creative thinking research paper

Enhancement of Creative Thinking Skills Using a Cognitive-Based Creativity Training

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• Published: 07 October 2016
• Volume 1 , pages 243–253, ( 2017 )

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• Simone M. Ritter 1 &
• Nel Mostert 2  

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Creative thinking skills can be considered one of the key competencies for the twenty-first century—they allow us to remain flexible and provide us with the capacity to deal with the opportunities and challenges that are part of our complex and fast-changing world. The increased focus on innovation combined with recent reports of decrements in creative performance brings attention to the need to develop creative thinking skills at both the educational and business levels. The main objective of the current project was to develop and scientifically test a brief, domain-unspecific creativity training. Undergraduate university students ( N  = 32) participated in the creativity training, which was a single session of 1.5 h and employed a cognitive approach (i.e., participants were shown how to apply creative thinking techniques in a systematic fashion). The effectiveness of the training was tested by means of a pre- and post-training comparison employing creativity measures that relied on divergent thinking, convergent thinking, and creative problem solving skills. To control for a possible instrumentation threat, two versions of each task were created and counterbalanced between the pre- and post-measure across participants. Following the creativity training, improvements were observed across a variety of creative performance measures. Importantly, the creativity level of the ideas generated during the divergent thinking task improved post-training. Moreover, the findings of the current study shed light on a possible underlying mechanism for these improvements in creativity, that is, cognitive flexibility. In addition to these divergent thinking skills, the training also improved convergent thinking and produced marginal improvements in creative problem solving skills. The current findings have important implications for educational and organizational settings, as they suggest that this brief creativity training (or one employing similar cognitive techniques) could be implemented to facilitate creative thinking skills.

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Introduction

Creative thinking can be considered one of the key competencies for the twenty-first century, and its effects are widespread. It allows us to fly to the moon, create art, develop computers, and cure illnesses. Creativity has not only been recognized in the sciences and the arts (Feist and Gorman 1998 ; MacKinnon 1962 ; Sternberg and Lubart 1996 ) but has also been shown to play an important role in everyday problem solving (Cropley 1990 ; Mumford et al. 1991 ; Runco 1994 ; Torrance 1971 ; Wallas 1926 ). The word creativity has its roots in the Latin term creō , which means “to create, to make,” and commonly refers to the ability to generate ideas or problem solutions that are original (i.e., novel) and useful (i.e., effective) (for example, Amabile 1983 ; Mumford 2003 ; Sternberg and Lubart 1999 ). In addition to its function of problem solving, creativity allows us to remain flexible. Cognitive flexibility provides us with the capacity to deal with the opportunities and changes that are part of our complex and fast-changing world (Cropley 1990 ; Reiter-Palmon et al. 1998 ). Due to its crucial role in innovation, the creation of new ideas and problem solutions has become a key concern for most organizations and businesses (Runco 2004 ), and some scholars refer to today’s economy as a creative economy (Florida 2002 ; Hawkins 2001 ). Supporting this trend, the US Council on Competitiveness has announced “innovation will be the single most important factor in determining […] success through the twenty-first century” (Wince-Smith 2006 ).

To meet the needs of the twenty-first century, academics, business leaders, and policy makers around the world have placed creativity high on their agenda. For example, 2009 was announced “the Year of Creativity and Innovation” to facilitate creative thinking skills among the entire population (European Commission 2008 ). Creativity is a skill that should be fostered in all disciplines and across all intellectual and social areas (UNESCO International Bureau of Education 2014 ). Initiatives to facilitate creativity are especially important since a creativity crisis has been identified, revealing significant decrements in creativity since the 1990s (Kim 2011 ; Kimbell 2000 ; Newton and Newton 2010 ). Both the heightened focus on creativity and innovation and the overall decline in creative performance bring attention to the need to develop creative thinking skills at both the educational and business levels. Creativity was long considered a topic not open to scientific research (Sternberg and Lubart 1999 ; Treffinger 2009 )—perhaps due to traditional beliefs that creativity has mystical origins—but in recent years, increasing insights have been gained into how creative ideas arise in the brain (e.g., see the review by Sawyer 2011 ). For example, it is now understood that creative thinking depends on fundamental cognitive processes, such as working memory, the ability to create new mental categories, and the ability to mentally manipulate objects (Ward et al. 1999 ). Creative thinking skills are thus inherent to normative cognitive functioning rather than an innate talent available to only a few genius minds. Importantly, research supports the idea that creative thinking can be trained (for a meta-analysis, see Scott et al. 2004a ).

Despite the urgent need for creativity, few curriculums devote much time or attention to developing creative thinking skills; in fact, the education system often discourages it (Edwards et al. 2006 ). This means that often, we are trained to consume knowledge but are not taught how to produce creative ideas and solutions. This is particularly problematic when graduates enter the workforce, as they have to be prepared for the needs of our creative economy (Florida 2002 ; Hawkins 2001 ). Similarly, those already established in work during adult life need to deal with twenty-first century problems but are not taught the creative thinking skills required to solve them. As such, people of all age groups could benefit from a training that enhances creative performance. Developing, evaluating, and implementing new content into the educational curriculum, such as creative thinking skills, take significant time. A valid alternative during this transition period might be to offer a short, well-developed, and scientifically tested creativity training—one that can be implemented easily in schools and business settings.

Previous creativity training approaches have been reported to differ across four main types of variables, including the cognitive processes targeted by the training, the techniques used in the training, the media used to deliver the training, and the types of exercises used during the training (for a more thorough discussion about these categories, see the meta-analysis of creativity training types by Scott et al. 2004b ). Of importance for the current study, the cluster analysis of creativity training techniques by Scott et al. ( 2004b ), revealed four broad themes: imagery training ( N  = 43, 27.6 %), idea production training ( N  = 83, 53.2 %), cognitive training ( N  = 17, 10.9 %), and thinking skills training ( N  = 13, 8.3 %). Thus, cognitive training approaches were found to be relatively uncommon. Although less common than idea production training, some cognitive training approaches (e.g., conceptual combination training) were found to have larger effects and higher success rates than did idea production training. These findings, plus the meta-analysis of training effectiveness by Scott et al. ( 2004a ), suggest that cognitive approaches will be effective, providing there is a focus on how to apply the technique (see Scott et al. 2004b ). One noted disadvantage of cognitive approaches, however, is that these techniques tend to be lengthy (see Scott et al. 2004b ). Thus, the length of cognitive training approaches could be a factor that limits the implementation of such creativity trainings in educational and business settings.

The aim of the current study was to develop and scientifically test a creativity training that anticipates these needs, and several requirements were specified for the training. First, it had to be domain unspecific ; that is, the training could be applied in various contexts irrespective of the trainee’s educational background. Second, the training had to employ a cognitive approach , as training programs that incorporate cognitive-oriented techniques have been shown to be effective (see Scott et al. 2004a ). Third, the training had to be brief (a single session, not exceeding 1.5 h) so that it could be implemented within an existing education program. Fourth, the current creativity training was developed by a scientist who holds a PhD in creativity and works as a creativity researcher, university teacher, and consultant and by a practitioner who has facilitated more than 900 creativity sessions with more than 14,000 participants worldwide. Thus, scientific insights and practical knowledge were combined when designing the training, which may strengthen the internal validity of the training (see Scott et al. 2004a ). Finally, the effectiveness of the training had to be scientifically tested by means of an extensive pre- and post-training assessment of participants’ creativity. We hypothesized that improvements in creative performance would be observed following the creativity training.

Materials and Methods

Participants.

A total of 32 (20 females) participants between the ages of 18 and 34 years old ( M  = 23.13, SD = 5.76) gave written informed consent to participate in the study, which was conducted according to the principles of the institutional review board (Ethics Committee Faculty of Social Sciences, Radboud University, the Netherlands) and the principles expressed in the Declarations of Helsinki. All the participants were Dutch and recruited for voluntary participation via the online research participation system (Sona) of Radboud University. The participants were from varied educational backgrounds, including MBO (EQ National Diploma or Vocational training; n  = 1), HAVO/VWO (EQ High School Diploma; n  = 2), HBO (EQ Applied Bachelor’s degree; n  = 2), and WO (EQ University Bachelor’s degree; n  = 27). Participants were given a choice of earning course credit (2.5 points) or €15 (approximately $16.70 USD) for their participation. Finally, the creativity training took place on March 30, 2015 at the laboratory of the Behavioural Science Institute, Radboud University, the Netherlands. Participants were subdivided across three training sessions (09:00–11:30, 10 participants; 11:45–14:15, 13 participants; 14:30–17:00, 9 participants). The same procedures were used during all sessions, which were conducted by the same experimenter and creativity trainer.

The overall effectiveness of the training was examined using a within-subjects design, with creative performance (pre, post) as the dependent variable. The techniques that were applied in the creativity training are described in the “ Training Techniques ” section, the measurement of creative performance is described in the “ Measures of Creative Performance ” section, and the procedure is described in the “ Procedure ” section.

