.81, P  < 0.01), and independent courses (ES = 0.27, P  < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2  = 12.18, P  < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P  < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2  = 9.03, P  < 0.01). The resource-supported learning scaffold (ES = 0.69, P  < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P  < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P  < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2  = 8.77, P  < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P  < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P  < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2  = 0.08, P  = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P  < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2  = 13.36, P  < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P  < 0.01), followed by science (ES = 1.25, P  < 0.01) and medical science (ES = 0.87, P  < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P  < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P  < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P  < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z  = 12.78, P  < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z  = 7.62, P  < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z  = 11.55, P  < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2  = 7.20, P  < 0.05), intervention duration (chi 2  = 12.18, P  < 0.01), subject area (chi 2  = 13.36, P  < 0.05), group size (chi 2  = 8.77, P  < 0.05), and learning scaffold (chi 2  = 9.03, P  < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2  = 3.15, P  = 0.21 > 0.05) and measuring tools (chi 2  = 0.08, P  = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

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Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

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Mindfulness and creativity: Implications for thinking and learning

Danah henriksen.

a Arizona State University, United States

Carmen Richardson

b Kamehameha Schools, United States

c Kalamazoo Public Schools, United States

• • This thematic literature review investigates the relationship between mindfulness and creativity
• • Mindfulness practices improve skills or habits of mind that can support creativity
• • The mindfulness-creativity relationship is complex, but generally positive
• • Deliberate/mindful mind-wandering can support creativity
• • Purposeful inclusion of mindfulness in learning settings can benefit student learning, creativity and wellbeing

Mindfulness and creativity have both come to the forefront of educational interest—but a better understanding of their relationship and the implications for education is needed. This article reviews the literature on the intersection of these topics in order to understand where and how these two related but distinctive areas of research connect, and how this pertains to the complexity of education settings. Our goal is to understand findings from the literature and consider the implications for educational practice and research, with an eye to how mindfulness can be supportive to learners’ creativity. This thematic review and qualitative analysis of extant literature identifies four themes that speak to the connection between mindfulness and creativity. There is solid evidence to show a generally beneficial and supportive relationship, in that practicing mindfulness can support creativity—but many factors affect this and there are a range of considerations for practice. This article reflects on the key findings of scholarly work on the mindfulness-creativity relationship with interpretative discussion and implications for educational research and practice.

1. Introduction

Existing research on creativity has examined its different relationships, connections, or variables—such as personality skills, neuroscientific or cognitive correlates of creativity, disciplinary knowledge, imagination, bodily thinking, or the ways that creativity emerges in real-world design settings, among others ( Runco, 2014 ). One relatively recent and growing area of literature involves the relationship between mindfulness and creativity ( Kudesia, 2015 ). These two areas have been increasingly discussed in education settings, yet there is little research-based guidance to help consider their interrelationship for teaching and learning. Here, we explore the relationship, and also seek to explore the practical applications and implications for education contexts.

Mindfulness has recently received attention across scholarly and popular discourse ( King & Badham, 2018 ). It is defined as a state of “nonjudgmental, moment-to-moment awareness” ( Kabat-Zinn, 1990, p.2 ), and has been studied across varied disciplines such as psychology, physiology, healthcare, neuroscience, the arts, and others. Most mindfulness research has examined its potential to regulate stress and improve cognitive, emotional, and interpersonal functioning ( Sedlmeier et al., 2012 ). Scholars have suggested that the effects of mindfulness also relate to other skills and abilities, such as creativity ( Carson & Langer, 2006 ). Creativity is frequently defined as the ability to develop novel and effective ideas, artifacts, or solutions ( Runco, 2014 ). While this so-called ‘standard definition’ represents many existing research definitions, it does not embody the diversity and divergence of ways that creativity has been defined across a range of practices, disciplines and traditions ( Henriksen, Creely, & Henderson, 2019 ). Creativity is a complex area of research and practice, yet neoliberal perspectives have often driven educational discourse on creativity, emphasizing instrumentalist and societal drive toward innovation ( Mehta, Creely, & Henriksen, 2020 ). But perhaps more importantly, creativity is a way of being in the world with substantive value for human-centered wellbeing and expression ( Goff & Torrance, 1991 ).

Both mindfulness and creativity are complex areas that have been independently touted in education practices. Yet there is a need for a synthesis of extant research findings in understanding the mindfulness-creativity relationship and how it matters in learning settings. There is a theoretical reason for presuming an important relationship between them. These are broad ideas with unique connections to emotions, attention, stress, wellness, and awareness of one’s self and the world ( Baas, Nevicka, & Ten Velden, 2014 ). Given the importance both areas have to thinking and learning, and their increasing presence in educational contexts, it is important to understand research on their relationship.

For our purposes in this thematic literature review, we seek to identify themes and trends in the research, and then discuss the implications for educational settings. While mindfulness and creativity individually arise in education discourses, they are rarely linked and there is little to guide teachers in identifying research takeaways for the complexity of learning settings. Very little existing research on the intersection of these topics is actually embedded in classrooms—so we aim to distill significant aspects of the relationship and share implications for teachers and learners.

In a world awash in distraction, stress, and often, distress—all of which can affect creativity and wellbeing—mindfulness becomes a valuable consideration for supporting learners in educational practice. Particularly in light of the recent COVID-19 pandemic, many teachers and learners are experiencing a sense of uncertainty, discomfort, or even trauma. While we do not suggest that mindfulness offers a “fix” for the kinds of systemic inequities or difficulties that many are facing—situations of stress or trauma underscore the value in paying attention to issues that relate to our sense of wellness and humanity, such as mindfulness and creativity.

We begin with background context about mindfulness, then we describe our literature review approach on the creativity-mindfulness relationship. We then qualitatively analyze and describe thematic findings and takeaways from this review. Finally, we discuss the implications for thinking and learning, with conclusions for educational practice and research.

1.1. Background on mindfulness

Mindfulness has roots in longstanding Eastern spiritual traditions, particularly Buddhist philosophy. Buddhist philosophy and practices teach a way of being present in the moment and letting go of the overreliance that humans tend to have on a sense of individualized identity (as a ‘thinker of thoughts’) in favor of a broader connection to a sense of oneness and integration with all things ( Shonin, Van Gordon, & Griffiths, 2014 ). However, Trammel (2017) notes that mindfulness has entered into secular practice and mainstream culture in recent decades. There has been valid concern about the ways in which the authenticity of Buddhist truths might be stripped of their original values through this mainstreaming of mindfulness. However, scholars such as Sun (2014) have noted that this secular recontextualization of mindfulness has supported the emergence of the concept for use in broader social contexts or organizations such as schools, where they can benefit wellbeing for learning. Williams and Kabat-Zinn (2011) suggest that since Buddhist meditative practices are concerned with embodied awareness and cultivating clarity, emotional balance, equanimity, and compassion—all of which can be developed by intentional deployment of attention—that “the roots of Buddhist meditation practices are de facto universal” (p. 1).

The work of Kabat-Zinn (1990) and his Mindfulness-Based Stress Reduction (MBSR) program (developed at University of Massachusetts Medical School) are partly responsible for bringing mindfulness to broader audiences, with intentional development of secular-based practices for health and wellbeing needs. Since then, many programs and studies have documented the physical and mental benefits of mindfulness, inspiring adaptations into schools, prisons, hospitals, veterans centers, and more.

The previously-noted definition of mindfulness can be elaborated as the ability to be fully present, and aware of where we are and what we are doing, without becoming overly reactive or overwhelmed by the present.. Mindfulness is often associated with meditation practices, aimed at building skills for present-moment awareness as a mental habit (e.g. just as physical exercise aims to make the body more healthy even beyond exercise sessions—meditation or mindfulness practices aim to cultivate healthy psychological awareness and wellbeing, beyond the practices themselves). Berkley’s Greater Good Science Center (n.d.) suggests, “Mindfulness means maintaining a moment-by-moment awareness of our thoughts, feelings, bodily sensations, and surrounding environment, through a gentle, nurturing lens.” Despite the simple, intuitive nature of such definitions, achieving it is often not simple or intuitive.

O’Donnell (2015) suggests that mindfulness has gained widespread interest precisely because states of distraction, anxiety, suffering, and lack of connection are so common and detrimental. As society veers toward more chaotic, techno-centric, globally-connected and distracted modes, mindfulness offers an antidote to internalized unrest—particularly for learners who face ever expanding sources of difficulty from stress and distraction. The buzz of popular interest and excitement around the concept has increased, such that mindfulness appears ubiquitous, from healthcare or corporate settings, to schools and classrooms ( Shapiro, 2009 ).

Researchers have sought to study interventions related to different components of mindfulness, often through the central practice of meditation. Because meditation offers specific practices for awareness of one’s own thoughts, it provides an intervention to study the development and effects of mindful states, helping people connect with thoughts and emotions in the present moment ( Shapiro, 2009 ). Research has demonstrated that by developing awareness about one’s own mind and the present moment, people experience less anxiety, more positive emotions and engagement, and other mental and emotional benefits ( Weinstein, Brown, & Ryan, 2009 ). In becoming more aware of their thinking, learners in particular become more skilled at navigating thought processes in psychologically healthy ways ( Bennett & Dorjee, 2016 ). Importantly, it also connects to creative thinking skills ( Kudesia, 2015 ).

While creativity and mindfulness may work synergistically, the relationship is complex. Researchers and practitioners in educational contexts require a better sense of a nascent but growing body of literature to understand implications for the future of research and practice.

2. Methods for review

We explore scholarly literature at the intersection of mindfulness and creativity to understand how it relates to thinking and learning settings. This is a thematic literature review and our work is guided by the following questions:

• ● What is the nature of the mindfulness-creativity relationship as outlined in existing research and literature?
• ● Based on the literature on mindfulness and creativity, what are the implications for teaching and learning settings? And what takeaways and ideas can be used to inform educational practice?

2.1. Approach and rationale for review

A thematic literature review is not based around the progression of time in a body of work as a chronological review might be ( Yun, Lee, & Kim, 2019 ), nor does it describe the emergence of a body of work as a narrative review might ( Bower & Gilbody, 2005 ). Instead, a thematic review is organized based on topics, issues, ideas, or takeaways from within a relevant body of work ( Hart, 2018 ). Unlike meta-reviews or systematic reviews, such as the one conducted by Lebuda, Zabelina, and Karwowski (2016) , we do not aim to extract empirical data findings to quantify the relationship.

We elected a thematic approach for important reasons. Our purpose was to narrow the scope of inquiry and dive into a qualitative exploratory analysis of relevant work on creativity and mindfulness skills. Such an approach provides space to explore insights from literature and then consider how broader takeaways might be used to inform practice. A thematic review was also deemed most appropriate because extant literature on this topic is not fully representable as systematized data, constraining the ability to present literature as a quantified ‘dataset’ for empirical dissection ( Tranfield, Denyer, & Smart, 2003 ). Although high-quality, quantifiable studies do exist in this space [see Lebuda et al. (2016) ] we wished to consider a more open swath of literature, including not only quantitative, but also theoretical, practical or qualitative works that are not amenable to systematic analysis. To allow for a comprehensive stance toward relevant literature, our review is framed in an exploratory, thematic way. This allowed us to go deeper into varied stances to later use these in discussion of implications and applications. We also aimed to be methodical about our search processes, using review criteria/approach as described.

2.2. Criteria and process for literature search

The research we reviewed is situated mostly within psychology or education. Our sources of literature were primarily drawn from two main databases, those being: 1.) Science Direct , and 2.) Scopus —as these two databases comprise a significant swath of ‘mainstream’ research papers in English. Additionally, we performed a search of both Google and Google Scholar to ensure that nothing was missed in the primary research database searches and to identify any useful non-empirical pieces.

We began by identifying keywords and search terms, which we selected based on the scope of study and the literature; we then chose the search strings most appropriate for the study ( Charmaz, 2003 ). We were able to keep the search relatively straightforward by pairing keywords and terms that precisely defined one of four areas: ‘ mindfulness’ , ‘ meditation’ , ‘ creativity’, or ‘ creative thinking .’ This yielded articles or studies that specifically referenced the theory/terminology within the text ( Grant & Booth, 2009 ).

This initial scoping process produced copious results, many of which were outside the scope of our topics ( Paré & Kitsiou, 2016 ). Common search terms of “mindfulness” and/or “meditation” and “creativity” yielded hundreds, in some instances thousands, of articles. By narrowing the scope using database functions, to include only articles that used both key terms as foci in titles and/or abstracts, we were able to clarify and tighten the search. This makes sense, as inquiry-driven intersection of these constructs has mostly emerged within recent decades and is a comparatively small space in the larger arena of creativity research. We then sifted through articles to identify work exploring the relationship between the constructs.

Our review criteria were agnostic as to the types of sources included, and this article explores varied academic sources, including books, chapters, and peer-reviewed journal articles. However, peer-reviewed empirical journal articles encompass most of the sources reviewed, allowing us to focus on understanding the state of the field of research findings, without entirely excluding important ideas that emerged in other sources.

2.3. Approach to thematic analysis

To assess and distill the key ideas from the literature into useful takeaways, we sought to extract ideas/findings and categorize them into “meaning units” ( Moustakas, 1994 ). Therefore, we engaged in several rounds of collective thematic coding from the articles identified, using a shared digital space to collectively document key findings identified in every piece of literature used ( Saldaña, 2015 ).

We first familiarized ourselves with the ‘data,’ which in this case were the key ideas/findings in varied studies or papers ( Moustakas, 1994 ). Through shared discussions of meaning-making, we coded thematically, by looking across the findings for patterns of organization ( Braun & Clarke, 2006 ). This resulted in takeaways that were less specific than most thematic coding, because the documented findings tended to focus around several broad areas that categorized the research on mindfulness and creativity—such as the generally positive nature of the relationship, or the observed lack of applied research. Several iterations of organized coding brought us to four themes that emerged from the literature. These were driven by our stated questions and are shared in the findings and discussion.

