Paper ID #26926

Linking Mind-set to Creativity

Dr. George D. Ricco, University Of Indianapolis

George D. Ricco is an assistant professor of engineering and first-year engineering coordinator at theUniversity of Indianapolis. He focuses his work between teaching the first two years of introductory en-gineering and engineering design and research in student progression. Previously, he was a special titleseries assistant professor in electrical engineering at the University of Kentucky, and the KEEN ProgramCoordinator at Gonzaga University in the School of Engineering and Applied Science. He completedhis doctorate in engineering education from Purdue University’s School of Engineering Education. Pre-viously, he received an M.S. in earth and planetary sciences studying geospatial imaging, and an M.S.in physics studying high-pressure, high-temperature FT-IR spectroscopy in heavy water, both from theUniversity of California, Santa Cruz. He holds a B.S.E. in engineering physics with a concentration inelectrical engineering from Case Western Reserve University. His academic interests include longitu-dinal analysis, visualization, semantics, team formation, gender issues, existential phenomenology, andlagomorph physiology.

c©American Society for Engineering Education, 2019

New Connections in Mindset: Exploring Creativity and Curiosity

Abstract

The nature of student mindset has been often probed in the recent engineering education literature. In this paper, we revisit a multi-year study to provide updated on a particular facet of mindset in engineering education we find particularly revealing – the misunderstood link between creativity and other mindset factors such as creatvity. This entrepreneurial mindset instrument used to investigate mindset of engineering and computer science students was utilized at a private liberal arts university in the United States and first reported as a study on Dweck mindset among first through fourth year students.

In this brief work in progress paper, we revisit this survey to determine the link between creativity and curiosity that has previously been uninvestigated, including variations of our original themes: (i) what diversity of curiosity and creativity is carried by first-year students into the university experience, (ii) how curiosity and creativity evolve during the undergraduate experience, and (iii) whether differences in curiosity and creativity can be identified by gender or discipline. While previously, the evidence suggested that mindset shifts somewhat towards fixed mindset as students progress towards their final year, the same contention may not exist for creativity or curiosity.

Background

In the years following the preliminary venture into exploring engineering mindset, the community interested in such studies has indeed grown and flourished (Ricco, Silliman, & Girtz, 2017). While Dweck studies are still increasingly rare and the lack of empirical evidence for many anecdotally-predicted phenomena does provided hurdles for the field, it also provides a need and an opportunity for more work. It is in this spirit that we present a number of findings that were uncovered in our original Dweck study that encompassed items not discussed within the paper, namely, the initial findings of creativity and curiosity.

The work permeating the field of entrepreneurial mindset in engineering education – while significantly more robust and vibrant than three years ago – still lacks strong empirical evidence (or connection) to validated survey instruments. The Dweck survey, when we released it, was one of very few papers in engineering education to employ a validated mindset survey, and only one that was directly relevant to our work (Reid & Ferguson, 2011). Subsequently, we have found ourselves in the middle of a five-year study at one school, with another school having provided a limited deployment of our survey, and at least one more institution preparing to implement our instrument. In the intervening time, the nature and understanding of entrepreneurial mindset has certainly expanded in new and exciting ways. Of particular interest is the recent field review of Commarmond (Commarmond, 2017), where the author notes a transition from the Dweck interpretation of mindset, to a field developing with its own potentially validated survey instruments. The field has indeed shifted from broad domains or domains that are related to entrepreneurial mindset to specific dimensions, such as those found in any psychometric protocol. Furthermore, this particular sentiment is mimicked by the strict and more modern defining of the entrepreneurial mindset as being one that “reflects deep cognitive phenomena,” including “particularly deep beliefs and assumptions” (Krueger, 2015). This increased rigor in the definition of entrepreneurial mindset coupled with an expansion of survey instruments designed to create dimensions endemic to it is promising, but it is still too early to determine the future of this line of study.

In the spirit of sharing our work, we have composed this brief work-in-progress as an addendum to a larger paper summarizing the longitudinal results of our Dweck study, plus further connections of the original mindset factors to creativity and curiosity.