Training Techniques

The training lasted 1.5 h. Based on the requirements outlined in the introduction, the following techniques were incorporated in the training: Silence , lines of evolution , random connections , and SCAMPER . Each of these techniques is described in detail below.

Technique 1: Silence

The participants were first provided with an explanation of the benefits of brainstorming individually and in silence. In particular, they were informed that brainstorming alone and in silence is beneficial for the creative process as it allows one to generate ideas without any restrictions, guidelines, or distractions. In addition, personal expertise and background knowledge can be used and individuals are not influenced by the ideas generated by other people. Moreover, during an individual brainstorming session, the creative thought process is not influenced by group processes (e.g., fear of criticism), idea loss due to turn-taking, and the dominance of certain group members (Nijstad and Stroebe 2006 ). If these group processes are at play at the beginning of a brainstorming session, the group may focus on a narrow range of idea directions—the ones mentioned by the participants who take the lead—and individual brainpower and expertise may be lost. After being introduced to the silent brainstorming technique, the participants generated ideas individually and in silence for 5 min.

Technique 2: Lines of Evolution

This technique relies on the findings of a Russian engineer, Genrikh Altshuller, who studied thousands of patents. He noticed that the evolution of breakthrough ideas—especially in the domain of technical innovation—follows universal principles. For example, a line of evolution could include changes in the form of an object using the following pattern: from solid, to powder or pieces, to liquid, to foam, to gel, to mechanics, to electronics, to spheres. A possible line of evolution for real-world inventions could be that what was once a chocolate bar can become mini chocolates or a chocolate drink, and what was once a solid $1 coin can become a virtual bit coin. This technique may facilitate the generation of creative ideas and solutions by examining how the current form of an idea or product can be changed into the next evolutionary form, that is, by “digging deeper.”

Technique 3: Random Connections

Creative ideas often come from making connections between seemingly unrelated concepts or objects. Accordingly, in some situations, creative thinking may not benefit from digging deeper, but instead from “digging elsewhere.” By digging elsewhere, one allows creative ideas to emerge from associative processes. The underlying approach of this technique is that one uses a random stimulus—for example, an object in the room or a picture in a newspaper—and tries to generate as many associations related to this stimulus as possible. Next, one can connect these associations to the problem that needs to be solved. To illustrate this process, imagine the following example: the problem at hand is “generate a new sun cream,” and the random object chosen is a “ballpoint pen.” Associations can be generated from the ballpoint pen, such as writing, color, and roller. By connecting these associations to the sun cream problem, one might generate the idea of colored sun cream (i.e., the sun cream is colored during application, which disappears once absorbed), a roll-on sun cream, or a roll-on sun cream containing colored sun cream. Thus, by facilitating the generation of random connections, this technique helps to create an environment that allows and encourages the generation of ideas that would very likely not emerge intentionally—a process which is called serendipitous creativity. The notion of serendipity is common throughout the history of creativity and scientific innovation, reportedly being involved in discoveries such as penicillin, the microwave, and the Post-it note.

Technique 4: Scamper

During the creative process, novel solutions may emerge when forced to think of possible changes to an existing idea or product. Hereby, a list of suggestions for possible changes can be helpful. A list with seven possible thinking techniques was provided using SCAMPER (Osborn 1953 ; Eberle 1971 ), and the participants could use any or all of the suggested approaches: substitute (remove some part of the accepted situation, thing, or concept and replace it with something else), combine (join, affiliate, or force together two or more elements of your subject matter and consider ways that such a combination might move you toward a solution), adapt (change some part of your problem so that it works where it did not before), modify (consider many of the attributes and change them if necessary; attributes can include size, shape, texture, color, attitude, position), purpose (put the product to some other use), eliminate (remove any or all elements of your subject, simplify it, or reduce it to its core functionality), reverse (change the direction or orientation; turn it upside-down, inside-out, or make it go backwards/against the direction it was intended to move or be used), and rearrange (modify the order of operations or any other hierarchy involved in the product). While applying these techniques, the participants have to remember the principle of force fitting; that is, if they cannot think of anything in response to the SCAMPER prompt they are using, they have to force a response (i.e., regardless of how ridiculous it seems) and then to think of ways to make any illogical responses work.

Measures of Creative Performance

Divergent thinking: the aut.

One of the creative skills to be developed by the current training program was divergent thinking, which is the capacity to generate multiple alternatives and solutions. There is a multitude of evidence suggesting that divergent thinking represents a distinct ability necessary for many forms of creative performance (Bachelor and Michael 1997 ; Mumford et al. 1998 ; Plucker and Renzulli 1999 ; Scott et al. 2004a ; Scratchley and Hakstian 2001 ; Sternberg and O’Hara 1999 ; Vincent et al. 2002 ). Divergent thinking tests can be considered the most widely used creativity test (Cropley 2000 ; Davis 2003 ), and they are applied in approximately 40 % of all creativity studies with college students and adults (Torrance and Presbury 1984 ). Divergent thinking can be assessed using open-ended tests, and several studies have documented its test-retest reliability (for example, see Yamamoto 1963a , 1963b ). Moreover, divergent thinking tests have been recommended as tests of effectiveness for creativity trainings (DeHaan 2011 ).

One of the most frequently used and well-validated divergent thinking test is the Alternative Uses task (AUT, Guilford 1967 ). During the AUT, the participants are asked to list as many different uses for a common object as possible and to make sure that the ideas they come up with are not too common and not completely impossible. The objects used in the current study were a brick and a newspaper and they were counterbalanced between the pre- and post-measure across the participants. The participants were given 3 min to perform the AUT and were instructed to list their ideas in the space provided. By coding the listed ideas, the participants’ creativity —the ability to generate ideas that are both novel and useful (for example, Amabile 1983 ; Mumford 2003 ; Sternberg and Lubart 1999 )—was examined. Moreover, the participant’s cognitive flexibility —the flexible switching among approaches—was assessed. Cognitive flexibility is characterized by global (as opposed to local) processing of information (for example, Ashby et al. 1999 ; Murray et al. 1990 ) and by the use of flat (as opposed to steep) associative hierarchies (for example, Mednick 1962 ). In other words, cognitive flexibility involves the ability to break cognitive patterns, to overcome functional fixedness, and to avoid a reliance on conventional ideas or solutions (Guilford 1967 ). Additionally, participant’s fluency —the total number of ideas generated by a participant—was measured. A more detailed description of the three measures is provided below.

Each idea was assigned a creativity score, ranging from not at all creative (=1) to very much creative (=5). Hereby, the two essential criteria of a creative idea—novelty and usefulness (for example, Amabile 1983 ; Mumford 2003 ; Sternberg and Lubart 1999 )—were taken into consideration. Two raters performed the creativity scoring. One rater assigned a creativity score to all of the ideas, and the other rater assigned creativity scores to 50 % of the ideas (50 % of the ideas generated for a brick and 50 % of those generated for a newspaper). The interrater reliability of the ratings was calculated using a two-way random intraclass correlation coefficient (ICC) analysis for consistency and can be considered substantial (ICC BothTasks  = 0.71, ICC Krant  = 0.65, ICC Baksteen  = 0.75). For each participant, across the ideas generated, a creativity sum score was calculated. The creativity sum score can be correlated with fluency (i.e., the total number of ideas generated by a participant). To control for the possibility that quantity confounds quality (e.g., that many less original and less useful ideas get a higher score than a few highly original and highly useful ideas) mean scores were calculated for each participant by dividing their creativity sum score by their fluency score.

Cognitive Flexibility

Cognitive flexibility can be quantified by the number of distinct idea categories used: each idea generated by a participant is assigned to a category from a predefined list of idea categories, and the total number of distinct idea categories is then calculated. For example, when asked to list possible uses for a brick, the ideas “build a house” and “build a bridge” would lead to a cognitive flexibility score of 1, as all ideas can be assigned to the category “building something.” On the other hand, the ideas build a house and “break a window” would lead to a score of 2, as the ideas can be assigned to two different idea categories (i.e., building something, and “destroying something”). For the flexibility scoring, a list of predefined idea categories was developed by two trained raters for each of the common objects (i.e., the brick and the newspaper). One of the raters assigned all of the ideas to the predefined idea categories, while the other rater did so for 50 % of the ideas (for 50 % of the ideas generated for a brick and for 50 % of those generated for a newspaper). The interrater reliability of the ratings was calculated using a two-way random ICC analysis for consistency and can be considered excellent (ICC BothTasks  = 0.97, ICC Krant  = 0.98, ICC Baksteen  = 0.95).

To calculate a participant’s fluency score, the number of complete and non-redundant ideas produced was counted.