2.4. Limitations

There are limitations in this work. First, we limited most of our examination to two databases, including Science Direct and Scopus, supplemented by peripheral searches of Google and Google Scholar as supplementary sources to check for additional work. Although these were selected because they are comprehensive sources of academic scholarship in English, encompassing most major and smaller journals that cover creativity research, there is still a limitation of scope.

Further, we would note that personal bias is always a potential issue in thematic review, and transparency is important. Our own interest in the topics as educational researchers could have influenced the process of analysis, as researchers naturally bring in their own preconceptions, assumptions or interests. Though we tried to minimize this effect through multiple rounds of reading and discussion, the possibility of bias influencing analysis exists.

3. Findings

We identified four broad thematic areas. The first theme describes how mindfulness enhances creativity. The second theme addresses the factors that complicate the nature of the relationship . The third theme addresses the relationship between mindfulness, mind-wandering and creativity ; and finally, the fourth theme concerns the need for more applied educational research on mindfulness and creativity . These are described in greater detail in the sections below.

3.1. Theme 1: mindfulness enhances creativity

Much literature suggests that the nature of the mindfulness-creativity relationship is positive and promising—in that mindfulness can enhance creativity. Research demonstrates that mindfulness improves a person’s ability to concentrate ( Sedlmeier et al., 2012 ), decreases the fear of being judged, and enhances open-minded thinking while reducing aversive self-conscious thinking ( Brown, Ryan, & Creswell, 2007 ). These points map directly onto key characteristics of creative habits of working, thinking, and being in the world, including: relaxation or flow states (improved concentration), risk-taking (requiring a lack of fear about judgment), and curiosity or open-mindedness/openness to experience (reducing self-conscious experience) ( Prabhu, Sutton, & Sauser, 2008 ). Logically, these effects suggest that mindfulness supports the skills associated with creativity, and research findings suggest that high levels of self-reported mindfulness correlate to creative practices ( Colzato, Szapora, & Hommel, 2012 ).

Many aspects of ‘trait mindfulness,’ or skills that are facilitated by mindfulness training, increase creativity. For example, mindfulness is associated with the ability to change perspectives by expanding empathy and open-mindedness ( Carson & Langer, 2006 ). It also increases a person’s capacity to respond to situations in a non-habitual fashion—which is at the crux of creativity ( Moore & Malinowski, 2009 ). Mindfulness training’s ability to reduce fear of judgment is conducive to creativity; as is its ability to improve working memory ( Chiesa, Calati, & Serretti, 2011 ). Specifically, experienced meditators are better problem solvers and have better verbal creativity ( Greenberg, Reiner, & Meiran, 2012 ). Jedrczak, Beresford, and Clements (1985)) found that meditation of any length strengthens creativity—even short meditation breaks. Thus, ontologically, mindfulness has the potential effect of improving or enhancing creativity by building skills or ways of being that support creativity. The ontological nature of the relationship show promise for educational settings where developing creativity is challenging. Anxiety, fear of risk or failure, and self-consciousness about one’s own thinking are often detrimental to classroom creativity—which opens up the possibility that mindfulness might offer practices that ameliorate barriers to learner’s creativity.

In their meta-review, Lebuda et al. (2016) hypothesized a positive relationship between mindfulness and creativity, wherein the former supports the latter. Their meta-analysis examined peer-reviewed, quantitative studies with direct measures of mindfulness and creativity—aiming to measure the relationship between the two and consider the role of moderators. Their study estimated the correlation between mindfulness and creativity at r = .22 (r = .18 without correction for attenuation). This suggests a significant correlation, with a small-to-medium effect size. Across all studies they found no evidence of publication bias, concluding that the estimation of the relationship is accurate and robust. This aligns with the proposed beneficial role of mindful meditation in creative thinking. The moderators included in their analysis clarify some important questions about the nature of this relationship. For instance, there were no differences between correlational and experimental studies—in both types of studies the effect size of the association was the same. This suggests not only a correlation between mindfulness and creativity, but more importantly reveals that developing mindfulness through meditation increases creativity—e.g. it goes beyond correlation into causation. This causal connection is something that educators and schools can potentially look to as they seek to address mounting calls to support students’ creativity, and as they also try to manage the socio-emotional needs of students in our tense and distractible society.

Despite this, varied kinds of moderators, such as the type of meditation practiced and the multifaceted character of mindfulness, create challenges in untangling the mindfulness creativity relationship ( Baas et al., 2014 ). The inherent complexity and emergent or experiential nature of both mindfulness and creativity could also be a confounding factor. Much like creativity, mindfulness is complex and involves different skills, such as: attention/observation, ability to act with awareness, capacity for nonjudgmental description, and ability to refrain from immediate evaluation. There is also no commonly agreed-upon mechanistic model of creative processes that could confirm how different types of meditations might affect such processes. All of this leaves educational practitioners with some foundations to work from in that mindfulness does seem to support creativity—but also some contested ground to navigate, in which the relationship can be nuanced by different contextual factors.

3.2. Theme 2: a relationship with complicating factors

Given the complexity of these areas it is not surprising that research also indicates a complicated relationship between the two. Different types of meditation (which are a vehicle for mindfulness) have differential relationships to creativity. Two of the main techniques discussed frequently in the literature on mindfulness include open-monitoring meditation and focused-attention meditation . Open-monitoring is the practice of observing and attending to any sensation or thought without focusing on any specific task or concept. Focused-attention meditation instead trains the participant to focus their attention and awareness to a particular task, item, thought or stimuli ( Colzato et al., 2012 ). These mindfulness skills can influence creativity differently. For example, while open-monitoring may increase creative thinking, some have found that focused-attention meditation may be either unrelated to creativity, or in certain instances may impede performance on creativity tests ( Zedelius & Schooler, 2015 ). For educators interested in facilitating a kind of mindfulness-supported creativity, that may leave questions as to which types of meditation to use in classrooms.

The Lebuda et al. (2016) meta-analysis noted that beyond the positive connection where mindfulness enhances creativity, there are areas of uncertainty. For instance, the Horan (2009) longitudinal study showed inconsistencies in the meditation-creativity relationship using the Torrance Test of Creative Thinking, a measure that distinguishes between verbal and figural dimensions of creativity. Specifically, groups practicing transcendental meditation showed significant gains in figural flexibility and originality, but no improvements in verbal creativity. This is interesting in teasing apart the relationship, however, it begs the question: To what degree would or should such individualized tests of creativity matter within the sociocultural dynamics of many learning settings?

Colzato et al. (2012) dissected the complexities by evaluating the impact of both types of meditation upon creativity tasks for either divergent or convergent thinking . Divergent thinking involves solving problems with many possible solutions—as opposed to convergent thinking, which involves solving problems with a more focused and narrowing approach. The researchers studied whether different types of meditation induce people toward particular cognitive-control states related to creativity. They hypothesized that open-monitoring meditation encourages divergent thinking and focused-attention meditation induces convergent thinking. Thus, open-monitoring meditation would be expected to improve divergent thinking but not convergent thinking (both of which were assessed by the AUT (Alternative Uses Task) creativity assessment).

Their data demonstrated that people excelled in the divergent thinking task after doing open-monitoring meditation. Although convergent thinking performance improved after focused-attention meditation, the increase was not significant. Interestingly, their measures of mood scores showed that both types of meditation elevated mood. Because elevated mood facilitates divergent rather than convergent thinking (elevated mood may even interfere with convergent thinking) mood effects might have been a confounding factor. In short, the focused-attention meditation may have improved convergent thinking, while the relaxing aspect of the procedure potentially could hamper it. Regardless, they identified a key mindfulness-creativity connection, showing the relationship between open-monitoring meditation and divergent thinking.

These findings point to some degree of nuance beyond the general assertion that mindfulness strengthens creativity. This suggests that if we are to seek more mindful creativity practices in schools, then it is important to consider what types of creative tasks or thinking might be called for in the given context, and consider what types of meditation practices might be beneficial.

3.3. Theme 3: mindfulness, mind-wandering and creativity

We have focused on the nature of the mindfulness-creativity relationship, which raises an important issue for this relationship—namely, mind-wandering. The relationship between mind-wandering to these areas is more uncertain and complicated than the relationship between mindfulness and creativity. Mind-wandering seemingly runs contrary to mindfulness, yet mind-wandering reliably correlates with creative thinking and creative achievement ( Baird et al., 2012 ). This is an issue for educators considering different facets of mindfulness practices, as it may affect creativity and related factors.

Mind-wandering is “a common everyday experience in which attention becomes disengaged from the immediate external environment and focused on internal trains of thought” ( Schooler et al. 2014, p. 1 ). It is differently important to both mindfulness and creativity. If mind-wandering is associated with getting lost in thought without realizing it—then mindfulness has an inverse purpose, bringing attention and awareness to thoughts in order to disentangle from them. Creativity has been positively associated with mind-wandering that stimulates novel ideas or fresh connections ( Baird et al., 2012 ).

Existing research points to a connection between mind-wandering and deficits in task performance or problems with task completion. However, mind-wandering may be beneficial in some areas, such as planning for the future, positive stimulation via interesting thoughts, and notably, creativity. Learners with ADHD often score higher on laboratory measures of creativity and assessments of creative arts achievement ( White & Shah, 2011 ), though they may struggle with some traditional tasks and outcomes of schooling.

Schooler et al. (2014) tested the mindfulness-creativity relationship directly, by assessing individual differences in mindfulness (via the Mindful Attention Awareness Scale or MAAS) as compared to measures of creative problem-solving performance (via the Remote Associates Test or RAT). They showed a negative correlation between mindfulness scores and RAT performance, and at first assumed that being less mindful helps one be more creative. However, they refined this interpretation by considering different strategies that can be used to solve the RAT problems. Creativity researchers have long been intrigued by the fact that the same creative problems can either be solved through analytic thought, or through spontaneous insight referred to as “Aha” experiences of insight/intuition ( Fleck & Kounios, 2009 ). Prior research has shown that analytic and insight problem-solving methods are associated with markedly different patterns of brain activity. For instance, default mode network activity in the brain is related to solving problems with insight/intuition ( Kounios et al., 2008 )—while the default mode network tends to quiet down through mindfulness.

Schooler et al. (2014) hypothesized that mindfulness might be related to creative analytic problem solving. To test this, after each problem they asked participants whether they had solved it mostly analytically or mostly with insight. They found that trait mindfulness correlated negatively with insight problem solving, but not with analytic creativity—suggesting that creative solutions can benefit from mindfulness, but specifically through a more analytically creative process. Others have actually found that insight problem solving can be enhanced through mindfulness. Ostafin and Kassman (2012) found that certain types of open-monitoring meditation improved insight problem solving. They noted that:

Insight problem solving is hindered by automated verbal-conceptual processes. Because mindfulness meditation training aims at “non-conceptual awareness,” which involves a reduced influence of habitual verbal–conceptual processes on the interpretation of ongoing experience, mindfulness may facilitate insight problem solving.

This helps to clarify how mindfulness can support creativity in terms of mind-wandering. The Schooler et al. (2014) body of work also makes assumptions which may limit the scope of their findings. For instance, they position mindfulness and mind-wandering in opposition to each other, and then carry this assumption out experimentally. However, while mindfulness and mind-wandering are often very different, they need not be mutually exclusive across all forms of practice—and in the messy spaces of implementation and educational practice, it is very possible that such ideas could coalesce. It might suggest that mindful meditations involving both conscious awareness and nonjudgment of thoughts could allow mindful mind-wandering in learning practices.

Certain forms of mind-wandering can be mindful/deliberate, while others are more uncontrolled/spontaneous. The role of these mental states on creativity was explored by Agnoli, Vanucci, Pelagatti, and Corazza (2018)) , who distinguished five constitutional dimensions of mindfulness: observing, acting with awareness, describing, nonreactivity, and non-judging. Results showed that mind-wandering and mindfulness predicted creative behavior both alone and in combination. Via path analysis they explored the value in distinguishing between deliberate and spontaneous mind-wandering. Deliberate mind-wandering positively predicted creative performance; however, spontaneous mind-wandering negatively associated with creative performance. Interestingly, more deliberative mind-wandering showed beneficial interaction effects with mindfulness toward producing creative and original ideas. This suggests that deliberate mind-wandering is a productive characteristic for creative work and potentially for creative learning in classrooms, which is supported by mindfulness.

Preiss and Cosmelli (2017) explored mindful mind-wandering for creativity using illustrative cases of creative writers and their processes. They noted that while their writers discussed the concepts of mind-wandering and creating in different ways, these were most often characterized by deliberation and awareness of their own mind. They termed this as, “mindful mind-wandering,” which nurtures creativity and differs from the absent-minded daydreaming of other mind-wandering:

Professional creators develop a sense of identity that is strongly grounded on their awareness of the mind wandering process. As authors become more expert, they gain a better understanding of the creative process and apprehend its phenomenological nature. Specifically, they become mindful mind wanderers (p. 303).

Research and practice suggest that despite what initially appears to be conflicting dynamics, mind-wandering and mindfulness can enhance each other toward creativity. Mindfulness in conjunction with mind-wandering may allow the mental wanderer more awareness and potential to imagine and think creatively—which may benefit creative imagination in learners’ skills and practices.

3.4. Theme 4: a need for applied and educational research

Finally, in reviewing the mindfulness-creativity relationship in scholarly literature for praxis, we noted a lack of educational literature in this space, which signals a need for more applied but still empirical research for thinking and learning settings. Fisher (2006) suggests that these topics may be most vital for young people in schooling:

For many children childhood is not a carefree time. In a materialistic, competitive world they are subject to many of the same stresses and strains as adults. They are bombarded by an information overload of words, images and noise. They are prey to the frustration and anger of others and often experience negative emotions more deeply and intensely than adults (p. 148).

