Examining influencing factors of teacher education students’ creativity in Chongqing Municipality

Abstract

In fostering creativity among teacher education students, attention to both internal and external factors is crucial. This study explores the impact of curriculum teaching on student creativity. Using a quantitative approach, 349 students from three Chongqing universities were surveyed. Results indicate that intrinsic motivation and teaching models significantly affect student creativity. Teacher influence and learner traits also play a role, although to a lesser extent. Surprisingly, the learning environment showed no significant relationship with creativity. These findings inform recommendations for innovative teaching strategies to enhance student creativity.

Keywords:

• Teacher education students
• creativity
• influencing factors
• Chongqing Municipality

REVIEWING EDITOR:

• Ramanathan Subramaniam, Nanyang Technological University National Institute of Education, Singapore

SUBJECTS:

• Higher education
• Creativity
• Teachers & Teacher Education

Introduction

Regarding the trends of the 21^st century skills learning, we deeply felt that the rapid development of science and technology has opened up a new era for us. The world is undergoing deepening multi-polarity, economic globalization, social informatization and cultural diversity. Science and technology are advancing rapidly, and the talents competition is becoming increasingly fiercer (Griffin et al., 2020; Weichert, 2021; Bastos & Souza, 2021).

Against this backdrop, countries have fully aware that the competition for economy, science and technology and comprehensive national strength ultimately boils down to competition for talents, particularly competition for their creativity talents (Sternberg & Lubart, 2020; Reiter-Palmon & Kaufman, 2018). In February 2019, China promulgated its first mid- to long-term strategic plan for education modernization, namely ‘China’s Education Modernization 2035 plan’, which includes ‘improving training process and innovation ability of top-notch talents’ as one of the ten major strategic tasks of China’s education modernization, reflecting the country’s urgent need for innovative talents (The Communist Party of China Central Committee & the State Council, 2019). To cultivate more high-quality innovative talents through a highly innovative and capable teaching force, it is crucial to focus on developing the creativity of teacher education students who will become future educators. As future teachers, their level of creativity will inevitably impact their ability to educate innovatively, which is directly related to the cultivation of China’s future innovative talents and the quality of national education (Huang et al., 2021; Zhou & Su, 2020). In the ‘Education Informatization 2.0 Action Plan’ issued by the Ministry of Education of the People’s Republic of China in 2018, the cultivation of creativity among teacher education students was identified as a priority. This demonstrates that creativity has become a core competency for teacher education students and an urgent requirement for future educators in contemporary society (China’s Ministry of Education, 2018).

The creativity of teacher education students is critical to improving the quality of education Liu, 2019), advancing educational reform and development in China, and most importantly, cultivating high-quality innovative talents (Zhao & Qu, 2022; Liang, 2019). In order to effectively enhance the creativity of teacher education students, it is imperative to conduct a thorough analysis of the factors influencing their creativity. Li (2020) conducted an analysis based on Sternberg’s six elements of creativity development, encompassing personality traits, motivation, and environmental factors among six dimensions. Chen (2023) measured the significant impact of emotional intelligence on creativity through relevant scales. Apart from psychological perspectives on the influencing factors of creativity, scholars have examined various aspects, such as teaching strategies employed by teachers (Cao, 2022), individual teacher characteristics (Zhang, 2024), teaching environment (Palanica et al., 2019; Fu et al., 2022; Wang & Liu, 2021), and teaching models (Xu, 2022; Wang & Song, 2023; Gao et al., 2022) on students’ creativity. From diverse research perspectives, the analysis of factors influencing creativity exhibits a multifaceted nature. To comprehensively understand this issue, the present study adopts a holistic approach, categorizing factors affecting creativity into internal and external dimensions, within the framework of curriculum teaching. Internal factors are primarily examined from a psychological standpoint, involving the learning activities’ subject – individual students. This encompasses students’ learning motivations, personality traits, and the knowledge and skills they possess. External factors are predominantly viewed from the perspective of curriculum teaching implementation, involving teachers, instructional models, and the teaching environment. This comprehensive analysis of influencing factors provides valuable insights for reforming curriculum teaching to enhance students’ creativity.

Literature review

Definition of creativity

The concept of creativity has had a long history of definitions and redefinitions (Dacey, 1999). Despite decades of research of this field, there is still no universally accepted definition of creativity. Creativity is defined as the ability to generate ideas that are original, novel, and appropriate to the situation (Guilford, 1950; Pontis et al., 2024). It involves the creation of innovative and practical ideas within the framework of social contexts (Amabile 1996; Karunarathne & Calma, 2024), with an emphasis on the subjective experience of the creator (Csikszentmihalyi, 1996; Toval-Gajardo et al., 2023). Sternberg and Lubart (1999) suggested that creativity involves both the ability to generate novel ideas and the ability to judge their quality. While there is no consensus on a single definition of creativity, it is generally agreed that it involves the production of something novel and valuable, often within a social or cultural context (Reiter-Palmon et al., 2019). Moreover, creativity is not limited to artistic or intellectual pursuits, but can be found in all areas of human endeavor, from science and technology to business and politics.