Convergent Thinking: the RAT

Although important, divergent thinking is only one component of creative thinking. Many scholars emphasize the need for an additional cognitive ability, convergent thinking; that is, the cognitive process of deriving the single best, or most correct, answer to a problem or question (Fasko 2001 ; Guilford 1967 ; Nickerson 1999 ; Treffinger 1995 ). This component of creative thought was assessed using the Remote Associates Test (RAT), which was originally developed by Mednick ( 1962 ). In the RAT, the participants are presented with three-word combinations and are required to generate a fourth word that connects the three seemingly unrelated words (e.g., bar–dress–glass, fourth word: cocktail; cocktail bar, cocktail dress, cocktail glass). The structure of the RAT—finding a highly constrained, single solution—fits well with the concept of convergent thinking. As the English RAT version is rather difficult for non-native speakers of English (e.g., Estrada et al. 1994 ), in the current study, the Dutch version of the RAT (adapted from Chermahini et al. 2012 ) was used. The participants were presented with a list of ten three-word combinations. Two versions of the RAT were provided and counterbalanced between the pre- and post-measure across participants.

Creative Problem Solving

A creative activity that requires the interplay of divergent and convergent thinking is creative problem solving — the cognitive process of searching for a novel and inconspicuous solution to a problem. Creative problem solving can be blocked by fixations—a persistent impasse in problem solving in which unwarranted assumptions, typical thinking, or recent experiences block awareness of the solution. Two common forms are perceptual and functional fixations. The participants’ ability to overcome perceptual fixation was measured by a pattern perception task and the nine-dot-problem; the ability to overcome functional fixation was measured by insight tasks. Two different versions of the tasks were used and counterbalanced between the pre- and post-measure across participants.

In the pattern perception task, participants are presented with a picture consisting of various black patches on a white background and they have to indicate which pattern is presented in the picture. In the nine-dot-problem, nine dots are arranged in a square pattern. The task is to join the dots using four straight lines. Although there are no borders surrounding the task, people often feel constrained by the assumption that they must only draw within the square boundary formed by the dots. In fact, the task can only be solved if one draws outside of the square.

The insight tasks used in the current study were the two-string problem, the ball problem, the candle problem, and the switch problem. To solve these tasks, one has to use a displayed object in an unfamiliar manner (i.e., in the two-string and candle problems) or one has to complete the task in a manner which is different from prior experience or expectations (i.e., in the ball and switch problems). For example, in the two-string problem, participants are required to tie together two strings hanging from the ceiling. However, the strings are arranged so far apart that they cannot be reached at the same time. The solution requires the use of one of the objects available in the room so that one string can be set in motion as a pendulum. This swinging string can then be caught, while holding the other string, and thus can then be tied together.

Demographics

In addition to the various measures of creative performance, participants completed several demographic questions, determining the gender, age, nationality, and educational background of the participants.

Participants were welcomed individually at the BSI entrance. Once all of the participants who were scheduled for the training session had arrived, they were accompanied to the room in which the training was held. In the training room, the experimenter briefly introduced herself and the creativity trainer and informed the participants of how the 2.5-h session would be conducted.

During the first 20 min of the session, participants’ creative performance was measured (the pre-training, i.e., baseline measure) using several well-known creativity tasks (for information about the creativity tasks, see the “ Measures of creative performance ” section). Following the pre-training measure of creative performance, the participants received the creativity training for 1.5 h (for information about the training techniques, see the “ Training techniques ” section). The training itself started with a short word of welcome by the trainer as well as an explanation of the real-world problem that would be used for all brainstorming sessions during the training. The real-world problem required generating ideas for what the next generation sponge might look like (i.e., Hoe ziet de volgende generatie spons eruit? ). For each of the four techniques, the participants completed two procedures. First, the cognitive mechanism underlying the technique and how the technique can be applied were explained to them by the trainer. Second, the participants practiced and applied the technique to the real-world problem; first alone and then in a small group (the question whether brainstorming in groups has any benefit over-and-above brainstorming individually will be addressed in a separate paper). After the training, the post-measure of creative performance was administered. The post-measure lasted 20 min and employed equivalent versions of the tasks used in the creativity pre-measure (i.e., the versions did not differ in the types of questions nor in level of difficulty). To control for a possible instrumentation threat (i.e., the risk that an observed change from pre- to post-measure is due to the test that was used, rather than the training), two versions of each task were created and counterbalanced between the pre- and post-measure across participants. This meant that half of the participants performed one version as the pre-measure and the other version as the post-measure; the remaining half of the participants completed these versions in the reverse order. Finally, the participants ended the study by completing the demographic questions (for information about these questions, see the “ Demographics ” section). All questionnaires and training materials were provided on paper.

Impact of the Training on Creative Performance

The effectiveness of the training was scientifically tested by means of a pre- and post-test, employing creativity measures that relied on divergent thinking (the “ Divergent Thinking: the AUT ” section), convergent thinking (the “ Convergent Thinking: the RAT ” section), and creative problem solving skills (the “ Creative Problem Solving ” section).

An ANOVA was performed on the mean creativity rating of ideas generated during the AUT with training ( pre , post ) as the within-subjects variable and task order ( brick – newspaper , newspaper – brick ) as the between-subjects variable. The mean creativity level of ideas produced did not differ significantly across task order group ( F (1, 30) = 0.092, p  = .764), indicating that one group was not significantly more creative than the other, nor was a significant interaction effect found between task order and training ( F (1, 30) = 0.428, p  = .518). Importantly, a significant main effect for training was observed ( F (1, 30) = 5.709, p  = .023), suggesting that the mean creativity of the ideas generated following creativity training ( M  = 2.59, SD = 0.45) was significantly higher than that of the ideas generated prior to training ( M  = 2.36, SD = 0.41) (see Fig.  1 ).

Mean creativity of the ideas generated pre- and post-creativity training

Given that a significant improvement in creative performance was found following training, it is interesting to examine the possible mechanism for the observed change. Cognitive flexibility was examined as a possible mechanism, as the training employed a cognitive approach. That is, the increase in creativity after training could be partly explained by participants diversifying the categories of their given responses (Ritter et al. 2012 , 2014 ). As such, a 2 × 2 mixed ANOVA was performed on the number of distinct idea categories generated for the AUT ( cognitive flexibility ), with training (pre, post) as the within-subjects variable and task order (brick–newspaper, newspaper–brick) as the between-subjects variable. The analysis revealed that the cognitive flexibility of the participants in the different task order groups did not significantly differ ( F (1, 30) = 1.009, p  = .323), indicating that one group did not score higher on cognitive flexibility than the other. Importantly, a main effect of the training approached significance ( F (1, 30) = 3.788, p  = .061), suggesting that the mean number of idea categories generated on the AUT task could improve by approximately one distinct category from pre-training ( M  = 5.41, SD = 2.67) to post-training ( M  = 6.34, SD = 2.52), see Fig.  2 .

Cognitive flexibility pre- and post-creativity training

Finally, an interaction effect was found between training and task order ( F (1, 30) = 31.128, p  < .001) (see Fig.  2 ). Post hoc analyses revealed significant differences between the two tasks before and after training, such that the number of idea categories was higher for the newspaper task both prior to training ( p  < .001) and following training ( p  = .018). These results suggest that generating distinct ideas might be easier for the newspaper task overall, and this was confirmed by follow-up tests—the newspaper produced a larger number of distinct idea categories ( M  = 7.22, SD = 2.73) compared with the brick ( M  = 4.53, SD = 1.67; t (31) = 5.344, p  < .001). Importantly, follow up tests revealed that performance on the more difficult task (i.e., the brick) was significantly improved from pre-training ( M  = 3.75, SD = 1.18) to post-training ( M  = 7.06, SD = 2.74; t (30) = 2.970, p  = .006).

To examine whether the creativity training had any impact on divergent thinking, the participants’ number of correctly solved RAT word pairs prior to training were compared with that following creativity training. As no participants reported prior knowledge of the RAT word pairs used, all the participant responses were included in the analysis. Initially, a mixed ANOVA was performed to include an examination of task order. However, as no significant effects involving task order were found, a within-subjects t test was performed on the effect of creativity training on RAT scores (pre, post). The training appeared to have a significant impact on RAT task performance: on average, the participants solved approximately one more RAT word pair following creativity training ( M  = 4.73, SD = 2.32) compared with pre-training performance ( M  = 3.97, SD = 2.27; t (31) = 2.342, p  = .026) (see Fig.  3 ).

Performance on the RAT pre- and post-creativity training

To examine whether creativity training had any impact on creative problem solving skills, the problem solving performance scores prior to and following creativity training were calculated by adding the participants’ scores on the picture tasks, the dot problem task, and the two insight problems. Correct responses were excluded where participants reported prior knowledge of the task(s). Given the exclusion of scores for participants who reported prior knowledge of the tasks, mean problem solving scores were also calculated (i.e., an average score for the unknown tasks completed) and examined. As the overall findings did not differ for mean or sum scores, sum scores were retained in the analysis for improved ease of interpretation. A 2 × 2 mixed ANOVA was performed on the problem solving score with task order as the between-subjects variable. No significant main effect was found for task order ( F (1, 30) = 0.375, p  = .545), indicating that one group was not significantly better at solving the tasks than the other. Importantly, a main effect for training approached significance ( F (1, 30) = 3.695, p  = .064), such that performance on these tasks was higher following creativity training ( M  = 0.97, SD = 0.80) compared with performance prior to the training ( M  = 0.66, SD = 0.70).