Fisher notes that these kinds of stressors are commonly recognized as blocks to learning and creativity, making mindfulness a potentially beneficial approach and psychological support for creativity. He highlights a historical link harkening to the ancient Greeks and Romans, who believed that a quiet mind offered an opening to the creative muse.

Notably, meditation engages the mind in non-verbal ways, which learners do not always have the opportunity to use in schools. While the conscious mind is caught up in language, the brain’s linguistic structures can restrict the scope of human knowledge and action. Meditation may offer an experience of the mind that is not purely linguistic, expanding learners’ creativity by tapping into subconscious and intuitive thought. Claxton (1997) called this the “under-mind” and Malcolm Gladwell (2005) referred to it as the “adaptive subconscious.” Such intuitive experience is essential to learners’ creativity and requires a present-moment focus and freedom from distracting fears and desires.

Much of this connection between children in schools and mindfulness and creativity is still theoretical; and while the existing research is promising, it is greatly limited in volume and scope. As mindfulness has become more prevalent in real-world learning settings, more empirical research is needed to understand the mindfulness-creativity link and practices for learning settings ( Osten-Gerszberg, 2017 ).

A limited number of studies have considered the connection between increased creative outcomes and mindfulness in applied settings outside of university labs or psychological experiments, across disciplines. In education, Justo, Mañas, and Ayala (2014)) studied this with high school students, to analyze the impact of an extracurricular mindfulness program upon the figural creativity levels of a group of 50 teenagers. The authors used an experimental group of high school students who participated in the mindfulness training program, and a control group who did not. The results of the Torrance Test showed significantly higher levels of creativity in the treatment group, after a 10-week mindfulness intervention (of 1.5 h of training a week, with 30 min of daily meditation).

The school-based intervention focused on flow meditation ( Franco, 2009 ), which is meant to set thoughts free rather than control them, by nonjudgmentally noting any spontaneous thoughts that appear in mind. This technique does not aim to redirect thoughts back to an object of foci (the breath, etc.), but to develop attention and allow full awareness of whatever appears in consciousness, noticing the transience and impermanence of thoughts (e.g. a kind of meditation on thoughts). Though the study did not provide an effect size, their results are still promising as a step toward empirical support for mindfulness and creativity in educational environments. In their work, achievement goals and self-determination influenced mastery experience in creativity via mindful learning, which also has implications for teaching.

Yeh, Chang, and Chen (2019) investigated mindful learning and creativity among a younger school population of elementary students. They sought to understand mindfulness within digital game-based creative learning, using the Langer (2000) concept of mindful learning as a flexible state of mind in which people are actively engaged with the present, aware of new things, and sensitive to context. They developed an original training program for creativity and an instrument for measuring mindful learning during game-based learning. Their study focused on how players’ traits would influence their mastery experience during digital creativity game-based learning. Results suggested that mindful learning can support creativity within a game-based learning system; and participating students became more confident in their own creativity competences. This is interesting, because creative confidence has been found to be a driver of creative potential ( Beghetto, 2006 ). In educational settings, the notion of creative confidence is not often addressed, as many traditional education contexts are uncomfortable with the kinds of risk of failure associated with creativity, or do not promote the confidence to work through such discomfort toward creative ends. Thus, support for creative confidence, via mindfulness, may be an interesting pathway for future study.

4. Discussion of findings for education

The research we have described can serve to provide the field of education with ideas to utilize mindfulness to support learner creativity and well-being in educational settings. While the connection between mindfulness and creativity is complex, there is enough evidence to show a generally beneficial and supportive relationship between the two, wherein practicing mindfulness can support creativity. In the next section we discuss implications for the field of education.

4.1. Allowing purposeful mind-wandering

One way that educators can support students is through the teaching of mindful mind-wandering strategies. Preiss and Cosmelli (2017) describe how an awareness of the mind-wandering process is an essential component of the creative process. The more aware people are of these processes and of their own mind’s activities, the more capable they become to notice and attend to creative ideas in productive ways. Educators can help students become mindful mind-wanderers by teaching a creative process that includes stages where students purposefully diverge from the task or topic at hand. Rather than being “off-task” students may be purposefully led through activities that guide them through deliberate acts of mind-wandering ( Agnoli et al., 2018 ).

Intentional mind-wandering can stimulate novel ideas or fresh connections. The most important component here is intentionality. Open-monitoring meditation and flow meditation, as described earlier, allow the mind to notice thoughts or sensory stimuli without trying to change them. This awareness component of noticing may be beneficial for giving the mind space to expand, while also cultivating present moment awareness and observation. If educators can support students in being more aware of the type of mind-wandering they engage in they may be able to provide a valuable skill for metacognitive awareness.

While focused attention has its benefits and is necessary for concentration particularly around analytic creative problem solving, in terms of insight problem solving it can potentially be limiting to “Aha” moments or bursts of creative thought. Therefore, breaking up time used to solve problems with more open mindfulness inspired activities can be helpful when learners in any context get stuck. This may, in fact, be a metacognitive skill that educators can explicitly teach— in understanding how to allow for mental breaks or a shift in awareness, which may lead to higher levels of insight-related creative thought, helping learners to overcome challenges where they get stuck. Dijksterhuis and Meurs (2006) found that too much focused deliberation on problems blocks creativity, whereas strategic distraction improves it. Thus, there may be creative potential to mindfully observe one’s own mind-wandering, and allow it, observing where it goes and what it does.

Agnoli et al. (2018) found that high levels of originality were also associated with high levels of deliberate mind-wandering. Therefore, generating creative and original ideas is linked with the re-creation, redirection and reflection of thought. This has implications for how educators engage students in creative processes, suggesting consideration of how they are supporting students’ mind-wandering.

4.2. Time and space for meditation in curriculum

The simple act of meditating has been shown to benefit creativity in learning settings ( Holm, 2015 ). Practicing being more mindfully aware through meditation, even for a short amount of time each day, impacts learning holistically. Brief meditation breaks provide the downtime needed for creativity to be enhanced after returning to the task at hand. These breaks also may positively impact teachers who struggle to maintain their students’ focus in the midst of increasing curricular demands. Puccio et al. (2017) suggest that mindfulness involves the self-awareness of individuals within organizations, communities and group practices. Therefore is not just an individual pursuit, but it impacts the sociocultural settings and complex ecosystems in which people live, work, learn, and play—which underscores its importance within the ecologies of classrooms and schools.

Supporting the development of learners through the mindfulness-creativity connection, Fisher (2006) lays out the case for mindful meditation for children in schools, predicated upon the ways that mindfulness can expand creative thinking—and the degree to which young people often need these kinds of skills for wellbeing. The more general positive effects on student well-being may have other unmeasured values for creative thinking. It could be argued that in a stressful world, being able to learn strategies to increase well-being are an essential part of social-emotional learning and productive creativity.

4.3. Supporting creative thinking and reducing judgment or fear

There are more experiences and strategies that potentially support creativity and divergent thinking than we could cover here. Ideation to guide learners through processes of generating creative and original ideas may also be supported by intentional and mindful mind-wandering. In addition, open-ended tasks are an approach to supporting creativity in content learning, in that multiple solutions are both allowed and expected. Yet while such approaches have been identified as a key way to support student creativity ( Jeffrey & Craft, 2004 ), simply doing such types of activities in learning settings does not guarantee that learners will engage in creative thinking or even feel comfortable doing so.

Some of the most notable barriers to creativity are either fear or judgment—or fear of judgment—which is often the case for learners in school settings. Creativity inherently brings social risk, and people frequently report feeling uncertain about offering up new ideas for fear they might be judged or thought to be strange ( Beghetto, 2007 ). Given the social pressures for students in K12 and higher education contexts, it is critical that creative environments reduce fear or anxiety around judgment. The nonjudgmental awareness of mindfulness meditation is an important skill supporting this.

Educators often note that in attempting more creative lessons, students may be uncomfortable in open-ended, project-based spaces that lack single-correct-answer approaches. Since learners today grow up in standards-based high-stakes testing environments, teachers sometimes report that they can be nervous or uncomfortable with ambiguity ( Olivant, 2015 ). Opening up thinking and allowing for more divergent elements is important, and the connection between divergent thinking and open-monitoring meditation suggests that this might be a useful practice, particularly for instances when ideation and multiple possibilities are important.

By aiming to non-judgmentally expand awareness, mindfulness presents opportunities to open social acceptance of creative thinking and intellectual risk-taking in learning settings. As learners come to expect different ideas and solutions from themselves and their peers without judgment, there may be a decrease in the fear or risk associated with presenting novel ideas, thus enhancing creative thinking ( Brown et al., 2007 ).

5. Conclusion

This literature review investigated findings around the relationship between mindfulness and creativity with focus on educational contexts—scoping the field in a thematic, qualitative exploration of the research into mindfulness and creativity. Summing up the relationship between these two areas is challenging due to their complexity. The most accurate summation may be to point to the generally positive but also complex nature of the relationship—with much research suggesting that mindfulness enhances creativity, as well as areas that are more nuanced depending on contextual factors. We have explored the connection to mind-wandering around mindfulness and creativity—and the possibility of using mindfulness to support deliberative mind-wandering (vs. spontaneous mind-wandering) toward expanded creativity in learning. Finally, we have emphasized the relative lack of applied and/or educational studies around mindfulness and creativity, and the need for more research in this area to inform educational practice across contexts.

The theoretical foundations connecting mindfulness and creativity are strong, with regard to observing and understanding the world and noticing more possibilities without being clouded by mental blinders. This is exemplified by Justo et al. (2014) :

Mindfulness is a technique which allows introspective and perceptual awareness, encouraging the awareness towards our psychological processes and habits. It increases the interhemispheric communication, which is typical of creativity states, since the individual who meditates is able to perceive more and more subtle details of the stream of consciousness and mental processes (p. 233).

Empirically, research in this area has demonstrated promise but there is much room to develop a more nuanced understanding of the relationship. Going beyond correlation, meta-analysis has empirically inferred causation, suggesting that mindfulness training supports, strengthens, and expands creative thinking ( Lebuda et al., 2016 ). Mindfulness and creativity are not yet fully understood in many ways, and both are inherently complicated and variable areas unto themselves.

In investigating the relationship, the context, variables, and moderators or potential interactions are important. For instance, mindfulness generally supports creativity—but there are some concerns about the way it affects mind-wandering and the resultant effects on creativity. More nuanced and recent research has teased this apart to further break down mind-wandering into different types (spontaneous and deliberate) each of which affect creativity differently ( Agnoli et al., 2018 ). This remains a somewhat new and still relatively unexplored area of empirical work.

The number of potential moderators, such as different types of mindfulness and meditation, is a challenge for researchers seeking to dissect the relationship. For instance, it is likely that different practices, such as open-monitoring vs. focused-awareness meditations, play a role at different stages of the creative process. Thus, there are gaps in the literature in regards to fully understanding these different roles and different moderators. In order to apply mindfulness and creativity in practical education settings, more sustained, applied and ongoing research is needed.

For educators, it is vital to see more research on mindfulness and creativity embedded in real-world contexts, particularly in learning settings. This would support better understanding of the intersection of these constructs in-situ—or a more robust understanding of mindfulness and creativity ‘in the wild,’ beyond labs or testing situations. When we combine the correlational and causal links between creativity and mindfulness, there are important implications for learning psychology around creativity and creative education—both for creative abilities and self-concept.

In practice, helping educators to understand how different types of mindfulness might support their students across different needs and tasks could be beneficial; and this may be true in other contexts of thinking, learning and development. Existing research points to a promising intersectionbut we would suggest that more action research approaches in classroom settings could benefit our empirical-practical understandings. Mindfulness and creativity are critical to wellbeing and development at individual and societal levels, so understanding them in context is essential. The future of human thinking, wellness, and progress demands no less.

CRediT authorship contribution statement

Danah Henriksen: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing. Carmen Richardson: Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing. Kyle Shack: Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing.

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Taking critical thinking, creativity and grit online

• Published: 09 November 2020
• Volume 69 , pages 201–206, ( 2021 )

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• Miguel Nussbaum   ORCID: orcid.org/0000-0001-5617-5983 1 ,
• Camila Barahona 1 ,
• Fernanda Rodriguez 1 ,
• Victoria Guentulle 1 ,
• Felipe Lopez 1 ,
• Enrique Vazquez-Uscanga 1 &
• Veronica Cabezas 2  

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Technology has the potential to facilitate the development of higher-order thinking skills in learning. There has been a rush towards online learning by education systems during COVID-19; this can therefore be seen as an opportunity to develop students’ higher-order thinking skills. In this short report we show how critical thinking and creativity can be developed in an online context, as well as highlighting the importance of grit. We also suggest the importance of heuristic evaluation in the design of online systems to support twenty-first century learning.

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Introduction

This paper is in response to the article “Designing for 21st century learning online: a heuristic method to enable educator learning support roles” (Nacu et al. 2018 ). In this paper, the authors outline a framework for heuristic evaluation when designing online experiences to support twenty-first century learning.

Twenty-first century skills can be key to success in a modern knowledge society. Among these skills, critical thinking is important not only at work, where problem solving is essential, but also in any social setting where adequate decision making is required (Dwyer and Walsh 2020 ). Additionally, creativity helps ensure that the outcomes of critical thinking can be both culturally ingenious as well as treasured (Yeh et al. 2019b ). This is achieved by embracing cognitive abilities in order to create new combinations of ideas (Davis 1969 ).

Technology has been shown to facilitate the development of higher-order thinking skills in learning (Engerman et al. 2018 ). However, in general, schools have failed to take advantage of this by incorporating adequate use of technology into their practices (Olszewski and Crompton 2020 ). Therefore, the rush towards online learning by education systems during COVID-19 can also be seen as an opportunity to develop students’ higher-order thinking skills. One potential drawback with online learning is the distance it creates between peers, thus hindering student engagement and the development of higher-order thinking skills (Dwyer and Walsh 2020 ). We show how this barrier can be overcome when developing critical thinking and creativity in an online context.