Drawing upon these perspectives, creativity may be conceptualized as the ability of individuals to produce creative products or solutions. (Glăveanu, 2023) For teacher education students, their creativity refers to the ability to produce creative educational products or solutions (Chen & Zhang, 2021), such innovative instructional design, courseware, micro-courses and so on. (Du, 2020)

In order to gain a nuanced comprehension of creativity, careful consideration must be given to pivotal assessment metrics, notably fluency, flexibility, and originality. (Guilford, 1950; Kulish & Cheng, 2023). Fluency refers to an individual’s capacity to generate a large quantity of creative ideas, manifested as the continuous and unrestricted flow of creative thoughts. Subsequently, flexibility involves the ability to flexibly switch thinking modes when solving problems or generating ideas, demonstrated by the diversity and adaptability of thinking processes. Originality emphasizes the uniqueness and distinctiveness of creative ideas, indicating an individual’s capability to produce uncommon and distinctive creative concepts (Leroy et al., 2023; Jiang et al., 2023). These indicators form a crucial framework for assessing and understanding creativity in academic research, commonly employed within scholarly discourse.

Thus, within the cohort of education majors, creativity indicators can be gauged through their performance in developing creative educational products or solutions (Zhu, 2020). Fluency is evident in their ability to construct educational content coherently and innovatively, while flexibility is displayed through their adeptness in adeptly adapting and refining instructional designs. Originality is reflected in their ability to furnish students with distinctive and creatively enriched learning experiences. These benchmarks are instrumental in evaluating students’ manifested creative thinking abilities within the realm of education.

Factors affecting creativity development of teacher education student

Numerous previous studies indicate creativity is a multidimensional construct that can be influenced by a variety of factors. For instance, individual traits, environmental factors, educational approaches and so on. Personality traits such as openness to experience, divergent thinking, and intrinsic motivation have been found to be positively associated with creativity (Runco & Jaeger, 2012; Li et al., 2023). Environmental factors such as social support, access to resources, and exposure to diverse experiences can facilitate creative development (Amabile1983; Glaveanu & Kaufman, 2019). Educational approaches also play an important role in fostering creativity. Teachers can adopt various teaching methods to enhance students’ creative abilities, such as project-based learning, inquiry-based learning, and problem-based learning (Craft 2005; Eshqobilov, 2023; Guo & Dou 2018). These methods provide students with opportunities to explore and experiment, and encourage them to generate original ideas and solutions. Furthermore, the evaluation of students’ creative work is crucial to the development of creativity. Traditional assessments, such as standardized tests, tend to focus on convergent thinking and do not adequately measure creative abilities (Feng & Feng 2024; Sawyer 2011). Alternative assessment methods, such as rubrics, portfolios, and self-assessments, can better capture students’ creative potential and provide constructive feedback to further their growth (Xia 2022; Zhang &Luo 2022; Kim 2011). Some scholars also categorize these factors as internal and external influences. Csikszentmihalyi (1996) identified three internal factors that contribute to creativity: domain-relevant skills, creativity-relevant processes, and intrinsic task motivation. In contrast, external factors such as cultural and environmental influences have been identified as important factors that influence creativity (Wang & Song 2023; Runco & Jaeger 2012). Another studies (Yuan et al. 2023; Plucker & Makel, 2010) emphasized the importance of both internal and external factors in fostering creativity. They argued that both sets of factors interact to influence the development of creativity in individuals. Among the factors, this research will identify as following:

Internal factors

Internal factors affecting creativity among teacher education students are mainly analyzed from the perspective of the students themselves in this research. These factors include intrinsic motivation, personality traits, and related knowledge and skills.

Intrinsic motivation

The influence of internal factors on creativity development among education students has been widely explored, with internal motivation being recognized as a pivotal element. Empirical research has indicated that intrinsic motivation, characterized by a deep-seated interest and pleasure in the subject, can play a crucial role in stimulating the production of creative ideas (Mdhlalose 2024; Craft 2005). When examining the impact of intrinsic motivation on education students, it becomes evident that it correlates positively with their engagement in exploratory and innovative thinking, willingness to take risks, and perseverance in problem-solving tasks, all of which are fundamental aspects of creativity (Geng 2023).

Personality traits

Personality traits are also recognized as crucial factors that impact the development of creativity (Brás et al., 2024; Plucker & Makel, 2010). Research has identified specific personality traits associated with creativity among teacher education students. For example, Klassen et al. (2011) found that openness to experience and extraversion were positively related to creativity in Canadian pre-service teachers. Similarly, it is found that nonconformity, risk-taking, and independence were linked to creativity (Ozturk & Ozturk, 2023). In addition, personality traits can also influence individuals’ motivation to engage in creative activities (Zheng et al. 2023). Overall, these findings highlight the importance of considering personality traits when examining the development of creativity among teacher education students.

