Assessing Young Students’ Design Thinking Disposition and Its Relationship With Computer Programming Self-Efficacy

Abstract

To explore the role of design thinking in contemporary computer literacy education, this study aimed to examine the relationship between young students’ design thinking disposition and their computer programming self-efficacy. To assess students’ design thinking disposition, this study developed the Design Thinking Disposition Scale (DTDS) with a sample of 350 junior high school students who had computer programming experience in a STEAM course. A principle axis factor analysis with the promax rotation method was used to verify the DTDS’s construct under the four dimensions: empathize, define, ideate and prototype. The Cronbach’s alpha reliability was .90 for the overall scale. Correlation analyses results showed that all the four dimensions were significantly correlated with computer programming self-efficacy assessed by CPSES. A significant regression model was found in which the three factors, ideate, prototype and define, significantly predicted the overall computer programming self-efficacy. Meanwhile, except for the ideate subscale, no gender difference was found in the young students’ design thinking dispositions. The students’ self-directed programming learning experience was shown to benefit their design thinking disposition. The DTDS can be applied to design-thinking-embedded computer literacy curricula such as makers, STEAM, or robotics education. Several further studies are also suggested.

Keywords

digital literacy evaluation programming self-efficacy design thinking disposition

Design Thinking Disposition and 21st Century Competencies

In the 21st century, the capabilities of communication, collaboration, complex problem solving, critical thinking, and creativity are required for all students. Many studies have extensively discussed how to help students enhance these capabilities through instructional approaches and courses (Cook & Bush, 2018; van Laar et al., 2017). For example, the implementation of STEAM (science, technology, engineering, art, and mathematics) curricula is one of the educational approaches used to engage students in constructivist learning and improve their 21st century capabilities (Cook & Bush, 2018). It has been regarded as being able to improve students’ abilities of inquiring, discussing, problem-solving, and critical thinking through a creative and design process (Cook et al., 2017), and has being increasingly carried out in formal and informal education. Art is one of the important factors in STEAM curricula since it is associated with students’ aesthetic feeling and creative thinking in their daily life as well as the creativity to design a work (Boy, 2013; Cook & Bush, 2018). Design thinking is important for solving problems creatively. The process of design involves some mindsets or strategies for information collecting, peer discussion, problem-solving, and understanding the needs of others (Adams & Nash, 2016; Wang & Tsai, 2018). Design thinking disposition has been regarded as an important role in creative learning and teaching in 21st century education (Koh et al., 2015). Understanding students’ design thinking dispositions has gradually become one of the main focuses for improving students’ 21st century competencies.

Design thinking has gained increasing attention and discussion during the past decade for fostering the development of 21st century skills (Henriksen et al., 2017; Kangas et al., 2013; Noel & Liub, 2017; Razzouk & Shute, 2012; Vanada, 2010). Brown (2008) defined design thinking as a human-centered design approach that considers people’s needs, behaviors, and the feasibility of technology or business. Also, design thinking is a process whereby designers search for some innovative solutions to ill-defined and complex problems (Adams & Nash, 2016; Micheli et al., 2019; Vanada, 2010). Brown (2008) and Brown and Wyatt (2010) proposed that design thinking involves three spaces: inspiration, ideation, and implementation. Inspiration indicates the process of searching for solutions, ideation indicates the process of developing ideas, and implementation indicates the process of practicing the idea. In addition, Stanford d.School (2010) introduced a conceptual framework of the design thinking process which includes the following five steps: empathize, define, ideate, prototype, and test. This framework is usually used to enhance learners’ design thinking skills in various educational contexts (Cook & Bush, 2018; Henriksen et al., 2017; Vanada, 2010).

Regarding the assessment of design thinking, in the past, individuals’ design thinking processes and strategy uses were observed and understood mainly via qualitative analyses such as protocol analyses, content analyses, and video-recorded analyses (Henriksen et al., 2017; Kangas et al., 2013; Kavakli & Gero, 2002; Mentzer et al., 2015; Seitamaa-Hakkarainen & Hakkarainen, 2001). However, the qualitative-based methods usually take tremendous time for data analysis, which limits the opportunity to conduct studies with a larger sample size. Also, in school teaching, it is impossible for teachers to understand students’ backgrounds by conducting a qualitative analysis before teaching. A questionnaire would be much more convenient and useful for teachers to diagnose students’ backgrounds in schools. Therefore, developing a design thinking disposition self-rating scale can help educators efficiently assess and understand individuals’ dispositions as the core of design thinking, as well as efficiently examine its relationships with other variables on a large scale. However, in the prior literature, a limited number of studies have examined students’ design thinking dispositions using a self-rating measurement. Therefore, there is a need to develop a scale to examine students’ dispositions as the core of the design thinking process.