• Critical thinking

Critical thinking includes the ability to identify the main elements and assumptions of an argument and the relationships between them, as well as drawing conclusions based on the information that is available, evaluating evidence, and self-correcting, among others. It is seen as a self-regulated process that comes from developing skills such as interpretation, analysis, evaluation and explanation; going beyond technical skills. It can therefore be considered a metacognitive process (Saxton et al. 2012 ; Facione 1990 ).

By taking learning online, both self-study and teacher-led sessions can be enhanced through a problem-based learning strategy. In the first stage, students build on a question or topic posed by the teacher, e.g. a mathematical problem or an essay writing assignment. In the second stage, students peer-review their classmates’ responses or essays using a rubric provided by the teacher. Students break down their classmates’ responses and see how they relate to the objective of the activity. They then compare this analysis with the rubric in order to provide feedback. In a third stage, the students develop a new response based on their initial response, the experience of giving feedback, and the feedback they received. This process develops self-evaluation as the students compare their own response with their classmates’ and discover any gaps in their knowledge. It can also develop metacognition as they integrate various sources of knowledge (initial response, feedback received and the experience of giving feedback) when developing a new response. In the final stage, the teacher discusses the different responses with the class. The teacher then compares the students’ work with the expected response and provides a general summary, transferring the responses to different domains.

While Stages 1 through 3 are asynchronous and computer-aided, stage 4 can be synchronous and supported by the use of a web-based video conferencing tool. Active student participation and teacher mediation are both key since interactive and instant feedback has been shown to improve critical thinking (Chang et al. 2020 ).

In addition to the problem-based strategy presented here, other active learning strategies can also be used to develop critical thinking, e.g. structured questioning, role playing, and cooperative learning (Cruz and Dominguez 2020 ). How these might be implemented online is still open to discussion, though heuristic evaluations may be a good alternative given the possibilities presented by online learning as a resource provider, learning broker and learning promoter (Nacu et al. 2018 ).

Creativity is an essential element of the problem-solving process. Creative people often find ways of addressing a problem that others cannot see, while also having the ability to overcome barriers where others may otherwise give up (Kaufman 2016 ). There are different techniques for developing creativity. In-depth learning is facilitated when students represent concepts based on their own personal perceptions (Liu et al. 2018 ). In this sense, analogy can be a powerful tool for boosting creativity. Analogical transfer includes the idea of making analogies by analyzing objects, ideas or concepts across domains, i.e. information is transferred from the known (the original domain) to the unknown (the new domain) by searching for similarities (Shen and Lai 2014 ).

We propose an analogical transfer strategy. In the first stage, the teacher identifies a concept with examples from different domains. This might include showing a video that not only introduces the concept but also provides a context that is both familiar and relatable for the students. In the second stage, students reflect on situations from their own lives where they can apply the concept that is being studied. Here, the use of open-ended questions allows the students’ creativity to be explored in greater depth, while adapting to their different backgrounds and levels of prior knowledge. In the third stage, which is mediated by the teacher, the students discuss their responses from stage 2. The teacher should focus on original responses from different domains, or responses where it is not clear whether the solution is correct.

Stages 1 and 2 can be conducted asynchronously and scaffolded using technology through the inclusion of multimedia and student guides. However, stage 3 should be synchronous and supported by the use of a web-based video conferencing tool. In this way, technology facilitates the development of creativity by facilitating the discovery process, the collection of ideas, and the integration of knowledge (Yang et al. 2018 ). Mediation in stage 3 is therefore key (Giacumo and Savenye 2020 ). Effective teacher-student dialogue can improve the teacher-student relationship and enhance the creative process. Heuristic evaluation can therefore help us understand this relationship by looking at these interactions on the online platform (Nacu et al. 2018 ).

As with any learning process, critical thinking and creativity require students to be both present and focused, which in turn requires grit (Yeh et al. 2019a ). In other words, the way in which students approach their schooling is just as important as what and how we teach them (Tissenbaum 2020 ). Grit should therefore not only be considered an essential element of academic achievement but also as a mental process that activates and/or directs people’s behavior and actions (Datu et al. 2018 , Lan and Moscardino 2019 ). This is particularly relevant in a COVID-19 context, where the pandemic is affecting the wellbeing and mental health of many students, families & communities (OECD 2020 ).

In order to achieve effective student engagement, the objective must be attainable, interesting and accessible (i.e. in their zone of proximal development). The means used to complete the task must be attractive and feel more like a reward than an assignment. Finally, the teacher should work on the students’ persistence, not just in order to complete the task but as an essential quality for everyday life (Barnes 2019 ).

Teacher grit may also be key. As Haderer ( 2020 ) suggests “Why do some teachers stay when others run from the challenges?” In this sense, reflection has been shown to be relevant for teacher efficacy and grit (Haderer 2020 ). Heuristic evaluation methods may therefore allow the educator to understand the learning system as a whole (Nacu et al. 2018 ).

Ending remarks

As indicated in (Nacu et al. 2018 ) we are “faced with the need to create youth-centered spaces that also provide adult facilitation of learning”. Heuristic evaluation can therefore help connect online platforms with students, teachers and twenty-first century skills needs.

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The research results informed in this report were supported by ANID/FONDECYT 1180024.

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Nussbaum, M., Barahona, C., Rodriguez, F. et al. Taking critical thinking, creativity and grit online. Education Tech Research Dev 69 , 201–206 (2021). https://doi.org/10.1007/s11423-020-09867-1

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Research Article

Fostering students’ creative thinking skills by means of a one-year creativity training program

Roles Conceptualization, Formal analysis, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing

Affiliation Institute for Management Research, Nijmegen School of Management, Radboud University, Nijmegen, The Netherlands

Roles Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands

Roles Conceptualization, Investigation, Resources, Writing – original draft, Writing – review & editing

Affiliation Brainnovation Foundation, Eindhoven, The Netherlands

Affiliation Fontys University of Applied Sciences, Venlo, The Netherlands

• Simone M. Ritter, 
• Xiaojing Gu, 
• Maurice Crijns, 
• Peter Biekens

• Published: March 20, 2020
• https://doi.org/10.1371/journal.pone.0229773
• Reader Comments

Creative thinking is among the most sought-after life and work skills in the 21 st century. The demand for creativity, however, exceeds the degree to which it is available and developed. The current project aimed to test the effectiveness of a one-year creativity training program for higher education. The creativity of students following the training was measured before, halfway, and after the training. In addition to the within-subjects comparison across time, performance was compared to a matched control group. At each of the measurement points, different versions of seven well-validated creativity tasks (capturing divergent and convergent creative thinking skills) were employed. The creativity training increased students’ ideation skills and, more importantly their cognitive flexibility. However, no difference in originality was observed. Finally, an increase in performance was observed for one of the convergent creativity tasks, the Remote Associate Test. Implications for educational settings and directions for future research are discussed.

Citation: Ritter SM, Gu X, Crijns M, Biekens P (2020) Fostering students’ creative thinking skills by means of a one-year creativity training program. PLoS ONE 15(3): e0229773. https://doi.org/10.1371/journal.pone.0229773

Editor: Dongtao Wei, Southwest University, CHINA

Received: June 27, 2019; Accepted: February 14, 2020; Published: March 20, 2020

Copyright: © 2020 Ritter et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available from the Radboud University repository at https://doi.org/10.17026/dans-zuz-q6zd .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

From the first wheel to the latest microprocessor creativity has continuously enriched our lives. It plays a vital role in science, innovation, and the arts [ 1 – 3 ]. Moreover, the significance of creativity has also been recognized in daily life problem solving [ 4 ], in maintaining and fostering our well-being [ 5 ], and in successful adaptation to change [ 4 , 6 ]. Creativity—the ability to generate original and useful ideas [ 7 – 9 ]—drives us forward, and it is among the most sought-after life and work skills in our complex, fast-changing world.

We have moved from an Industrial Age, to a Knowledge Age, to an Innovation Age. Many jobs are disappearing, and new jobs are emerging, for example, due to the transformative impact of digital technologies. On average our future generation of employees will change jobs more than 10 times before they reach the age of 50 [ 10 ]. As we don’t know how the future work field will look like, it is difficult to predict for what kind of jobs we have to prepare our current generation pupils and students. Whereas for decades content knowledge was a prerequisite for work, in the era of google we need individuals who are capable to creatively use and generate knowledge. To remain competitive, nations, organizations and individuals have to be able to think differently and to make connections between seemingly unrelated things. Global surveys have revealed that organizational leaders are mostly satisfied with their employees’ content knowledge or technical skills [ 11 ]. However, what they complain about is the lack of creativity in many otherwise qualified graduates [ 11 ]. For example, as reported by a UK employment survey, information technology graduates fail to grasp job opportunities due to a lack of creativity [ 12 ]. Creativity is not anymore, a ‘nice to have’, but has turned into a ‘must have’. Interestingly, the majority of employees indicate that they wish they had more creative ability (75%), and that they lacked exposure to creative thinking during their education (82%; [ 13 ]). Supporting these findings, recruiters denoted that creative thinking is a skill that is hard to find in job applicants [ 14 ]. All in all, the demand for creativity exceeds the degree to which it is available at all levels of the system. To meet the needs of the 21st century, academics, business leaders, and policy makers around the world have stressed that creativity should be fostered in the entire population [ 15 ].

Evolution has equipped us with a creative mind. However, we often do not use our creative thinking skills to the best of our ability. Some scholars even state that the educational system diminishes our creativity. In the most watched TED talk of all time, educationalist Ken Robinson claims that schools kill creativity—schools do not foster growing into but out of creativity. This is a rather radical view, as schools cultivate the knowledge on which creativity often depends. In schools, children develop the literacy skills necessary for all further learning. Creativity does not happen in a vacuum, it is based on knowledge. However, what schools mostly don’t focus on is teaching and practicing how existing knowledge can be used to come up with creative ideas and problem solutions. In schools that focus on creativity, it is often observed that creativity development is embedded in arts subjects, but not in subjects such as writing and mathematics [ 16 ]. Cotter, Pretz and Kaufman [ 17 ] studied the relationship between university applicants’ creativity, extracurricular involvement and traditional admission criteria (e.g., SAT scores, high school rank). The results revealed that applicants’ extracurricular activities positively predicted their creativity, whereas their academic performance or the traditional admission criteria even showed a negative relationship with creativity.

Creativity is a mental phenomenon that results from the application of ordinary cognitive processes such as working memory, and the ability to categorize and manipulate objects (creative cognition approach; [ 18 , 19 ]). Importantly, the ability to think creatively can be taught and developed—creativity is not a fixed inborn trait [ 20 – 23 ]. However, this is often not what is happening in education. While the world has gone through revolutionary changes, teaching practices have not changed much. The main focus in education is still on rote learning. In classroom activities as well as in the curricula, little attention is paid on introducing and practicing cognitive strategies proven to foster creative thinking skills.

By now, a variety of reports stress that creative thinking is a crucial 21 st century skill [ 24 – 26 ], and a skill that should be fostered in schools [ 9 , 27 ]. Schools allow not only the training of a creative elite, but of our entire future generation. To illustrate, simply the way a question is asked can either stimulate or undermine creative thinking: Example ‘What is three plus three?’ requires convergent thinking (i.e., finding the single, correct answer). However, if the teacher instead asks ‘Which calculation will result in six’, divergent thinking is stimulated—after all, the answer could be three plus three, two plus four, or twelve divided by two, and infinitely many others. Instead of focusing on calculations, the teacher could also ask a broader question: ‘What is six?’ The answer might be a triangular pyramid, the sixth sense, or an ice crystal. To boost creativity further, the teacher may ask ‘What can you do with six?’ Next day, she asks for answers. A dreamer or gifted visionary may answer: I see an array of hexagons, which you can use to build spaces. This example demonstrates that creativity is a skill that can be taught and developed within different academic domains and school subjects [ 28 ]. We can think of the brain as a muscle. To run a couple of kilometres, people must practice. By exercising regularly, our muscles and condition become strong enough to run a longer distance. It is no different for the brain. Regular exercise is required to develop a creative thinking style and to keep our brain in shape. A potentially helpful framework for fostering creativity in educational settings is the 4 P’s model of creativity: how to promote the cognitive processes that lead to creativity (Process), how to recognize and support creative individuals (Person), how the school/classroom environment impacts creativity (Press), and how to recognize and evaluate creativity in students’ work (Product).

The current project

During recent years notable efforts have been made to empower creativity in education [ 29 – 31 ]. However, empirical evidence on the effectiveness of creativity intervention programs is often lacking. As concluded by Davies and colleagues [ 32 ] in their review paper, “Much literature in this area tends to be either philosophical, anecdotal or polemical, which has led to a strong belief about the effectiveness but significant evidence gaps” (p.89). To fill this gap, the current study aimed to develop and scientifically test the effectiveness of a creativity training program. The main objective of the current project was to scientifically test the effectiveness of a recently developed one-year creativity training program for higher education, called the ‘Brainnovation Six Step Cycle of Creativity’. The training had to fulfil several requirements: First, it has to be suitable for students with various educational backgrounds (i.e., it has to be domain unspecific). Second, it applies a cognitive approach, as previous research has shown that cognitive-oriented training programs have larger effects [ 20 ]. Third, it has to combine scientific insight and practical experience. Brainnovation is based on linking practical experience and anecdotal evidence (e.g., sleeping on a problem, distraction, connecting seemingly unrelated things) with existing models of the creative process (e.g., preparation, incubation, illumination and verification [ 33 ]) and with brain science (e.g., the finding that creative thinking is related to the interaction of three major brain networks; the central executive network, salience network and the default mode network [ 34 – 36 ]. The core of the Brainnovation method is the ‘Six Step Cycle of Creativity’. The first three steps explore the resources of the central executive network, and the last three steps explore those of the default mode network. A set of assignments trains the fluent application of all six steps. The idea is that by following the training, the student can apply the Six Step Cycle of Creativity to problems that need a creative solution. Four tools are employed to facilitate walking through and practicing the Six Step Cycle. The Six Step Cycle and the four tools are described in more detail in the Method section of the current paper. Fourth, rigorous scientific testing of the effectiveness of the training has to be performed: The creativity of students following the creativity training was measured before the training, halfway the training, and after the training. In addition to the within-subjects comparison across time, the creativity of students following the training was compared to a matched control group. At each of the three measurement points, seven well-validated creativity tasks were employed to test participants’ divergent thinking, convergent thinking and creative problem solving ability. The creativity measurement tasks are described in more detail in the Method section of the current paper.