Domain-relevant knowledge and skills

Domain-relevant knowledge refers to the specific knowledge and expertise related to a particular field, while domain-relevant skills encompass the abilities and competencies required to perform tasks within that domain (Amabile, 1996). Individuals with greater domain-specific knowledge and skills were more likely to exhibit high levels of creativity in their respective fields (Zhao et al. 2023). Expertise in a particular domain positively correlated with creativity, as it allows individuals to develop a deep understanding of the field and generate creative solutions. (Emami et al., 2023)

External factors

Teacher

Teachers play a crucial role in influencing the creativity of students. Tang and Sternberg (2020) emphasize the importance of teachers’ attitudes towards risk-taking and tolerance for ambiguity in fostering students’ creativity. Runco (2018) highlights that the teacher-student relationship and the role of teachers can impact students’ creativity in various ways. Furthermore, teachers’ abundant abilities and experiences can positively impact students’ creativity (Simonton 2017).

Instructional model

Instructional model refers to a systematic approach or framework that guides the organization and design of instructional activities, including setting instructional goals, arranging teaching activities, utilizing instructional resources, selecting teaching methods, and evaluating instructional outcomes (Dick et al. 2015). Several researches provide evidence that it can significantly impact students’ creativity in educational settings (Fasko 2019; Karakaya & Ilhan 2019).

Learning environment

The learning environment refers to the physical, social, and psychological contexts in which learning takes place. It includes the physical spaces, resources, and tools available to support learning, as well as the social and cultural norms, expectations, and interactions that shape learners’ experiences (Alenezi 2023; Laurillard 2012; Jonassen & Land 2012). Creating a positive, flexible, open, and technology-enhanced learning environment can foster students’ creativity by promoting a sense of autonomy, curiosity, and intrinsic motivation (Kim & Song 2017; Chen & Chang 2018; Hsieh & Wu 2018; Kang 2020; Mattarelli et al., 2024).

Hypotheses and conceptual framework

Based on the preceding discussion, the purpose of this study is to demonstrate that the creativity of teacher education students is jointly influenced and shaped by both internal and external factors, while exploring the varying degrees of impact that these factors have on creativity.

So, the researcher has proposed the following research hypotheses and the study framework shown in Figure 1.

Figure 1. Conceptual framework of the study (Source: proposed by the researcher (2023)).

Hypothesis 1:

The creativity of teacher education students is jointly influenced by internal and external factors.

Hypothesis 2:

The impact of internal and external factors on the creativity of teacher education students varies in degree.

Materials and methods

The participants in current research were 2725 teacher education students enrolled in the Modern Educational Technology Course (MET Course) during the second semester of the academic year 2022 at three normal universities in Chongqing Municipality. Chongqing Municipality is the sole directly-administered city in the western region of China, embodying the distinctive characteristics of this area. It serves as a typical representation of the development level and educational system in western China. Chongqing’s higher education landscape includes a Ministry of Education-affiliated teacher education institution and multiple municipally administered teacher education colleges. For this study, we specifically opted for a Ministry of Education-affiliated institution and carefully selected the top two municipally governed teacher education colleges, ranked first and second, as the focal points of our research. Before administering the questionnaires to students, one of the authors consulted the leaders of the MET course in three universities regarding the encouragement of creativity in the classroom. The information obtained indicated that the cultivation of creativity permeates throughout all projects and activities at the normal universities, particularly within the MET Course. Next, the teachers responsible for teaching the MET course in the three universities also confirmed to us that they not only focus on assisting students in developing creative problem-solving skills but also encourage students to engage in creative learning in the classroom. Subsequently, the teachers were requested to assist in data collection.

A random sampling technique was employed to select participants from the population. The confidence level was set at 95%, with a confidence interval of 5%. Using Taro Yamane’s sampling theory, the calculated sample size was 349. Based on the proportion of sample size in the population, the specific sample size of each of the three universities is shown in Table 1.

Table 1. Target population and sample size of this research.

University	Population	Sample size
University A	1320	169
University B	1158	148
University C	247	32
Total	2725	349

Source: MET Course group from three normal universities.

Note: The sample size was calculated using a 95% confidence level, a 5% confidence interval, and a random sampling technique based on the sampling theory of Taro Yamane.

This study employed a quantitative research approach to systematically investigate and analyze the variables of interest. The aim was to collect numerical data and derive regression equations to comprehensively understand both internal and external factors influencing creativity. Quantitative research, characterized by its objective and structured methodology, utilizes measurable data to draw generalizable conclusions. Questionnaires served as the primary data collection tools, allowing for the quantification of participants’ responses. The statistical analysis of the collected data involved applying both descriptive and inferential statistical methods, facilitating a thorough examination of regression equations, correlations, and significance levels. The adoption of a quantitative research design enhances the reliability and generalizability of the research findings, contributing to a comprehensive understanding of the factors influencing creativity.

The instruments used in this study comprised a self-designed questionnaire that included both dependent and independent variables. The dependent variable was the creativity level of teacher education students, while the independent variables consisted of internal and external affect factors of creativity. Participants were asked to rate the impact of each item on their creativity development using a five-point Likert scale, ranging from very strong (5) to almost no impact (1).

