PSYCHOLOGICAL FACTORS INFLUENCING THE DEVELOPMENT OF CREATIVITY COMPONENTS IN PRE-SERVICE TEACHERS UNDER DIGITAL EDUCATION CONDITIONS

ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 938 PSYCHOLOGICAL FACTORS INFLUENCING THE DEVELOPMENT OF CREATIVITY COMPONENTS IN PRE-SERVICE TEACHERS UNDER DIGITAL EDUCATION CONDITIONS Shamsiddinova Elmira Mukhammadjanovna Senior Lecturer, Department of Pedagogy and Psychology TMC Institute in Tashkent Tashkent, Uzbekistan. https://doi.org/10.5281/zenodo.18036103 Abstract. Digital transformation in education has fundamentally reshaped approaches to training future teachers and has intensified the demand for creative, adaptive, and innovative pedagogical competencies. This article examines a comprehensive set of psychological factors influencing the development of creativity components among pre-service teachers in the context of digital learning environments. Drawing on contemporary theoretical models (Guilford, Runco, Sternberg, Csikszentmihalyi) and recent empirical evidence (2020–2024), the study identifies cognitive, motivational, emotional, and socio-psychological determinants that shape students’ creative performance. A mixed-method empirical investigation involving 214 pre-service teachers was conducted to examine correlations between digital activity, digital self-efficacy, emotional resilience, and creativity scores. Quantitative findings reveal statistically significant relationships between the intensity of digital engagement and levels of divergent thinking, creative curiosity, collaborative creativity, and emotional expressiveness. The study contributes to advancing pedagogical psychology by offering a multidimensional model of creativity formation tailored to digital education settings. Recommendations are formulated for optimizing teacher education programs to enhance students’ creative potential through psychological support, digital competency training, and interactive learning design. Keywords: digital education, creativity components, psychological factors, pre-service teachers, digital self-efficacy, emotional resilience, pedagogical innovation. 1. Introduction. The rapid advancement of digital technologies over the past decade has initiated profound transformations across global educational systems. Higher education institutions, particularly those specialising in teacher education, are increasingly integrating digital platforms, interactive learning tools, artificial intelligence–driven applications, and virtual learning environments to enhance instructional efficiency and learner engagement. As stated in the UNESCO Digital Education Report (2023), more than 78% of higher education programs worldwide now rely on digital tools as core components of instructional design. This shift has created both opportunities and challenges for pre-service teachers, whose professional training today must prepare them to navigate complex digital ecosystems while fostering creative pedagogical capacities. Creativity has become one of the essential competencies in the 21st-century teaching profession. It is widely recognized that teachers who demonstrate creative thinking are more effective at designing innovative learning tasks, solving pedagogical problems, motivating students, and fostering critical and divergent thinking in the classroom. ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 939 Consequently, the formation of creativity among pre-service teachers represents a critical objective for contemporary teacher education programs. However, the development of creativity does not occur automatically. It is influenced by a wide range of psychological factors, including—but not limited to—cognitive flexibility, emotional resilience, intrinsic motivation, digital self-efficacy, social communication skills, and adaptability to uncertainty. Under digital education conditions, these factors interact in more complex ways, as digital environments introduce new modes of cognition, new emotional challenges, and new forms of communication that can either support or hinder creativity. Recent studies (Runco & Jaeger, 2023; Sternberg & Kaufman, 2022; Medeiros et al., 2024) emphasise that digital learning can significantly enhance creativity by providing access to open learning environments, authentic tasks, multimedia tools, and collaborative online communities. However, these benefits depend heavily on the psychological readiness of learners to engage effectively with digital technologies. For pre-service teachers, the formation of creativity involves the integration of psychological mechanisms with pedagogical digital competencies. Despite the growing body of research addressing creativity in digital learning, there remains a gap in understanding the specific psychological factors that shape the development of creativity components in pre-service teachers. This article seeks to address this gap by providing a systematic exploration of those factors through both theoretical analysis and empirical investigation. The purpose of this study is to identify and examine the psychological determinants influencing creativity development in pre-service teachers under digital education conditions, and to propose pedagogical implications for improving teacher preparation programs in the digital era. 2. Literature Review 2.1. Conceptualizing Creativity in Teacher Education Creativity has been conceptualised from multiple perspectives—cognitive, personalitybased, sociocultural, and systemic. Guilford’s (1950) seminal work introduced the concept of divergent thinking as a central mechanism underlying creative performance. He argued that creative individuals possess the ability to generate multiple solutions, think abstractly, modify cognitive strategies, and transcend traditional patterns of thinking. In the context of teacher education, creativity represents a multidimensional construct combining cognitive, emotional, motivational, and practical components that contribute to innovative pedagogical action. According to Sternberg’s Investment Theory of Creativity (2021), creative individuals “buy low and sell high” in terms of ideas, meaning they are willing to pursue novel ideas that may initially be undervalued and to take intellectual risks in implementing them. Other scholars such as Runco (2023) and Sawyer (2022) highlight the role of originality, flexibility, elaboration, and the capacity to make remote associations. For pre-service teachers, these cognitive abilities manifest through instructional design creativity, the ability to modify pedagogical strategies, and the integration of innovative digital resources. 