Examining the effects of creativity training on creative production, creative self-efficacy, and neuro-executive functioning

Thinking Skills and Creativity 31 (2019) 70–78

Contents lists available at ScienceDirect

Thinking Skills and Creativity journal homepage: www.elsevier.com/locate/tsc

Examining the effects of creativity training on creative production, creative self-efficacy, and neuro-executive functioning

T

⁎

Zahir Vally , Leen Salloum, Dina AlQedra, Sara El Shazly, Maryam Albloshi, Safeya Alsheraifi, Alia Alkaabi Department of Psychology & Counseling, United Arab Emirates University, United Arab Emirates

A R T IC LE I N F O

ABS TRA CT

Keywords: Creativity Creative output Innovation Divergent thinking Training Self-efficacy Neuro-executive Arab Middle East

A plethora of evidence suggests that creativity can be enhanced following training. In the United Arab Emirates, where creativity and innovation are overtly promoted, especially among young adults, university students complete a semester-long course in creativity as part of their undergraduate degrees. The effectiveness of this course, however, remains undetermined. Thus, we examined, using a sample of 133 participants who completed the 13-week program, whether improvements to creative production, creative self-efficacy (CSE), and neuro-executive functioning would emerge. Pre to post-test differences were assessed and substantial improvements to originality, elaboration, and fluency were observed. CSE was enhanced. However, neuro-executive functioning remained unchanged following the program. These results contribute to the literature attesting to the efficacy of training in creativity skills.

1. Introduction There are few constructs in contemporary society that remain as sought after as creative and innovative skill, and the desire for the possession and enhancement of these skills transcend a great variety of contexts and cultures (Runco, 2015). In Western cultural contexts, the ability to engage in creative thought and production is deemed especially desirable in occupational and educational settings (Choi, Anderson, & Veillette, 2009; Pace & Brannick, 2010). Creative individuals are considered to be more confident compared to those with lesser developed creative skill (Bungay & Vella-Burrows, 2013), and are subjectively viewed as being special and unique (Garcia-Ros, Talaya, & Perez-Gonzalez, 2012). At the psychological level, those with higher creative skill have been shown to exhibit greater psychological well-being, higher levels of resilience when faced with challenging experiences, and greater flexibility in relation to the demands of everyday life difficulties (Amabile, 1997; Coholic, Eys, & Lougheed, 2012; Lynch, Sloane, Sinclair, & Bassett, 2013). Creativity is not just sought after in Western societies. The United Arab Emirates (UAE), for example, is a country characterized by exponential socio-economic growth and development across all spheres of society. The country’s continued success is driven by the active promotion of a culture of creativity and innovation within society and the recognition that its sustained socio-economic growth and global competitiveness is, in part, dependent on enhancing the creativity of the next generation of entrepreneurs and leaders (Moonesar, Saher, & Mourtada, 2015). To this end, the country has instituted a number of programs and initiatives to foster creative and entrepreneurial activities under the auspices of its National Innovation Strategy. The strategy provides a framework that

⁎ Corresponding author at: Department of Psychology & Counseling, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates. E-mail address: [email protected] (Z. Vally).

https://doi.org/10.1016/j.tsc.2018.11.003 Received 9 September 2018; Received in revised form 18 October 2018; Accepted 6 November 2018 Available online 16 November 2018 1871-1871/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