The questionnaire was developed by the researchers specifically for this study and was pilot-tested to ensure its reliability and validity. The questionnaire consisted of several sections, including demographic information, questions related to the internal affect factors of creativity (such as self-efficacy and autonomy), questions related to the external affect factors of creativity (such as supervisor support and job complexity), and questions related to the dependent variable of creativity level. The use of a self-designed questionnaire allowed for the measurement of the specific variables of interest in a way that was tailored to the context of this study.

To ensure the reliability and validity of the questionnaire, a pilot test was conducted during the initial stages of the research. In this testing phase, particular attention was given to ensuring the clarity of questionnaire items to avoid participant misunderstandings or ambiguities. Additionally, emphasis was placed on the consistency of item wording to ensure uniformity and coherence in expressing concepts across all questions. The pilot test involved a diverse group of participants representing various characteristics and backgrounds of the target audience. Participants were tasked with completing the questionnaire and providing feedback on issues related to comprehension, difficulty in answering, and suggestions for potential improvements. This iterative process assisted the researcher in identifying potential issues, and appropriate revisions were made based on participant feedback. The pilot testing phase specifically addressed the reliability and validity of the questionnaire. Regarding reliability, the researcher utilized Cronbach’s alpha coefficient to assess the internal consistency of the questionnaire, ensuring uniformity in measuring the same underlying constructs across different items. In terms of validity, focus was placed on whether the questionnaire authentically reflected the research objectives and the theoretical framework constructed, as well as whether it accurately measured participants’ perspectives and experiences. Building upon the feedback received during the pilot testing, necessary revisions were implemented to ensure the final version of the questionnaire possessed strong reliability and validity in both content and structure. Lastly, the internal consistency of the final questionnaire was measured using Cronbach’s alpha coefficient, demonstrating good reliability (α = 0.849).

The questionnaire was administered to the participants in an online format using Wenjuanxing platform. Participants were instructed to complete the questionnaire independently and honestly, and were assured that their responses would be kept confidential and used only for research purposes. The researcher also provided a brief explanation of the purpose and significance of the study to the participants before they began the questionnaire.

Regression model specification and analysis

The data were analyzed using descriptive analysis, correlation analysis, and multiple regression analysis with SPSS version 22. The model employed in this study was described by the formula proposed by Hair et al.: $$y={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+{\beta }_3{x}_3+{\beta }_4{x}_4+{\beta }_5{x}_5+{\beta }_6{x}_6+\dots +{\beta }_n{x}_n+e$$ Where ${\beta }_0$ is the intercept and ${\beta }_1,{\beta }_2,\dots {\beta }_n$ are the parameter associated with $x_1,x_2,\dots x_n$ respectively. e represents the error term. Therefore, the research model for this study is:y = Creativity, $x_1=$ Intrinsic motivation, $x_2=$ Personality traits, $x_3=$ Domain-relevant knowledge and skills, $x_4=\mathrm{\text{Teacher}},$ $x_5=$ Instructional model, $x_6=$ Learning Environment.

Ethical issues of the study

A request letter for data collection was submitted to three normal universities in Chongqing Municipality by the Dean of the X faculty at S University. Following communication with the leaders of the MET course, data collection procedures were initiated. Verbal consent was obtained from the leaders, and their colleagues and students volunteered to collect the data for the study. The collected data were treated with confidentiality, and the privacy of the respondents was ensured.

Results and discussion

Descriptive analysis

In this study, an online survey platform, Wenjuanxing, was utilized to administer the questionnaires. Out of 349 questionnaires dispatched, 332 were returned, resulting in a response rate of 95.13%. Among the 332 returned questionnaires, 4 were excluded from the analysis due to disqualified responses.

Reliability analysis

To ensure the consistency and stability of the collected data, it is crucial to conduct a reliability analysis. In this study, a reliability analysis was performed for the six independent variables. The Cronbach’s alpha values for intrinsic motivation, personal traits, domain-relevant knowledge and skills, teacher, instruction model, and learning environment were .795, .859, .789, .763, .792, and .795, respectively, as presented in Table 2.

Table 2. Reliability analysis of study variables.

	Variables	Number of items	Cronbach’s alpha
Internal factors	Intrinsic motivation	4	.795
	Personal traits	5	.859
	Domain-relevant knowledge and skills	3	.789
External factors	Teacher	3	.763
	Instruction model	5	.792
	Learning environment	4	.795
Total		24	.849

Source: Own survey (2023).

According to Cohen, Swerdlik, and Sturman (2018), reliability coefficients ranging from 0.80 to 0.90 indicate excellent reliability, coefficients between 0.70 and 0.80 reflect good reliability, and coefficients between 0.60 and 0.70 reflect acceptable reliability. In the present study, the variables demonstrated reliability scores above 0.7, indicating good reliability, as reported by Cohen et al. The reliability analysis conducted on the study variables revealed a high level of internal consistency. Thus, it can be inferred that the questionnaires employed in this study were effective in measuring the influence of various factors on the creativity of teacher education students, producing reliable data. It is worth noting that these results support the validity of the questionnaire and the generalizability of this study findings.