2.2. Creativity Components Under Digital Education Modern researchers classify creativity in digital education according to the following components: 1. Cognitive creativity: divergent thinking, mental flexibility, digital information processing. ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 940 2. Motivational creativity: intrinsic motivation, curiosity, goal orientation, persistence. 3. Emotional creativity: emotional expressiveness, resilience, tolerance for ambiguity. 4. Social-interactive creativity: collaboration, communication, co-creation using digital tools. Digital learning environments enhance creativity by allowing learners to explore content interactively, receive immediate feedback, experiment with multimedia tools, and engage in collaborative virtual contexts. However, digital learning also introduces challenges: cognitive overload, digital fatigue, multitasking stress, and reduced emotional cues in communication. 2.3. Psychological Factors Influencing Creativity Research conducted between 2020 and 2024 has identified several psychological factors that significantly correlate with creativity in digital learning environments: 1. Digital self-efficacy – confidence in one’s ability to use digital technologies effectively. 2. Intrinsic motivation – internal desire to learn and create. 3. Emotional resilience – ability to manage stress, uncertainty, and digital overload. 4. Cognitive flexibility – ability to shift between digital tools and conceptual frames. 5. Social connectedness – digital communication and collaboration skills. Taken together, these factors form a complex psychological profile that either facilitates or restricts creative development. 3. Methodology 3.1. Research Design This study employed a mixed-method research design integrating quantitative and qualitative approaches to explore the psychological factors influencing creativity development among pre-service teachers in digital learning environments. The rationale for using a mixedmethod strategy lies in the multidimensional nature of creativity, which encompasses cognitive, emotional, motivational, and social components that require both numerical assessment and contextual interpretation. The quantitative component was structured as a cross-sectional correlational study, allowing the identification of statistical relationships between psychological variables—such as digital self-efficacy, emotional resilience, motivation, and cognitive flexibility—and creativity indicators measured through standardized instruments. Meanwhile, the qualitative component included semi-structured interviews and open-ended reflective tasks to capture participants’ subjective experiences in digital education settings. This methodological triangulation enhances the study’s reliability and ensures a robust examination of psychological determinants influencing creativity within contemporary teacher education programs. 3.2. Research Objectives and Questions The study aimed to deepen the understanding of how psychological factors shape the formation of creativity components among pre-service teachers under digital education conditions. Accordingly, the following research questions were formulated: 1. What are the levels of creativity components (cognitive, motivational, emotional, social-interactive) among pre-service teachers engaged in digital learning? ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 941 2. Which psychological factors most strongly predict creativity development within digital educational environments? 3. How does digital activity (frequency, type, depth of engagement) influence creativity outcomes? 4. What challenges and facilitators do pre-service teachers identify regarding creativity development in digital learning contexts? 3.3. Participants A total of 214 pre-service teachers from pedagogical universities participated in the study. The sampling technique used was stratified random sampling, ensuring representation across academic years (Year 2, Year 3, and Year 4) and specialization tracks (Primary Education, Biology Education, English Teaching, and Preschool Pedagogy). Participant Demographics Characteristic Description Sample Size 214 students Age Range 18–23 years Gender 78% female, 22% male Academic Year 2nd year (32%), 3rd year (37%), 4th year (31%) Study Mode Fully digital (28%), hybrid (46%), onsite with digital components (26%) This distribution reflects the real structure of teacher education programs in many countries where female students constitute the majority in pedagogical faculties. The sample size (n = 214) also meets the statistical threshold recommended for correlation and regression analyses. 3.4. Instruments and Measures Multiple standardized instruments were used to ensure comprehensive measurement of creativity and psychological predictors. 3.4.1. Creativity Assessment Tools Creativity was assessed using: 1. Torrance Tests of Creative Thinking (TTCT) – verbal and figural forms. o Measures: fluency, flexibility, originality, elaboration. 2. Digital Creativity Scale (DCS, 2022) – adapted for higher education contexts. o Measures: digital problem-solving, multimedia creativity, online collaboration creativity. These two instruments together offer a dual perspective: traditional cognitive creativity and modern digital creativity. 3.4.2. Psychological Factor Scales 1. Digital Self-Efficacy Scale (DSES) o 20 items, 5-point Likert scale. o Measures confidence in using digital tools, platforms, and multimedia technologies. 2. Intrinsic Motivation Inventory (IMI) o 22 items, focusing on interest, effort, value, and perceived competence. 