We formulated the following hypotheses:

• There will be a significant improvement in students’ creative thinking skills from pre-measure to half-way and post-measure in the training group. For exploratory reasons, we will also compare creative performance in the creativity training group between the half-way measure and the post-measure, as this gives an indication whether the time duration of the training has a positive effect on students’ creativity development.
• In the control group no difference in creative performance is observed across the three measurement times.
• The training group significantly differs in creative performance from the control group on the half-way measure and on the post-measure.

Participants

The current study was conducted from September 2017 to May 2018 at an applied university in the Netherlands. The study was pre-registered on open science framework (see https://osf.io/znw5h/register/5730e99a9ad5a102c5745a8a ). An a priori power analysis using G* power [ 37 ] was calculated. To reach a statistical power of .80, 215 students should be recruited for the study. The total participant number is slightly lower (198 instead of 215), as less than expected freshmen students enrolled in the program in the study year 2017/18. From the 198 students, 133 students followed the creativity training, a 5 ECTS (i.e., 140 hours) course entitled ‘Applied Creativity’. Another 65 students, who were not enrolled in the course, formed the control group. The training and the control group are comparable in terms of educational level (all freshmen) and educational background (Business related study). As preregistered, participants who did not regularly (less than 2/3 of all lessons) attend the creativity training program were excluded from data analyses. From the 78 participants who met this criterion, 57 were in the creativity training group, and 21 in the control group. 27 of the 78 participants were female and 50 were male, and the average age was 19.72 ( SD = 1.82), ranging from 18 to 26 years. The study was conducted according to the principles expressed in the Declarations of Helsinki. The research was not of a medical nature, no minors or persons with disability were involved, and there were no potential risks to the participants; therefore, ethical approval was, when data collection started, not required by the Institution’s guidelines and national regulations. Importantly a lecturer prior to the study assigned each participant a subject identification code that was used in the current study. This code was not shared with the researchers, to make sure that personal data is staying within the educational institution.

The study employed a pre-post-test between-subject design. Participants were either in the creativity training group or in the control group. Participants’ creative thinking skills were assessed at three time points: at the beginning of the training program (pre-measure; beginning of the academic year, September 2017), after three months of the training (half-way measure; December 2017), and at the end of the training program (post-measure; May 2018). At each testing session, participants’ creative performance was measured by means of seven well-validated and frequently used creativity tasks (for tasks and task description, see the creativity measurement section).

Creativity training

The creativity training program is provided as a mandatory course that counts for 5 ECTS credits. According to Dutch law, 1 credit represents 28 hours of work, and 60 credits represents one year of full-time study. The creativity course (in total 140 hours) lasted two semesters, and the course entailed lectures (i.e., focus on theory) and factory lessons (i.e., focuses on practice exercises in the field of international business).

In the creativity training program, students learned to apply the Six Step Cycle of Creativity to a wide range of problems. The 6 steps—understanding the question, convergent thinking, divergent thinking, detached thinking, stop thinking, and sleeping—are described in more detail below.

Understand the question . The problem must be defined correctly; failing to do so interferes with the other steps of the creative cycle [ 38 ]. This step requires a high focus. Convergent thinking . Convergent thinking is logic reasoning, straightforward thinking from A to B. People in general are quite good in convergent thinking, as schools put heavy focus on convergent thinking. Divergent thinking . Divergent thinking is associating freely without criticizing ideas or thoughts: One tries to consider different kinds of alternatives. To illustrate these steps with an example: The question ‘What is three plus three?’ elicits convergent thinking—there is one single correct answer, six; whereas the question ‘What is six?’ stimulates divergent thinking, it could be three plus three, nine minus three, and infinitely many other options. Detached thinking . In this stage, one tries to look at a problem with defocused attention [ 39 ] and without emotions or personal concern [ 40 ]. One can observe a problem, object, or image from all sides, upside down, turn it around, and toss and touch it. Central in this stage is a playful mood or a meditative mind set [ 41 ]. When answering the question ‘What is six?’ with detached thinking, the answer might be a triangular pyramid, a dice, an ice crystal, a hexagon, and so on. Stop thinking . If convergent, divergent and detached thinking did not provide a solution, a possible avenue may be to stop thinking about the problem. Let the problem ‘go’ for a while, create an incubation period [ 42 , 43 ], for example, go shopping, go for a run, watch TV, dance, bike, bath, shower, drive, or listen to music [ 44 ]. Without conscious awareness, the unconscious is working hard [ 45 ] to re-assemble the information obtained in the previous steps of the cycle in new networks. Suddenly, an idea may pop-up, and experience that is described as an Euraka moment—often experienced at times when people expect it the least [ 46 ]. In terms of our example, the question ‘What is six?’, stop thinking may result in abstract, remote associations and the answer may be the Six Thinking Hats, or the sixth sense. Sleeping . A very powerful step of the cycle is sleeping on it. Research has shown a positive relationship between creativity and sleep [ 47 , 48 ]. This step starts with deliberatively re-activating the problem just before going to sleep; this gives guidance to the unconscious where to focus on during sleep. It has, for example, been shown then even in rats [ 49 ] during sleep the unconscious starts to replay many scenarios to find a solution to a given problem [ 50 , 51 ]. During sleep, the brain takes into account different kind of scenarios. An important advantage of the unconscious mind is that it is not hindered by social conventions or prejudices, and in that setting, mood and other variables are changing at an incredible speed, thereby allowing a diversity of option to be explored. This process may take one or more nights, and eventually it may lead to a creative idea. It may be helpful to put a notebook next to the bed. In case one wakes up in the night with a hunch, one must write it down, as people often do not remember their dreams and solutions the next morning [ 52 ]. For example, sleeping on the question ‘What is six?’, an architect may dream of floating images of hexagons, which in the case of Li Hu of the Beijing-based Open Architecture office, resulted in the design of the HEX-SYS—a reconfigurable construction system of hexagons in response to the proliferation of temporary structures erected by property developers during China’s recent construction boom.

Four tools are provided to facilitate walking through the Six Step Cycle: simplify (i.e., reduce the complexity of questions), differentiate (i.e., wonder what is more and less important; what is the big picture what are details), visualize (i.e., use real objects, make sketches, or imagine comparable processes from everyday life) and tag the problem (i.e., link the problem to one of the five senses: sight, smell, sound, taste, touch). Students are repeatedly provided with four different types of assignments, which trigger them to practice the different steps of the Six Step Cycle: The Detox assignments are provided at the start of the course, and they aim to train the flexibility of the mind by questioning prejudices and by fostering an open mind. The Training assignments focus on the first three steps of the Six Step Cycle, which train students’ cognitive creativity and their ability to quickly form remote associations. The Jump assignments are complex problems that are not easy to solve, and they challenge the students to employ and practice step four to six of the Six Step Cycle. Whereas the Training assignments must be solved within a short amount of time, for the Jump assignments students have one week, allowing unconscious processes to come into play. Often the Six Step Cycle must be repeated to find a solution. In addition to the three assignments, students practice with neural headsets. Two types of headset are used: the Mindwave and the Mindflex. The neural headsets visualize the brainwaves of a student in real time and so, the student can monitor the level of attention or relaxation. By playing with the headsets, the students are challenged to smoothly rotate between a state of focused and defocused attention—a process that is vital for creativity and a skill that can be learned [ 53 ].

Each of the training sessions starts with a warming-up: A short video clip that is not aimed at developing creativity, but at making students wonder. The warming-up prepares the mind for the theory and training provided.

Creativity measures

Seven creativity tasks were employed to measure students’ divergent thinking, convergent thinking and creative problem solving skills. Creative performance was measured at three time points (pre-measure, half-way measure, post-measure) and, therefore, three versions of each task (except the number task) were used. The task versions were counterbalanced across participants and time points. Importantly, the creativity measures differed from the trained exercises.

Divergent thinking.

Alternative Uses Task (AUT) . The AUT requires participants to think of as many uses of an object as possible [ 54 ]. Participants were asked to think of as many different uses for a brick (newspaper, paperclip; depending on the task version) within 3 min. After data collection, four trained raters screened the generated ideas and eliminated incomplete or unclear ideas.

The following creativity indices were used to measure the participants’ performance on the AUT: (a) Fluency , the total number of ideas. (b) Flexibility , the total number of different categories that a participant’s ideas could be assigned to. Therefore, a pre-defined list of categories was developed based on the ideas generated by all participants. (c) Originality , the originality level of an idea. (d) Creativity , the creativity level of an idea. (e) U sefulness , the usefulness level of an idea. Two raters scored Originality, Creativity and Usefulness, using a 5-point scale (ranging from 1 “not at all [dimension]” to 5 “very [dimension]”). The two raters first assigned scores to a random sample of 30% of participants’ ideas, based on which the raters’ reliability (two-way random, consistency) was calculated. The results showed good intraclass coefficients (ICC) for Originality (.893), Creativity (.898), and Usefulness (.822). Hereafter, the two raters worked independently—each rater assigned scores to 50% of the remaining ideas. For each participant, a mean score of Originality, Creativity or Usefulness was calculated across all his/her ideas.

Visual imagination task . Participants were presented with a picture of randomly combined shapes, and they were asked to think of as many ideas as possible of what the randomly combined shapes could represent within 3 min. Participants’ performance was measured using three indices: Fluency, Flexibility and Originality. For a detailed description of these indices, see AUT above. The intraclass coefficient (ICC) for Originality was good (.811).

Convergent thinking.

Remote Associates Test (RAT) . In the RAT, a task originally developed by Mednick [ 55 ], participants were presented with six sets of three cue words, and they were asked to think of a fourth word that associates with each of the three given words. For example, for the three-word set “bar, dress, glass”, the solution word is ‘cocktail’ (cocktail bar, cocktail dress, cocktail glass). Participants had 3 min to come up with answers, and an overall RAT performance was calculated (i.e., number of correct solutions).

Convergent visual imagination task . This task required participants to rearrange a set of coins (e.g., arranged in a triangle) into a new shape with limited moves. Participants’ performance was measured by whether they solved (score of 1) or did not solve (score of 0) the task within the given time of 3 min.

Idea selection task . In the idea selection task, participants had to rank order three pictures of business ideas from most creative to least creative within 4 min. These business ideas had been evaluated beforehand on creativity by 9 creativity experts. For each participant, a final creativity score was calculated using a weighted score: 3* top-1 idea (ranked as the most creative) + 2* top-2 idea (ranked as the second creative) + 1* top-3 idea (ranked as the third creative).

Creative problem solving.

Insight problems . In the current study, insight problems were used to measure participants’ creative problem solving ability. Three different insight problems were used, and one is illustrated in more detail here, the two-string problem. Participants were confronted with the following situation: two strings are hanging from the ceiling, and they are further away from each other than an arm’s length. The question is how to hold the two strings at the same time. The solution is to first set one string in motion (i.e., like a pendulum), then hold the other one, and catch the swinging string. Participants’ performance was measured by whether they solved (score of 1) or did not solve (score of 0) the insight problem within the given time of 4 min.

Number task . In this task, participants were presented with a picture of a parking lot. The number of one parking space was invisible due to a parked car. The task is to figure out the number of the parking space. One can only solve the task if one turns the picture up-side down. This task had one version, and to measure incubation effects it was measured on all three measurement times (pre-measure, half-way measure and post-measure). Participants’ performance was measured by whether they solved (score of 1) or did not solve (score of 0) the task within the time limit of 2 min. If a participant already solved the number task at the pre-measure, his/her performance was not included in the analyses of subsequent measurement points.

Demographics

Students’ age, gender, and educational background were investigated at the pre-measure.

Divergent thinking

To examine whether the creativity training improved participants’ creative performance, we performed mixed ANOVAs in which treatment (creativity training group, control group) served as the between-subjects factor and measurement time (pre-measure, half-way measure, post-measure) as the within-subjects factor. Results are shown in Fig 1 .

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https://doi.org/10.1371/journal.pone.0229773.g001

For Fluency, there was a significant interaction effect between training and measurement time, F (2,152) = 7.62, p = .001, η p 2 = .092 (according to Cohen [ 56 ], η p 2 = 0.01 refers to small effect, η p 2 = 0.06 refers to medium effect, η p 2 = 0.14 refers to large effect). A significant main effect was found for training, F (1,76) = 48.52, p < .001, η p 2 = .393, and for measurement time, F (2,152) = 16.0, p = .006, η p 2 = .095. Simple main effects (bonferroni corrected) revealed that in the training group participants’ creative performance at the pre-measure ( M = 5.88, SD = 2.36) significantly differed from the half-way measure ( M = 9.63, SD = 3.48, p < .001) and the post-measure ( M = 10.95, SD = 4.49, p < .001), while there was no significant difference between the half-way measure and the post-measure ( p = .141). Participants generated significantly more ideas after having followed the training, and this effect was already found after a couple of training sessions (half-way measure),and did not further increase with duration of the training (post-measure). Importantly, the control group showed no significant change from pre-measure ( M = 4.43, SD = 1.83) to half-way measure ( M = 5.48, SD = 2.86, p = .581) and post-measure ( M = 5.24, SD = 2.14, p = 1.00), and also not from half-way measure to post-measure ( p = 1.00).

