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Article

Effects of Creativity Styles on Learning Engagement and Motivation in STEAM Education

by
Yenchun Jim Wu
1,
Chih-Hung Wu
2,* and
Kang-Lin Peng
3
1
Graduate Institute of Global Business and Strategy, National Taiwan Normal University, Taipei 106, Taiwan
2
Department of Digital Content and Technology, National Taichung University of Education, Taichung 400, Taiwan
3
Faculty of International Tourism and Management, City University of Macau, Macau, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(6), 2755; https://doi.org/10.3390/su17062755
Submission received: 12 January 2025 / Revised: 12 March 2025 / Accepted: 14 March 2025 / Published: 20 March 2025
(This article belongs to the Section Sustainable Education and Approaches)
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Abstract

:
Participating in science, technology, engineering, arts, and mathematics (STEAM) fosters learning engagement and improves student learning outcomes. This study explored the effects of creativity style on learning engagement and motivation in STEAM education to emphasize the critical inner process of learning. The curriculum content was established based on STEAM with artificial intelligence (AI) game development. A Creativity Assessment Questionnaire was designed to measure students’ learning motivation through STEAM creativity. Before the experiment, the participants completed a creativity questionnaire to determine their creativity style. This study included 65 undergraduate volunteers from a national university in central Taiwan. Before the experiment, all participants completed a questionnaire to determine their creativity style, classified as either low or high ACT (actively engage in generating ideas) or low or high FLOW. During the experiment, they were asked to participate in our designed STEAM-with-AI-game learning activity, and their motivation and engagement were evaluated. Although there was a limitation of generalizability due to nonrandom sampling, this study revealed the effects of creativity style on the relationship between motivation and engagement in STEAM education. Our findings showed that creativity styles played a critical role in learning motivation and engagement. In addition, the creativity styles of ACT and FLOW enhanced learning engagement and motivation in STEAM education through STEAM–AI gamification.

1. Introduction

Research has shown that science, technology, engineering, arts, and mathematics (STEAM) education can guide student inquiry, dialogue, and critical thinking, resulting in ideal learning outcomes [1]. In STEM education, the pedagogies do not involve traditional subject-oriented but technological object-oriented learning. However, the emphasis of STEM education on learning outcomes has led to the incorporation of arts into STEAM to supplement creativity in the learning process, which is essential for lifelong learning. Close attention should be paid to learning motivation and engagement in studying the effects of STEAM to bridge the research gap in the intermediate education process [2,3]. Learning engagement refers to the extent to which students are actively involved in the content of learning activities [4,5]. Students’ participation in STEAM encourages learning engagement for better learning outcomes.
To address the insufficiency of STEAM, studies have investigated the relationship between creativity and engagement [6]. Active engagement indicates that students take action or extend contact with the subject to increase their understanding [4]. Such level of engagement has a profound effect on learning outcomes because students who are truly engaged are more likely to succeed in learning [7]. According to most studies, cognitive engagement refers to cognitive activities that students engage in while being involved in academic learning tasks. Cognitive engagement is thoughtful learning with reflection, postulated cognition, and self-adjustmen strategies [8]. One study examined the association between engagement profiles, key motivations (science achievement goals and self-efficacy), and the science achievement of secondary school students, revealing that being moderately and behaviorally engaged was associated with higher science achievement [9,10] and well-being [11]. Engagement predicted a higher grade point average (GPA), educational ambition, lower dropout rates, higher college enrollment, and lower depression rates, whereas the opposite trend was found in students with low engagement [12].
The association between creativity and motivation has been investigated thoroughly in the literature [13]. Agnoli et al. [14] showed the key role of motivation in explaining creative achievement within the school environment. Liao, et al. [15] reported that creative pedagogy, which nurtures language learners’ creativity in the classroom, considerably improves young children’s motivation to learn English. Furthermore, individuals with creative thinking can master their emotions and use their creativity to achieve personal fulfillment [16] or create new product designs, such as the nonfungible token [17]. Motivation is strongly associated with creativity [18,19] and learning outcomes [20]. Teachers structured their subjects around STEM or STEAM education initiatives, emphasizing the core values of critical thinking and creativity, as demonstrated by [21,22].
However, the research on the effect of creativity styles ACT (actively engage) and FLOW on engagement and motivation in STEAM education remains limited. Thus, this study aimed to close the research gap by examining the influence of creativity styles on learning engagement and motivation in STEAM education, with a focus on the critical inner processes of learning. The creativity style of ACT assesses the cognitive activity necessary for creativity and generating creative solutions. Using ACT, people actively engage in generating ideas, often breaking down problems into smaller pieces, and systematically explore solutions. The creativity style of FLOW is a remarkable state in which creativity seems to occur naturally. It implies a mental state of intense concentration that is free from fear, boredom, and self-doubt and requires an open-minded environment with support and encouragement [19]. We extended the concept of engagement to include cognitive, behavioral, emotional, and social engagement, aiming to investigate the differences in learning engagement between the ACT and FLOW creativity styles before and after the implementation of the STEAM materials. This study offers a learning model for creativity styles, with learning engagement and motivation in the artificial intelligence (AI)-driven STEAM education system. This connection between AI and STEAM can be utilized to echo the abovementioned education theories. The STEAM–AI gamification could be designed in education to trigger students’ learning engagement and motivation for creativity. The following sections present the literature review, methodology, results, and discussion, and address the research questions in the conclusion for the empirical study.