Of utmost importance in this analysis is the notion of talent and intelligent groupings, that are fundamental to Dweck-style survey analysis. As Appendix 1 indicates the survey instrument for curiosity and creativity, Appendix 2 indicates the original Dweck instrument. In the Dweck instrument, the positive and negative formulations of the questions are listed. In Dweck analysis, these questions are often times grouped together in positive and negative sets, and further sorted into talent and intelligence questions. Our original work uncovered a significant overlap in distributions for each of these Dweck sets via reliability analysis (Cronbach’s alpha), thus confirming anecdotal suspicions these sets would overlap for engineering students.

Administering the Instrument

The instrument was administered at a college of engineering and computer science undergraduate program at a mid-sized (approximately 4300 total undergraduates), private, Catholic university. The university has one online master’s program and otherwise issues bachelor of science degrees. The survey was and continues to be administered by the dean’s office in engineering.

At the time of administration of the first survey, the school housed approximately 850 students. The dean’s office used multiple emails to encourage participation. No incentives were provided for participation. Based on this strategy, 267 students responded to the survey over a period of approximately one month. Of these respondents, 251 provided answers to all questions. It is this reduced set of student responses that are analyzed. All individually identifiable information provided with the survey was removed from the data base prior to analysis of the data.

Both initial survey instruments developed employed a 6-point Likert scale indicating degree of agreement with a number of statements on creativity or curiosity – both with the affirmative answer being anchored to 1 and the negative answer anchored to 6. It has not been altered in any way in the following years of survey deployment. Much of the reasoning for this is to compel the students to make a choice, and to maintain parity with the original surveys.

Methodology

The survey instrument was highly influenced by the work of Karwowski’s (2014), who adapted Dweck’s original strategy and assumed positive and negative formultions in order to test for reliability of student responses. (We did not adapt Karwowski’s approach of a percent “open” or “fixed”). As an example, when we refer to positive intelligence and negative intelligence questions in the Dweck domain, we are referring to the sets of questions 3,5,7,8 and 1,2,4,6, respectively. In the case where in the original paper where we needed to compare distributions sets between positive and negative formulations, we simply flipped the scales of the individual answers and centered them.

The creativity and curiosity protocols were adapted from the established works of Fulcher and Karwowski (Fulcher, 2004; Maciej Karwowski, 2014). Before delving into curiosity, we found simply no works outside of Fulcher’s as developed and significant within the outside literature. This was, admittedly, one of primary impetuses for initially engaging in this work. With such significant efforts from groups such as the Kern Entrepreneurial Engineering Network (KEEN) at promoting curiosity, we found it stunning that the field had little to no empirical underpinnings. The reason we chose Fulcher as the focus of the creativity domain questions was due to Fulcher’s exhaustive probe of the creativity protocol through a thesis and subsequent publications. Not only had Fulcher produced a robust digression of a creativity protocol, but had iterated it multiple times, producing a number of validation metrics in the process.

Other tests of both creativity and curiosity do exist and are relevant here. One of the first that comes to mind and transferable to the field of engineering education is the work of Kaufman (Kaufman,

2012). Within his K-DOCS continued study from 2012, he outlines and summarizes a significant portion of the modern creativity testing field, and provides a thoroughly in-depth analysis of his instrument. Kaufman – like Fulcher – finds significant results and useful mappings to other known quantities, such as the Big 5, and other studied quantities in psychological testing. He also continues to probe the question of whether or not creativity is in any way related to Spearman’s G or a universal attribute that is inherent and somehow measurable at the same time. The reason we did not choose to use his protocol at the time is simple – combining multiple metrics with a K-DOCS or similar instrument would have been exceedingly time consuming, and no doubt would have led to survey creep and a significantly lower response rate.