encourages the development of an innovation-enabling environment, one that enables innovation by providing appropriate regulatory frameworks, enhances the technology infrastructure needed for innovation, and ensures the availability of funding and incentives to enable the realization of innovation. Moreover, a number of events and related awards take place annually to identify champions of innovation in schools, universities, government, and private industries, to reward these individuals and teams, and to encourage future entrepreneurship (Moonesar et al., 2015). Despite this explicit focus on the development of creativity and innovation in this region of the world, and the prominence, in schools and universities, of training programs designed to entrain these desirable thinking skills, there has been no formal examination of their efficacy. Conversely, a wealth of research from elsewhere in the world demonstrates that creativity skills can be enhanced following deliberate training, with effect sizes ranging in magnitude from moderate to large (Bott, Quintin, Saggar, Kienitz, & Royalty, 2014; Byrge & Tang, 2015; Karpova, Marcketti, & Barker, 2011; Kienitz et al., 2014; Ma, 2006; Perry & Karpova, 2017; Scott, Leritz, & Mumford, 2004). These favourable effects have been demonstrated for training programs across a number of modalities, settings, target populations, and duration, with some training regimens lasting only a few days (West, Tateishi, Wright, & Fonoimoana, 2012) or, in some cases, just a single session (Ding, Tang, Tang, & Posner, 2014). Despite the general agreement that creativity training tends to be beneficial, a number of gaps are present in the literature. First, the vast majority of creativity training studies have been conducted with populations from primarily first-world, industrialized, Western countries. Given that there are significantly less from developing countries and none from the Middle East it is presently unclear where training is beneficial to individuals resident in this part of the world. Second, studies have mainly focused on children and adolescents in school settings, or else, where adults have been the targeted sample, the training has tended to be domain-specific and occurred within the constraints of the workplace. Third, studies have usually employed a single measure of creativity to assess change following training, despite the recognition of creativity being a multidimensional construct, and there being agreement among scholars that the assessment of creativity should be conducted using multiple assessment measures (Cheung, Roskams, & Fisher, 2006). 1.1. Aims of the study The present study has three primary aims. First, we examine the effectiveness of creativity training on creative production which, in this study, is operationalized as consisting of three sub-skills; originality, elaboration, and fluency. Second, the intervention’s effect on neuro-executive functioning is examined, specifically, both low-level and higher-level functions. And third, we assess whether participants’ creative self-efficacy is enhanced following receipt of the training program. The ensuing sections of this paper are organized as follows. We provide a literature review of the constructs/ variables that are typically assessed in creativity outcome studies, such that a rationale is provided for the development of each hypothesis relating to the effects of our training program. Then, the methodology for the conduct of the study is described, including a thorough description of the program’s content and the approach to training. Finally, the results of the study are presented, and their implications and limitations are discussed. 2. The efficacy of creativity training and development of hypotheses A central component of many creativity training programs is creative production (Clapham, 2011; Milgram & Livne, 2006) which represents a reasonable estimate of one’s potential for creative thinking (Runco, Millar, Acar, & Cramond, 2010). Guilford (1967) defined creative production as the ability to generate multiple or alternate and varied solutions, ideas, or associations to a given problem or stimuli. Creative production has been operationalized into various component parts, namely, fluency (or the number of relevant responses or ideas), flexibility (the number of different categories of ideas), elaboration (the level of detail of the ideas), and originality (the extent to which the ideas are unique) (Csikszentmihalyi & Getzels, 1971; Kettner, Guilford, & Christensen, 1959; Scott et al., 2004; Torrance, 1999). Creative production tests are deliberately open-ended in their design so as to elicit as many alternative responses as possible (Yeh, 2011). The early work of Guilford (1967) prompted the development of a large number of creative production tests that have been rigorously tested and standardized. The Torrance Test of Creative Thinking (TTCT; Torrance, 1987) is perhaps the most widely used standardized measure of creative production, however, standardized versions and associated norms are rarely available for populations outside of Western, first-world contexts. In the present study, we employed a creative production assessment measure designed by Byrge and Tang (2015) as we required a measure that was brief, contained stimulus material that could be administered at both pre and post-test, and did not require normative data to score and interpret. Hypothesis 1. The creativity training program will have a positive effect on participants’ creative production skills operationalized as originality, elaboration, and fluency. Many researchers engaged in the study of creativity propose that a high degree of creative self-efficacy (CSE) is needed to ensure the attainment of creative outcomes (e.g., Chin, 2013; Lee & Kemple, 2014; Lim & Choi, 2009; Yu, 2013). CSE is defined as the individual’s subjective belief in his or her own ability to create unusual outcomes or masterful solutions (Tierney & Farmer, 2002). It has been suggested that individuals high in CSE are able to access greater levels of self-motivation and varied cognitive states to meet the demands of a given situation, tolerate a high risk of potential failure, manage a variety of obstacles, and are more impervious to the potential negative self-evaluation that often accompanies creative production (Hsu, Hou, & Fan, 2011; Tierney & Farmer, 2002). Empirically, evidence suggests that CSE is positively correlated with creative output (Beghetto, Kaufman, & Baxter, 2011), life satisfaction and subjective happiness (Tan, Ho, Ho, & Ow, 2008), mastery and performance, and achievement goal orientations (Beghetto, 2009). Moreover, individuals who receive training in creativity tend to display greater belief in their own creative abilities 71

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

as well as demonstrate an inclination towards greater risk-taking in relation to idea generation (Byrge & Tang, 2015; Perry & Karpova, 2017). Hypothesis 2. The creativity training program will have a positive effect on participants’ CSE. Creativity is also, increasingly, being investigated in relation to neuro-executive functioning. Notably, those who score highly on creativity tests also demonstrate increased goal-directed attention skills (Kasof, 1997; Zabelina & Beeman, 2013); greater cognitive control (Groborz & Necka, 2003), enhanced capacity for cognitive switching (Gilhooly, Fioratou, Anthony, & Wynn, 2007), and inhibition and cognitive flexibility (Golden, 1975; Groborz & Necka, 2003; Zabelina & Beeman, 2013). Longitudinal investigation of changes in executive functioning following training, however, has been rare. Bott et al. (2014) found that a 5-week creative capacity building program produced appreciable benefit to the development of lower-level executive functions (goal-directed attention and information processing) but not to higher-level functions. Given these preliminary findings and the demonstrated relationship between creativity and neuro-executive functions using cross-sectional data, further investigation of this relationship using a repeated measures design is needed. Hypothesis 3. The creativity training program will have a positive effect on participants’ neuro-executive functioning. 3. Method 3.1. Training approach and program content The Department of Psychology and Counseling at the authors’ university offers a 13-week, credit-bearing, creativity and innovation course that can be taken by students registered for an undergraduate degree. Students may elect to take the course during any semester of their undergraduate degrees. Students attend two 90-minute classes each week for thirteen weeks and in class sizes of approximately 30 students. The classes included in the present study took place during the Spring and Fall semesters of 2017 and were all taught by the same instructor who was blind to students’ pre-test performance on the assessment measures. The course is designed to entrain students’ creative and innovative thinking skills. Specifically, it introduces students to the theoretical conceptualizations of creative and innovative thinking as well as to the practical application of a variety of techniques to solving realworld problems. The sessions are primarily centred around group activities. In the first session of each week, the instructor provides an introductory lecture to the given technique, its background, uses, and examples of its application. Then, the following session, the second session of each week, focuses on its application. Students work collaboratively to apply the technique to a set of problems, reflect on their experiences of using the technique, troubleshoot any issues that arise, and brainstorm potential solutions to issues that might impede successful application of the technique. Student progress is assessed continuously throughout the course. The weekly application tasks form part of the continuous process of assessment, some of which are completed individually while others are completed in collaborative groups. The course concludes with a final assignment, an invention, in which students work in small groups, and are challenged to apply the collection of skills learned throughout the semester to create a new product, process, or service. Ideas are presented to the class in seminar format in which students are encouraged to reflect on the creative process that they engaged in, the process of idea generation, the application of the thinking techniques that were used, and how the invention contributes to individuals, its field, or society at large. 3.2. Participants All students who registered to complete the undergraduate creativity and innovation module during the Spring and Fall semesters of 2017 were invited to participate in this evaluation study and 100% consented to participate. The sample consisted of 133 participants aged between 18 and 22 years of age (M = 20.51, SD = 2.02). Students from across the university will complete the creditbearing creativity and innovation course at some point during the tenure of their undergraduate degrees and therefore classes typically comprise of students from a variety of faculties and fields of study. This was reflected in the study sample’s composition. The majority of students were registered in the humanities and social sciences (40.5%), while 21.4% were from engineering, 14.5% from business, and education, law, science, food and agriculture, and information technology each representing less than 10% of the sample. Students in their second (33.6%), third (28.2%), and fourth (21.4%) years of registration were most frequently represented in the sample. First years and those beyond a fourth year of registration represented the remaining 15% of the sample. 3.3. Assessment measures 3.3.1. Creative production While the TTCT (Torrance, 1987) is the most frequently used test of creative production, it is time-consuming, requiring more than 1.5 hours to administer, and 2–3 hours to analyse. Therefore, we employed the test developed by Byrge and Tang (2015) which consists of three tasks, two drawing tasks (task 1 and 2) and a drawing and writing task (task 3). The pre-test and post-test assessments are similar in terms of their design but contain different stimulus material to avoid sensitization effects (see Byrge & Tang, 2015 for more detail). The three tasks are briefly described below.