2. Literature Review

2.1. The Development of STEM Education

Since the establishment of the National Science Foundation in 1950 to support research and education in scientific study fields, the crucial role of STEM education has been widely recognized as a key strategy for sustainable development in modern society [23]. From a student’s perspective, Moore [24] summarized three benefits of STEM education: providing students with a deeper knowledge of contextualizing concepts in each study discipline; enriching students’ knowledge of STEM disciplines through extensive exposure to socially and culturally relevant STEM contexts; and increasing their interest in participating in STEM disciplines [25]. From a macro perspective, Breiner, et al. [26] believed that STEM education is an important element supporting a country’s global competitiveness and economic future by cultivating professionals skilled in science and technology.

2.2. STEAM Education: The Integration of Creativity in STEM Education

Building on a process-oriented perspective of STEM education, the emphasis has shifted from defining its individual disciplines to exploring strategies that enable students to actively engage in innovation and creativity, thus solving real-world problems and seizing opportunities [27]. Given the increasing importance of creativity in fostering a competitive workforce in modern society [28], many scholars have argued that creativity-related components are fundamentally missing in STEM education and proposed adding an additional letter “A” into the acronym to coin a new term known as “STEAM education” [27]. The letter “A” in STEAM refers to art education that focuses on cultivating students’ creativity and innovation [29,30].
Creativity has been widely recognized as a broad and complex construct because it can be defined from various perspectives [31]. As a pioneering attempt to quantify and measure creativity in education, Miller and Dumford [32] conceptualized creativity as a bidimensional construct comprising two distinct types of processes—deliberate and intuitive—with the former emphasizing and the latter deemphasizing the role of educators’ instruction in cultivating students’ creativity. Besides this conceptualization, Conrady and Bogner [33] proposed two other dimensions in STEAM education—ACT and FLOW—developing an 8-item measurement scale for measuring students’ creativity in STEAM education. The ACT dimension is similar to deliberate process, highlighting students’ conscious and trainable cognitive processes. Unlike Miller and Dumford [32], who conceptualized creativity exclusively as cognitive processes, Conrady and Bogner [33] took individuals’ mental states into account and proposed FLOW as a replacement for intuitive process to describe the extent to which students fully immerse themselves in a learning activity. This instrument provided new opportunities to investigate how STEAM education should be designed to overcome the barriers of traditional STEM education [34] because the letter “A” distinguishes STEM education from STEAM education by highlighting the important role of creativity. However, Perignat and Katz-Buonincontro [35] asserted that research on the possible learning outcomes, such as learning motivations and engagements, remain scarce in the field of educational creativity.