As such, we chose Karwowski’s work as the creativity wing of our analysis for a few simple reasons: first, Karwowski has deeply probed mindset studies in the past; second, Karwowski’s work is directly linked and related to Dweck mindset, and makes explicit connections to it throughout the 2014 paper; third, Karwowski’s work was significant to us in the fact it was one of the first papers that made an attempt to probe whether or not mindset was indeed malleable; and, finally, Karwowski had previously delved into analysis we found useful, such as gender identity and creative self-efficacy (M Karwowski, Lebuda, Wisniewska, & Gralewski, 2008).

Discussion of Data Integrity

Our original study employed the use of Cronbach’s alpha for a variety of reasons, the primary of them being that Dweck survey questions are grouped into fours, and an inquisitive quantitative mind would want to know whether or not these groupings are interchangeable – either amongst themselves or as a group relative to other groups. Cronbach’s alpha is a good first test to determine estimate the similarity of two sample distributions. Whereas in the original study, it was uncovered that positively and negatively-worded talent and intelligence questions shared a high Cronbach’s alpha for reliability, it was only postulated that other factors could possess the same properties. The original questions are included in

Appendix 3 indicates our results for a Cronbach analysis of the distributions for creativity and curiosity questions when paired with talent and intelligence ones. Upon first glance, this does not seem to bear much fruit, but upon closer inspection, one can note that most of the positive-leaning questions, have a low Cronbach’s alpha value when analyzed with Dweck positive-leaning questions. This is novel and, in fact, somewhat surprising, as this potentially means that creativity and curiosity questions that are positive-leaning may not have distributions that mimic Dweck questions. Conversely, it appears a number of questions that are negative-leaning, have a significantly higher Cronbach’s alpha, including Very few things interest me, (curiosity question 13) and four of the ten creativity questions have Cronbach’s alpha values above 0.6, which certainly merits further study.

Assessment of Full (Complete Response) Population

The student responses here are telling. Students tended to have an overall positive response to creativity and an even more positive response to curiosity, on average to most of the questions. As evident in both Appendices 5 and 6, we see a distribution for one set of questions that is more well distributed relative to the centroid (3.5). This is an unexplored phenomenon and poorly understood in the literature. Students had the most positive response to questions creativity 1 and 5, and curiosity 3 and 16. Creativity questions 1 and 5 are phrased in a slightly more encompassing way than the others, example, Everyone can create something great at some point if he or she is given appropriate conditions and Practice makes perfect - perseverance and trying hard are the best ways to develop and expand one's capabilities are both positive statements with question 5 practically being an adage taken from folk wisdom. A similar question to 5, question 7, is a similar commonplace saying compared to 5 and may be positively-loaded for today’s student populations.

Curiosity 3 and 16 are pithy but positive statements - I like variety in my life and I find myself fascinated by lots of different things – compared to the other statements in the set, and thus students have gravitated towards them in a positive light. Analagously, the most negative of all the questions, number 13 - Very few things interest me – was nearly ubiquitously rejected by the students! Another negatively-loaded question, number 7 - I rarely try new things – follows a similar trend to 13 among all students.

Assessment by Gender

As expected from previous work, the distribution by gender is not too different. A simple paired-T or non-linear pairing test confirms that while there are questions that have significant differences in their distribution, the overall responses are well within the error values. That being said, the near statistical equivalence of a number of questions is affirming of decades of work within and outside of engineering education that demonstrates that the differences (if any) between the genders is nonexistent – which holds for macroscopic values such as persistent in engineering, as well (Ohland et al., 2008). Questions 4, 6, and 8 in the creativity protocol and 1, 2, and 3 in the curiosity protocol are the only outliers of any statistical significance between the genders.

Strangely enough, the most seemingly recalcitrant question, number 6, achieved the greatest difference between genders. Similar to the Dweck analysis, Creativity can be developed, but one either is or is not a truly creative person, evoked a visceral difference between the sexes, with men tending to reject the immutability of creativity.

Assessment by Class Standing

A number of assessments here that directly correlate to our original study are apparent and some that are not so. To begin with, the most blatant differences in the survey are between the extremities of class standing – freshmen and seniors. It appears that over most questions, class standing is a significant factor, with students in fourth-year standing having different views on questions such as I spend a great deal of time researching areas that I wish to learn about in depth, which is practically expected as students develop research and overall study capacities in their student progression.