Design Thinking Disposition and Computer Programming Self-Efficacy

Computer programming has been merged into computer literacy education in many countries in the last decade (Department for Education, 2013; Ministry of Education, 2016; National Science Foundation, 2016). The major reason for this merger was due to the plausible benefits of enhancing students’ 21st century skills via computer programming. However, few studies have examined the plausible effects or the relationships between them. This may be related to the fact that not many prior instruments could be applied to contemporary computer literacy education in schools. Specific to computer programming, M.-J. Tsai et al. (2019) recently developed a scale, the Computer Programming Self-Efficacy Scale (CPSES), for assessing all students’ computer programming self-efficacies from a computer literacy perspective. Self-efficacy refers to individuals’ perceptions of their own abilities to perform some specific tasks (Bandura, 1977), and it has close relationships with learning experience, learning strategies, learning attitudes and learning performance in many learning domains or tasks (C.-C. Tsai et al., 2011; Tsai & Tsai, 2010; M.-J. Tsai et al., 2019; Wang & Tsai, 2017; Wu et al., 2019). Thus, computer programming self-efficacy can be regarded as one index of students’ learning outcomes in computer literacy education, including computer programming activities.

The computer programming process is basically a design process of problem solving. In fact, the computational thinking process (Wing, 2006) rooted in computer programming activities shares some similar elements with the design thinking process. For example, decomposing a problem in the computational thinking process is a similar concept of defining a problem in the design thinking process. That is, both thinking processes define issues from analysis (Park & McKilligan, 2018). Therefore, some relationships may exist between students’ design thinking disposition and their computer programming self-efficacy. Since computer programming has been included in curricula for young students in many countries for 21st century literacy, the relationship between students’ design thinking disposition and computer programming self-efficacy is worth exploring for computer literacy education. Demographic variables usually play important roles in computer-related learning domains; for example, a gender difference and the effect of prior learning experience level have been found in university students’ CPSES (M.-J. Tsai et al., 2019). Therefore, this study further explored the effects of these two demographic variables on design thinking disposition.

In sum, although some previous studies (Mehmood et al., 2020; Omer et al., 2020) have made efforts to understand computer science majors’ programming learning in higher education, few have examined the computer programming self-efficacy of middle to high school students and university non-computer-science majors as well as its relation to the students’ design thinking dispositions. Thus, the current study aimed to develop a scale, the Design Thinking Disposition Scale (DTDS), to examine young students’ dispositions of design thinking, so that we can examine the relationship between young students’ design thinking dispositions and their computer programming self-efficacy. The gender difference and the role that programming learning experience played in design thinking disposition were also examined.

Purpose

To understand the role of design thinking in the 21st century’s computer literacy education, this study aimed to explore the relationships between students’ design thinking dispositions and their computer programming self-efficacy. Since few instruments are available for assessing young students’ design thinking dispositions, this study had two main purposes: One was to develop a valid and reliable scale for assessing design thinking dispositions of students at the junior high school level or above. The other was to examine the correlation between young students’ design thinking dispositions and their computer programming self-efficacy in a computer literacy course; and, if a correlation was found, to further examine whether the young students’ design thinking dispositions can predict their computer programming self-efficacy.

Method

For the purposes of this study, a survey comprising two instruments, one developed for assessing design thinking disposition and the other to assess computer programming self-efficacy, was administered to a sample of junior high school students with computer programming learning experience in the computer literacy curricula.

Sample

A total of 427 junior high school students were originally selected in this study; however, 77 students did not complete the questionnaires. Thus, the final sample consisted of 350 students, including 183 eighth graders and 167 ninth graders. They were selected from a junior high school in a suburban area of northern Taiwan. All of them had taken a one-semester required STEAM course in the school. This STEAM course was a project-based curriculum coordinated by science and technology teachers. The learning goal was to design a robot for solving some problems related to natural science or ecological environmental issues. In the performance-based course, both artifact designs and computer programming learning activities were provided; therefore, these students were suitable for serving as the sample of this study. The sample included 226 males and 124 females, and their ages ranged from 14 to 16 years old. Regarding their computer programming experience, 27%, 54%, 14% and 4% of the sample self-reported having experience of less-than-one year, one-to-three years, three-to-five years and above-five years, respectively.