• Task 1: “Use the following image as a starting point, make additions to it to create a new image/ object. The starting image should 72

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

• •

be an integral part of the final image. Try to make the drawing as interesting and unique as possible. When you have finished the drawing, give it an original title. Try to make a story out of the drawing. You have 3 min to complete this.” Task 2: “Draw as many complete drawings as you can by using the figures below. Draw as many different drawings as possible. Try to think of pictures no one else will think of. Try to make a story using the drawings. Give your pictures titles. You have 3 min to complete this.” There were 12 identical figures on the page. Task 3: “Draw and/ or write ideas to improve the pencil. Don’t think about the cost or feasibility of the improvement. Only think about what would make it better, more interesting, and original. You have 3 min to finish your ideas.”

These tasks were scored by two raters who worked independently from the rest of the research team and were blind to the purpose of the study. Raters were graduate psychology students with previous research experience and were trained and closely supervised by the first author. Each task was rated for three variables, again following the recommendation of Byrge and Tang (2015): originality was rated from 0 (low) to 2 (high); fluency scored as the number of ideas produced within the time limit; and elaboration was scored from 0 (a simple idea with one or a few elements) to 1 (an elaborated idea that has several elements). Inter-rater reliability was acceptable with intra-class correlation values of 0.81, 0.77, and 0.83 for originality, fluency, and elaboration, respectively. We used the average values for the combined scores of both raters in all analyses. The scores of the three sub-skills were also combined to reflect a global score for creative production. 3.3.2. Neuro-executive functioning We used the Color–Word Interference Test (CWIT) from the Delis–Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) to measure neuro-executive functioning. The CWIT consists of 4 subtests. In subtest one, color naming, a series of red, green and blue squares are displayed, and the subject is instructed to name the color of the respective square. In subtest two, word reading, the words red, green, and blue are printed in black ink, and the subject is instructed to read the word. These two subtests target the lower-level executive functions of goal-directed attention, working memory, visuomotor sequencing, and processing speed. In subtest three, the words red, blue, and green are printed incongruently in colored ink, and the subject is required to name the color of the ink that each word is printed in. In the final condition, subjects are again required to name the color of the ink that each word is printed in, as is the case for subtest three; however, half of these words are enclosed in a box, and for these, the word should be read rather than the color of the ink named. These two tasks are designed to assess the higher-level executive functions of inhibition and cognitive switching. As is customary with the D-KEFS scoring procedure, we recorded speed and accuracy for each subtest as well as the number of self-corrected errors on trials 3 and 4. 3.3.3. Creative self-efficacy The creative self-efficacy inventory (CSEI) by Abbott (2010) was employed. The CSEI is a 28-item scale consisting of two dimensions: creative thinking self-efficacy (16 items) and creative performance self-efficacy (12 items). According to Abbott (2010), the first dimension is composed of four subscales: elaboration (e.g. “connect daydreams or new ideas to things you have already learned”), flexibility (e.g. “come up with different kinds of responses, not just different responses”), fluency (e.g. “get a large number of different ideas or responses”), and originality (e.g. “be the first in a group to come up with an original suggestion”). The second dimension is composed of three subscales: domain (e.g. “teach yourself how to do something new”), field (e.g. “convince others that you have made a valuable contribution”), and personality (e.g. “be motivated to come up with new ideas”). Participants rated their confidence on each statement by recording a number from 0 (Not at all confident) to 100 (Highly confident). Previous investigations of the CSEI have demonstrated its adequate content validity, internal consistency, and test-retest reliability (Abbott, 2010; Alotaibi, 2016). In the current study, internal consistency was acceptable (α = 0.76). 3.4. Procedure The study received approval from the university’s social sciences research ethics committee (Ref. No.: ERS_2017_5508). Students were informed of the study during the first meeting of the semester and their participation invited. They were informed that their participation was entirely voluntary. They could elect not to participate in the study while continuing to complete the course without penalty. All students within the potential sampling frame agreed to participate. They received course credit for participation. During the 5 days preceding the first official lecture, students visited the research lab and completed a protocol of pre-test assessments. Tasks 1 (creative production) and 3 (CSEI) were completed on paper and pencil while task 2 (CWIT) was administered by a research assistant using digital images of the stimulus material displayed on a hand-held electronic device. Assessments were administered to each student individually. All research assistants were graduate psychology students, who were trained by the first author in the administration of the protocol. A senior graduate student (the second author) was also assigned to monitor and supervise the team of research assistants during the data collection period of the study. Completed assessment protocols were also checked daily. All instructions were administered using standardized assessment scripts to ensure that consistency was maintained in relation to the instructions used by the group of assessors. These measures ensured that assessments were administered and completed with the necessary precision and fidelity. Assessments were administered in English. Although Arabic is the first language of the citizens of the UAE, the university where the study was conducted is an English-medium institution, all classes are conducted in English, and students are required to demonstrate a substantially high level of proficiency in English to gain admission. Thus, we were confident that assessments did not require translation to Arabic. 73