For Flexibility, a significant interaction effect was found between treatment and time, F (2, 152) = 7.04, p = .001, η p 2 = .086. A significant main effect was found for treatment, F (1,76) = 49.3, p < .001, η p 2 = .397, and for measurement time, F (2,152) = 14.4, p < .001, η p 2 = .161. An analysis of simple effect (bonferroni corrected) showed that the training group showed a significant improvement from the pre-measure ( M = 5.07, SD = 1.98) to the half-way measure ( M = 7.75, SD = 2.62, p < .001) and the post-measure ( M = 8.52, SD = 3.02, p < .001). For the training group, participants improved significantly from pre-measure to half-way measure in generating ideas from different categories, but the improvement between half-way measure and post-measure was non-significant ( p = .298). For the control group, there was no significant change neither from the pre-measure ( M = 3.86, SD = 1.80) to the half-way measure ( M = 4.52, SD = 2.11, p = .083) and the post-measure ( M = 4.38, SD = 2.60, p = 1.00), nor from the half-way measure to the post-measure ( p = 1.00).

For Originality, results yielded no significant interaction effect of treatment and time, F (2,152) = 0.023, p = .977, η p 2 = .000. The main effect of measurement time was not significant, F (2,152) = 0.828, p = .439, η p 2 = .011; but a significant main effect of treatment was revealed, F (1,76) = 9.49, p = .003, η p 2 = .112; a difference between the training and control group was found regardless of measurement time.

For Creativity, the interaction effect between treatment and time was non-significant, F (2,152) = 0.235, p = .772, η p 2 = .003. There was a significant main effect of training, F (1,76) = 7.86, p = .006, η p 2 = .095, but the main effect of measurement time was non-significant, F (2,152) = 0.746, p = .476, η p 2 = .010. The training and control group differed on creativity, but as shown in Fig 1 , performance of the training group was higher on all the three measurement times as compared to the control group.

For Usefulness, there was a significant interaction effect between treatment and time, F (2, 152) = 3.87, p = .026, η p 2 = .049. A significant main effect was found for training, F (1,76) = 8.85, p = .004, η p 2 = .106, and for measurement time, F (2,152) = 6.17, p = .003, η p 2 = .076. Interestingly, further analyses showed that performance of the training group decreased significantly from pre-measure ( M = 3.85, SD = 0.632) to half-way measure ( M = 3.46, SD = 0.810, p = .020) and post-measure ( M = 3.16, SD = 0.526, p < .001), and from half-way measure to post-measure ( p = .042). Though there was a decrease tendency in the training group, the average score of usefulness was still higher than the medium level (> 3). The control group showed no significant change from pre-measure ( M = 3.76, SD = 0.873) to half-way measure ( M = 3.94, SD = 0.582, p = 1.00) and post-measure ( M = 3.65, SD = 0.370, p = 1.00), and not from half-way measure to post-measure ( p = .377).

Visual imagination task.

As in AUT, the training effect was analysed by means of a mixed ANOVA with treatment (creativity training group, control group) as the between-subject variable and measurement time as the within-subjects variable.

For Fluency, the mixed ANOVA showed a significant interaction effect between treatment and time, F (2, 152) = 28.8, p < .001, η p 2 = .275. A significant main effect was found for training, F (1,76) = 45.3, p < .001, η p 2 = .373, and for measurement time, F (2,152) = 35.1, p < .001, η p 2 = .316 ( Fig 2 ). Simple main effect analyses using bonferroni correction indicated that in the creativity training group participants generated significantly more ideas at the half-way measure ( M = 9.05, SD = 3.15, p < .001) and the post-measure ( M = 9.37, SD = 3.83, p < .001) than at the pre-measure ( M = 4.26, SD = 1.76), whereas no significant training effect was observed from the half-way measure to the post-measure ( p = 1.00). For the control group, no significant change was observed from the pre-measure ( M = 3.76, SD = 1.14) to the half-way measure ( M = 3.62, SD = 1.28, p = 1.00) and the post-measure ( M = 4.33, SD = 1.28, p = 1.00), and also not from the half-way measure to the post-measure ( p = .755).

https://doi.org/10.1371/journal.pone.0229773.g002

For Flexibility, a significance interaction effect was observed between treatment and measurement time, F (2, 152) = 21.4, p < .001, η p 2 = .219. There was a significant main effect of training, F (1,76) = 39.3, p < .001, η p 2 = .341, and of measurement time, F (2, 152) = 29.8, p < .001, η p 2 = .282. The training significantly increased participants’ performance from the pre-measure ( M = 3.89, SD = 1.59) to the half-way measure ( M = 7.44, SD = 2.59, p < .001) and the post-measure ( M = 7.20, SD = 2.57, p < .001); no difference was found between the half-way measure and the post-measure ( p = 1.00). For the control group, no significant difference was found between the pre-measure ( M = 3.48, SD = 1.21) and the half-way measure ( M = 3.38, SD = 1.28, p = 1.00) and the post-measure ( M = 4.19, SD = 1.33, p = .495); moreover, no difference was found between the half-way measure and the post-measure ( p = .333).

For Originality, the interaction effect of treatment and measurement time was not significant, F (2, 152) = 0.306, p = .737, η p 2 = .004, indicating that the training didn’t lead to an increase in the originality of the ideas generated.

Convergent thinking

Using a mixed ANOVA, there was a significant interaction effect between treatment and measurement time, F (2, 152) = 3.55, p = .031, η p 2 = .045. Moreover, results indicated a significant main effect of training, F (1, 76) = 9.05, p = .004, η p 2 = .106; the main effect of measurement time was non-significant, F (2, 152) = 0.561, p = .572, η p 2 = .007. For the training group, a simple main effect analysis (bonferroni corrected) revealed that the performance of the training group on the pre-measure ( M = 1.86, SD = 1.37) differed significantly from the half-way measure ( M = 2.75, SD = 1.46, p = .001) and the post-measure ( M = 2.61, SD = 1.76, p = .023). However, there was no significant difference between the half-way measure and the post-measure ( p = 1.00) in the training group. For the control group, participants’ performance on the pre-measure ( M = 1.90, SD = 1.38) did not differ from the half-way measure ( M = 1.52, SD = 1.44, p = 1.00) and the post-measure ( M = 1.57, SD = 1.57, p = 1.00), and no difference was found between the half-way measure and the post-measure ( p = 1.00) ( Fig 3 ).

https://doi.org/10.1371/journal.pone.0229773.g003

Convergent visual imagination task.

Before data analyses, participants’ familiarity with the tasks were checked. On the pre-measure, 75 participants reported “unfamiliar” with the task; on the half-way measure, 67 participants were unfamiliar with the task; on the post-measure, there were 64 participants who reported “unfamiliar” with the task (see Table 1 ). For each measure, only participants who were unfamiliar with the tasks were included in the data analyses.

https://doi.org/10.1371/journal.pone.0229773.t001

Given that there were some cells with expected value < 5, Fisher’s exact tests were performed to determine whether there were any differences between and within groups. Results indicated that there was no significant difference between the training and control group on the pre-measure, p = .124, the half-way measure, p = .432 and the post-measure, p = .268. For the training group, there was a significant improvement from the pre-measure to the half-way measure, p = .020; no difference was observed between the pre-measure and the post-measure, and the half-way measure and the post-measure, p = 1.00, p = .328, respectively. For the control group, no difference was found between the pre-measure and the half-way measure, and the pre-measure and the post-measure, p = .600, p = .608, respectively; the control group demonstrated a marginally significant improvement from the half-way measure to the post-measure, p = .050.

Idea selection task.

Some participants did not complete this task; the performance of 64 participants could be analysed on the selection task. Mixed ANOVAs revealed that there was no interaction effect between treatment and measurement time, F (2, 124) = 0.517, p = .597, η p 2 = .003. The main effect of training, F (1, 62) = 1.74, p = .192, η p 2 = .033, and measurement time, F (2, 124) = 1.06, p = .348, η p 2 = .004, were not significant.

Creative problem solving

Insight problems..

Before data analyses, participants’ familiarity with the insight problems were checked. At pre-measure, 63 participants reported that they were unfamiliar with the problems; at half-way measure, 69 participants reported “unfamiliar”; and at post-measure, 70 participants reported “unfamiliar” (see Table 2 ). At each measure, only participants who were unfamiliar with the insight problems were included in the data analyses.

https://doi.org/10.1371/journal.pone.0229773.t002

Given that there were some cells with expected value < 5, Fisher’s exact tests were performed to determine whether there were any differences between and within groups. We first compared the difference between the training and control group at each measurement time. On the pre-measure, Fisher’s exact test yielded a non-significant result, p = .662, indicating that there was no difference between the two groups prior to the training. On the half-way measure, the results were non-significant, p = .499. On the post-measure, the training group performed significantly better than the control group, p = .017. We also compared the difference within groups at each measurement time. For the training group, the difference between the pre-measure and half-way measure couldn’t be computed because the pre-measure data was a constant; there was no difference between the pre-measure and the post-measure, p = 1.00, and between half-way measure and post-measure, p = .273. For the control group, Fisher’s exact test couldn’t be computed because the half-way measure data was a constant.

Number task.

On the pre-measure, Fisher’s exact tests revealed no difference between the training and the control group, p = .127. Because we aimed to examine whether participants could come up with the solution after an incubation period, we also administered the same task on the half-way measure and the post-measure. Only those participants who failed to solve the task on the pre-measure, that is, 45 participants, were included for further data analyses. Using Fisher’s exact test, their performance on the half-way measure and the post-measure were examined between groups. Results showed no significant difference between the training and control group on the half-way measure, p = .695, and on the post-measure, p = .190 (see Table 3 below).

https://doi.org/10.1371/journal.pone.0229773.t003

Creativity is important for innovation [ 57 ], everyday problem solving [ 58 ], and emotional health and wellbeing [ 57 , 59 ]. It has been recognized that the need for people who are able to think creatively exceeds the degree to which creativity is available. Academics, business leaders, and policy makers around the world have stressed that creativity should be developed throughout the entire population [ 15 ]. Although creativity can be fostered [ 20 ], in most educational settings little attention is paid on developing students’ creative thinking skills. There is a strong need for well-developed, domain-unspecific, scientifically tested creativity trainings that can be easily implemented in educational settings.

The main goal of the current research was to establish whether a creativity-training designed to meet these requirements enhances students’ creative thinking skills. After having followed the creativity-training course provided in the current study, improvements in creativity were observed. On both divergent thinking measures (the verbal AUT, and the visual VIT) students generated significantly more ideas. This effect was already found after three months of training (i.e., on the half-way measure), and did not further increase with duration of training (i.e., on the post-measure). Importantly, the control group showed no change in the number of ideas generated during time.

In addition to looking at ideation skills, the current study also allows to examine the quality of the ideas generated. As mentioned earlier, creative ideas have to be both original and useful [ 9 , 60 , 61 ]. Thus, an idea without originality is merely a good but mundane solution to a problem, whereas an idea without usefulness is considered weird. Often organizations need workable ideas—then, mundane ideas (i.e., highly useful ideas) are fine. However, there are situations in which individuals or organizations are explicitly looking for novel ideas—when conventional ideas don’t work effectively, original ideas are of importance [ 62 ]. In the current study, the usefulness of the ideas was always on a satisfactory level as, for all measurement moments, the usefulness was higher than 3 on a 5-point scale. However, after following the training, it seems like as students in the training condition focused less on the usefulness of the ideas, as a significant decrease was observed—both from the pre-measure to the half-way measure, and from the half-way measure to the post-measure. Research has shown that people tend to perceive an incompatibility between the originality and the usefulness of an idea [ 63 , 64 ], and that most individuals focus on ideas that are consistent with social norms and reject highly original ideas [ 65 ]. A decreased focus on usefulness can be considered a first step towards focusing on the originality of an idea. In the current study, however, this does not translate into an observed increase in idea originality—the originality of the ideas did not increase, and no difference in originality was found between the ideas generated by participants in the training and the control condition.

Besides the significant improvement in creative ideation (i.e., number of ideas generated), the cognitive-oriented training program also significantly enhanced participants’ ability to diversify the categories of the ideas they generated (i.e., cognitive flexibility) [ 45 , 48 ]. Indeed, in the group of students that followed the creativity-training course, the cognitive flexibility was evidenced by a significant increase in the number of distinct idea categories generated half-way and post-training. There was no difference in the training condition between half-way and post-measure, suggesting that cognitive flexibility did not further increase with duration of the training. Importantly, the increase in cognitive flexibility that was observed in the training group was not observed in the control group. The creativity training, thus, enhanced students’ ability to break cognitive patterns and to overcome functional fixedness.

As the first challenge in moving from creativity to innovation is to recognize whether the available ideas have creative potential, we also examined whether the training has a positive effect on participants’ ability to recognize creative ideas. In the idea selection task, participants had to rank order three business ideas from most to least creative. The training had no effect on participants’ idea selection performance. The training also did not substantially affect participants’ performance on the convergent visual imagination task, the task where they had to re-arrange coins. For the training group, a significant increase in performance was observed from pre-measure to half-way measure, but this difference was not present anymore on the post-measure. A possible explanation for this inconsistent finding could be the way the convergent visual imagination task was administered. No actual coins—which would allow playing with the coins to find a solution—were provided due to practical considerations during the testing session. Instead, the convergent visual imagination task was handed out on paper, and participants had to draw the solution on paper. This slightly changed the essence of the task and, most importantly, formed a misfit with the creativity training program, in which students were used to play and experiment with real objects, hereby making problems tangible as much as possible. Participants’ convergent creativity was further examined by means of the RAT. Participants’ number of correctly solved RAT word pairs prior to training was compared with that following half-way and post-measure training. Compared to the pre-measure, improved performance was observed half-way and post-measure in the creativity training condition, but not in the control condition. The difference in the training condition between half-way and post-measure was not significant, suggesting that RAT performance did not further increase with duration of training.