2.3. Creativity and Learning Engagement

Students’ learning engagement is an important learning outcome of STEAM education [36]. Learning engagement holds international priority in any educational context because it is known to improve learning performances [8,10,37]. As a concept rooted in the flow theory [38], learning engagement is expected to demonstrate a direct link with creativity [39] as students tend to be more immersed when looking for creative responses to a particular challenge [40]. This argument was verified by McGrath and Brown [41], who discovered that creativity education (i.e., participations in arts workshops) significantly enhanced the learning engagement level of STEM students. Hence, Richardson and Mishra [42] proposed learner engagement as a key dimension of an effective learning environment that cultivates student creativity. Learning engagement had been commonly framed as a unidimensional construct until [8] highlighted conflicting unidimensional views of this psychological variable and proposed a tridimensional conceptualization that involved cognitive, behavioral, and affective engagement to reflect its complex, dynamic, and multidimensional structure [4].
Cognitive engagement refers to how students think about and pay attention to learning activities [7], thus determining their self-regulated behaviors [8]. Behavioral engagement illustrates the amount of time and energy students devote to a learning activity [37]. Affective engagement focuses on the extent to which students show positive emotional states toward teachers, classmates, and schools, encompassing one’s sense of belonging in terms of various learning aspects [43]. Given the extensive use of technology (e.g., online teaching) in education, many scholars have started to recognize the incomprehensive tridimensional conceptualization of learning engagement in online learning contexts [44,45] because social networking learning environments, such as Second Life® and Knowledge Forum®, emphasize the role of social interactions in sharing knowledge, collaboration, and facilitating reflections in the learning process [46]. Specifically, social engagement has been proposed as a supplementary dimension to the tri-dimensional conceptualization of learning engagement [8]. Deng et al. [47] further described it as the degree to which students engage in social interactions with teachers and peers.
Hence, this study conceptualized learning engagement as a construct consisting of cognitive, behavioral, affective, and social dimensions, examining how creativity in STEAM education fosters students’ learning engagement. In addition to traditional in-classroom engagement, engagement-related research has been used in cross-campus and organized off-campus settings (e.g., community organizations and extracurricular activities).

2.4. Creativity and Learning Motivations

As an important concept in education, creativity has widely been recognized as a crucial driver of student learning motivation, especially for difficult subjects such as mathematics [48,49]. While initial studies in education suggested that extrinsic motivation results in better learning performance [50] and higher quality learning [51], more recent evidence has proven the importance of intrinsic learning motivations, specifically curiosity and creativity, on boosting learning motivations [52,53,54]. Csikszentmihalyi [55] asserted that creative people tend to recognize the process of learning as an enjoyable experience, thus perceiving STEM education as attractive [56]. This argument was adopted by Urban [57], who identified motivation as one of the six key components for the development of individuals’ creativity. Many scholars have suggested that successful learning requires an appropriate learning environment in which personal psychological variables such as creativity and motivation can be cultivated [15,58]. However, few studies have investigated the extent to which students’ learning motivations are affected by creativity in the STEAM education context [19,35]. While several scholars have identified a significant correlation between science students’ creativity and learning motivation [59,60], most recognized learning motivation as a unidimensional construct and overlooked its multidimensional nature. Tuan et al. [61] proposed six components of students’ learning motivations—self-efficacy, active learning strategies, science learning value, performance goal, achievement goal, and learning environment stimulation—to encourage a multidimensional conceptualization of students’ learning motivations in the education literature.
Amabile [62] claimed that motivation drives creativity, which echoes the pedagogy of arts learning within STEAM education. Lin and Tsai [63] revealed that implementing the pedagogical STEAM model enhanced students’ project competence and learning motivation in an experimental group. Evidently, the reciprocal causation of creativity and motivation can transform the knowledge of learners’ cognition and epistemic beliefs in the STEM learning process [64]. Regarding the link between creativity styles (active [ACT] and FLOW) and creativity, studies have shown that the subscales of ACT and FLOW measure different cognitive aspects of creativity [19,22]. Creativity styles presents strong causal links with personality type [65], creative capacity [66], fantasy proneness [66], and the characteristics of products at work [67]. Creative teaching methods associated with games may enhance learners’ engagement. Specifically, by experiencing and immersing oneself in the game flow, players may gain knowledge and skills [68,69]. By contrast, active learning has been found to be strongly linked to student engagement [70,71]. Cicuto and Torres [72] documented that students in an active learning environment had a higher level of motivation than in pharmacobiochemistry courses. Furthermore, Julià and Antolí [73] stated that implementing a long-term STEM-based active learning course has a beneficial effect on student motivation. The flow experience is a motivational factor in the learning process and can be measured as an outcome variable [69]. Furthermore, intrinsic motivation is potentially associated with creativity flow [22].