Where students did not differ by class standing is also of significant interest. Creativity question 9 - It doesn't matter what creativity level one reveals - you can always increase it – achieved a nearly universal positive response and the means practically overlap across all class standings.

Discussion and Conclusions

Given the initial results in 2017, we are pleased to see a number of analogous results and some that require further inquiry:

We have uncovered multiple questions within a established surveys in creativity and curiosity that appear to have a significantly high distribution relationship to Dweck questions. One in particular has a particularly high correlation with the positive intelligence Dweck grouping,, It doesn't matter what creativity level one reveals - you can always increase it (creativity question 9). A similar set of questions in the original study also had a high reliability rating, indicating student may survey highly positive questions on the plasticity of intelligence and talent in similar ways.

Of the remaining member of the curiosity and creativity set whose distributions score a high Cronbach’s alpha when paired with Dweck, they all appear to be negatively formulated ones – creativity questions 2, 4, and 8). This could mean that students tend to have a higher tendency to

score negatively-formulated questions in a similar manner, but at this juncture further study is needed to properly determine this relationship.

It appears that curiosity questions are not as relatable in distribution to Dweck mindset questions as creativity questions are. The reasons for this at this juncture are potentially multiple: students could have been attracted to seemingly most negative questions and grouped them together; creativity questions appear to test the same as Dweck questions; and/or curiosity is a fundamentally diverging construct compared to Dweck and creativity concepts.

In a society where we are bombarded with social norms or myths that women are “more creative” or divergent in thought, it is interesting to see that men in a number of instances have engaged in thinking that indicates they are more open to the flexibility and neuroplasticity of creativity. A similar phenomenon was noted in the original Dweck survey among students overall. This could indicate that women in engineering believe they have fewer opportunities for growth in creativity and curiosity – which is of concern and worth further study.

Overall, students find themselves gravitating towards positive interpretations of creativity and curiosity across all metrics relative to the centroid. This was mimicked in the original Dweck survey analysis in a few domains.

Students gravitate in general towards opinions that they are intellectually interested in the world around them, from being open to trying new things or being fascinated by the novel, to believing anyone can be creative. These are all overwhelmingly accepted by students in this study.

Similar to our first study, it appears that juniors in some domains deviate the most from other classes on across instruments. They clearly overestimate their own perception of their grasp of learning new concepts (curiosity question 15).

Deviating from our first study, juniors overall do not appear to exhibit the same radical deviation from seniors over a significant number of questions and domains. In other words, when they do differ significantly from freshmen, they tend to do so in tandem with seniors.

Students across class standings have a strong belief that in a general way, creativity can be increased – mimicking the results of our Dweck study that students across standings believed intelligence or talent could be increased in various ways.

Given these positive results, we are delighted to continue our work on mindset and will be presenting more results from the multi-year study that include non-linear analysis between Dweck parameters, creativity, and curiosity.

Bibliography

Commarmond, I. (2017). In Pursuit of a Better Understanding of and Measure for Entrepreneurial Mindset. The Allan Gray Orbis Foundation. 

Fulcher, K. (2004). Towards Measuring Lifelong Learning: The Curiosity Index. James Madison University. Karwowski, M. (2014). Creative Mindsets: Measurement, Correlates, Consequences. Psychology of 

Aesthetics, Creativity, and the Arts, 8(1), 62‐70.  

Karwowski, M., Lebuda, I., Wisniewska, E., & Gralewski, J. (2008). Big Five Personality Factors as the Predictors of Creative Self‐Efficacy and Creative Personal Identity: Does Gender Matter? The Journal of Creative Behavior, 47.  

Kaufman, J. C. (2012). Counting the muses: Development of the Kaufman Domains of Creativity Scale (K‐DOCS). Psychology of Aesthetics, Creativity, and the Arts, 6(4), 298‐308.  