In addition, the analyses revealed a significant interaction effect between training and task order ( F (1, 30) = 5.320, p  = .028). Post hoc tests indicated that prior to training, participants who completed the problem solving task set that included the ball and rope insight tasks performed significantly better than those who completed the set containing the candle and switch tasks ( p  = .041). Interestingly, no task order effect was observed post-training ( p  = .387). Moreover, participants who completed the set of problem solving tasks including the candle and switch insight tasks prior to training showed a significant improvement in task performance post-training ( p  = .006), while such a difference was not observed for the group who completed the problem solving tasks in the reverse order ( p  = .788). Taken together, these results suggest that the task set containing the candle and switch tasks were harder to solve than that containing the ball and rope problems and that the training increased performance for the more difficult tasks (Fig.  4 ).

Creative problem solving performance pre- and post-creativity training

Summary of Research Aims and Findings

Creativity has a crucial role in innovation, and the creation of new ideas and problem solutions has become a key concern for most organizations and businesses (Runco 2004 ). This goal is further supported by findings showing that creativity plays an important role in everyday problem solving (Cropley 1990 ; Mumford et al. 1991 ; Runco 1994 ; Torrance 1971 ; Wallas 1926 ) and in emotional health and well-being (Runco 2004 ; Simonton 2000 ). Given the importance of creativity and that creative thinking skills can be trained (Scott et al. 2004a ), the goal should be to train creative skills throughout the entire population. As such, there is a strong need for a well-developed, domain-unspecific creativity training that has been scientifically tested. In addition, such creativity training would be relatively easier to implement in educational and organizational settings if it was a single, brief session. Thus, the main objectives of the current research were to develop a brief creativity training that meets these requirements and to establish whether this training can enhance creative performance.

The findings of the current study demonstrate that a short training (i.e., a single training session of just 1.5 h), which develops cognitive skills necessary for creativity, can have an impact on creative performance. Following the creativity training session, improvements were observed across a variety of creative performance measures. Importantly, the creativity level of the ideas generated during the divergent thinking task improved post-training. In addition, the findings of the current study shed light on a possible underlying mechanism for these improvements in creativity, that is, cognitive flexibility. This is evidenced by a marginal improvement in the number of distinct idea categories generated post-training. Next to these divergent thinking skills, the training also improved convergent thinking, as improved performance on the RAT was observed post-training. Finally, the training provided marginal improvements in creative problem solving skills by reducing perceptual and functional fixations and mental blocks. Interestingly, it seems that the training benefitted the more difficult versions of some tasks, as demonstrated by the interaction effects for the AUT and the problem solving tasks.

The current findings provide support to the creative cognition model of creativity (for example, Ward et al. 1999 ), which states that individual differences in creativity can be explained by variations in the efficiency of cognitive processes underlying creativity (for example, Ward et al. 1999 ), and to the idea that creative thinking can be trained (Scott et al. 2004a ). Moreover, the current findings have important implications for educational and organizational settings. If the goal is to train creative skills among the entire population, effective creativity training programs need to be successfully implemented—this is particularly important if we want to meet the needs of the twenty-first century. The increases in creative performance reported here are impressive and promising since the training was only short (1.5 h), and the effects were demonstrated across a variety of well-validated measures.

Strengths and Contributions

Previous research has shown that creativity trainings with a focus on developing cognitive skills contribute to effectiveness (Scott et al. 2004a ). However, cognitive approaches tend to take longer to explain and implement and appear to be relatively less common (see Scott et al. 2004b ). The current training employed a cognitive approach, with the techniques used targeting multiple divergent, convergent, and problem-solving processes (i.e., not just idea generation) (see Scott et al. 2004a , b ). As such, the current creativity training makes a distinct contribution by employing a cognitive training approach in a brief, single-session, creativity training. Importantly, the exercises used during the creativity training differed from those used to evaluate the effectiveness of the training; that is, participants were not trained to the criterion (see Scott et al. 2004a ). Given that significant improvements were found following the current training employing a cognitive approach, this demonstrates a transfer of cognitive skills required for creative performance—and further supports the domain-unspecific nature of the training. In line with variables thought to strengthen training quality and efficacy (see Scott et al. 2004a ), the current creativity training did not include prizes, overt praise, or external motivation for creative performance.

Limitations and Suggestions for Future Research

While the current study provides evidence that the combined effects of various cognitive skills training methods work, there are some limitations of the study that should be addressed in future research. The current study included a within-subjects design with pre- and post-test creativity measures. Given the nature of the tasks included in the study, it is unlikely that the observed increase in creative performance on the post-measure was due to practice or learned effects (e.g., different objects were used in the AUT versions and different problems were presented in the insight task versions). Moreover, interaction effects were observed for some of the creativity measures (i.e., the training benefitted the more difficult task versions), suggesting that these effects would not be improved by practice alone. However, to eliminate any practice or learned effects on creative performance with certainty, a future study could employ a between-subjects design, or a mixed design, employing a control group. In future research, it could also be interesting to investigate whether the training is particularly effective for specific creativity domains. Importantly, the current study does not allow any conclusions to be made about the long-term effects of the training. In future research, a follow-up measure could be included to gain information about the maintained effects of creativity training.

The four techniques employed in the current study were carefully selected by the authors, and it was assumed that their combined effects would have a greater impact on creative performance. It remains unclear whether just one of the training methods would be necessary to obtain these observed effects or whether their combined effects were necessary to observe significant improvements in creative performance. Future research could answer this question by examining the impact of each of these techniques on creative performance in isolation. Such a test may, moreover, provide valuable information to further improve the form of the techniques applied during the training.

Finally, the western participant sample had a high education level and a relatively high proportion of females, which could limit the ecological validity of this study. On the other hand, findings of a meta-analysis by Scott et al. ( 2004a ) suggest that creativity training may be more effective in organizational than academic settings and may have greater effects on men than on women. Considering that this study relied on a population and setting for which the a priori chance of finding a training effect was not high, the ecological validity and generalizability of the current findings may be enhanced. However, it is still unknown what impact such training would have on eastern participants and on other age groups, for example, school-aged children and elderly people. Future research could include examining how this or a similar training can be adapted in eastern cultures and for other age groups.

Conclusions

Creative thinking can be considered one of the key competencies for the twenty-first century and is viewed as being essential for entrepreneurial activities and long-term economic growth (Amabile 1997 ; Wise 1992 ). If a goal is to train creative thinking skills, effective creativity training programs need to be developed and successfully implemented. The current study provided further evidence that creative potential is inherent to cognitive functioning and can be facilitated with training. Impressively, following a short (a single session lasting 1.5 h) domain-unspecific training, which develops cognitive skills necessary for creativity, improved creative performance on a variety of well-validated measures. These findings have important implications for educational and organizational settings, as they suggest that the present brief creativity training (or one employing similar cognitive techniques) could be implemented to facilitate creative thinking skills among the entire population.

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Acknowledgments

Financial support was provided by a Netherlands Organization for Scientific Research (NWO) Veni grant awarded to Simone M. Ritter (016.155.049. Veni 2014. Division Social Sciences).

We would like to thank Bernice Plant for her help with the data analysis and the writing of the paper.

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Ritter, S.M., Mostert, N. Enhancement of Creative Thinking Skills Using a Cognitive-Based Creativity Training. J Cogn Enhanc 1 , 243–253 (2017). https://doi.org/10.1007/s41465-016-0002-3

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REVIEW article

The role of metacognitive components in creative thinking.

• 1 Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University, Chongqing, China
• 2 Institute of Psychology, Zhejiang Normal University, Jinhua, China
• 3 Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China

Metacognition refers to the knowledge and regulation of one’s own cognitive processes, which has been regarded as a critical component of creative thinking. However, the current literature on the association between metacognition and creative thinking remains controversial, and the underlying role of metacognition in the creative process appears to be insufficiently explored and explained. This review focuses on the roles of three aspects of metacognition (i.e., metacognitive knowledge, metacognitive experience, and metacognitive monitoring and control) in creative thinking and offers a primary summary of the neurocognitive mechanisms that support metacognition during creative thinking. Future research is needed to explore the interactive effects of the metacognitive components on creative thinking and to elucidate the function of metacognition during different stages of the creative process.

Introduction

Metacognition is viewed as the ability to think about one’s current cognitive processes ( Flavell, 1976 ). It is also called “cognition about cognition,” which plays a top-down regulation role in various cognitive processes, such as learning, memory, decision-making, and other high-level cognition ( Son and Metcalfe, 2000 ; Metcalfe, 2002 ; Ariel et al., 2009 ). Creativity, a unique ability of human beings, refers to generating original and useful ideas or developing novel solutions to problems under a given context ( Runco, 2010 ; Runco and Acar, 2012 ; Abraham, 2013 ). In the past decade, researchers have hypothesized that creative thinking may rely on metacognition components ( Davidson and Sternberg, 1998 ; Berkowitz and Ansari, 2008 ; Lizarraga and Baquedano, 2013 ; Erbas and Bas, 2015 ; Preiss et al., 2016 ). We believe that a relevant review and discussion of this topic can not only enrich the current theories of creative thinking but also provide a new direction for the cultivation of creativity.