3. Material and Methods

To investigate the correlation between creativity styles, learning motivation, and learning engagement in STEAM education, this study employed a research model that included the ACT and FLOW styles. We also used a mixed-method experimental design with quantitative and qualitative data collected using a creativity style questionnaire, learning motivation assessment, and learning engagement assessment, which was divided into the following four sections: cognitive, behavioral, emotional, and social. An experimental design was chosen because it eliminates bias, ensures reproducibility, and generates reliable data through systematic, controlled investigation methods. The types of experimental design include completely randomized design, randomized block design, factorial design, Latin square design, matched-pairs design, and quasi-experimental design. This study adopted a quasi-experimental design because it is typically used in situations where not all variables can be controlled in real-world settings, such as in school education research. The conditions of this study did not allow for the complete randomization required by a true experimental design.

3.1. Research Model

This study proposed a research model to depict the correlation between creativity styles, learning motivation, and learning engagement in STEAM education (Figure 1). The creativity styles included the ACT and FLOW styles. Learning engagement had four dimensions—cognitive, behavioral, emotional, and social. Learning motivation had six dimensions—self-efficacy, active learning strategy, science learning value, performance goal, achievement goal, and learning environment stimulation.

3.2. Experimental Design

This study included 65 undergraduate volunteers from a national university in central Taiwan. Before the experiment, all participants completed a questionnaire to determine their creativity style, classified as either low or high ACT or low or high FLOW. At the beginning of the experiment, the students received lectures on STEAM knowledge with AI game design. Personal computers, Micro:bits, and webcams were provided before starting the learning activities. After completing the learning activities, each student underwent an assessment of learning motivation and engagement to find and analyze differences across various creativity styles. The following section describes in detail the design of our STEAM activity, which utilized AI.
STEAM concepts: Our activity required students to use the image-recognition function with micro:bit hardware and webcam to complete a task. Science: Students learned about AI (specifically neural network theories and algorithms) to understand image recognition. Technology: Students learned how to write AI programs using Kittenblock software, which connects micro:bit hardware through a Blockly interface programming language. Engineering: To assemble the system, students had to understand the structures of various hardware parts such as micro:bit and motors. Arts: The activity introduced concepts and the programming language of graphic design, allowing students to design their own images for the AI’s response to users after receiving the results of image recognition. Mathematics: Students had to understand the necessary mathematical formulas to calculate correct rates and optimize the results of neural networks for image recognition.