Instruments

To assess young students’ design thinking dispositions in computer literacy curricula, the current study designed the Design Thinking Disposition Scale (DTDS) based on the design thinking process of Stanford d.School (2010): Empathize, Define, Ideate, Prototype, and Test. Since formal testing is not emphasized for young students, Test may not be suitable for the assessment of young students. Thus, only the first four stages of the design thinking process were considered to be designed as subscales of a new scale to measure young students’ design thinking. The following are the definitions of each subscale: Empathize means that designers should think about the feelings of the audience for whom they are designing. Define indicates that designers need to identify the needs or the problems of users clearly. Ideate indicates that designers use brainstorming and then offer various creative solutions. Prototype means that designers need to present their preliminary ideas or show a typical model for a solution to others. According to the above definitions, this study designed and developed five candidate items for each subscale; thus, a pool of 20 candidate items was constructed for the initial version of the Design Thinking Disposition Scale (DTDS). In addition, a stem question “When I design a work, …….” was put at the top of the scale as a guide. Each item was evaluated using a 5-point Likert rating scale ranging from very much like me to not like me at all. The higher the score, the higher the disposition of design thinking. The validity and reliability of this scale were also verified in this study, and all the results are presented in the results section.

The young students’ computer programming self-efficacy was assessed by the CPSES (M.-J. Tsai et al., 2019). The CPSES (M.-J. Tsai et al., 2019) was designed based on Berland and Lee’s (2011) computational thinking framework to understand young students’ perceptions of their own computer programming abilities. It is a literacy level scale targeted at all students above middle school level, and consists of 16 items for assessing young students’ computer programming self-efficacy in five dimensions: Control (e.g., “I can open and save a program in a program editor”), Logical Thinking (e.g., “I can understand the basic logical structure of a program”), Debugging (e.g., “I can find the origin of an error while testing a program”), Algorithm (e.g., “I can figure out program procedures without a sample”), and Cooperation (e.g., “I know programming work can be divided into sub-tasks for people”). The original overall reliability coefficient (Cronbach’s alpha) is .96, which is excellent for research. In the present study, each item was evaluated using a 5-point Likert rating scale, and the overall reliability coefficient (Cronbach’s alpha) was .91.

Data Collection

A survey was conducted to collect the data in this study. A questionnaire including the above two scales as well as students’ background information was administered. The background information included demographic variables such as age, gender and computer programming learning experience. Self-directed learning experience was especially asked since self-directed learning seems to be a common learning approach or learning strategy for computer programming. The questionnaire was administered to all students online in the computer classrooms at one junior high school in the suburban area of northern Taiwan; however, only the 350 students who met the sample selection criteria of this study were selected for final data analysis.

Data Analyses

For the first purpose of this study, an explorative factor analysis with the principal axis method plus promax rotation was used to validate the construct of the DTDS. The reliabilities of Cronbach’s alpha were also examined for the overall scale and subscales. Independent t tests were used to compare the roles of demographic variables such as gender on students’ DTDS scores. For the second purpose of this study, Pearson’s correlation analyses were conducted to examine the relationship between the DTDS and CPSES. If there was a significant finding in the correlation analyses, a stepwise regression was conducted to examine if design thinking dispositions could significantly predict computer programming self-efficacy.

Results

Factor Analysis for DTDS

The results of the principal axis factoring procedure with the promax rotation method are summarized in Table 1. An item was retained only when its factor loading (pattern matrix coefficients) was greater than .50. Finally, 18 items were retained for the DTDS under the four designed subscales, i.e., the Ideate, Prototype, Empathize and Define subscales. The total explained variance of the scale was 58.61%, suggesting that the DTDS is acceptable to explain young students’ design thinking disposition. Meanwhile, the reliability of Cronbach’s α was .90 for the overall scale, and ranged from .73 to .86 for the subscales. This suggests that the DTDS has good reliability for assessing the young students’ design thinking dispositions. The item mean of each subscale ranged from 3.16 (Prototype) to 3.69 (Empathize) on a 5-point scale, suggesting that the junior high school students had attained all aspects of design thinking dispositions above a median level.

Table 1.
Rotated Factor Loadings (Pattern Matrix Coefficients), Cronbach’s Alpha Values, Factor Means, and Standard Deviations of the Four DTDS Factors (N = 350).