Investigations of the processing mechanism that underlies creative thinking have typically considered metacognition as a single cognitive component, such as self-regulation during representational change and metacognitive self-monitoring or self-confidence when outputting the answer ( Hong et al., 2016 ; Rudolph et al., 2017 ). Although researchers in the creativity field have emphasized the special role of metacognition, to the best of our knowledge, very few theoretical or empirical studies have clarified how metacognition affects creative thinking. Early on, researchers emphasized creative thinking as a self-regulated metacognitive process ( Pesut, 1990 ). For example, some researchers have advanced the concept of “creative metacognition,” which is a combination of self-knowledge (e.g., knowing one’s own creative advantages and disadvantages in a certain field) and contextual knowledge (e.g., knowing when/where/how/why to be creative, Feldhusen and Goh, 1995 ; Davidson and Sternberg, 1998 ; Kaufman and Beghetto, 2013 ). In addition, a few empirical studies have examined the relationship between metacognition and creative thinking in terms of the following three aspects: (1) exploring the positive/negative correlation between metacognition and creative thinking via behavioral investigation ( Lizarraga and Baquedano, 2013 ; Erbas and Bas, 2015 ; Hong et al., 2016 ; Preiss et al., 2016 ); (2) understanding the function of brain regions activated in creative thinking from the metacognition perspective, for example, the anterior cingulate gyrus (ACC) and dorsolateral prefrontal cortex (DLPFC, Geake and Hansen, 2005 ; Berkowitz and Ansari, 2008 ; Kounios et al., 2008 ); and (3) enhancing individual creative thinking by metacognitive training ( Hargrove, 2013 ; Abdivarmazan et al., 2014 ; Hargrove and Nietfeld, 2015 ). Although theoretical and empirical studies have indicated that metacognition may be critically involved in creative thinking, the conclusion regarding whether metacognition has a positive or negative effect on creative thinking and how it engages in the creative process remains controversial. Therefore, this article systematically disentangles the roles of the three components of metacognition in creative thinking and discusses several central issues in the current literature to guide future research.

The Construct of Metacognition

In general, metacognition refers to individuals’ ability to have knowledge, awareness, and control of their cognitive activities ( Nelson, 1990 ). The concept of metacognition is regarded as being fuzzy with indistinct boundaries, as researchers have often classified it into the three interconnected components of metacognitive knowledge, metacognitive experience, and metacognitive monitoring and control ( Flavell, 1979 ). Specifically, metacognitive knowledge, which refers to the declarative knowledge of cognitive processes and products ( Dowson and Mcinerney, 2004 ; Efklides, 2011 ), has generally been divided into personal knowledge (e.g., hobbies, memory characteristics, ways of thinking, and ability limitations); task knowledge (e.g., task structures, task goals); and strategic knowledge (e.g., advantages or disadvantages and the applicability of each strategy). Metacognitive experience, the cognitive or emotional experience that accompanies cognitive activity, can occur in the early, middle, and late stages of cognitive activity ( Flavell, 1979 ). Metacognitive experience is not a cognitive operation itself but an individual’s subjective perception of the ease or difficulty of certain cognitive operations ( Rummer et al., 2016 ). In addition, metacognitive monitoring and control refers to an individual’s self-conscious supervision and regulation of the cognitive processes. Specifically, metacognitive monitoring includes individuals ability to plan, monitor, and evaluate their cognitive activities, followed by subsequent metacognitive control that allows individuals to regulate their cognitive processes, such as adjusting task goals, distributing study time, and selecting cognitive strategies ( Flavell, 1979 ).

The Construct of Creative Thinking

A standard definition of creativity has lacked consensus, as the construct of creativity is complex and different disciplines have distinct focuses. Early researchers were more likely to consider creativity as a personal trait, such as personality ( Guilford, 1950 ; Eysenck, 1993 ). With the development of experimental technology in the field of psychology, especially neuroimaging methods, a clear operational definition may benefit from investigations into the nature of creativity; thus, most researchers have viewed creativity as a problem-solving ability, namely, the ability to imagine novel or useful ideas or products in a given context ( Sternberg and Lubart, 1999 ; Runco, 2010 ). In addition, some comprehensive frameworks have attempted to describe a profile of creativity. Batey (2012) proposed an integrative perspective of the 4-P model of creativity by emphasizing the following four dominant factors of creativity: person—individual traits or characteristics; process—thought process involved in the creation of ideas; press—environmental influences; and product—output from creative activity. These four factors are highly interrelated, as a product is created by a series of cognitive processes that a person uses in a specific environment. Furthermore, it can be recognized that the lack of a consensual definition of creativity has led to a multitude of measurement approaches. A review of research methods in creativity studies (2003–2012) revealed that researchers have relied heavily on divergent thinking tests, problem-solving tasks or products to assess creativity ( Long, 2014 ).

Eysenck (1993) framed divergent-convergent interactions as important to conceptualizations of creativity. That is, creativity could be described as a constant oscillation between divergent and convergent thinking ( Finke et al., 1992 ; Bink and Marsh, 2000 ). Specifically, divergent thinking refers to the expansive generation of novel ideas for an open-ended problem, whereas convergent thinking emphasizes producing a single response from all possible answers to a given problem ( Guilford, 1967 ). The differences between these two types of creative thinking lead to distinct measurement approaches. Generally, divergent thinking can be assessed by a diverse set of tasks, such as the classic Alternative Uses Task (AUT, Guilford, 1967 ), Torrance Test of Creative Thinking ( Wallach and Torrance, 1968 ), and Multiple Choice Test ( Auzmendi et al., 1996 ). The degree of divergent thinking (i.e., scoring) mainly depends on the sum of fluency, flexibility, and originality of ideas. In contrast, convergent thinking is typically assessed by the Remote Associations Test (RAT, Mednick, 1962 ), insight problem-solving tasks ( Luo and Knoblich, 2007 ), and creative analogical reasoning ( Zhang et al., 2014 ). Although these two prominent measures do not guarantee actual creative thinking performance, compelling evidence well supports the construct validity of the two psychometric tasks for creative thinking.

The Intersection of Metacognition and Creative Thinking

Creative thinking can be regarded as a metacognitive process in which the combination of individual’s cognitive knowledge and action evaluation results in creation. Specifically, creative thinking involves a series of cognitive processes, such as the acquisition of knowledge and skills, the transformation of knowledge into new forms, and the verification of products from internal and external standards ( Amabile, 1983 ). It seems to be appropriate to involve metacognition in these stages due to its crucial role in high-level cognition. For example, for any creative action to be successful, relevant prior knowledge must be consciously selected, and a work plan must be implemented. Moreover, the strategies must be flexibly adjusted, and the originality and utility of products must be evaluated. In fact, all of these functions are metacognitive in nature, and their use would likely enhance creativity ( Armbruster, 1989 ). Accordingly, we systemically review the role of the three components of metacognition in creative thinking.

Metacognitive Knowledge and Creative Thinking

Metacognitive knowledge guides individuals to select, evaluate, and correct cognitive strategies, which are important for creative thinking. Empirically, several works have shown that individual’s metacognitive knowledge contributes to domain-specific creativity. For example, Lizarraga and Baquedano (2013) found a moderate correlation between metacognitive knowledge and visual-spatial creativity (e.g., drawing and titling four drawings from provided lines), and similar findings were reported on mathematic creativity ( Erbas and Bas, 2015 ). Fayenatawil et al. (2011) adopted a protocol analysis to examine both artists and non-artists during the creation of original drawings. The results revealed that artists who possess much more metacognitive knowledge of plans, goals, and descriptions performed better than non-artists in an artistic creation task. Additionally, Zeng et al. (2011) constructed a conceptual model of the IT creativity of studying and designing computer hardware or software and found that the metacognitive knowledge about explicit problem analysis, remote association, abstraction, and domain-specific knowledge played important roles in the analysis, ideation, evaluation, and implementation of IT creativity, respectively.

Several intervention studies have found that the training of metacognitive knowledge promotes creative problem solving. For instance, Abdivarmazan et al. (2014) used a pretest-posttest design to examine the effect of training metacognitive knowledge for problem solving. The subjects were divided into an experimental group and a control group. The experimental group received metacognitive strategy knowledge training a total of eight times (50 min each time), while the control group did not receive any intervention. The results showed that metacognitive knowledge training can significantly improve creative problem solving. This intervention effect is consistent with previous findings ( Hargrove, 2013 ).

Nevertheless, Preiss et al. (2016) found no correlation between individual metacognitive knowledge and creative thinking. In their study, the AUT and the compound word association task were used to measure creative thinking, and the self-reporting scale was used to evaluate individual declarative strategic knowledge about planning, monitoring, and regulating ( Dowson and Mcinerney, 2004 ). The results showed that metacognitive knowledge did not significantly predict the performance in either of the two creative thinking tasks after controlling for fluid intelligence and reading difficulties.