3.3. Measurement Instruments

We employed the Creativity Assessment Questionnaire for STEM or STEAM creativity measurement [19]. An 8-item questionnaire was proposed to examine the reliability of the data of 2713 students (11–19 years old). The questionnaire was divided into two domains: ACT, which contained the cognitive process of awareness and training; and FLOW, which measured creativity at the psychological level. The questionnaire was not gender specific and was validated for use in the STEAM program assessment. We used creativity measurement to categorize students’ creativity style as low or high ACT or FLOW styles. The ACT creativity style denoted participants’ preference for covering conscious and trainable cognitive processes. It also indicated that the cognitive processes were conscious and active operations that could be taught and trained [19]. The FLOW creativity style indicated that participants preferred to perform an activity in which they could become fully immersed in a feeling of energized focus, complete involvement, and enjoyment. Flow usually causes high intrinsic motivation [19,74]. To explore the changes in students’ motivation to learn after the integration of creativity into the STEM learning environment, the Science Learning Motivation Questionnaire developed by Tuan et al. [61] was used. The questionnaire included 35 questions, scored on a 5-point Likert scale, of which 9 were reversed and 26 were positive questions. The scale measured the following six domains: self-efficacy (7 questions), active learning strategies (8 questions), science learning value (5 questions), performance goal orientation (4 questions), achievement goal orientation (5 questions), and learning environment stimulation (6 questions). The motivation questionnaire is provided in the Appendix. We adopted a questionnaire that measures students’ motivation toward science learning, which included the following six dimensions: self-efficacy, active learning strategies, science learning value, performance goal, achievement goal, and learning environment stimulation [61]. To measure the learning engagement in STEAM education, we adapted the STEM Education Engagement Questionnaire developed by Fredricks et al. [75]. This questionnaire was originally designed to assess students’ engagement in mathematics and science and comprised four main components: behavioral, emotional, cognitive, and social engagement. The questionnaire has been proven to be reliable and valid.

3.4. Material

In this study, AI games served as an educational tool and research instrument. They provided a modern, interactive platform, allowing researchers to study how different creativity styles affect learning outcomes in STEAM education while serving as an engaging learning medium for students. The orientation of the AI game was as an educational and experimental research tool. It was based on the assessment for measuring learning outcomes, focused on integrating STEAM disciplines, and emphasized the individual creativity styles and engagement of students. This approach represents a modern evolution in STEAM education, where technology (AI games) is used not just as a subject to learn about but also as an active tool for learning and assessment.
This study designs an AI-integrated STEAM teaching material, guiding students to create an AI-based image recognition game. The AI-STEAM project-based learning material consists of both hardware and software components. Hardware includes Micro:bit, motors, a webcam, and an expansion board. Software includes Kittenblock, a block-based programming language that supports Micro:bit and allows the integration of machine learning libraries for AI-based image recognition projects. Teaching and learning activity process have three phases. 1. The teacher instructs students on how to build an image recognition project. 2. Students engage in hands-on practice with the provided AI-STEAM materials. 3. Students apply what they have learned to create their own image recognition projects, including designing their own interface.

4. Results and Discussion

The results were analyzed and discussed and are presented in the following four sections. Section 4.1 compares the differences in learning engagement and low or high ACT/FLOW creativity styles between the control and experimental groups. Section 4.2 analyzes how learners’ low or high ACT/FLOW creativity styles influence their learning engagement. Section 4.3 discusses the findings and implications of the study.

4.1. Effect of Creativity Styles on Learning Engagement

Table 1 shows significant differences between low and high ACT for all dimensions except for the social dimension, indicating that the high ACT group experienced higher learning engagement than the low ACT group. Table 2 shows significant differences between low and high FLOW, which means that the high FLOW group had higher learning engagement than the low FLOW group.

4.2. Effect of Creativity Styles on Learning Motivation

Table 3 shows that the difference between the low and high ACT groups is significant only for the active learning strategies and science learning value scales, indicating that the high ACT group had higher learning motivation for active learning strategies and science learning value than the low ACT group. Table 4 shows the significant differences between the low and high FLOW groups for all scales except for the performance goal scale, implying that the high FLOW group had higher learning motivation than the low FLOW group.