Factor 1 Factor 2 Factor 3 Factor 4 M SD α

Factor 1: Ideate (ID)

ID 11 0.74 −0.01 −0.09 0.06 3.50 0.73 0.79

ID 12 0.78 0.01 0.04 −0.02

ID 13 0.69 0.05 0.02 0.05

ID 14 0.78 −0.06 0.07 −0.11

ID 15 0.52 0.08 0.07 0.14

Factor 2: Prototype (PR)

PR 16 0.08 0.75 0.01 −0.05 3.16 0.85 0.86

PR 17 −0.06 0.83 0.20 −0.08

PR 18 0.07 0.73 0.08 −0.01

PR 19 −0.00 0.82 −0.13 0.07

PR 20 −0.03 0.87 −0.13 0.01

Factor 3: Empathize (EM)

EM 1 −0.02 −0.04 0.81 0.04 3.69 0.82 0.76

EM 2 0.01 −0.06 0.84 0.03

EM 3 0.09 0.07 0.75 −0.03

Factor 4: Define (DE)

DE 6 0.13 0.14 −0.17 0.61 3.62 0.67 0.73

DE 7 −0.02 −0.20 0.03 0.88

DE 8 −0.23 0.21 0.13 0.66

DE 9 0.05 0.01 0.15 0.55

DE 10 0.22 −0.01 −0.06 0.55

Note. Overall α = .90; Total variance explained = 58.61%. Bold values indicate that the factor loadings are greater than .50.

Table 2 shows the final 18 items of the DTDS categorized in the four designed dimensions. Following are the descriptions of the four subscales:

Table 2.
The Retained 18 Items in the Final DTDS.

Subscale No. Item

Guidance: When I design a work, ……….

Ideate ID1 11 I usually generate solutions via brainstorming.

ID2 12 I usually come up with various solutions.

ID3 13 I usually come up with innovative solutions.

ID4 14 I usually come up with new solutions.

ID5 15 I usually think of different problem-solving approaches.

Prototype PR1 16 I usually make a model of the design.

PR2 17 I usually draw a picture of the design.

PR3 18 I usually provide an example for showing an idea.

PR4 19 I usually present an idea by assembling objects.

PR5 20 I usually try to design a preliminary model.

Empathize EM1 1 I usually try to imagine the feelings of the users.

EM2 2 I usually try to consider the practice of the work.

EM3 3 I usually try to feel the needs of the users.

Define DE1 6 I usually make it clear about the problem that I am facing.

DE2 7 I usually try to clarify the problems that must be solved.

DE3 8 I usually try to find out the criteria of successful works.

DE4 9 I usually see the gap between the current work and the goal.

DE5 10 I usually try to clearly know the goal to achieve.

Ideate: examining an individual’s disposition to generate various solutions via brainstorming when designing a work. A sample item is ‘I usually generate solutions via brainstorming.’

Prototype: examining an individual’s disposition to present their ideas using an example or using a typical or preliminary model when designing a work. A sample item is ‘I usually make a model of the design.’

Empathize: examining an individual’s disposition to understand the feelings or perspectives of the users of his/her work when making the design. A sample item is ‘I usually try to imagine the feelings of the users.’

Define: examining an individual’s disposition to identify the needs of users or the problems users face when designing a work. A sample item is ‘I usually make it clear about the problem that I am facing.’

Inter-Correlation Among DTDS Subscales

Inter-correlation analyses among the four subscales were conducted to further observe the convergent validity and the discriminant validity of the DTDS. The results are summarized in Table 3. It is clear that all four factors are significantly (p < .001) correlated to each other, and all of the correlation coefficients ranged from .37 to .62, suggesting that medium levels of correlations existed among the factors. This means that the four factors of the DTDS coherently measured the students’ design thinking dispositions.

Table 3.
Inter-Correlation Matrix, CR and AVE of the DTDS (N = 350).

Cronbach’s alpha CR AVE Ideate Prototype Empathize Define

Ideate .79 .83 .50 .71

Prototype .86 .90 .64 .50* .80

Empathize .76 .84 .64 .45* .37* .80

Define .73 .79 .44 .62* .54* .48* .66

***p < .001. Bold values on the diagonal of the correlation matrix are the square root of AVE.

Further, this study calculated the composition reliability (CR) and the average variance extracted (AVE) for each factor (see Table 3). The CR of each factor ranged from .79 to .90, suggesting a good composition reliability for the DTDS. The values of AVE ranged from .44 to .64 for each factor. Although some of them were below .5, they were all greater than .36, which was still acceptable for convergent validity (Fornell & Larcker, 1981). The square root of AVE of each factor ranged from .66 to .80 and is listed in bold in Table 3. All correlation coefficients between factors were smaller than the square roots of the corresponding AVE, suggesting a good discriminant validity of the DTDS. Therefore, the DTDS was a valid and reliable instrument to assess the junior high school students’ design thinking dispositions.