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

Table 1 Bivariate correlations between all the creative production and CSE variables at both pre- and post-training.

1. Originality PRE 2. Originality POST 3. Elaborations PRE 4. Elaborations POST 5. Fluency PRE 6. Fluency POST 7. Creative Production PRE 8. Creative Production POST 9. CSE PRE 10. CSE POST

M

SD

1

2

3

4

5

6

7

8

9

10

2.22 3.56 .59 .85 15.52 18.26 18.33 22.67 205.92 214.69

.97 1.14 .27 .24 6.20 6.82 8.63 7.24 35.98 38.62

1 .55** .18* .21* .64** .24** .48** .32** .14 .15

1 .15 .26** .25** .23** .32** .38** .11 .22*

1 .32** .09 .14 .15 .17 .13 .07

1 .26** .29** .28** .35** .13 .20*

1 .30** .99** .33** .20* .19*

1 .32** .99** .18* .25**

1 .36** .21* .20*

1 .19* .27**

1 .69**

1

Note: CSE = creative self-efficacy; *p < .05; **p < .001.

Students then attended the 13-week course. The four classes included in this study sample were conducted using the same curriculum, facilitated by the same instructor, and using the same materials. Thus, we ensured that all participants received equivalent input. Moreover, according to this university’s policy, students are required to attend a minimum of 85% of all lecture sessions. Therefore, we are confident that all participants attended a sufficient number of sessions to qualify as having received and completed the course satisfactorily. Students then visited the research lab once more approximately 7–10 days following completion of the last lecture session to ensure that any post-test changes were the result of the training program rather than a recency effect. At this second visit, students completed a post-test protocol of assessment measures again administered by a research assistant to each individual student. We recognize that inclusion of a control group in this study’s design would be exponentially preferable, however, this is a naturally-occurring sample and a program that is delivered within the confines of an existing university calendar. Randomization to more than one group and implementation of a delayed start for half of the students (i.e. constituting the control group) would not be possible as the course is required to commence and end with the start and conclusion of the university semester. Additionally, previous studies have shown that during the conduct of creativity interventions, the change in creativity of participants in control groups tend to display no significant change (Karakelle, 2009; Memmert, 2007). Moreover, a number of studies in which the efficacy of creativity programs has been evaluated have successfully implemented single experimental group designs without control conditions (e.g. Byrge & Tang, 2015; Perry & Karpova, 2017).

4. Results Table 1 provides correlation coefficients between the three creative production variables (originality, elaboration, and fluency), total creative production, and CSE at both pre and post-test. Results reveal that all the variables included in the analyses were highly correlated with each other at their pre and post-test assessment points (e.g., CSE at pre-test was correlated with CSE at follow-up, p < 0.001). CSE was correlated with most of the creative production variables. Moreover, the creative production variables were highly associated with each other at each respective level of measurement; for example, originality at pre-test was associated with elaboration at pre-test (r = 0.18, p < 0.05) and with fluency at pre-test (r = 0.64, p < 0.001) as well as with total creative production (r = 0.48, p < 0.001) while originality at post-test was significantly correlated with elaboration (r = 0.26, p < 0.001), with fluency (r = 0.23, p < 0.001), and with total creative production (r = 0.38, p < 0.001) at post-test. Table 2 illustrates descriptive statistics and the results of a series of repeated measures analysis of variance (ANOVA) that were Table 2 Descriptive statistics and repeated measures ANOVA results for the creative production and creative self-efficacy outcomes. PRE-TEST

Originality Task 1 Originality Task 2 Originality Task 3 Total Originality Elaboration Task 1 Elaboration Task 2 Total Elaboration Fluency Task 2 Fluency Task 3 Total Fluency Total Creative Production Creative self-efficacy

POST-TEST

M

SD

M

SD

F

df

p

ŋp2

.73 .59 .89 2.22 .26 .33 .59 2.09 13.43 15.52 18.33 205.92

.44 .41 .49 .97 .33 .35 .27 1.38 5.89 6.20 8.63 35.98

1.12 .98 1.47 3.56 .35 .51 .85 3.53 14.73 18.26 22.67 214.69

.51 .45 .50 1.14 .44 .46 .24 1.97 6.35 6.82 7.24 38.62

70.556 100.63 159.85 232.81 7.834 27.272 103.125 62.782 4.010 16.714 40.681 11.789

132 132 132 132 132 132 132 132 132 132 132 132

.000 .000 .000 .000 .006 .000 .000 .000 .047 .000 .000 .001

.35 .43 .55 .64 .06 .17 .44 .32 .03 .11 .24 .08

74

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

Table 3 Descriptive statistics and repeated measures ANOVA results for the neuro-executive outcomes. PRE-TEST