Not all empirical studies have found a positive correlation between metacognitive knowledge and creative thinking, and several limitations should be considered. First, an individual’s metacognition knowledge assessed through a self-report approach ( Antonietti et al., 2000 ; Hargrove and Nietfeld, 2015 ; Preiss et al., 2016 ) has been debated due to potential problems with its reliability and validity. Previous studies have suggested that unskilled individuals always exaggerate their self-assessment because they have poor analytical ability ( Kruger and Dunning, 1999 ) and are overly interested in motivations and intentions ( Kruger and Gilovich, 2004 ; Pronin, 2008 ). Similarly, Preiss et al. (2016) suggested that the self-report method may not accurately reflect metacognitive knowledge—especially the metacognitive strategic knowledge of planning, monitoring, and regulation—for individuals who have difficulty in recognizing their abilities. Second, there is a dissociation between self-report metacognitive knowledge and its application to specific tasks, and self-report metacognitive knowledge may not directly affect task performance ( Scherer and Tiemann, 2012 ; Hargrove and Nietfeld, 2015 ). Third, existing studies have mainly focused on the role of metacognitive strategic knowledge in creative thinking, whereas an examination of the other two variables (personal knowledge and task knowledge) is disregarded. For example, creative mindsets, a type of metacognitive knowledge that refers to individuals’ incremental or entity-mindset view of creativity (e.g., Creative mindsets, O’Connor et al., 2013 ), may influence their creative performance ( O’Connor et al., 2013 ; Karwowski, 2014 ). That is, individuals with different types of creativity mindsets have different cognitive processing characteristics, such as having different ways of learning, orienting toward a target, making strategic choices, and cognitive persistence ( Dweck and Leggett, 1988 ; De Dreu et al., 2008 ; Baas, 2010 ; Benedek et al., 2011 ; Roskes et al., 2012 ), which are regarded as critical aspects of creativity. It is inferred that creative mindsets may indirectly influence creativity through other cognitive variables. Therefore, more empirical studies are needed to uncover the mechanism of the different components of metacognitive knowledge in creative thinking.

Metacognitive Experience and Creative Thinking

Numerous empirical studies have confirmed that metacognitive experience can be indicated by the metacognitive cue of processing fluency ( Koriat et al., 2004 ; Oppenheimer, 2008 ; Alter and Oppenheimer, 2009b ; Jia et al., 2016 ). Processing fluency, the subjective feeling of the ease of information processing ( Koriat et al., 2004 ), influences a variety of cognitive tasks, such as factual preferences, aesthetic appreciation, brand assessment, and reading comprehension ( Alter and Oppenheimer, 2009a ; Miele and Molden, 2010 ). For the relationship between processing fluency and creative thinking, previous research has revealed that processing fluency affects a series of cognitive activities involved in creative thinking ( Gilhooly et al., 2007 ) such as goal setting ( Storbeck and Clore, 2007 ), work efforts ( Miele and Molden, 2010 ), strategy choice ( Lucas and Nordgren, 2015 ), and processing styles ( Alter et al., 2007 ).

Mehta et al. (2012) asked 95 participants to complete the AUT and RAT with different levels of background noise. Meanwhile, a 7-point scale that contained three questions was used to assess the subjective level of processing disfluency. The results showed that a moderate (vs. low) level of noise induced higher processing disfluency and consequently enhanced creative thinking performance. Alter and Oppenheimer (2009a) suggested that processing disfluency could induce individual’s higher construal thinking and less attention-focused, which were beneficial to creative thinking.

Moreover, processing fluency could also influence creative thinking by inducing different types of processing styles. Alter et al. (2007) argued that processing fluency can induce different degrees of intuition and analytical processing. That is, if information processing is perceived as easy and fluent, much more intuitive processing will be activated; conversely, if information processing is perceived as difficult and disfluent, a much greater analytical processing style will be activated ( Kuhl et al., 2014 ). Mehta et al. (2012) found that the disfluent processing experience allows individuals to use more analytical processing, which, in turn, promotes creative thinking performance as measured by both the AUT and RAT. In addition, neurophysiological evidence has revealed that processing disfluency induces the activation of the anterior cingulate cortex ( Boksman et al., 2005 ) and the prefrontal cortex (PFC), which allows people to think thoughtfully and use analytical processing to complete creative tasks ( Goel et al., 2000 ; Botvinick et al., 2001 ; Lieberman et al., 2002 ). Taken together, these results suggest that processing disfluency could promote creative thinking by activating a much higher level of analytical processing.

Nevertheless, the notion that overly analytical processing induced by processing disfluency impedes convergent thinking has been supported by some studies ( Friedman and Forster, 2005 ; Aiello et al., 2012 ). For example, in the study by Aiello et al. (2012) in which both bilingual and monolingual participants completed the RAT before or after an artificial grammar task with or without the “use your gut” instruction (just go with your “gut feeling” to make a decision), the results showed that the completion of an artificial grammar task with the “use your gut” instruction before enhanced the RAT performance, suggesting the beneficial role of a less analytic approach in the RAT performance. Similarly, another effective indicator of convergent thinking—insight problem solving, which involves an “aha!” experience that the solution could occur in a sudden and unpredictable manner with little or no conscious processing, has been confirmed to be inhibited much more by analytical processing ( Metcalfe and Wiebe, 1987 ; Qiu and Zhang, 2008 ).

Whether the metacognitive experience reflected by processing fluency promotes or inhibits creative thinking is controversial. There are several reasons for this controversy. First, different types of creative thinking, such as divergent and convergent thinking, may have different relationships with processing fluency. According to Benedek et al. (2011) , different types of creative thinking have significant differences in processing mechanisms. Specifically, divergent thinking tasks involve analytical processing ( Unsworth et al., 2011 ), whereas too much analytical processing may inhibit convergent thinking tasks as a requirement of a novel representation for problems and the search for remote connections to memory ( Metcalfe and Wiebe, 1987 ). Therefore, the differentiated roles of processing fluency in divergent and convergent thinking should be considered. Second, the problem of the classification and operation of the metacognitive experience may be partly responsible for the controversial results. Previous studies, however, have paid less attention to exploring this issue. To be more specific, processing fluency, an indicator of the metacognitive experience which has always been used in previous studies, could be divided into perceptual fluency, encoding fluency, and retrieval fluency, whereas these distinct types of processing fluency may have different effects on different types of creative thinking ( Koriat et al., 2004 ). For example, the AUT, which requires individuals to generate as many novel ideas as possible, was relied on the fast and effective strategic memory retrieval ability ( Forthmann et al., 2019 ). That is, the retrieval fluency could play a key role in the creative ideas production. Conversely, the RAT requires individuals to generate a target word from a set of cue words, which means that perceptual and encoding fluency may influence the results. Third, the indirect ways of manipulating processing fluency, such as pre-experiment tasks or noise activation, are greatly affected by additional factors beyond the experiment ( Mehta et al., 2012 ). Therefore, the direct ways of disrupting subjective feelings of fluency, such as font style manipulation ( Alter and Oppenheimer, 2009b ; Jia et al., 2016 ), semantic priming ( Winkielman and Cacioppo, 2001 ), and statement-background color contrast ( Hansen et al., 2008 ), should be investigated in future studies.

Metacognitive Monitoring and Control and Creative Thinking

It is worth mentioning that metacognition can be divided into the “knowledge of cognition” and the “regulation of cognition” by using a dichotomy ( Brown, 1978 ). The regulation of the cognition component includes individual’s planning, examining, monitoring, testing, and evaluating cognitive activities, which corresponds to “metacognitive monitoring and control.” Thus, we now comprehensively introduce the relationship between “metacognitive monitoring and control” and the “regulation of cognition” and creativity.

Sternberg (1985) argued that the process of creative thinking involved “self-monitoring” by monitoring other components through metacognition. Evidence from cognitive neuroscience studies reveals that the brain regions responsible for creative thinking overlap with the activated brain regions in metacognition monitoring and control, which mainly involve the dorsolateral prefrontal and ventrolateral prefrontal cortexes ( Carlsson et al., 2000 ; Zysset et al., 2001 ). Empirically, Zhang and Xiao (1996) asked participants to complete the Mutilated Chickboard problem, which requires people to change the representation from the space of all possible coverings to the “meta-level” space to find the correct problem representation. Their results showed that successful problem solvers were better at monitoring, transforming, and adjusting their search strategies according to changeable problem conditions, suggesting the positive effect of metacognitive monitoring and control on creative problem solving. Similarly, Xing and Chen (2009) further revealed that individuals with higher metacognitive monitoring and control abilities showed better performance at solving a Chinese logogriph task (i.e., a type of creative problem-solving task in which participants respond to puzzles) than individuals with a lower ability. The process monitoring theory proposed by Macgregor et al. (2001) explains that metacognitive monitoring and control ability can constantly monitor the gap between the existing state and the target state and then adjust cognitive strategies to access creative problem solving.