Gender Difference in DTDS

Since gender and learning experience are usually the significant factors in computer-related learning domains, this study further explored the roles that gender and programming learning experience played in the respondents’ design thinking dispositions.

Table 4 shows that a significant gender difference was found only in the Ideate subscale, but not in any of the other three subscales. In the Ideate subscale, the male students’ item mean (Mean = 3.55, SD = 0.71) was significantly higher (t = 2.01, p < .05) than the female students’ (Mean = 3.39, SD = 0.75), with a small effect size (Cohen’s d = 0.22). This means that, for the junior high school students, the male students had slightly higher dispositions than the female students only in brainstorming for various solutions for designing a work. No gender differences were found regarding the dispositions to understand the users’ feelings, to know the users’ needs regarding their work, or to present a preliminary model of their work.

Table 4.
Independent t Tests of the DTDS Scores Between Genders (N = 350)

Male (n = 226)
Female (n = 124)
t Effect Size Cohen’s d

DTDS Mean SD Mean SD

Ideate 3.55 0.71 3.39 0.75 2.01* 0.22^#

Prototype 3.22 0.85 3.05 0.84 1.87 0.20

Empathize 3.70 0.80 3.67 0.87 0.31 0.04

Define 3.64 0.67 3.60 0.67 0.58 0.06

p < .05; ^#0.2≦|d|< 0.5 indicates a small effect.

Self-Directed Programming Learning Experience for DTDS

Regarding the junior high school students’ computer programming learning experience, 117 out of 350 junior high school students self-reported that they had self-directed programming learning experience* such as online learning or textbook self-studying. Therefore, the students were divided into two groups (With vs. Without) regarding their self-directed programming learning experience. Table 5 shows that significant differences were found in the following three subscales: Ideate (p <.001), Prototype (p <.001) and Define (p <.05). In the three subscales, the item means of the With group were significantly higher than those of the Without group, with small to medium effect sizes (Cohen’s d ranged from 0.27 to 0.57). This means that the junior high school students with self-directed programming learning experience tended to have significantly higher dispositions to define, ideate and prototype the work for their designs. This may suggest that the self-directed programming learning experience benefitted the young students’ design thinking dispositions.

Table 5.
Comparing the DTDS Scores of the Groups With and Without Self-Directed Programming Learning Experience

With self-directed learning experience (n = 117)
Without self-directed learning experience (n = 233)

DTDS Mean SD Mean SD t Cohen’s d

Ideate 3.71 0.73 3.39 0.70 3.97* 0.45^#

Prototype 3.47 0.84 3.00 0.81 5.01* 0.57^##

Empathize 3.78 0.77 3.64 0.84 1.50 0.17

Define 3.74 0.57 3.56 0.70 2.59* 0.27^#

p < .05; p < .001; ^#0.2≦|d|< 0.5; ^##0.5≦|d| < 0.8.

Correlations Analyses Between DTDS and CPSES

In order to explore the relationships between the students’ design thinking and their computer programming self-efficacy, Pearson’s correlation analyses were conducted between the subscales of the DTDS and the CPSES. According to Table 6, although no significant correlation was found between the DTDS’s Emphasize scores and the CPSES’s Algorithm scores, all the DTDS sub-scores were significantly and positively correlated with all the sub-scores of the CPSES, with correlation coefficients r ranging from .17 to .43 (i.e., small to medium levels). This showed that almost all the sub-dimensions of the two scales were more or less correlated to each other, suggesting that design thinking dispositions are significantly associated with computer programming self-efficacies.

Table 6.
Correlation Analyses Between the DTDS and the CPSES (N = 350).

CPSES DTDS

Ideate Prototype Empathize Define

Control .35* .34* .14 .34*

Logical thinking .38* .36* .17 .34*

Debug .43* .41* .19* .38*

Algorithm .38* .41* .10 .33*

Cooperation .37* .34* .28* .42*

Total .49* .47* .23* .46*

p < .01; p < .001.

The total score of CPSES were even more significantly correlated with the DTDS scores, when compared to the subscales of CPSES. That is, the total CPSES had significant correlations with the Ideate (r = .49, p < .001), Prototype (r = .47, p < .001), Empathize (r = .23, p < .001) and Define (r = .46, p < .001) dimensions of the DTDS. This means that the junior high school students’ computer programming self-efficacies had significant correlations with their dispositions to Ideate, Prototype, Empathize and Define when they were designing a work. Therefore, this study further conducted a regression analysis to examine whether the four DTDS sub-scores could significantly predict the total CPSES score. If they could, then that DTDS factor would be included in the prediction model.