POST-TEST

M

SD

M

SD

F

df

p

ŋp2

Completion Time Trial 1 Trial 2 Trial 3 Trial 4

4.71 8.15 49.52 49.79

2.08 1.50 13.65 16.02

4.50 8.29 50.91 50.80

1.21 1.29 11.85 12.16

1.23 1.33 1.36 .571

132 132 132 132

.27 .25 .25 .45

.01 .01 .01 .00

Total Errors Trial 1 Trial 2 Trial 3 Trial 4

.05 .13 1.10 1.77

.02 .34 1.66 2.02

.07 .09 1.34 2.02

.02 .31 1.79 1.80

.598 .528 1.37 1.26

132 132 132 132

.44 .47 .24 .26

.00 .00 .01 .01

Self-corrected errors Trial 3 Trial 4

.87 1.40

1.03 1.38

.41 1.03

.79 1.15

19.356 6.064

132 132

.000 .02

.13 .04

used to examine the differences between the pre and post-test scores for the three creative production tasks (H1), the CSE outcome measure (H2), and the neuro-executive outcomes (H3). Effect sizes are reported for each analysis using partial eta squared (ŋp2) and the rules of thumb by Cohen (1988) are used for determining magnitude (an effect size of 0.02–0.12 was deemed small, from 0.13–0.25 was medium, and 0.26 and higher was large). H1: For the three creative production tasks, we recorded three variables, originality in tasks one, two, and three, elaboration on task one and two, and fluency on tasks two and three. A series of repeated measures ANOVA with a Greenhouse-Geisser correction indicated that participants produced substantial increases in the ratings for all three variables across all tasks in which they were rated. All pre to post-test differences were statistically significant (p < 0.05) and produced effect sizes ranging from small to large in magnitude (see Table 2). H2: A repeated measures ANOVA with a Greenhouse-Geisser correction determined that the program yielded a positive improvement to participants’ CSE, a change that was statistically significant and small in magnitude (F(1, 132) = 11.789, p = .001, ŋp2 = .08). H3: Analysis of the pre- to post-test change in the completion time of all tasks revealed no statistically significant improvement on any of the four trials. Moreover, the total number of errors made in each task similarly showed no change. However, while the number of errors made between the two measurement points did not diminish, self-corrected errors, the number of responses that were self-identified by participants as being incorrect, did show a statistically significant improvement with an effect size of moderate magnitude on trial 3 (F(1, 132) = 19.356, p < .001, ŋp2 = .13) and small magnitude on trial 4 (F(1, 132) = 6.064, p < .05, ŋp2 = .04). Table 3 illustrates the descriptive and inferential results for all the neuro-executive outcomes. 5. Discussion This study examined the effects of a semester-long creativity training program offered to a group of university students resident in the UAE. The results of this study provide supportive evidence in concurrence with a wealth of existing literature that domain-general training in creativity, implemented with an adult population outside of a scholastic or occupational setting, can successfully enhance creative production skills (Kienitz et al., 2014; Meinel, Wagner, Baccarella, & Voigt, 2018; Perry & Karpova, 2017). Specifically, after receipt of our training program, participants demonstrated improvement to all three aspects of creative production that were assessed; namely, originality, elaboration, and fluency. Thus, at post-test, participants were able to produce a greater number of ideas, that were more original in nature, and contained substantially more elaborative elements than at pre-test. Moreover, our analyses suggest that the improvements to creative production were substantial with computed effect sizes that ranged from 0.11 for overall fluency to 0.64 for overall originality. While the sample produced statistically significant improvements across all creative production skills, the magnitude of these changes varied markedly. Generally, our results are in keeping with those previously reported in the literature. For example, a review of creativity training programs by Ma (2006) found that effect sizes generally ranged from 0.47 to 0.76. However, our sample’s performance in relation to originality, in particular, consistently exceeded the effects sizes of the other skills across all three trials. Our program was more effective in the entrainment of originality (ŋp2 = .64) than elaboration (ŋp2 = .44) and fluency (ŋp2 = .11). Additionally, the program was more effective in promoting creative production skills assessed by trained raters (ŋp2 = .24) than increasing belief in their own abilities in relation to creativity (ŋp2 = .08). This is in contradiction to Bertrand’s (2005) suggestion that no differences will be evident between participants’ performance on a TTCT-type test and other creativity assessments. Our findings underscore the importance of carefully selecting assessment measures when conducting evaluations of creativity training programs as the assessed efficacy may vary depending on the measures employed. Moreover, the significant changes to all the creative production variables are likely the result of the training program’s content which very directly and overtly provided instruction and excessive practice in the fluid generation of original and elaborative ideas. Much time within the course was devoted towards the promotion of creative production and many of the activities were specifically 75