With regard to creative problem solving skills, no difference was observed between the training and the control condition at the pre-measure, indicating equal creative problem solving skills between both groups at the start of the project. However, at the post-measure, a significant difference in creative problem solving skills was observed between the two groups; in the creativity training group a larger percentage of participants was able to solve the creative problem solving tasks as compared to the control group. Though, when looking at the training group, no difference between pre-measure and post-measure was observed. This indicates that we have to be cautious in drawing any firm conclusions with regard to creative problem solving skills.

Strengths and limitations

The current research project included a between-subjects design with three creativity measurement points: pre-, half- and post-measure. In addition, a control condition has been used. This makes it possible to rule out any practice or learning effects on the creative performance measures. Importantly, the training exercises differed from the tasks that were used to test the effectiveness of the training—participants were therefore not trained to the criterion [ 20 ]. This shows that the training succeeded in enabling a transfer of creative thinking skills, specifically ideation skills and cognitive flexibility. The enhanced creative thinking style, however, did not translate into the generation of more original ideas—the originality score of participants’ ideas did not increase in the creativity training group, nor did the training group generate more creative ideas than the control condition. This finding suggests that the creativity training should be further fine-tuned to optimally benefit students’ creativity development.

Moreover, an important question for future research is to focus on the optimal duration of the creativity training. The current training was a one-year creativity training, and students’ creative performance was measured prior to, half-way, and after the training. Importantly, whereas a significant increase was observed on ideation skills and on cognitive flexibility from pre- to half-way measure, no further increase was observed from half-way to post-measure, suggesting that creative performance did not increase further with a longer duration of the training. A follow-up study with addition measurement moments during the first months of training can provide insight into the time that is needed to observe a training effect. This question is also interesting in the light of earlier studies showing a creativity training effect after a 2.5 hour of creativity training in both children and adults [ 21 , 66 ].

In the current study, mainly Western adults participated. It is important to examine the effectiveness of the current training among Eastern participants and among other age groups, for example, among children and the elderly. Moreover, the domain generality of the training could be further examined. We assume that the training is applicable in various domains; in the current study, however, the effect of the training has been tested on standardized and well-validated creativity tasks, but not in different domains such as science, arts, and product development. Moreover, the standardized and well-validated creativity tasks that were employed in the current study during the pre-, half-way and post-measure were of relatively short nature, participants had a couple of minutes to solve the creativity task (e.g., 3 min for the AUT, and 3 min for the RAT). Time taken to creatively solve a problem is an important component of the Six Step Cycle; specifically, during step 4, 5 and 6 time plays a vital role. For practical reasons, the creativity assessment did not include tasks that needed a longer time to be solved. In a follow up study it would be interesting to more extensively test whether students’ ability to apply step 4, 5 and 6 of the Six Step Cycle increased from the pre-measure to the half-way and post-measure. Finally, no conclusions can be drawn about the long-term effects of the training. In the current study three measurement moments have been employed, but no follow-up data are available. In future research a follow-up measurement, for example 6 months after the training, could be administered to obtain information about potential long-term effects of the creativity training.

Future generations will need to think creatively in order to thrive in our fast-changing world. This brings attention to the need to foster creativity. Education plays a central role in fostering creativity—not merely in elites, but in all learners. While the world has undergone revolutionary changes, teaching practices have not changed much: learning continues to focus primarily on rote learning, instead of stimulating creativity. The current findings demonstrate the effectiveness of a one-year training program in fostering creative thinking skills in applied university students. The current findings suggest that by spending some curriculum time on creativity development, we can contribute to preparing learners for a rapidly changing world after graduation.

Acknowledgments

We would like to thank Marianne Pütz and Olga Boon for their help with data collection.

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• Published: 05 February 2018

The role of problem solving ability on innovative behavior and opportunity recognition in university students

• Ji Young Kim 1 ,
• Dae Soo Choi 1 ,
• Chang-Soo Sung 1 &
• Joo Y. Park 2  

Journal of Open Innovation: Technology, Market, and Complexity volume  4 , Article number:  4 ( 2018 ) Cite this article

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Universities engage in entrepreneurship education to increase social value creation, through students’ new opportunities recognition. However, there are not enough of empirical researches on whether the current entrepreneurship education can be differentiated from other curriculum to improve the opportunity recognition process. This study argues that it is very important for cognitive abilities to be manifested as behavior when students in university are new opportunities recognition. For this purpose, the relationship between problem solving ability, innovation behavior, and opportunity perception was verified empirically. This study was conducted on 203 students who took entrepreneurship education courses at Korean universities. The results of this study showed that problem solving ability positively influenced innovation behavior and opportunity perception. Innovation behavior was identified as a key parameter that partially mediated the relationship between problem solving ability and innovation behavior. The implication of this study is to prove the relationship between individual ‘s problem - solving ability considering the characteristics of education in Korea and the opportunity through innovative behavior and various learning strategies to help entrepreneurship education to design better courses for the future It has important implications for strategic pedagogy that can enhance behavioral elements in development.

It is the new opportunity recognition that all firms focus on for a new economic paradigm (Ancona and Caldwell, 1992 ). Recognizing high opportunities can significantly improve profit, growth, and / or competitive positioning. And this new opportunity leads to innovation. From a conceptual point of view, research is continuing on the question of ‘what is opportunity’ and ‘where is opportunity’ (Gartner and Carter, 2003 ; Venkataraman & Sarasvathy, 2001 ). Research on the discovery and realization of new opportunities is a very important research area that suggests how to discover and utilize creative opportunities that create new value and profit for pre-service workers, and is the ultimate goal of entrepreneurship education. (Kim et al., 2016 ). Particularly, there is a lot of debate about the relationship between opportunity perception and personal characteristics. Despite many arguments, however, research on individual characteristics and opportunity perceptions is still insufficient, and a unified opinion has not been created due to differences between cognitive and behavioral theories (Ko & Butler, 2003 ). In particular, there is much controversy over the relationship between opportunity recognition and personal traits, and research has been continuing to demonstrate that organizational learning in organizations can influence opportunity recognition (Shane & Venkataraman, 2000 ). In particular, learning enhances cognitive ability, which is an opportunity that leads to opportunity recognition through the manifestation of behavior (Lumpkin and Dess, 2004 ). Many studies have also demonstrated the difference in behavior that successful entrepreneurs see as contributing to their ability to recognize opportunities and create innovative business ideas (Dyer et al., 2008 ; Kim et al., 2017 ). For example, Alvarez and Barney ( 2005 ) argue for mountain climbing and mountain building to understand the implications of entrepreneurial behavior in relation to these theories. In other words, a new opportunity for entrepreneurs is not a passive case that is generally found and climbed by climbers such as mountains, but rather by the actions of entrepreneurs, creating competition for the market, creating another market, Is the same. Therefore, in order for a person’s cognitive ability to recognize a new opportunity, it must focus on manifesting an action that can realize an innovative idea. In this regard, Kanter ( 1988 ) proved the relationship between new opportunity recognition and those with innovative tendencies and regarded this new opportunity recognition as innovation activity through organizational education. Scott and Bruce ( 1994 ) have integrated a number of research flows into innovation pioneers to develop and test individual innovative behavioral models. In particular, they argued that individual problem-solving styles are very important to induce innovative behavior. Although there are a number of studies on problem solving ability, innovation behavior, and new opportunities, most of the opportunistic researches have been conducted in organizational units of companies. Is still insufficient. Furthermore, unified opinions were not created due to differences between cognitive theory and behavioral theory (Ko & Butler, 2003 ). It is also true that the effects of entrepreneurship education in university have not been studied empirically because they are mainly focused on promoting cognitive ability and applied to various kinds of teaching methods.

This study argues that it is very important for cognitive abilities to be manifested as behavior that. “Through” courses, In other words, it is very important to induce students to act through ‘learning through process’ learning through behavioral learning by providing students with some (virtual or real) business to start doing some of the actions of the entrepreneur. When students in university are new opportunity recognition. Especially, entrepreneurship education, which ultimately focuses on whether it is a new opportunity, is very important to induce behavior through behavior learning beyond the cognitive ability as the general education curriculum. Particularly, innovative behaviors that create and realize innovative ideas are very important for new opportunity recognition (Paine & Organ, 2000 ).In order to achieve this, various kinds of teaching methods are being pursued in the university, but studies on the effectiveness of behavioral learning have not been studied yet. In this study, we are based on team-based learning among various teaching methods for behavior learning that leads to innovative behaviors. Team learning instructional activity sequence designed by Michaelsen and Sweet ( 2008 ), the most well known team-based learning in entrepreneurship education as in class-primarily group work and outside class-primarily individual work. In this way, we demonstrate empirically the relationship between individual problem solving ability and opportunity through innovative behavior, and develop a variety of learning strategies that help entrepreneurship education to design better courses for the future. I would like to point out some implications for strategic pedagogy to increase the element.

The paper proceeds as follows: Initially we present the theory of innovative behavior with individual problem-solving ability, innovative behavior and opportunity recognition. We develop hypotheses to confirm its basic predictions in the student context. Finally, we link the findings with the wider social effect of entrepreneurship literature and highlight the theoretical contributions and practical implications.

Theoretical background

‘opportunity recognition’ as entrepreneurship education unit of analysis.

A commonly focused analysis in entrepreneurship research over the last 30 years has been the ‘opportunity’, most simply defined as any situation in which new products or services can be development of production (Casson, 1982 ; Shane & Venkataraman, 2000 ; Venkataraman, 1997 ). The definition of opportunity recognition is defined in many ways, but opportunity is defined as a perceived means of generating economic value (ie, profit) that has not been exploited previously and is not currently exploited by others. If opportunity is defined in this way, opportunity recognition can be defined as a cognitive process (or process) that concludes that an individual has identified an opportunity (Baron and Ensley, 2006 ). Kirzner ( 1997 ) pointed out that the distribution of information in society affects the discovery of entrepreneurial opportunities and that only a few individuals can identify and recognize specific opportunities in the market. The process of finding opportunities also depends on the individual’s ability and discovery (Stevenson & Gumpert, 1985 ). For example, people may miss opportunities due to a lack of cognitive ability to change external environments (Stevenson & Gumpert, 1985 ). Only those who recognize and value the existence of opportunity can benefit from new opportunities (Ardichvili et al., 2003a , b ; Shane & Venkataraman, 2000 ). Opportunity recognition is an early step in transforming value into a business concept that creates value and generates revenue and distinguishes it from the aggressive stages of detailed assessment and development of recognized opportunities and potential economic value. The focus of the new venture business is also an innovative opportunity to create new opportunities rather than merely expanding or repeating existing business models (Gaglio & Katz, 2001 ). As a result, universities need to make use of a variety of initiatives to educate students to recognize innovative opportunities. Therefore, entrepreneurship education aimed at a new opportunity recognition should be able to provide learning opportunities based on various theories of favorable conditions for new business creation and the types of traits required for new ventures (Garavan & O’Cinne’ide, 1994 ).

Based on these considerations, we also define opportunity recognition as the formation of beliefs that can be translated into actions in order to understand the signals of change (new information on new conditions) and respond to these changes.

Problem-solving ability and innovative behavior of education for students

Problem-solving abilities have been proven to be one of the key factors for success in organizations and personal careers (Anderson & Anderson 1995 ). Through decades of research data, organizations and schools have studied factors that affect improvement. Problem-solving abilities are defined in a number of prior studies, and problem-solving abilities in a volatile and sophisticated knowledge- and technology-based industry are an important ability to drive innovation and sustainable growth and development in the industry. Table  1 show the concept of problem solving ability defined in previous research.

There have been a number of previous studies, emphasis has been placed on the importance and meaning of rational problem-solving processes in order to improve problem-solving abilities, and research has focused on individual problem solving styles (Woodman et al., 1993 ; Scott & Bruce, 1994 ). According to the personal innovation behavior model of Scott and Bruce ( 1994 ), climate has shown individual innovative behavior as a result of individuals signaling the organization’s expectations of behavior and the potential consequences of action. Innovative organizations are, last but not least, equipment, facilities and time, including the direction of creativity and innovative change (Kanter, 1983 ; Siegel & Kaemmerer, 1978 ) Proper supply of such resources is important to innovation (Amabile, 1988 ; Van de Ven & Angle, 1989 ; Dubickis & Gaile-Sarkane, 2017 ). Based on a study of Koestler’s ( 1964 ) creative thinking, Jabri conceptualized a problem-solving style consisting of two independent thinking styles. He uses a structured problem-solving styles that is based on associative thinking, follows a set of rules, resolves reasonably logically, and uses an intuitive problem-solving ability that focuses on problem-solving, not tied to existing rules with multiple ideas. Intuitive problem solving styles tend to process information from different paradigms simultaneously. It is therefore more likely to create new problem solutions as possible (Isaksen, 1987 ; Kirton, 1976 ). However, style assessment is not desirable because the style of problem solving affects style differently depending on the individual problem-solving situations (Scott & Bruce, 1994 ). We are proposing a role for the University to encourage innovative behavior based on the individuality of our students in order to recognize new opportunities through education about Scott and Bruce’s innovative behavioral models and diverse entrepreneurship education approaches. And involvement of resources, such as entrepreneurship awareness programs, ultimately leads to the identification of individual characteristics and innovation. In addition, current Korean entrepreneurship education is mainly focused on cognitive learning to improve problem solving ability, and one aspect of cognitive learning plays an important role in learning process of new venture firms. This study has a more direct focus on behavior learning such as team-based learning.

Hypothesis development

Problem-solving ability and innovative behavior.