4.3. Discussion

This study provided a systematic conceptual framework for explaining the relationship between creativity styles and engagement and motivation as shown in Figure 2. Furthermore, this study clarified and highlighted the significance of the level of creativity style (ACT and FLOW) in capturing student engagement and motivation. Our study makes the following contributions to current literature.
Figure 3 and Figure 4 showed the graphical outcomes of this study. First, the ACT style was identified as a critical factor affecting learners’ engagement. Specifically, the results showed that the high ACT group had higher learning engagement than the low ACT group. Students with high self-directed learning ability demonstrated significantly more reading engagement and engaged significantly more in self-directed learning behaviors than students with low self-directed learning ability [71]. Rashid and Asghar [76] presented a direct effect of student engagement on self-directed learning. This study extended these findings by examining the predictive role of the ACT style in the interplay between creativity and engagement.
Second, the results of the present study support those of previous scholars, who concluded that by immersing students in video-based learning methods, game-integrated learning pedagogy, and project-based learning methods, educators can drive students’ deep engagement in courses [70,77,78]. In a more general way, our research highlighted the effect of the FLOW style on learning engagement. Accordingly, in the context of STEAM education, the high FLOW group attained more learning engagement than the low FLOW group.
Third, the relationship between creativity styles and learning motivation was confirmed in this study, which is similar to the results of Conradty and Bogner [19], who demonstrated that creativity is correlated with motivation. Similarly, it has been widely accepted that creativity and intrinsic motivation are interdependent [33]. Our conclusion further clarifies the effect of creativity styles (ACT or FLOW) on learning motivation. Therefore, our study contributes to the existing literature on this topic.
According to our study, students with high ACT exhibited greater learning motivation in the active learning strategies and science learning value dimensions compared with the low ACT group. Our findings support and extend those of Li et al. [71], who pointed out that students with a higher level of self-directed learning ability exhibited noticeably more autonomous behavior, motivation, and self-governance in their extensive reading practice than those with lower levels of self-directed learning ability. This study also makes a significant contribution by expanding the idea that active learning is an effective strategy for increasing students’ motivation, facilitating knowledge construction, and encouraging higher-order thinking [33,79].
Finally, research has focused on the relationship between flow experiences and elements of learning motivation, including self-efficacy, learning strategy, performance goals, achievement goals, learning environment, and values. In addition, Wang and Chen [80] showed how the flow experience of learners in game-based learning is affected by game strategies. The integration of flow experiences into an e-learning environment and game-based science learning environment can enhance student learning outcomes [81] and perceived learning values [69]. Simultaneously, integrating augmented reality technology into education can facilitate students’ experience of the psychological state of flow, leading to improved learning performance [82]. Moreover, Yen and Lin [83] reported that in business simulation-based learning settings, the flow experience serves as an antecedent of learning performance. Our research delves deeper into how creativity flow and learning motivation are closely interrelated. We found that the group that experienced high FLOW exhibited a higher level of learning motivation than the low FLOW group. Specifically, the social cognitive theory can be employed to explain the relationship of creativity with engagement and motivation [84]. Teachers’ feedback on creativity significantly contributes to motivating students to learn. When teachers encourage creative thinking and stress its significance, students are more likely to engage in productive behaviors [84,85].

4.4. Recommendations for Practice

The STEM and STEAM approaches have been demonstrated to be effective in fostering learners’ creativity, according to numerous educational theories, including behaviorism, cognitive approaches, constructivism, humanism, and connectivism. Owing to the distinctive demands of gamified environments, conventional learning management systems and learning tools may not able to fully utilize the potential of gamification. The STEAM–AI system can perform well for the effectiveness of gamification to enhance learners’ creativity. Based on our findings, educators should consider implementing several key practices in their STEAM classrooms. First, they should assess their students’ creativity styles (i.e., ACT and FLOW) before designing learning activities as these styles significantly affect engagement and motivation. They may consider using the Creativity Assessment Questionnaire as a preliminary tool at the start of each term. Once they understand their students’ creative approach, educators can tailor AI game development activities and other STEAM projects to complement these different learning styles. For instance, students with an ACT style may benefit from more structured, goal-oriented AI programming tasks, whereas leaners with a FLOW style may thrive with open-ended, exploratory projects. Furthermore, teachers should create a learning environment that supports both creativity types, perhaps by offering flexible project options or varying the structure of assignments. Regular monitoring of student motivation through tools such as the STEAM questionnaire could help teachers adjust their approaches as needed, ensuring that both ACT- and FLOW-style learners remain engaged throughout the learning process.