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

directed towards the entrainment of the sub-skills which we assessed. Moreover, many of the activities contained in the curriculum entailed rapid prototyping and improvisation with constraints related to the allotted time, available materials, and the topic. Thus, students received direct and repeated exposure to experiences that promoted rapid idea generation, the processing and synthesis of complex information, and resilience when facing potential failure within the pressurized constraints of the task at hand. There have been previous indications in the literature of a suggested association between creativity and CSE but despite evidence of a correlation from cross-sectional data (Lee & Kemple, 2014; Tierney & Farmer, 2002), few investigations have measured CSE at more than one time point when evaluating creativity training programs and the few studies that have done so have produced inconsistent findings. A recent evaluation by Meinel et al. (2018) failed to find a significant change following training and concluded that CSE was less amenable to change than creativity. However, our results concur with those of Perry and Karpova (2017) and Byrge and Tang (2015) in demonstrating that training in creativity promotes appreciable changes, not just to skills, but also to participants’ awareness of and belief in their own creative abilities. The trainees in Byrge and Tang (2015) cited potential reasons for the increase in their self-efficacy as the result of their improved ability to generate new ideas, a larger set of creativity skills which had been learned during the course, and the changed perception towards idea generation, one that was now more open-minded and positive. Similarly, the participants in the current sample demonstrated evidence of an expanded repertoire of creativity skills and the capacity to execute these skills in generating novel, original, and expansive ideas as was evidenced by the substantial increases on the creative production tasks. The finding of enhanced CSE may be the result of having acquired and successfully applied creativity skills. Additionally, a key feature of the curriculum is the notion of ‘welcoming failure’. A single, hour-long session is spent reflecting on the concept, participants are encouraged to reflect on previous experiences of perceived failure, their emotional and behavioural reactions, and to envision alternate interpretations to these experiences with the intention of reconceptualizing their view of these experiences. As Mathisen and Bronnick (2009) suggest, training that enhances individual’s self-belief in their capacities may have the effect of improving creative productivity by nurturing a sense of perseverance when faced with difficulties and the capacity to recover relatively quickly following setbacks. Our study makes a novel contribution to the literature by extending the variables that are usually measured following training in creativity to include assessment of potential changes to neuro-executive functioning. This follows suit with the steadily growing body of evidence that shows a relationship between creativity and executive functioning (Ansburg & Hill, 2003; Zabelina & Beeman, 2013). However, the existing literature suggests that creativity does not appear to benefit executive functioning uniformly. Specifically, research demonstrates that lower-level executive skills such as goal-directed attention and the capacity for information processing is greatly benefited by training in creative thinking skills (Bott et al., 2014; Vartanian, Martindale, & Kwiatkowski, 2007). Higher-level functions, on the other hand, appear less amenable to improvement following training. Studies that have sought to test participants’ capacities for inhibition and cognitive switching often fail to find any enhancement in this domain (Ansburg & Hill, 2003; Bott et al., 2014; Vartanian et al., 2007; Zabelina & Beeman, 2013). Our results contribute to this growing body of literature but rather than finding differential improvement, participants in our sample did not show any statistically significant improvement in either lowlevel or higher-level functions. This is in contradiction to both the existing literature (e.g., Bott et al., 2014) and Martindale’s (1999) contention that creative people are likely to demonstrate enhanced performance on tasks involving goal-directed attention and information processing that are low in cognitive interference (equivalent to tasks 1 and 2 in the current study). This is usually a sound suggestion as attention is known to vary depending on the cognitive demands of a given task (Dorfman, Martindale, Gassimova, & Vartanian, 2008). Additionally, many of the activities completed during creativity programs are often practiced under time pressure, thus entraining the completion of tasks that require attention and information processing, and particularly those that involve automatic, pre-potent processes such as color naming or word reading (Bott et al., 2014). The lack of significant findings in respect of neuro-executive functioning in this study may have a number of possible explanations. First, studies that have indeed found an improvement have done so following the delivery of an embodied, skills-based program specifically designed to entrain the processing of information and ideas, under time pressure, and within both ambiguous and unambiguous situations. Our program was a credit-bearing university course and thus was required to be both didactic and practical in nature. This may have shifted the program’s foci across a number of possible outcomes, not solely on executive functioning. Second, executive functioning may simply be less amenable to change than creative production. Future work is required to investigate this more fully. An additional explanation to consider is that high pre-test abilities tend to limit the scope for change following intervention. If participants possess pre-test experience of the skill in question, they are likely to produce relatively high scores at pre-test. Then, often a plateau is observed in which post-test scores show no change or, in some cases, a decline. This phenomenon has been observed in previous evaluations of creativity training programs (Meinel et al., 2018; Perry & Karpova, 2017) and its consequence is to diminish the effect of the intervention. It is noteworthy that while participants did not improve their overall speed of completion on the neuro-executive measures, the accuracy with which tasks were completed did improve, as evidenced by a significant improvement to self-corrected errors. The capacity to self-monitor is an important cognitive tool as it promotes task accuracy (Groborz & Necka, 2003), but when it is a newly developed skill, it may not necessarily be implemented with speed, especially for more complex tasks involving greater degrees of cognitive distraction. This may therefore be a potential explanation for the finding that completion times for tasks showed no improvement. 6. Limitations The following limitations should be noted. The strategy for inclusion of participants in the sample was one of convenience. We sampled a naturally-constituted group of subjects and thus inclusion of multiple experimental groups with randomization was not possible as the training program was required to commence and end with the stipulated university calendar. Inclusion of a control 76