Regression Analyses for Predicting CPSES by DTDS

The collinearity among the subscales of the DTDS was tested before conducting the regression analyses. The values of variance inflation factor (VIF) ranged from 1.50 to 1.84, indicating that there were no collinearity problems (VIF < 5) (Hair et al., 2019) for the predictors in the regression model. Since the four factors of the DTDS were all correlated with students’ overall computer programming self-efficacy, this study conducted a stepwise regression analysis including all four DTDS sub-scores in the model for predicting the CPSES total score. Table 7 shows that a significant prediction model was obtained from the analysis. The result showed that the CPSES total score can be significantly predicted by three DTDS factors: Ideate (β = .26, SE = .06, t = 4.40, p < .001), Prototype (β = .26, SE = .04, t = 4.77, p < .001) and Define (β = .16, SE = .06, t = 2.54, p < .01). Only Empathize was not included in the prediction model. The overall explained variance of the model was 31%. This means that students who had higher dispositions to ideate, prototype* and define thinking when designing a work would be more likely to have higher computer programming self-efficacies. This suggests that the design thinking dispositions of brainstorming, presenting examples and identifying needs may benefit junior high school students’ computer programing self-efficacies.

Table 7.
Stepwise Regression of Predicting CPSES by the DTDS Factors (N = 350).

Predictors S.E. β t Adjusted R²

CPSES total Ideate 0.06 0.26 4.40 0.31

Prototype 0.04 0.26 4.77

Define 0.06 0.16 2.54*

Constant 0.18 6.37**

*p < .01; **p < .001.

Discussion

The Design Thinking Disposition Scale

The present study developed the Design Thinking Disposition Scale (DTDS) to assess young students’ design thinking dispositions, with good validities and reliabilities, under the four dimensions: Empathize, Define, Ideate, and Prototype. The DTDS was designed to examine young students’ general design thinking dispositions while designing a work. Therefore, it can be applied to all students involving the design of a work in all learning contexts at schools above the junior high school level. It can also be applied to adult learners, teachers, faculty and school administrators at all levels. Researchers can use the DTDS to conduct large-scale studies relating to creative learning and teaching for 21st century education (Koh et al., 2015), such as the evaluation of teaching computer programming in computer literacy education. Educators can use the DTDS to understand students’ design thinking dispositions before and after courses, and further design the curricula to enhance students’ design thinking strategies.

Demographic Differences in Young Students’ Design Thinking Disposition

Gender differences in junior high school students’ design thinking dispositions were examined in this study. No significant gender differences were found in the Empathize, Define and Prototype dispositions while they design a work. However, a significant gender difference was found in the Ideate disposition. The male students had higher brainstorming dispositions than the female students. Since the sample of this study was selected from a STEAM course in which computer programming was used to design a work, this result may imply that, in the junior high school, the male students had a higher disposition to brainstorm a work via computer programming than the female students. This may be related to the male students’ higher self-efficacy of computer programming (M.-J. Tsai et al., 2019). Moreover, the higher disposition to brainstorm a design may explain why some studies in creativity (Stoltzfus et al., 2011; Proudfoot et al., 2015) have mentioned that males’ working outcomes of creativity were generally better than females’.

The plausible differences in young students’ design thinking due to the experience of self-directed programming learning was explored in this study. The young students who had self-directed computer programming learning experience had higher dispositions to Define, Ideate, and Prototype a work than those without such experience. This implies that self-directed computer programming learning experience may enhance young students’ dispositions to identify problems, to brainstorm solutions, and to present ideas when designing a work. This may be related to the high motivation and the higher order metacognitive skills such as control and self-management required in self-directed learning or self-regulated learning (Zimmerman et al., 1990). Razzouk and Shute (2012) indicated that designers’ design strategy derives from their previous experience of designing. The expert designers should be able to adopt multiple problem-solving strategies with flexible use and divergent thinking skills to deal with and achieve the task (Kavakli & Gero, 2002; Mentzer et al., 2015; Seitamaa-Hakkarainen & Hakkarainen, 2001). Such an enhancement of design thinking dispositions due to self-directed computer programming learning experience may further enhance students’ creative learning (Dorst & Cross, 2001; Florida, 2002). Self-directed learning experience may be further examined in the future computer programming courses.