Participant demographics analysis

Table 3 presents an overview of participant demographics. Out of the total respondents, 71.0% were female and 29.0% were male. Concerning age, 5.5% of respondents were under 18 years old, 93.6% were between 18 and 25 years old, and .9% were between 26 and 30 years old, with none above 30 years old. The participants were recruited from three universities: 48.5% from university A, 42.4% from university B, and 9.1% from university C. As for their academic background, 39.0% of respondents majored in humanities, 41.2% in science, 11.0% in arts, and 8.8% in information technology.

Table 3. Descriptive statistics of demographic characteristics.

Variables	Frequency	Percent	Cumulative percent
Gender
Male	95	29.0	29.0
Female	233	71.0	100.0
Total	328	100.0
Age of respondents
Below 18	18	5.5	5.5
18-25	307	93.6	99.1
26-30	3	.9	100.0
Above 30	0	0	100.0
Total	328	100.0
Universities
University A	159	48.5	48.5
University B	139	42.4	90.9
University C	30	9.1	100.0
Total	328	100.0
Academic Background
Humanities	128	39.0	39.0
Science	135	41.2	80.2
Arts	36	11.0	91.2
Information Technology	29	8.8	100.0
Total	328	100.0

Source: Own survey (2023).

Mean value analysis

As demonstrated by previous studies in the field of education, analyzing the mean values of variables is a commonly used method to gain preliminary insights into the impact of various factors on the development of creativity (Glăveanu 2014; Plucker et al., 2004). Based on the results presented in Table 4, the mean value of intrinsic motivation was rated as ‘very important’ (M = 4.155), whereas the mean score for personality traits was ‘neutral’ (M = 3.282). The mean value of domain-specific knowledge and skills was also at the ‘neutral’ level (M = 3.377), while the mean values of teacher (M = 3.455) and instructional model (M = 3.933) were rated as ‘important’ and ‘very important’ respectively. In contrast, the independent variable of learning environment was rated as ‘unimportant’ (M = 2.508). It is worth noting that the mean value of teacher education students’ creativity was at the ‘neutral’ level, indicating that the impact of the factors on creativity may require further analysis.

Table 4. Descriptive statistics of the variables studied.

Variables	N	Mean	SD
Intrinsic motivation (IM1)	328	4.155	.566
Personality traits (PT)	328	3.282	.783
Domain-specific knowledge and skills (KS)	328	3.377	.815
Teacher (Tchr)	328	3.455	.558
Instructional model (IM2)	328	3.933	.557
Learning environment (LE)	328	2.508	.608
Creativity	328	3.274	.789

Source: Own survey (2023).

Effect analysis of the study

Multiple regression analysis is a statistical method utilized to investigate the impact of multiple independent variables on a dependent variable. In this study, we will employ multiple regression analysis to explore the factors influencing creativity, with creativity being the designated dependent variable. The independent variables in this study encompass intrinsic motivation, personal traits, domain-relevant knowledge and skills, teacher, instructional model and learning environment. This rigorous statistical approach will allow us to analyze the unique contributions of these variables to the development of creativity in our study population.

The results presented in Table 5 indicate a well-fitting multiple regression model with a high degree of explained variance. The coefficient of determination (R-squared) is 0.869, indicating that the independent variables account for approximately 86.7% of the observed variability in the dependent variable. The adjusted R-squared, which accounts for the number of predictors and sample size, is 0.867, demonstrating the model’s robustness and reliable estimation of the relationship between the variables. The model exhibits a standard error of 0.288, indicating its high predictive accuracy. Furthermore, the Durbin-Watson value of 1.849 confirms the independence of the model’s residuals, indicating the absence of significant autocorrelation. These findings collectively support the adequacy and reliability of the multiple regression model in elucidating the relationship among the variables.

Table 5. Model summary.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Durbin-Watson
1	.932^a	.869	.867	.2879	1.849

Source: Own survey (2023).

^aPredictors: IM₁ PT, KS, Tchr, IM₂, LE.

^bDependent Variable: Creativity.

An overall significance F-test using one-way analysis of variance (ANOVA) was conducted to assess the model, as presented in Table 6. The results indicated a highly significant F-value of 355.173 (p < .01), suggesting a robust fit of the constructed model to the data.

Table 6. ANOVA^a.

Model		Sum of Squares	df	Mean Square	F	Sig
1	Regression	176.690	6	29.448	355.173	.000^b
	Residual	26.615	321	.083
	Total	203.305	327

Source: own survey (2023).

^aDependent Variable: Creativity.

^bPredictors: (Constant), IM₁ PT, KS, Tchr, IM₂, LE.