Krueger, N. (2015). Entrepreneurial Education in Pracetice: Part 1 ‐ The Entrepreneurial Mindset.  Ohland, M. W., Sheppard, S. D., Lichtenstein, G., Eris, O., Chachra, D., & Layton, R. A. (2008). Persistence, 

Engagement, and Migration in Engineering Programs. Journal of Engineering Education, 97, 259‐278.  

Reid, K., & Ferguson, D. (2011). Enhancing the Entrepreneurial Mindset of Freshman Engineers. Paper presented at the American Society for Engineering Education.  

Ricco, G., Silliman, S., & Girtz, S. (2017). Exploring Engineering Mindset. Paper presented at the American Society for Engineering Education.  

Appendix 1 – Combined Curiosity and Creativity Survey Questions

Curiosity_1 I spend a great deal of time researching areas that I wish to learn about in depth.

Curiosity_2 When learning about something new, I try to find out everything I can about it.

Curiosity_3 I like variety in my life.

Curiosity_4 I rarely spend time investigating one thing.

Curiosity_5 I am always finding new things to do.

Curiosity_6 The more I learn about something, the more I want to learn about it.

Curiosity_7 I rarely try new things.

Curiosity_8 I seldom research a topic in great depth.

Curiosity_9 I am always trying out new things.

Curiosity_10 I often spend sustained periods of time investigating a topic of interest to me.

Curiosity_11 I prefer to mix up my days with a variety of activities.

Curiosity_12 I immerse myself in information pertaining to a topic that I find fascinating.

Curiosity_13 Very few things interest me.

Curiosity_14 Please indicate your leveI like to get involved in a wide variety of activities.

Curiosity_15 When learning something, I try to gain the fullest possible understanding of the phenomenon.

Curiosity_16 I find myself fascinated by lots of different things.

Creativity_1 Everyone can create something great at some point if he or she is given appropriate conditions.

Creativity_2 You either are creative or you are not - even trying very hard you cannot change much.

Creativity_3 Anyone can develop his or her creative abilities up to a certain level.

Creativity_4 You have to be born a creator - without innate talent you can only be a scribbler.

Creativity_5 Practice makes perfect - perseverance and trying hard are the best ways to develop and expand one's capabilities.

Creativity_6 Creativity can be developed, but one either is or is not a truly creative person.

Creativity_7 Rome wasn't built in a day - each creativity requires effort and work, and these two are more important than talent.

Creativity_8 Some people are creative, others aren't - and no practice can change it.

Creativity_9 It doesn't matter what creativity level one reveals - you can always increase it.

Creativity_10 A truly creative talent is innate and constant throughout one's entire life.

Appendix 2 – Dweck Questions

Question Number

Positive (P) or Negative (N) Formulation?

Statement

1 N You have a certain amount of intelligence, and you really can't do much to change it 2 N Your intelligence is something about you that you can't change very much. 3 P No matter who you are, you can significantly change your intelligence level 4 N To be honest, you can't really change how intelligent you are 5 P You can always substantially change how intelligent you are 6 N You can learn new things, but you can't really change your basic intelligence 7 P No matter how much intelligence you have, you can always change it quite a bit 8 P You can change even your basic intelligence level considerably 9 N You have a certain amount of talent, and you can't really do much to change it

10 N Your talent in an area is something about you that you can't change very much 11 P No matter who you are, you can significantly change your level of talent 12 N To be honest, you can't really change how much talent you have 13 P You can always substantially change how much talent you have 14 N You can learn new things, but you can't really change your basic level of talent 15 P No matter how much talent you have, you can always change it quite a bit 16 P You can change even your basic level of talent considerably

Appendix 3 – Cronbach’s Alpha for Dweck Groups Compared to Creativity and Curiosity