Design Thinking Disposition Predicts Computer Programming Self-Efficacy

This study found a significant relationship between design thinking disposition and computer programming self-efficacy for junior high school students. Almost all sub-dimensions in the two conceptual constructs were inter-correlated with each other. Moreover, the students’ computer programming self-efficacy can be significantly and positively predicted by their design thinking dispositions of define, ideate and prototype. The results may be explained by the similar processes in design thinking and in computer programming (Park & McKilligan, 2018). Meanwhile, the results may suggest that not only computational thinking (Wing, 2006) but also design thinking (Brown, 2008) is an important factor in computer programming learning. This advances our understanding of computer programming learning processes. Future studies may examine the relationships between students’ design thinking and computational thinking as well as their predictions of students’ computer programming self-efficacy.

For teaching practice, the results suggest that design thinking disposition is important for young students’ computer programming learning, because it can contribute positively to their computer programming self-efficacy via defining a problem, brainstorming an idea and presenting a typical model for solving the problem. That is, encouraging young students to identify problem needs, to brainstorm ideas and to present preliminary models designed for solutions in computer literacy curricula may be helpful to improve their computer programming self-efficacy. On the other hand, an interdisciplinary curriculum in which design thinking work is integrated or embedded into computer programming tasks could simultaneously enrich young students’ creative thinking, critical thinking and complex problem-solving competencies (Ananiadou & Claro, 2009). Therefore, interdisciplinary and integrated STEAM or robotics curricula could become a solution for young students’ 21st century competencies.

Conclusion

The study developed and validated the DTDS scale for assessing young students’ design thinking disposition under the four dimensions of Empathize, Define, Ideate, and Prototype. The DTDS can be used to understand their design thinking dispositions in all learning contexts relating to design thinking, such as Maker, STEAM, Robotics and computer literacy curricula. The DTDS can be applied to all students above junior high school level. Additionally, gender difference was found only in the disposition of Ideate, and self-directed programming learning experience was found to benefit design thinking. Meanwhile, this study confirmed the relationship between students’ design thinking disposition and their computer programming self-efficacy. Furthermore, a significant prediction model was obtained in which the Define, Ideate, and Prototype dispositions of design thinking significantly predicted young students’ computer programming self-efficacy. This suggests that enhancing young students’ design thinking dispositions in identifying problems, brainstorming ideas and presenting models of solutions may improve their computer programming self-efficacy.

Future studies can be suggested in several dimensions. One, researchers can explore more about the roles that individual factors such as age, grade level, school level, parents’ socio-economic status as well as learning strategies in general play in students’ design thinking disposition. Two, the relationships among design thinking, computational thinking and computer programming self-efficacy can be further examined for the development of student’s 21st century key capabilities. Three, the impacts of a curriculum in which design thinking dispositions are enhanced, such as a STEAM, Maker or Robotics curriculum, on students’ computer programming self-efficacy can be examined. Finally, the DTDS can be further validated for teachers or school administrators, and the relationships among the design thinking dispositions among students, teachers and school administrators can be explored.

Limitations

The DTDS developed in this study may be limited in its application of targets due to the lack of a testing domain. Since formal testing is not emphasized in young students’ design curricula, the DTDS does not include a “Testing” domain. However, testing may be an important phase in the design curricula of higher education. Thus, although the DTDS can be applied to all students above middle school level, it is unable to explain students’ dispositions to test a design work, which could often occur in higher education. Future work could develop an additional Testing domain that could be applied as necessary to enhance the utility of the DTDS.

	Factor 1	Factor 2	Factor 3	Factor 4	M	SD	α
Factor 1: Ideate (ID)
ID 11	0.74	−0.01	−0.09	0.06	3.50	0.73	0.79
ID 12	0.78	0.01	0.04	−0.02
ID 13	0.69	0.05	0.02	0.05
ID 14	0.78	−0.06	0.07	−0.11
ID 15	0.52	0.08	0.07	0.14
Factor 2: Prototype (PR)
PR 16	0.08	0.75	0.01	−0.05	3.16	0.85	0.86
PR 17	−0.06	0.83	0.20	−0.08
PR 18	0.07	0.73	0.08	−0.01
PR 19	−0.00	0.82	−0.13	0.07
PR 20	−0.03	0.87	−0.13	0.01
Factor 3: Empathize (EM)
EM 1	−0.02	−0.04	0.81	0.04	3.69	0.82	0.76
EM 2	0.01	−0.06	0.84	0.03
EM 3	0.09	0.07	0.75	−0.03
Factor 4: Define (DE)
DE 6	0.13	0.14	−0.17	0.61	3.62	0.67	0.73
DE 7	−0.02	−0.20	0.03	0.88
DE 8	−0.23	0.21	0.13	0.66
DE 9	0.05	0.01	0.15	0.55
DE 10	0.22	−0.01	−0.06	0.55