Additionally, the histogram of residuals depicted in Figure 2 reveals a normal distribution pattern, with a mean value near zero and a standard deviation close to one (resembling a standard normal distribution). This indicates that the linear regression model meets the assumption of normality. Moreover, Figure 3, displaying the probability-probability (P-P) plot, provides further evidence supporting the fulfillment of the normality assumption for the model.

Figure 2. Histogram.

Figure 3. P-P plot.

The scatter plot in Figure 4 illustrates the relationship between the standardized predicted values and the standardized residuals. Visual inspection of the plot reveals that the standardized residuals are centered around zero and exhibit a symmetrical distribution. This indicates that the assumptions of homoscedasticity and independence are satisfied. Moreover, the distributional characteristics of the residuals remain consistent across different levels of predicted values, providing evidence for the fulfillment of the assumptions of homogeneity of variance and independence.

Figure 4. Scatterplot.

Based on the aforementioned analysis, it can be concluded that the data in this study meet the basic assumptions of regression analysis, and the obtained results hold significance. As regards regression analysis as shown in Table 7. The results indicate significant positive effects of intrinsic motivation (β = .719, t = 20.790, p < .001), personality traits (β = .228, t = 7.289, p < .001), domain-relevant knowledge and skills (β = .146, t = 6.105, p < .001), teachers (β = .309, t = 7.896, p < .001), and instructional models (β = .544, t = 15.337, p < .001) on students’ creativity. However, the variable of learning environment does not have a significant impact on students’ creativity (β = .041, t = 1.004, p > .001). These findings suggest that higher levels of intrinsic motivation, favorable personality traits, a solid knowledge and skills base, competent teachers, and effective instructional models contribute to enhanced creativity among students. The lack of significance for the learning environment variable implies that it does not play a significant role in influencing students’ creativity.

Table 7. Coefficients^a.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
1	(Constant)	−4.268	.181		−23.642	.000
	IM1	.719	.035	.516	20.790	.000	.661	1.513
	PT	.228	.031	.226	7.289	.000	.422	2.368
	KS	.146	.024	.151	6.105	.000	.663	1.507
	Tchr	.309	.039	.219	7.896	.000	.532	1.880
	IM2	.544	.035	.384	15.337	.000	.649	1.540
	LE	.041	.041	.031	1.004	.316	.416	2.405

Source: own survey (2023).

^aDependent Variable: Creativity.

In addition, collinearity diagnostics involve two measures: variance inflation factor (VIF) and tolerance. It is worth noting that VIF is the reciprocal of tolerance (1/tolerance). Therefore, in this study, the examination of tolerance values is primarily employed to assess the collinearity among the independent variables. From Table 6, all tolerance values exceed 0.1, with a minimum value of 0.663. This indicates that there is no severe multi-collinearity among the six independent variables, satisfying the necessary criteria. Thus, the relationship between creativity and the following variables: intrinsic motivation (IM₁), personality traits (PT), domain-relevant knowledge and skills (KS), teachers (Tchr), and instructional models (IM₂) can be represented by the following equation: $$\mathit{\text{Creativity}}=-4.268+.719I{M}_1+.228 \mathit{\text{PT}}+.146 \mathit{\text{KS}}+.309 \mathit{\text{Tchr}}+.544I{M}_2$$

Upon considering the significant variables in the multiple regression equation, it becomes evident that intrinsic motivation (IM₁) and instructional models (IM₂) exert a significant impact on students’ creativity. Additionally, teachers (Tchr) and personal traits (PT) demonstrate a moderate level of influence. However, the relationship between domain-relevant knowledge and skills and creativity, while statistically significant, exhibits a relatively small regression coefficient in this study. This suggests that the effect of domain-specific knowledge and skills on students’ creativity is relatively limited within the context of this research. It is crucial to acknowledge that this finding does not diminish the importance of domain-specific knowledge and skills in the creative process. Acquiring a profound understanding of a particular domain remains essential for effectively navigating its intricacies and generating innovative solutions. Nonetheless, in this particular study, other factors such as intrinsic motivation, instructional models, and personal traits have shown a stronger influence on students’ creativity.

Discussion

This study revealed a significant positive correlation between the development of creativity in teacher education students and their intrinsic motivation, which is consistent with prior research by Amabile (1996). Students who have a strong motivation to learn and are interested in the subject matter are more likely to engage in creative thinking and problem-solving, as they exhibit greater curiosity, persistence, and risk-taking propensity during the learning journey. Recent studies have also emphasized the significance of intrinsic motivation in fostering creativity among teacher education students. For example, Cai and Kim (2021) showed that highly intrinsically motivated teacher education students were more creative in lesson planning and teaching practices, while Liu and Zhang (2018) found that intrinsic motivation positively impacted teacher creativity in developing innovative teaching strategies. These findings collectively suggest that intrinsic motivation plays a crucial role in the development of creativity among teacher education students. Therefore, we conclude that intrinsic motivation not only exhibits a significant positive correlation with the creativity of teacher education students but also plays a substantial facilitating role in educational practices. This conclusion not only provides robust theoretical support for previous research but also receives further empirical validation, underscoring the pivotal role of intrinsic motivation in enhancing the creativity of teacher education students.