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

condition was not possible and is an acknowledged limitation of the study. Despite this, the argument cannot be made that this particular group of individuals possessed a specific level of motivation, personality characteristics, or espoused a particular level of interest in the course’s subject matter as all students from across the university are required to undertake this course, or else, a similar course from amongst a small selection encompassing similar content. Our use of a convenience sample of, primarily female, university students carries an additional limitation. Specifically, those engaged in tertiary education may possess superior cognitive abilities, which we did not measure on this occasion, and are therefore not necessarily representative of the larger population. Most student samples tend to have intellectual abilities approximately 1.5 standard deviations above the norm (Kienitz et al., 2014). However, our study’s aim was to examine the effects of this universitybased program, a naturally occurring program, not one that was designed specifically for experimental evaluation. Those who designed and implemented this program followed the government-mandated recommendation that it be specifically targeted at young adults registered at university. Additionally, the predominantly female sample is unlikely to impact the results of this study given that the efficacy of creativity training has rarely been shown to vary according to gender (Kim & Pierce, 2013). We did not assess personality which would have been a worthwhile addition to our study. Specifically, personality traits such as openness and extraversion have been shown to be positively associated with creative output when assessed at one time point in the context of correlational studies (e.g., Batey & Furnham, 2006; Furnham & Bachtiar, 2008). Few studies have investigated whether changes to objective measurements of creative output across time may vary according to participants’ underlying personality characteristics, specifically those demonstrated to be associated with creativity. 7. Conclusion Our study supports the contention that creative production and CSE can be enhanced following training. It is the first to demonstrate that this contention holds for individuals from an Arab, Middle Eastern country. This finding is especially pertinent given the focused efforts and significant funding directed towards the promotion of creativity and innovation in this region of the world. Conflicts of interest statement The authors declare that there are no conflicts of interest. Funding This study was funded with the kind support of Dr. Fadwa Al Mughairbi, the Assistant Dean for Research and Graduate Studies in the College of Humanities and Social Sciences at the United Arab Emirates University. References Abbott, D. (2010). Constructing a creative self-efficacy inventory: A mixed methods inquiry (Doctoral dissertation). Lincoln, NE: The University of Nebraska. Alotaibi, K. N. (2016). Psychometric properties of creative self-efficacy inventory among distinguished students in Saudi Arabian universities. Psychological Reports, 1–16. Amabile, T. M. (1997). Entrepreneurial creativity through motivational synergy. The Journal of Creative Behavior, 31(1), 18–26. Ansburg, P. I., & Hill, K. (2003). Creative and analytic thinkers differ in their use of attentional resources. Personality and Individual Differences, 34(7), 1141–1152. Batey, M., & Furnham, A. (2006). Creativity, intelligence, and personality: A critical review of the scattered literature. Genetic, Social, and General Psychology Monographs, 132(4), 355–429. Beghetto, R. A. (2009). Correlates of intellectual risk taking in elementary school science. Journal of Research in Science Teaching, 46, 210–223. Beghetto, R., Kaufman, J., & Baxter, J. (2011). Answering the unexpected question: Exploring the relationship between students creative self-efficacy and teacher ratings of creativity. Psychology of Aesthetics, Creativity, and the Arts, 5, 342–349. Bertrand, J. R. (2005). Can individual creativity be enhanced by training? (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3196767). Bott, N., Quintin, E.-M., Saggar, M., Kienitz, E., Royalty, A., et al. (2014). Creativity training enhances goal-directed attention and information processing. Thinking Skills and Creativity, 13, 120–128. Bungay, H., & Vella-Burrows, T. (2013). The effects of participating in creative activities on the health and well-being of children and young people: A rapid review of the literature. Perspectives in Public Health, 133(1), 44–52. Byrge, C., & Tang, C. (2015). Embodied creativity training: Effects on creative self-efficacy and creative production. Thinking Skills and Creativity, 16, 51–61. Cheung, C., Roskams, T., & Fisher, D. (2006). Enhancement of creativity through a one‐semester course in university. The Journal of Creative Behavior, 40, 1–25. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge ISBN 1-134-74270-3. Chin, Y. (2013). The relationship between undergraduate students’ creative self-efficacy, creative ability and career self-management. International Journal of Academic Research in Progressive Education and Development, 2, 181–193. Choi, J. N., Anderson, T. A., & Veillette, A. (2009). Contextual inhibitors of employee creativity in organizations: The insulating role of creative ability. Group & Organization Management, 34(3), 330–357. Clapham, M. M. (2011). Testing/measurement/assessment. In S. R. Pritzker, & M. A. Runco (Eds.). Encyclopedia of creativity (pp. 458–464). Boston, MA: Academic Press/Elsevier. Coholic, D., Eys, M., & Lougheed, S. (2012). Investigating the effectiveness of an arts-based and mindfulness-based group program for the improvement of resilience in children in need. Journal of Child and Family Studies, 21(5), 833–844. Csikszentmihalyi, M., & Getzels, J. (1971). Discovery-oriented behavior and the originality of creative products: A study with artists. Journal of Personality and Social Psychology, 19(1), 47–52. Delis, D. C., Kaplan, E., & Kramer, J. H. (2001). Delis–Kaplan executive function system (D-KEFS): Examiner’s manual. San Antonio, TX: The Psychological Corporation. Ding, X., Tang, Y.-Y., Tang, R., & Posner, M. (2014). Improving creativity performance by short-term meditation. Behavioral and Brain Functions, 10(9), 1–8. Dorfman, L., Martindale, C., Gassimova, V., & Vartanian, O. (2008). Creativity and speed of information processing: A double dissociation involving elementary versus inhibitory cognitive tasks. Personality and Individual Differences, 44(6), 1382–1390.