Subscale		No.	Item
Guidance: When I design a work, ……….
Ideate	ID1	11	I usually generate solutions via brainstorming.
ID2	12	I usually come up with various solutions.
ID3	13	I usually come up with innovative solutions.
ID4	14	I usually come up with new solutions.
ID5	15	I usually think of different problem-solving approaches.
Prototype	PR1	16	I usually make a model of the design.
PR2	17	I usually draw a picture of the design.
PR3	18	I usually provide an example for showing an idea.
PR4	19	I usually present an idea by assembling objects.
PR5	20	I usually try to design a preliminary model.
Empathize	EM1	1	I usually try to imagine the feelings of the users.
EM2	2	I usually try to consider the practice of the work.
EM3	3	I usually try to feel the needs of the users.
Define	DE1	6	I usually make it clear about the problem that I am facing.
DE2	7	I usually try to clarify the problems that must be solved.
DE3	8	I usually try to find out the criteria of successful works.
DE4	9	I usually see the gap between the current work and the goal.
DE5	10	I usually try to clearly know the goal to achieve.

	Cronbach’s alpha	CR	AVE	Ideate	Prototype	Empathize	Define
Ideate	.79	.83	.50	.71
Prototype	.86	.90	.64	.50***	.80
Empathize	.76	.84	.64	.45***	.37***	.80
Define	.73	.79	.44	.62***	.54***	.48***	.66

	Male (n = 226)	Female (n = 124)	t	Effect Size Cohen’s d
Ideate	3.55	0.71	3.39	0.75	2.01*	0.22^#
Prototype	3.22	0.85	3.05	0.84	1.87	0.20
Empathize	3.70	0.80	3.67	0.87	0.31	0.04
Define	3.64	0.67	3.60	0.67	0.58	0.06

	With self-directed learning experience (n = 117)	Without self-directed learning experience (n = 233)
Ideate	3.71	0.73	3.39	0.70	3.97***	0.45^#
Prototype	3.47	0.84	3.00	0.81	5.01***	0.57^##
Empathize	3.78	0.77	3.64	0.84	1.50	0.17
Define	3.74	0.57	3.56	0.70	2.59*	0.27^#

CPSES	DTDS
Control	.35***	.34***	.14**	.34***
Logical thinking	.38***	.36***	.17**	.34***
Debug	.43***	.41***	.19***	.38***
Algorithm	.38***	.41***	.10	.33***
Cooperation	.37***	.34***	.28***	.42***
Total	.49***	.47***	.23***	.46***

	Predictors	S.E.	β	t	Adjusted R²
CPSES total	Ideate	0.06	0.26	4.40**	0.31
Prototype	0.04	0.26	4.77**
Define	0.06	0.16	2.54*
Constant	0.18		6.37**

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Ministry of Science and Technology in Taiwan (MOST 106-2511-S-003-065-MY3) and the ‘Institute for Research Excellence in Learning Sciences’ of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

Author Biographies

Meng-Jung Tsai is currently a distinguished professor in the Program of Learning Sciences, National Taiwan Normal University. Her research interests include students’ learning strategies utilized in computer literacy education and in various digital learning environments. She also uses eye-tracking technology to examine students’ visual attention in various learning environments, especially in online inquiry learning and game-based learning environments. Recently, she is interested in examining the roles of computational thinking and design thinking dispositions played in young students’ computer programming self-efficacy.

Ching-Yeh Wang is currently a postdoctoral research fellow in the Program of Learning Sciences, National Taiwan Normal University. He is interested in exploring effective factors in digital learning such as self-efficacy, learning attitudes and interests. He is also interested in using eye-tracking technology to explore students' online information reading such as conflicting information about socio-scientific issues and online multimedia recipe reading. Recently, he is interested in understanding the role of design thinking dispositions played in students' learning.

ORCID iD

Meng-Jung Tsai

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	Male (n = 226)		Female (n = 124)		t	Effect Size Cohen’s d
DTDS	Mean	SD	Mean	SD	t	Effect Size Cohen’s d
Ideate	3.55	0.71	3.39	0.75	2.01*	0.22^#
Prototype	3.22	0.85	3.05	0.84	1.87	0.20
Empathize	3.70	0.80	3.67	0.87	0.31	0.04
Define	3.64	0.67	3.60	0.67	0.58	0.06