Teacher education students, as the future teachers, possess a profound understanding of curriculum and instructional design through their other coursework (Du 2020). This heightened sensitivity and attentiveness to the application of instructional models in their classrooms have been demonstrated by the regression equation derived from this study, indicating that students widely recognize instructional models as a significant factor in fostering their creativity. Therefore, this study underscores the importance of incorporating instructional models as a critical component of teacher education programs and providing students with opportunities to explore and enhance their creativity through the use of innovative instructional models, such as Steam-6E model (Lin et al., 2023), design-based learning model (Ladachart et al., 2023), project-based model (Sari et al., 2023), problem-based, and other similar approaches and instructional models. In conclusion, the integration of innovative instructional models into teacher education programs emerges as a crucial factor for fostering the creativity of prospective educators. This integration aligns seamlessly with the findings of recent relevant studies.

The critical role that teachers play in fostering students’ creativity development has been widely demonstrated in researches (Fredagsvik, 2023; Plucker & Makel, 2010; Runco & Jaeger 2012). Teachers’ pedagogical beliefs, instructional strategies, and behaviors are highly influential external factors that significantly impact students’ attitudes towards creativity and their creative thinking skills (Žarnauskaitė 2023; Beghetto & Kaufman 2014). The finding of this study has revealed that teachers who encourage creativity in their teaching practices, provide opportunities for students to engage in creative activities, and possess experience and ability to cultivate students’ creativity can substantially enhance students’ creativity development. It also aligns with Dewey’s experiential education theory, which emphasizes that educators’ experiences and methods have a profound impact on students’ cognition, emotions, and behavior—all of which are closely associated with the cultivation of creativity.

Personality traits play a critical role in the manifestation of creative thinking into creative behavior (Li, 2020). While not a prerequisite for creativity, personality traits provide robust support and assurance for its development (Sternberg & Lubart, 1999). For instance, students who possess traits such as openness, curiosity, and imagination tend to exhibit higher levels of creativity (Schutte & Malouff 2020). They are more inclined to explore new perspectives, embrace novel ways of thinking, and employ innovative problem-solving approaches. Additionally, students who possess traits like perseverance, self-confidence, and autonomy often demonstrate heightened creativity, as they are more likely to overcome various challenges (Alamanda et al., 2024). Therefore, the conclusion of this study aligns with Eysenck’s perspective, stating that creativity is a personality trait. However, the impact of personality traits on creativity is complex, as different traits may exert varying effects on creative outcomes (Li et al. 2023). Understanding and comprehending this relationship can aid in formulating more effective strategies and interventions in fostering student creativity.

The relationship between domain-relevant knowledge and skills and creativity can be explained by the ‘tension theory’, which suggests that there exists a tension or balance between knowledge and creativity (Sternberg & Lubart, 1999). The empirical results, where domain-relevant knowledge and skills show statistically significant but relatively small regression coefficients, seem to support this theory. Domain-relevant knowledge and skills serve as the foundation for creative thinking (Emami et al., 2023). Acquiring and mastering knowledge and skills within a specific domain expands individuals’ thinking by providing them with a broader range of materials and concepts (Thornhill-Miller et al., 2023). This knowledge acts as a valuable resource for generating innovative ideas and solutions. However, an excessive focus on specialized knowledge can impose limitations on creativity (Li, 2020). Deep knowledge within a specific domain can confine individuals to rigid thinking frameworks, hindering their ability to break free from traditional thought patterns. This limitation becomes particularly apparent in tasks that demand interdisciplinary or innovative approaches, where flexibility is crucial. To enhance students’ creativity, it is vital to emphasize the integration of knowledge and skills across disciplines rather than confining them to a single domain (Leng 2023). Promoting cross-domain connections and encouraging exploration of diverse perspectives and ideas can foster a more flexible and expansive creative mindset (Ke & Liang 2023). Striking a balance between domain-specific knowledge and the ability to think beyond conventional boundaries enables educators to effectively cultivate students’ creativity.

In this study, the statistical analysis did not reveal a significant relationship between the learning environment and the development of creativity, which contrasts with several previous research findings (Palanica et al., 2019; Fu et al. 2022; Wang & Liu 2021; Chen 2020). However, it is crucial to consider the specific context of this study, which focused on students enrolled in the ‘Modern Educational Technology’ course, known for its resource-rich and technologically advanced smart learning environment. Firstly, the course offers a wide range of learning resources and advanced technological tools, providing robust support for fostering students’ creativity. Through the internet access, students can explore diverse information, multimedia materials, and learning tools, facilitating the stimulation of their creative thinking and expression of ideas. Secondly, the course emphasizes active participation and collaborative learning, fostering a positive learning atmosphere. Students have opportunities to engage in discussions and collaborate with peers, working together to solve problems and complete projects. This collaborative learning environment contributes to the stimulation of students’ creativity, fostering their teamwork and innovative abilities. Additionally, the course places significant emphasis on practical application and real-world experiences, providing ample opportunities for students to engage in hands-on activities. By designing and implementing practical projects, participating in simulated scenarios, and engaging in practical activities, students can apply their acquired knowledge and skills to real-life situations. This practice-oriented learning environment helps cultivate students’ abilities to creatively solve problems and think innovatively. Although the statistical analysis in this study did not yield significant results regarding the relationship between the learning environment and creativity development, it is reasonable to infer that within the resource-rich and technologically advanced smart learning environment of the ‘Modern Educational Technology’ course, students have received the necessary support and motivation for fostering their creativity. Therefore, despite the absence of statistical significance, it can still be argued that the learning environment plays a positive role in developing students’ creativity.