Moreover, intervention studies have shown that metacognitive skills training could promote creative thinking ( Atman et al., 2005 ). For example, Hargrove (2013) divided participants into an intervention group and a control group by counterbalancing their professional categories and genders. The participants in the intervention group received 1–2 semesters (17 h/semester) of metacognitive skills training to learn how to plan and implement thinking strategies, how to monitor and evaluate the quality of thinking, and how to amend incorrect thinking, whereas the participants in the control group received only professional courses every semester. The participants in the intervention group showed a significantly higher level of creative thinking as measured by the RAT and an art design task. A similar effect was also reported by Hargrove and Nietfeld (2015) .

Since Kaufman and Beghetto (2013) proposed the concept of creative metacognition, a growing number of studies have attempted to examine creative metacognitive monitoring accuracy, which can be assessed by comparing a general self-external assessment. Silvia et al. (2008) required participants to complete the AUT and then asked them and external raters to indicate the most creative responses from the reaction pool. The results showed that when the level of creative ability was higher, the participants more accurately monitored their responses. Beghetto et al. (2011) further examined this issue by asking primary school students to assess their creative ability in mathematics and science and observed that creative metacognitive monitoring accuracy (one type of metacognitive knowledge) can significantly explain the instructor’s assessment of creative ability. Priest (2006) and Kaufman et al. (2010) , however, did not find significant correlations between creative thinking and metacognitive monitoring and control in the art, writing, and musical fields.

The lack of a correlation between creative thinking and metacognitive monitoring and control can be found in other empirical studies. Metcalfe (1986) asked participants to make feeling-of-knowing judgments, an index of metacognitive monitoring ( Maclaverty and Hertzog, 2009 ), for creative problems and then give corresponding answers within 5 min. If the participants realized that the correct answer was closer, the value of the feeling-of-judgment would be higher. However, the results showed that the value of the feeling-of-judgment did not relate to the probability of producing the correct answer. Hong et al. (2016) asked participants to complete a divergent thinking task of creating a new cultural environment and to answer eight questions, such as “I always monitor my job completion process,” in order to measure their metacognitive plans and monitoring. The results showed that individuals’ metacognitive monitoring had no significant effect on their divergent thinking performance.

Overall, the conclusion that a positive correlation exists between metacognitive monitoring and control and creative thinking may not be as stable as we expected. In fact, metacognitive monitoring and control includes a set of subcomponents, such as goal setting, planning execution, strategy selection, and cognitive assessment ( Flavell, 1976 ). Many previous studies have either focused on either one or some subcomponents of metacognitive monitoring and control. For example, Hong et al. (2016) explored only the effects of planning and monitoring subcomponents on divergent thinking but summarized the results at the overall level. This method is likely to result in biased or conflicting evidence. In addition, according to Kelemen et al. (2000) , there are both “trait” and “situational” metacognitive monitoring and control, which have different concepts and measurements ( Veenman et al., 2004 ; Preiss et al., 2016 ). Hong et al. (2016) found that individuals’ situational metacognitive monitoring and control have no significant effect on creative thinking while controlling for the variable of an individual’s trait metacognitive monitoring and control. Therefore, their confusion in related studies could at least partly account for the inconsistency in the related results. Generally, all of the above problems should be considered to obtain a better understanding of the relationship between creative thinking and metacognitive monitoring and control.

Neurophysiological Evidence of Metacognition and Creative Thinking

The general framework of metacognition is characterized by the interplay of meta-level and object-level information ( Nelson, 1990 ). The object-level refers to one’s current cognitive processes (e.g., perception, attention, and decision making), which are monitored or controlled at the meta-level. Previous cognitive neuroscience evidence suggests that the PFC plays a central role in the processing of meta-level top-to-bottom adjustment of the object-level ( Fernandezduque et al., 2000 ). Specifically, the PFC regulates the posterior cortical circuit involvement in object-level processing through a filtering mechanism. In recent years, there has been increasing interest in identifying the regions in the PFC involved in metacognition, including the lateral prefrontal cortex (LPFC), medial prefrontal cortex (mPFC), and DLPFC. These brain regions are responsible for different functions in metacognition ( Christoff et al., 2003 ; Fleming et al., 2010 ; Fleming and Dolan, 2012 ). For example, a metacognitive assessment of cognitive tasks (e.g., working memory, episodic memory retrieval, and abstract thinking) induces greater activation of the lateral PFC ( Braver and Bongiolatti, 2002 ; Christoff et al., 2009 ), whereas metacognitive judgment generally activates the rostral medial prefrontal cortex (RMPFC, prospective judgment), the rostral lateral prefrontal cortex (RLPFC, retrospective judgment, Fleming and Dolan, 2012 ) and the DLPFC. An fMRI study found that the DLPFC and VLPFC were activated when tasks involved the metacognitive inhibition of sensory information, whereas the DMPFC and DLPFC were activated when tasks (e.g., the Stroop task) involved metacognitive control for concurrent conflicts ( Zysset et al., 2001 ).

Recently, some noninvasive brain stimulation and lesion studies have suggested that a disabled PFC can affect metacognitive monitoring in perceptual decision making ( Cul et al., 2009 ; Rounis et al., 2010 ; Ham et al., 2014 ). Despite this, the neural mechanism that underlies individual metacognition remains controversial. One core component of this controversy is whether functional segregation exists in the prefrontal system that is specific to metacognition. Qiu et al. (2017) used a novel decision-redecision paradigm, in which participants make an initial decision on perceptual and rule-based decision-making tasks (decision phase) followed by another decision on the same tasks (redecision phase), to examine the underlying neural substrates of metacognition on decision making. The results revealed that the dACC is responsible for decision uncertainty monitoring, while the FPC is responsible for the metacognitive control of decision adjustment, suggesting a disconnected role in the PFC and a distinct role in metacognition.

Interestingly, these studies of metacognition show brain recruitment (e.g., ACC, IFG, mPFC, and DLPFC) similar to that in creative thinking ( Dietrich and Kanso, 2010 ; Fink et al., 2012 ; Fox and Christoff, 2014 ). Specifically, the LPFC (including the IFG and DLPFC) is essential to various creativity ( Aziz-Zadeh et al., 2010 ). Similarly, the ACC, which plays a role in the solutions monitoring, was also confirmed to be activated during the creative process ( Geake and Hansen, 2005 ; Berkowitz and Ansari, 2008 ; Kounios et al., 2008 ). A study regarding musical improvisation found that pianists exhibited stronger activation in the ACC under conditions of rhythmic and melodic freedom, suggesting the positive effect of metacognition for monitoring the conflicts among different melodies or rhythms on the creative process ( Berkowitz and Ansari, 2008 ).

In addition, several fMRI studies have reported that metacognition is associated with the anterior insula, which is responsible for promoting the individual consciousness of emotional and physical states ( Craig, 2009 ), and for delivering this information to PFC areas ( Fleming and Dolan, 2012 ). For example, people who have had mindfulness training are more likely to perceive their thoughts, emotions, and physical state; in addition, they show stronger activation in the insula and the lateral prefrontal cortex ( McCaig et al., 2011 ). Similarly, the mPFC and the anterior insula can also be activated in the generation stage in multiple creative tasks ( Geake and Hansen, 2005 ; Howard-Jones et al., 2005 ; Limb and Braun, 2008 ). According to the two-stage theory of the creative process ( Ellamil et al., 2016 ), metacognition might play different roles in idea generation and idea evaluation. A low level of metacognitive control can make more diverse pieces of information appear to enter the mind to construct more novel ideas at the stage of idea generation, whereas at the stage of idea evaluation, the activation of the metacognitive system can contribute to evaluations of the novelty and utility of the spontaneous ideas generated during the previous stage. Based on this framework, the latter process may possibly be associated with cognitive activation and positive emotion, which further guides the ensuing idea generation. This neurophysiological evidence from the aforementioned studies reveals that the prefrontal regions related to metacognition are involved not only in monitoring but also in value evaluation and emotion in the creative process.

Concluding Remarks and Future Directions

The present study primarily focuses on the intersection between metacognition and creative thinking. Although increasing research points out that metacognition may play an important role in creative thinking, the empirical studies reviewed in the present study have not reached a consensus. Some obvious limitations remain. First, previous studies have mainly applied correlational approaches to investigate the intersection between metacognition and creative thinking and have neglected to reveal the cause-and-effect between the two constructs. Future research is particularly essential to explore the internal mechanism of metacognition that affects creative thinking. Second, the reliability and validity of metacognition measurements are controversial. Specifically, self-reporting is greatly influenced by subjective expectations, whereas a think-aloud protocol is time consuming, and discourse analysis is subject to the quality of the interpersonal interaction among groups ( Desoete, 2008 ). To avoid unexpected factors generated by these methods, objective measurement indexes such as prospective monitoring, retrospective monitoring, and the judgment of confidence ( Bjork et al., 2013 ) could be promising ways to assess metacognition. Third, the three components of metacognition are independent but closely interrelated ( Dowson and Mcinerney, 2004 ; Efklides, 2011 ). Previous research has always focused on metacognition as a whole or a single component, which has led to the lack of an interaction effect of the three metacognitive subcomponents on creativity. Fourth, the differentiated effects of metacognition on different types of creative thinking have not yet been described. Accordingly, we discuss two important directions in future research as follows.