Problem solving is to discover knowledge and skills that reach the target country by interfering with a set of processes and goals where the solution is unknown, unfamiliar, or reaching a new state of goal (Jonassen, 2004 ; Inkinen, 2015 ). There are various approaches to solve this problem. To solve problems and improve problem solving with a successful solution experience, you should adopt the method that best suits your problem solution. You need to select the appropriate inputs for the solution elements and a flexible process structure. Problem solving ability has been recognized as a key element of innovative behavior in responding to rapid changes with the ability to find various alternatives and predict outcomes from these alternatives to maximize positive results, minimize negative consequences, and select solutions to problems (Barron & Harrington, 1981 ; Jabri, 1991 ; Kirton, 1976 ). We pose the following hypotheses:

Hypothesis 1: Individual problem-solving ability has an effect on the innovative behavior of students.

Innovative behavior and opportunity recognition

Innovation involves introducing ideas from outside the organization, through creative processes, and linking these ideas to products or processes. Many scholars studying innovation recognize that designing ideas is only one step in the innovation process (Kanter, 1988 ). Innovation is changing at the organizational or individual level. Kanter, Scott and Bruce defined personal innovation. In other words, an innovation act starts with recognition of a problem, adoption of a new idea, or creation of a solution, and an individual with an innovative tendency wants to create a realistically realizable group with the sympathy of such an idea. Innovative individuals create prototypes for innovations that enable ideas to be realized specifically with goods or services and become productive use and social day merchandising. According to previous studies, opportunity perception can be seen as an individual’s corporate strategy that focuses on the perception and exploitation of individuals about potential business ideas and opportunities and finds resources to create innovative outcomes (Manev et al., 2005 ). New Venture Ideas (NVI) are imaginary combinations of product/service offerings; potential markets or users, and means of bringing these offerings into existence (Davidsson, 2015 ). From the viewpoint of a potential entrepreneur like a university student, entrepreneurship starts with an idea. This process continues with a range of practices including attractiveness and feasibility of an idea, gathering information to minimize value-related uncertainty and possibility and perhaps the main idea’s conformity ratio in terms of newly discovered needs (Hayton & Cholakova, 2012 ). Earlier we proposed that the program as a whole increases the students’ innovative behavior and that innovative performance is the new venture ideas. Since it is logical to assume that the relationship between innovative behavior and opportunity recognition. We pose the following hypotheses:

Hypothesis 2: Innovative behavior will be a more potent inducer of opportunity recognition.

Problem-solving ability and opportunity recognition

Among the many factors influencing opportunity perception, the problems that arise in the fourth industry, the knowledge-based industry of the twenty-first century, are unpredictable and unstructured; they cannot be solved with existing solutions and require creative problem-solving skills. In order to determine how to solve problem situations that are different from the current situation and have unknown results, problems are solved through the process of adjusting previous experience, knowledge, and intuition (Charles & Lester, 1982 ). Experience, knowledge, and intuition are applied simultaneously to a single problem, not individually or collectively, and the intellectual and creative results that can be quickly and effectively solved in problem solving are seen as problem solving abilities (Ardichvili et al., 2003a , b ). Empirical studies of problem-solving abilities and opportunity perceptions have provided strong evidence that there is a positive relationship between theoretical integrative processes and corporate opportunity recognition (Ucbasaran et al., 2009 ). Therefore, we hypothesized that:

Hypothesis 3: Problem solving ability has an effect on the opportunity recognition.

The respondents for this study were randomly selected from three universities in Korea. Most of the respondents in this study were Korean university students who experienced team-based learning during behavioral learning through entrepreneurship education. Since then, we have been guided by two main criteria when choosing these universities. First, students who take entrepreneurship courses are critical to their innovation behavior. This led us to realize that innovative behavior is an important factor in an individual’s survival and growth. The second is that the parallel process of theoretical and behavioral learning is highly satisfied. A pilot study was conducted to verify the reliability and validity of the research measurements with 28 students at a university. The results of the pilot study showed high clarity and reliability (Cronbach ‘s alphas were all above 0.70) ​​of the research measurements. The sample of the pilot study was not incorporated in the present study.

This study was conducted in a four - year undergraduate course (various majors) that took entrepreneurship courses in Korea university programs. Students in this course have a mix of students who have previously experienced entrepreneurship and those who have not. During the course, students were taught the theoretical lessons for 8 weeks and the team for the 8 weeks. The questionnaire was administered during the last week of the course.

The data were analyzed from 203 participants, out of a total of 209, of which 7 were not appropriate. Of the 203 participants, 27% were female and 73% were male and the grade distribution was 3% for freshmen, 12% for grade 2, 26% for grade 2, and 59% for grade 2. The main distribution is 26% in social science, 16% in business and economics, 39% in engineering, 11% in music and athletics and 7% in others (see Table  2 ).

Measurement

The structure of the model was measured by questionnaires (problem-solving ability, innovation behavior and opportunity recognition questionnaire) consisting of the scale taken from questionnaires verified in previous studies. Tool selection was performed on two criteria. First, the selected tool should measure the same structure (ie, the original measured structure had to be conceptually identical to the way the structure was defined in this study model). Secondly, the psychometric qualities of the instrument for the student had to be high.

Assessment of the factors was carried out through principal component analyses (varimax rotation with eigenvalues of 1.0 or above) of the scales connected to the same level of the model to confirm the uniqueness of the scales with respect to each other. This was supplemented by the computation of the internal consistency reliability of the scales (Cronbach’s α). These analyses were executed using the individual participants’ responses (Nunnally & Bernstein, 1994 ).

Problem- solving ability was measured on a 7-point Likert-scale (1 = ‘completely disagree’; 7 = ‘completely agree’). Jabri ( 1991 ) used a measurement tool to measure individual problem solving ability.

Innovative behavior was measured on a 7-point Likert-scale (1 = ‘completely disagree’; 7 = ‘completely agree’). In order to measure innovation behavior, we modified the questionnaire items to fit the intention of this study among the questionnaire items used by Scott and Bruce ( 1994 ) and Kim and Rho ( 2010 ).

Opportunity recognition was measured on a 7-point Likert-scale (1 = ‘completely disagree’; 7 = ‘completely agree’). In order to measure opportunity recognition, we modified the questionnaire items to fit the intention of this study among the questionnaire items used by Kim and Rho ( 2010 ).

Methods of analysis

The first two parts of the analysis were primarily based on (multiple) regression analyses. The last part of the analysis was informed through the path analyses. The adequacy of the models was assessed by AMOS 18(Arbuckle & Wothke, 2003 ). Models were all tested with standardized coefficients obtained from the Principal Component Analysis. To ascertain the model fit, we analyzed the comparative fit index (CFI), the normed fit index (NFI), the Root Mean Square Err of Approximation (RMSEA), the standardized root mean square residual (SRMR) and the chi-square test statistic.

Reliability and validity are essential psychometrics to be reported. The first step to evaluate those aspects was to use the Cronbach’s alpha and the composite reliability to test reliability of the proposed scales. The usual threshold level is 0.7 for newly developed measures (Fornell and Larcker, 1981 ). Values range from 0.69 to 0.79 in the case of Cronbach’s alpha, and from 0.85 to 0.92 in the case of composite reliability (see Table  3 ). Therefore, these scales may be considered as reliable. Next, we estimated the research model, displayed in Fig.  1 , using structural equation modeling (SEM) and AMOS 18 (Arbuckle & Wothke, 2003 ). Our analysis revealed an adequate measurement model with high factor loadings for all the items on the expected factors and communalities of each item exceeding 0.50. We discuss three fit indices that are generally considered as important (Hu & Bentler, 1998 ). First, the CFI-value represents the overall difference between observed and predicted correlations. A value of 0.04 which is situated well below the cut-off value of 0.08, suggests that the hypothesized model resembles the actual correlations. Secondly, Bentler’s CFI (comparative fit index) greater than 0.90 and 0.95 which is above the cut-off of 0.90 (Schumacker & Lomax, 1996 ). Thirdly, NFI greater greater than 0.90 and 0.95 which is above the cut-off of 0.90 (Schumacker & Lomax, 1996 ). Fourthly, the standardized root mean square residual (SRMR) value of 0.0392 which is situated well below the cut-off value of 0.05(Hu & Bentler, 1998 ), and the chi-square value of 3581.622 which is situated well below the cut-off value of 0.0005. Finally, the RMSEA (root mean square error of approximation) equals 0.04 with a 90% confidence interval between 0.03 and 0.05.

Analysis of mediation effect

The value and confidence interval are situated over but below the cut-off value of 0.1 which suggests not a great but a good fit. Factor analysis was verified by factor analysis using principal component analysis and only factors with an eigenvalue of 1 or more by orthogonal rotation method were selected. Factor loading was considered to be significant at 0.5 or more (Hair et al., 2006a , b ). As a result of the analysis, cumulative explanation for 72.4% of the total variance. Confirmatory factor analysis thus supported the differentiation of the three components Also we tested the confirmatory validity of the construct by testing whether the structural linkage of each square is greater than the mean variance extraction (AVE) of each structure. The AVE ranged from 0.52 to 0.53, reaching the recommended level of .50 for both Fornell and Larcker ( 1981 ). Therefore, all constructs showed sufficient convergent validity (see Table 3 ).

As shown in Table  4 , the AVE value of each variable has a higher value than that of other factors. Therefore, the discriminant validity of the proposed model can be judged as appropriate.

Means, standard deviations, and correlations among the study variables are shown in Table  5 .

The mean scores for the conceptual model were as follows for problem-solving ability (MD. 5.20, SD.1.08), innovative behavior (MD.5.20, SD.1.03), and opportunity recognition (MD. 5.14, SD. 1.06) conditions. The means of problem-solving ability, innovative behavior, and opportunity recognition were high. Furthermore, those variables correlated positively with each other.

Figure  1 showed that all paths and their significance levels are presented in Table  6 . The path between the latent variables problem-solving ability and innovative behavior was significant (p, 0.001), consistent with Hypotheses 1. In addition, there was innovative behavior and opportunity recognition (p, 0.01), this result provide empirical support for Hypothesis 2.

H3 proposed that Problem-solving ability is positively related to opportunity recognition. The results of the correlation analysis: The coefficient of problem solving and opportunity perception weakened from .717 to .444, but it is still partly mediated because it is still significant (C. R  = 7.604 ***). This supports H3 (see Table 6 ).

In order to verify the significance of the indirect effect, the bootstrapping must be performed in AMOS, and the actual significance test should be identified using two-tailed significance. As a result, the significance of indirect effect is 0.04 ( p  < 0.05), which is statistically significant (see Table  7 ).

Discussion and conclusion

We have tried to demonstrate the effects of behavior and its significance by differentiating from the general curriculum emphasizing cognitive effects as a model of problem solving ability emerging as innovative behavior through opportunity of university entrepreneurship education.. This supports the premise that entrepreneurship education can improve opportunities or processes through behavioral learning. The results of this study support the role of entrepreneurship education in creating opportunities for innovative behavior and problem solving abilities. Entrepreneurship education should provide different types of learning for new opportunities and focus on what is manifested in behavior.

In addition, based on previous research, we propose whether the following contents are well followed and whether it is effective. First, the emergence of innovative behavior in problem-solving abilities increases as the cognitive diversity of students with diverse majors and diverse backgrounds increases. Second, the more entrepreneurial learning experiences, the greater the chance of new opportunities. Third, it is necessary to investigate students’ problem solving style and problem-solving ability first, and then a teaching strategy based on this combination of systematic and effective theory and practice is needed. Of course, as demonstrated by many studies, it may be easier to enhance the effectiveness of opportunity recognition through cognitive learning. This is because it emphasizes the achievement of knowledge and understanding with acquiring skills and competence. This process, however, is not enough for entrepreneurship education. However, we do not support full team-based behavioral learning in the class designed by Michaelsen and Sweet ( 2008 ). As with the results of this study, problem solving ability is positively related to opportunity perception directly. As previously demonstrated in previous studies, problem solving ability can be enhanced by cognitive learning (Anderson et al., 2001 ; Charles & Lester, 1982 ).

Therefore, it has been demonstrated that it is more efficient to balance a certain level of cognitive learning and behavior learning in consideration of the level of students in a course. Also this study satisfies the need for empirical research by Lumpkin and Lichtenstein ( 2005 ) and Robinson et al. ( 2016 ) and others. This will help to improve understanding of how entrepreneurship training is linked to various learning models and their effectiveness and to design better courses for the future. Finally, this study sought to provide an awareness of entrepreneurship education as the best curriculum for solutions that evolved into innovative behaviors that create new values and ultimately represent new opportunities. This study shows that it can positively influence the social effect of creating new value, that is, not only the cognitive effect of general pedagogy, but also the innovation behavior. By providing this awareness, we have laid the groundwork for empirical research on entrepreneurship education in order to create more opportunities for prospective students in education through education and to expand their capabilities.

Limitation and future research

Indeed, the concepts presented here and the limitations of this study have important implications that can fruitfully be addressed in future research. First, we selected a sample of college students taking entrepreneurship training. However, since it is not the whole of Korean university students, it is difficult to extend the research results to all college students in Korea. Second, there is no precedent research on the role of innovation behavior as intermedia in college students. Therefore, we were forced to proceed as an exploratory study.

The ability to recognize opportunities can provide significant benefits that can remain firm and competitive in an ever-changing environment. Future research should therefore expand these insights and try to empirically test more ways in which entrepreneurship pedagogy teaches how learning methods can be integrated into venture creation and growth processes to help new process opportunities. By providing this study, we will help entrepreneurship education in the university to create more opportunities and expand the capacity of prospective members.

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Ji Young Kim, Dae Soo Choi & Chang-Soo Sung

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Kim, J.Y., Choi, D.S., Sung, CS. et al. The role of problem solving ability on innovative behavior and opportunity recognition in university students. J. open innov. 4 , 4 (2018). https://doi.org/10.1186/s40852-018-0085-4

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Received : 12 September 2017

Accepted : 22 January 2018

Published : 05 February 2018

DOI : https://doi.org/10.1186/s40852-018-0085-4

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• Problem-solving ability
• Innovative behavior
• Opportunity recognition
• Entrepreneurship education