Conclusions

The present study focused on analysis the influence factors of internal motivation, personality traits, knowledge and skill proficiency, teachers, instructional model, and the learning environment on the creativity of teacher education students. The findings reveal that factors such as internal motivation and instructional model exert a positive and statistically significant impact on enhancing the creativity of teacher education students. In the contemporary era of rapid technological advancement, the field of education is undergoing profound and swift transformations. The integration of technology is driving continuous innovation in teaching methods, approaches, and materials within higher education. Novel instructional models, methods, and materials are emerging, characterized by a dynamic iteration of content, diverse presentation formats, and an increasingly varied array of teaching methods, accompanied by a more nuanced set of assessment approaches. These innovative shifts in instructional paradigms are designed to effectively stimulate students’ interest in learning, cultivate stronger motivation for learning, and encourage deep engagement in the learning process, ultimately leading to enhanced learning outcomes. This research seeks to make a substantive contribution to the enhancement of creativity among teacher education students. Through a comprehensive analysis of various factors influencing the creativity of teacher education students, we aim to raise awareness among educational management departments, teacher training institutions, and educators about the pivotal importance of this topic. This endeavor not only facilitates a profound understanding of key factors in fostering creativity but also provides robust support for driving reforms and innovations in curriculum and instructional models within the education sector. By actively exploring and experimenting with new instructional models in practical educational settings, we aspire to infuse renewed vitality into the motivation of teacher education students. Emerging teaching methods are expected to better cater to the needs and learning preferences of contemporary teacher education students, more effectively inspiring their academic interests. This positive transformation is anticipated to assist teacher education students in better comprehending and adapting to the evolving demands of education, consequently achieving more significant accomplishments in their future educational careers. Ultimately, by elevating the learning motivation of teacher education students, this research not only aids them in coping better with academic challenges but also contributes to cultivating more creative educators within their respective professional domains. This is anticipated to have a profound positive impact on the overall upgrade of the education system and the comprehensive development of students.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Xiaoyan Zhong

Xiaoyan Zhong, 1. Position: Associate Professor, Master’s Supervisor. College of Teacher Education, Southwest University. 2. Courses taught: Intelligent teaching, science and technology education, teacher education. 3. Publications: (1) Led 8 projects, including: ① “Central University Basic Research Industry Special Project" of the Ministry of Education and the Ministry of Finance of China, ②Chongqing Higher Education Teaching Reform Research Project, ③ Southwest University Education Teaching Reform Research Project, and the Network and Continuing Education Teaching Research Project. (2) Published over 10 papers in core journals such as “China Educational Technology”, “E-education Research”, “Education and Information Technologies”, and related important international academic conferences. (3) Editor in chief or co-editor of 7 relevant academic works, university planning textbooks, and primary and secondary school textbooks. 4. Representative achievements: (1) Design and Practice of High School Programming Teaching Mode Based on Deep Learning Theory [J]. Journal of Southwest University (Natural Science Edition), 2023,45 (06): 23–34, (2) Exploring the Path to Solving the Teaching Dilemma of Information Technology Courses [J]. The Guide of Science & Education, 2023 (15): 125–127, (3) Comparative Study on the Application of Information Technology Teaching from the Perspective of Reciprocity: A Case Study Based on Sister Schools of the Sino Canadian Reciprocal Learning Project [J]. China Information Technology Education, 2021 (06): 107–112.

Kun Qu

Kun Qu, 1. Position: Associate Professor, Master’s Supervisor. College of Education, Southwest University. 2. Courses taught: Educational technology. 3. Publications: (1) Led 10 projects, including: ① “Central University Basic Research Industry Special Project" of the Ministry of Education and the Ministry of Finance of China, ② Chongqing Higher Education Teaching Reform Research Project, ③ Southwest University Education Teaching Reform Research Project, and the Network and Continuing Education Teaching Research Project. (2) Published over 20 papers in core journals such as “China Educational Technology”, “E-education Research”, “Education and Information Technologies”, and related important international academic conferences. (3) Editor in chief or co-editor of 10 relevant academic works, university planning textbooks, and primary and secondary school textbooks.

Deyuan Zhang

Deyuan Zhang, 1. Position: Teacher education student, College of Mathematics and Statistics, Southwest University.