Exploring the Role of Metacognition in the Creative Process

According to the aforementioned 4-P model of creativity ( Batey, 2012 ), it should be acknowledged that most of the previous studies have tended to explore the relationship between metacognition and creativity outcomes (e.g., responses in the AUT) but have neglected to discuss the role of metacognition during the dynamic creative process. The creative process, namely, the sequence of thoughts and actions that leads to novel, adaptive productions ( Lubart, 2001 ), has been identified as the combination of a series of cognitive processes. According to the classic four-stage model proposed by Guilford (1950) , the creative process can be divided into the following four stages: reparation—consciously define and establish the problem; incubation—no conscious mental work on the problem; illumination—the promising idea breaks through to conscious awareness; and verification—evaluate and refine ideas. Whether metacognition plays a different role in different stages of the creative process remains an open question. Armbruster (1989) suggested that the role of metacognition in incubation may be unconscious, whereas it is conscious in verification. Similarly, the geneplore model of the creative process (i.e., idea generation, namely, operating on unstructured, illogical thoughts to produce ideational materials, and idea evaluation, namely, controlling, evaluating, and selecting the best ideas) suggests that the idea generation stage requires no participation of metacognition to produce many more ideas, whereas the idea evaluation stage needs the participation of metacognition to assess the originality and usefulness of ideas ( Fox and Christoff, 2014 ). In addition, Shen et al. (2013) demonstrated that P2 in processing creative problems, as a stimulus-driven frontal metacognitive mechanism, reflects preconscious awareness of the mental impasse at a relatively early rather than the late stage of creative problem solving.

Nevertheless, in investigations of the current issue, regarding metacognition as a whole remains controversial due to its complex construct. Perhaps different components of metacognition have different effects on the creative process. In a recent study, for example, Jankowska et al. (2018) integrated the psychometric approach, eye-tracking methodology, and thinking-aloud protocols and found that the three categories of metacognition play different roles in the creative process. Specifically, one category of exploratory activities was demonstrated to be essential in the initial phase of the creative process, while another two categories, decision-making and control activities and affective-evaluation activities, were involved in the entire creative process. From this independent point of view, we propose that the effect of metacognitive monitoring can be separated from metacognitive control on the creative process. According to the monitoring-affect-control hypothesis ( Nelson and Leonesio, 1988 ), metacognitive control may be the result of prior metacognitive monitoring ( Metcalfe and Finn, 2008 ). For instance, individuals can adjust the strategy selection (an indicator of metacognitive control, Beaty and Silvia, 2012 ) during the generation of the next idea according to self-assessment of previous ideas originality (an indicator of metacognitive monitoring, Silvia et al., 2008 ). In this case, we believe that separating the two subcomponents leads to a better understanding of the dynamic monitoring-affect-control process in creative thinking.

Although the three key components of metacognition have been discussed separately, these components are not independent as an interactive system ( Efklides, 2011 ). That is, metacognitive monitoring and control could be activated by relying on metacognitive knowledge and the information provided by metacognitive experiences about the flow of cognitive processing. Accordingly, how these three factors interact in the process of creative thinking remains unclear. Here, we take a creative metacognitive monitoring accuracy, processing fluency (an index of metacognitive experience), and metacognitive monitoring accuracy as examples. Previous studies have demonstrated that individuals with different types of creative mindsets exhibited significant differences in their interpretation of the experience of process disfluency ( Miele et al., 2011 ) and in their level of metacognitive monitoring accuracy ( Blackwell et al., 2007 ). When completing a creative thinking task, individuals with an incremental creative mindset could interpret their processing disfluency as lacking in effort and would show much greater cognitive persistence, whereas individuals with an entity creative mindset could interpret it as an ability deficiency and would give up on further cognitive persistence. Meanwhile, individuals with an incremental creative mindset showed better performance in the metacognitive monitoring of the selecting and evaluating strategies than individuals with an entity creative mindset.

Under the framework of the dynamical creative process, the effect of metacognition components and their interaction on the creative process could be helpful for understanding the current work. Future research could examine the independence and interaction effect of the metacognitive components on the creative process using multiple methods.

Cultivating Creativity From the Perspective of Metacognition

The practical implication that should be considered is how to foster individual creative thinking from the perspective of metacognition. Apart from teaching individuals’ metacognitive skills ( Scott et al., 2004 ; Hargrove, 2013 ; Hargrove and Nietfeld, 2015 ), a new training perspective based on metacognition knowledge could prove to be a novel avenue for creativity cultivation in future studies. This promising example of a metacognition training is creative mindset intervention. A creative mindset, metacognitive knowledge that refers to individuals’ domain-specific implicit theories of creativity aforementioned, could have independent and interactive effects on creativity ( Blackwell et al., 2007 ; Miele et al., 2011 ). The main idea is that an incremental creative mindset (viewing creativity as malleable and changeable) is beneficial to creativity compared with an entity creative mindset (viewing creativity as stable and unchangeable). More importantly, similar to the idea that it is possible to successfully intervene in a general mindset ( Hong et al., 1999 ; Blackwell et al., 2007 ; Paunesku et al., 2015 ), it is possible to intervene in a creative mindset.

A general mindset intervention has been a popular topic in many disciplines such as learning, writing, anxiety, and musicality ( Donohoe et al., 2012 ; Müllensiefen et al., 2015 ; Paunesku et al., 2015 ; Schleider and Weisz, 2018 ), and improving creativity through creative mindset intervention shows promise. The general mindset intervention methods that aim to encourage an incremental mindset could be transferred to and borrowed by the creativity field. For example, Hong et al. (1999) asked students to read articles in popular magazines to emphasize the importance of environmental factors rather than genetic components to mindset development. Blackwell et al. (2007) succeeded in altering the mindsets of middle school students over the course of eight intensive sessions that focused on the study strategies of brain plasticity and ways that their mindset changes over time. Other researchers have also used a similar design in their intervention methods ( Aronson et al., 2002 ; Yeager et al., 2013 ). Bostwick (2015) summarized that the key commonalities of these intervention designs included three factors, namely, the “saying is believing aspect” (the article was read), “students formalized it in their own words” (the article was understood), and the “interventional time point of students’ most susceptible to the intervention.” Future research could attempt to create a series of standardized creative mindset interventions to contribute to creativity cultivation.

Author Contributions

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

This work was supported by the Social Science Planning Project of Chongqing (2018BS93) and the Fundamental Research Funds for the Central Universities (SWU1809709).

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.

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Keywords: creative thinking, metacognitive knowledge, metacognitive experience, metacognitive monitoring and control, creative process

Citation: Jia X, Li W and Cao L (2019) The Role of Metacognitive Components in Creative Thinking. Front. Psychol . 10:2404. doi: 10.3389/fpsyg.2019.02404

Received: 06 May 2019; Accepted: 08 October 2019; Published: 24 October 2019.

Reviewed by:

Copyright © 2019 Jia, Li and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xiaoyu Jia, [email protected]

Disclaimer: 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.

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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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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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Critical Thinking, Creative Thinking, and Learning Achievement: How They are Related

A Fatmawati 1,2 , S Zubaidah 2 , S Mahanal 2 and Sutopo 3

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1417 , Mathematics, Informatics, Science and Education International Conference (MISEIC) 2019 28 September 2019, Wyndham Hotel, Surabaya, Indonesia Citation A Fatmawati et al 2019 J. Phys.: Conf. Ser. 1417 012070 DOI 10.1088/1742-6596/1417/1/012070

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1 Department of Biology Education IKIP Mataram, Jl. Pemuda No. 59A Mataram 83125, Indonesia

2 Department of Biology Education, Universitas Negeri Malang, Jl. Semarang No. 5 Kota Malang Jawa Timur 65145, Indonesia

3 Department of Physics Education, Universitas Negeri Malang, Jl. Semarang No. 5 Kota Malang Jawa Timur 65145, Indonesia

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This study aimed to investigate the correlation between (1) critical and creative thinking skills; (2) critical thinking skills and learning achievement; (3) creative thinking skills and learning achievement; and (4) critical thinking, creative thinking, and learning achievement. The current study was conducted in April 2019 and employed a correlational research design. The participants of this study consisted of 30 fourth-semester students from the Department of Biology Education of IKIP Mataram, Indonesia, who were currently studying Plant Physiology. Data were collected using a test that contained 19 essay questions on photosynthesis. The critical thinking instrument was composed of five aspects, whereas the creative thinking instrument comprised of eight aspects. Besides, the instrument used to determine learning achievement incorporated six aspects. Each of the elements was represented by one test item. The results of the data analysis indicated correlations between (1) critical and creative thinking skills; (2) critical thinking skills and learning achievement; (3) creative thinking skills and learning achievement; (4) creative thinking, critical thinking, and learning achievement. Since critical and creative thinking skills affect learning achievement, the empowerment of these skills may lead to the enhancement of learning achievement.

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