5. Conclusions

We applied the pedagogy of STEAM–AI game education to explore the cognitive processes of creativity, motivation, and engagement. The results showed that computer training does not influence learning engagement, which should be intrinsically motivated by learners. The learning activity design of ACT and FLOW on the STEAM–AI program is crucial for learning engagement that could benefit the learning process. The learning motivation of active learning strategies and science learning value significantly affected learning actions. The learning motivation of active learning strategies, science learning value, achievement goal, and learning environment stimulation exerted considerable influences on mental flow. Thus, we concluded that training the creativity styles of ACT and FLOW in the STEM–AI program can enhance learning engagement and motivation. Specifically, STEAM education aims to cultivate the ability to face real-world challenges. The intrinsic learning process of STEAM–AI programs, combined with exciting and challenging life situations, ignites learners’ curiosity and desire to explore. It allows learners to implement ideas in practice and undertake actions based on mental flow to test whether the ideas can meet the implementation needs. Through educational activities combined with life situations and applied practice, learners can acquire the ability and perspective to face life’s challenges.

Limitations

Our findings are limited in generalizability owing to the small, nonrandom sample size, the specific cultural and educational context of Taiwan University, and the focus on AI-driven gamification in STEAM education. Future studies could increase the sample size or employ alternative sampling methods to further validate our findings. The variables of learning engagement and motivation can be extended to other learning constructs to compare the effects of creativity styles on novel constructs.

Author Contributions

Conceptualization, Y.J.W. and C.-H.W.; invstigation, Y.J.W. and C.-H.W.; methodology: C.-H.W. and K.-L.P.; software: C.-H.W.; supervision: Y.J.W.; data curation: C.-H.W.; resources: C.-H.W.; validation: K.-L.P.; writing-original draft preparation: Y.J.W. and C.-H.W.; writing-review and editing: C.-H.W. and K.-L.P. funding acquisition: Y.J.W. and C.-H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Science and Technology Council with grant number [MOST 110-2511-H-142-008-MY2; NSTC 113-2410-H-003-144-MY3].

Institutional Review Board Statement

This study was waived by Ministry of Science and Technology (MOST) in Taiwan. As part of the MOST funding application process, the research proposal underwent a rigorous review by a panel of external experts.

Informed Consent Statement

Participants were informed that their responses would be collected and analyzed solely for academic purposes, and no identifiable personal information would be collected. Additionally, semi-structured interviews were conducted anonymously through an open-ended online questionnaire to gather more detailed insights. Participation in both the questionnaire and the interviews was entirely voluntary, and completion of either was considered as implied consent.

Data Availability Statement

No new data were created or analyzed in this study.