77

Thinking Skills and Creativity 31 (2019) 70–78

Z. Vally et al.

Furnham, A., & Bachtiar, V. (2008). Personality and intelligence as predictors of creativity. Personality and Individual Differences, 45(7), 613–617. Garcia-Ros, R., Talaya, I., & Perez-Gonzalez, F. (2012). The process of identifying gifted children in elementary education: Teachers’ evaluations of creativity. School Psychology International, 33(6), 661–672. Gilhooly, K. J., Fioratou, E., Anthony, S. H., & Wynn, V. (2007). Creative production: Strategies and executive involvement in generating novel uses for familiar objects. British Journal of Psychology, 98(Pt 4), 611–625. Golden, C. J. (1975). The measurement of creativity by the stroop color and word test. Journal of Personality Assessment, 39(5), 502–506. Groborz, M., & Necka, E. (2003). Creativity and cognitive control: Explorations of generation and evaluation skills. Creativity Research Journal, 15(2), 183–197. Guilford, J. P. (1967). The nature of human intelligence. New York, NY: McGraw-Hill. Hsu, A., Hou, S., & Fan, H. (2011). Creative self-efficacy and innovative behavior in a service setting: Optimism as a moderator. The Journal of Creative Behavior, 45, 258–272. Karpova, E., Marcketti, S. B., & Barker, J. (2011). The efficacy of teaching creativity: Assessment of student creative thinking before and after exercises. Clothing and Textiles Research Journal, 29(1), 52–66. Karakelle, S. (2009). Enhancing fluent and flexible thinking through the creative drama process. Thinking Skills and Creativity, 4, 124–129. Kasof, J. (1997). Creativity and breadth of attention. Creativity Research Journal, 10(4), 303–315. Kettner, N. W., Guilford, J. P., & Christensen, P. R. (1959). A factor-analytic study across the domains of reasoning, creativity, and evaluation. Psychological Monographs General and Applied, 73(9), 1–31. Kienitz, E., Quintin, E.-M., Saggar, M., Bott, N. T., Royalty, A., ... Reiss, A. L. (2014). Targeted intervention to increase creative capacity and performance: A randomized controlled pilot study. Thinking Skills and Creativity, 13, 57–66. Kim, K. H., & Pierce, R. A. (2013). Creativity and age. In E. G. Carayannis (Ed.). Encyclopedia of creativity, invention innovation and entrepreneurship (pp. 364–368). New York, NY: Springer. Lee, I. R., & Kemple, K. (2014). Preservice teachers’ personality traits and engagement in creative activities as predictors of their support for children’s creativity. Creativity Research Journal, 26, 82–94. Lim, H. S., & Choi, J. N. (2009). Testing an alternative relationship between individual and contextual predictors of creative performance. Social Behavior and Personality, 37, 117–136. Lynch, M., Sloane, G., Sinclair, C., & Bassett, R. (2013). Resilience and art in chronic pain. Arts & Health: An International Journal of Research, Policy and Practice, 5(1), 51–67. Ma, H.-H. (2006). A synthetic analysis of the effectiveness of single components and packages in creativity training programs. Creativity Research Journal, 18, 435–446. Martindale, C. (1999). Biological bases of creativity. In R. J. Sternberg (Ed.). Handbook of creativity (pp. 137–152). New York: Cambridge University Press. Mathisen, G. E., & Bronnick, K. S. (2009). Creative self-efficacy: An intervention study. International Journal of Educational Research, 48, 21–29. Meinel, M., Wagner, T. F., Baccarella, C. V., & Voigt, K. (2018). Exploring the effects of creativity training on creative performance and creative self-efficacy: Evidence from a longitudinal study. The Journal of Creative Behavior. https://doi.org/10.1002/jocb.234. Memmert, D. (2007). Can creativity be improved by an attention-broadening training program? An exploratory study focusing on team sport. Creativity Research Journal, 19(2–3), 281–291. Milgram, R. M., & Livne, N. L. (2006). Research on creativity in Israel: A chronicle of theoretical and empirical development. In J. C. Kaufman, & R. J. Sternberg (Eds.). The international handbook of creativity (pp. 307–336). New York, NY: Cambridge University Press. Moonesar, I., Saher, S., & Mourtada, R. (2015). Persistence in the Abu Dhabi STEM pipeline: Preparing emirati youth for careers in the UAE innovation economy. Report published by the Emirates Foundation and Mohamed Bin Rashid school of government. Pace, V. L., & Brannick, M. T. (2010). Improving prediction of work performance through frame-of-reference consistency: Empirical evidence using openness to experience. International Journal of Selection and Assessment, 18(2), 230–235. Perry, A., & Karpova, E. (2017). Efficacy of teaching creative thinking skills: A comparison of multiple creativity assessments. Thinking Skills and Creativity, 24, 118–126. Runco, M. A. (2015). Meta-creativity: Being creative about creativity. Creativity Research Journal, 27(3), 295–298. Runco, M. A., Millar, G., Acar, S., & Cramond, B. (2010). Torrance tests of creative thinking as predictors of personal and public achievement: A fifty-year follow-up. Creativity Research Journal, 22, 361–368. Scott, G., Leritz, L. E., & Mumford, M. D. (2004). The effectiveness of creativity training: A quantitative review. Creativity Research Journal, 16, 361–388. Tan, A. G., Ho, V., Ho, E., & Ow, S. (2008). High school students’ perceived creativity self-efficacy and emotions in a service learning context. The International Journal of Creativity and Problem Solving, 18, 115–126. Tierney, P. A., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. The Academy of Management Journal, 45, 1137–1148. Torrance, P. E. (1987). Guidelines for administration and scoring/comments on using the Torrance Tests of Creative thinking. Bensenville, IL: Scholastic Testing Service, Inc. Torrance, P. E. (1999). The Torrance tests of creative thinking: Norms and technical manual. Bensenville, IL: Scholastic Testing Service, Inc. West, R. E., Tateishi, I., Wright, G. A., & Fonoimoana, M. (2012). Innovation 101: Promoting undergraduate innovation through a two-day boot camp. Creativity Research Journal, 24(2–3), 243–251. Vartanian, O., Martindale, C., & Kwiatkowski, J. (2007). Creative potential, attention, and speed of information processing. Personality and Individual Differences, 43(6), 1470–1480. Yeh, Y. C. (2011). Research and methods. In S. R. Pritzker, & M. A. Runco (Eds.). Encyclopedia of creativity (pp. 291–298). Boston, MA: Academic Press/Elsevier. Yu, C. (2013). An empirical examination of a four-component of creative self-efficacy among undergraduate students. Journal of Applied Sciences, 13, 4092–4095. Zabelina, D. L., & Beeman, M. (2013). Short-term attentional perseveration associated with real-life creative achievement. Frontiers in Psychology, 4(191), 1–8.

78