   Dweck Set Cronbach's 

Alpha  Dweck Set Cronbach's 

Alpha Curiosity_1 IntPos  0.147  TalPos  0.219 Curiosity_2 IntPos  0.23  TalPos  0.344 Curiosity_3 IntPos  0.321  TalPos  0.18 Curiosity_4 IntPos  0.174  TalPos  0.221 Curiosity_5 IntPos  0.389  TalPos  0.369 Curiosity_6 IntPos  0.16  TalPos  0.352 Curiosity_7 IntPos  0.283*  TalPos  0.308* Curiosity_8 IntPos  0.041  TalPos  0.262* Curiosity_9 IntPos  0.343  TalPos  0.255 

Curiosity_10 IntPos  0.161  TalPos  0.271 Curiosity_11 IntPos  0.37  TalPos  0.232 Curiosity_12 IntPos  0.052  TalPos  0.221 Curiosity_13 IntPos  0.344*  TalPos  0.196* Curiosity_14 IntPos  0.249  TalPos  0.189 Curiosity_15 IntPos  0.13  TalPos  0.284 Curiosity_16 IntPos  0.247  TalPos  0.117 Creativity_1 IntPos  0.402  TalPos  0.241 Creativity_2 IntPos  0.644*  TalPos  0.462* Creativity_3 IntPos  0.459  TalPos  0.341 Creativity_4 IntPos  0.658*  TalPos  0.515* Creativity_5 IntPos  0.503  TalPos  0.371 Creativity_6 IntPos  0.493*  TalPos  0.474* Creativity_7 IntPos  0.349  TalPos  0.36 Creativity_8 IntPos  0.679*  TalPos  0.488* Creativity_9 IntPos  0.666  TalPos  0.546 

Creativity_10 IntPos  0.427*  TalPos  0.265* An * indicates that the distribution was analyzed compared to the inverted distribution of the Dweck set.

Appendix 4 – Raw Averages for Questions Separated by Gender and Class Standing

Question Male Female Freshmen Sophomore Junior Senior Curiosity_1 2.58 2.85 2.88 2.61 2.75 2.34 Curiosity_2 2.50 2.72 2.65 2.54 2.58 2.47 Curiosity_3 1.82 1.57 1.68 1.94 1.86 1.59 Curiosity_4 3.50 3.37 3.16 3.67 3.46 3.61 Curiosity_5 2.65 2.25 2.45 2.56 2.55 2.72 Curiosity_6 2.26 2.35 2.32 2.24 2.24 2.39 Curiosity_7 4.57 4.70 4.55 4.61 4.47 4.71 Curiosity_8 3.99 3.84 3.81 3.84 4.07 4.12 Curiosity_9 2.59 2.61 2.55 2.58 2.66 2.59 Curiosity_10 2.48 2.93 2.78 2.63 2.41 2.53 Curiosity_11 2.55 2.37 2.37 2.55 2.53 2.66 Curiosity_12 2.24 2.37 2.39 2.20 2.27 2.21 Curiosity_13 5.03 5.12 5.01 5.06 4.93 5.07 Curiosity_14 2.35 2.23 2.18 2.32 2.51 2.36 Curiosity_15 2.34 2.51 2.55 2.39 2.24 2.40 Curiosity_16 1.92 1.77 1.86 1.85 1.98 1.83 Creativity_1 1.71 1.76 1.67 1.70 1.73 1.85 Creativity_2 4.30 4.28 4.27 4.23 4.44 4.20 Creativity_3 2.36 2.40 2.44 2.30 2.48 2.29 Creativity_4 4.52 4.68 4.52 4.59 4.64 4.51 Creativity_5 1.70 1.65 1.67 1.70 1.66 1.75 Creativity_6 3.63 3.35 3.51 3.46 3.73 3.51 Creativity_7 2.10 2.10 2.10 2.15 2.24 1.93 Creativity_8 4.52 4.72 4.48 4.61 4.53 4.64 Creativity_9 2.38 2.51 2.40 2.44 2.46 2.46 Creativity_10 3.59 3.46 3.37 3.62 3.46 3.69

Appendix 5 – Mean Questions Distributed by Gender

Appendix 6 – Mean Questions Distributed by Class