Acknowledgments

We gratefully acknowledge Wai Ching Wilson AU for his valuable input that helped refine this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Research Model of Creativity Style, Learning Engagement, and Learning Motivation.
Figure 1. Research Model of Creativity Style, Learning Engagement, and Learning Motivation.
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Figure 2. Relationship between creativity styles and engagement and motivation for STEAM education enhancement.
Figure 2. Relationship between creativity styles and engagement and motivation for STEAM education enhancement.
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Figure 3. Comparisons of creativity styles (ACT and FLOW) for engagement. * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001.
Figure 3. Comparisons of creativity styles (ACT and FLOW) for engagement. * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001.
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Figure 4. Comparisons of creativity styles (ACT and FLOW) for motivation. * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001.
Figure 4. Comparisons of creativity styles (ACT and FLOW) for motivation. * denotes p < 0.05; ** denotes p < 0.01; *** denotes p < 0.001.
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Table 1. t-test results of the engagement in two groups (low vs. high ACT).
Table 1. t-test results of the engagement in two groups (low vs. high ACT).
Low ACT Group (n = 21)High ACT Group (n = 44)
EngagementMeanStd.MeanStd.tSig.
Cognitive3.6540.3934.1280.476–3.9530.000 ***
Behavioral3.3450.3813.6930.508–2.7820.007 **
Emotional3.5100.5444.0390.558–3.6020.001 **
Social3.9930.3684.2010.571–1.7670.083
** p < 0.01; *** p < 0.001.
Table 2. t-test results of the engagement in two groups (low vs. high FLOW).
Table 2. t-test results of the engagement in two groups (low vs. high FLOW).
Low FLOW Group (n = 13)High FLOW Group (n = 52)
EngagementMeanStd.MeanStd.tSig.
Cognitive3.4420.3054.110.449–5.049 0.000 ***
Behavioral3.1250.3923.6940.454–4.149 0.000 ***
Emotional3.3230.4824.0030.555–4.053 0.000 ***
Social3.8790.3544.1980.538–2.021 0.047 *
* p < 0.05; *** p < 0.001.
Table 3. t-test results of the learning motivation in two groups (low vs. high ACT).
Table 3. t-test results of the learning motivation in two groups (low vs. high ACT).
Low ACT Group (n = 18)High ACT Group (n = 43)
MotivationMeanStd.MeanStd.tSig.
SE3.2210.4863.4910.671–1.5430.128
AS3.8010.4844.2230.414–3.4490.001 **
SV3.6560.6614.1350.557–2.9010.005 **
PG3.2010.7593.1691.2320.1270.900
AG3.6560.5973.9000.592–1.4660.148
LE3.7030.5413.9990.561–1.9030.062
** p < 0.01.
Table 4. t-test results of the learning motivation in two groups (low vs. high FLOW).
Table 4. t-test results of the learning motivation in two groups (low vs. high FLOW).
Low FLOW Group (n = 11)High FLOW Group (n = 50)
MotivationMeanStd.MeanStd.tSig.
SE2.9860.5763.5060.608–2.5860.012 *
AS3.6520.3114.1970.447–3.8340.000 ***
SV3.4180.5624.1200.567–3.7220.000 ***
PG3.6140.8393.0851.1431.4460.154
AG3.4000.4383.9220.592–2.7540.008 **
LE3.4250.4834.0130.537–3.183 0.002 **
* p < 0.05; ** p < 0.01; *** p < 0.001. SE: Self-efficacy; AS: Active learning strategies; SV: Science learning value; PG: Performance goal, AG: Achievement goal; LE: Learning environment stimulation.
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Wu, Y.J.; Wu, C.-H.; Peng, K.-L. Effects of Creativity Styles on Learning Engagement and Motivation in STEAM Education. Sustainability 2025, 17, 2755. https://doi.org/10.3390/su17062755

AMA Style

Wu YJ, Wu C-H, Peng K-L. Effects of Creativity Styles on Learning Engagement and Motivation in STEAM Education. Sustainability. 2025; 17(6):2755. https://doi.org/10.3390/su17062755

Chicago/Turabian Style

Wu, Yenchun Jim, Chih-Hung Wu, and Kang-Lin Peng. 2025. "Effects of Creativity Styles on Learning Engagement and Motivation in STEAM Education" Sustainability 17, no. 6: 2755. https://doi.org/10.3390/su17062755

APA Style

Wu, Y. J., Wu, C.-H., & Peng, K.-L. (2025). Effects of Creativity Styles on Learning Engagement and Motivation in STEAM Education. Sustainability, 17(6), 2755. https://doi.org/10.3390/su17062755

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