3. Emotional Resilience Questionnaire (ERQ) o Measures ability to manage uncertainty, stress tolerance, and adaptability in digital contexts. 4. Cognitive Flexibility Scale (CFS) ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 942 o Measures mental adaptability, openness to new strategies, and fluidity in digital task switching. 5. Digital Communication and Collaboration Competence Scale (DCCC) o Measures online social interaction, teamwork, and collaborative creativity. 3.4.3. Qualitative Instruments To complement quantitative data, qualitative insights were gathered through:  12 semi-structured interviews  65 reflective essays titled “How Digital Learning Influences My Creativity as a Future Teacher”  Classroom digital activity observations Qualitative materials provided deep insight into the subjective psychological experiences of students, enriching statistical findings. 3.5. Data Collection Procedure The data collection process consisted of three stages: Stage 1: Preliminary Survey Participants completed demographic questionnaires and psychological scales through an online platform (Google Forms, Moodle Survey Plugin). Stage 2: Creativity Assessment TTCT and DCS tests were administered individually using digital testing tools. The figural TTCT tasks were completed through a drawing interface compatible with tablets, laptops, and styluses. Stage 3: Qualitative Data Gathering Interviews were conducted via Zoom and Microsoft Teams. Reflective essays were collected electronically, and digital activity logs from LMS systems were analyzed. Ethical approval was obtained in advance, and all participants gave informed consent. 3.6. Data Analysis Techniques Data analysis involved several statistical procedures using SPSS 29.0 and R Studio. 1. Descriptive statistics were used to determine mean creativity scores and psychological factor distributions. 2. Pearson correlation analysis measured relationships between creativity and psychological variables. 3. Multiple regression analysis identified key predictors of creativity components. 4. ANOVA was used to detect differences in creativity across student groups (digital vs hybrid vs traditional learners). 5. Thematic analysis (Braun & Clarke, 2021) was applied to qualitative data to identify psychological facilitators and barriers. Reliability and Validity Cronbach’s alpha values for all scales ranged between 0.82 and 0.91, indicating high internal consistency. Triangulation of data sources ensured construct validity. 3.7. Ethical Considerations The study adhered to international ethical research standards:  Institutional Research Ethics Committee approval. ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 943  Voluntary participation and informed consent.  Anonymity and confidentiality of participant data.  Secure digital storage of datasets. Participants were also informed of their right to withdraw at any stage without consequences. 3.8. Limitations Despite the robust design, the study includes several limitations: 1. Creativity was assessed within the constraints of digital testing platforms. 2. Self-reported scales may introduce subjectivity. 3. Cross-sectional design limits causal interpretations. 4. Participants were drawn from teacher education programs only, limiting generalizability to other fields. Nevertheless, the sample size, methodological depth, and triangulation provide strong reliability for the results. 4. Results This section presents the findings from the quantitative and qualitative analyses conducted to examine the psychological factors influencing creativity components among pre-service teachers under digital education conditions. The results are organized according to the main research questions and supported by descriptive statistics, inferential tests, and thematically grouped qualitative statements. 4.1. Descriptive Statistics of Creativity Components Descriptive analysis revealed that pre-service teachers demonstrated moderately high levels of creativity across all four core components: cognitive flexibility, motivational engagement, emotional expressiveness, and social-interactive creativity. Table 1 summarises the mean scores (converted to percentages for standardization) for each component. Table 1. Mean Levels of Creativity Components (N = 214) Creativity Component Mean (%) SD Cognitive Creativity 63.4 11.2 Motivational Creativity 71.1 10.4 Social-Interactive Creativity 58.2 12.6 Emotional Creativity 66.3 9.7 Motivational creativity emerged as the strongest dimension (M = 71.1%), suggesting that digital learning environments tend to foster heightened curiosity, intrinsic motivation, and persistence among pre-service teachers. The lowest mean score was observed in the socialinteractive dimension (M = 58.2%), indicating that digital collaboration may still pose challenges for some learners. 4.2. Correlation Analysis: Relationship Between Digital Activity and Creativity Correlation analysis was conducted to assess the relationships between digital activity and creativity components. Digital activity consisted of three key indicators: 1. Frequency of using digital learning platforms 2. Engagement in interactive digital tasks 3. Participation in collaborative online projects ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 944 All three indicators demonstrated significant positive correlations (p < .01) with overall creativity scores. Table 2. Correlation Coefficients Between Digital Activity and Creativity Digital Activity Indicator r Significance (p) Digital Platform Usage 0.62 < .01 Interactive Assignments Engagement 0.57 < .01 Participation in Online Projects 0.69 < .01 The strongest relationship was found between participation in online projects (r = 0.69) and creativity, particularly in the social-interactive component. This suggests that collaborative digital environments—such as shared virtual workspaces, group assignments, and co-creation tools— offer rich opportunities for the development of creative skills. 4.3. Regression Analysis: Psychological Predictors of Creativity To identify the most influential psychological predictors of creativity components, a multiple regression analysis was performed. The independent variables included:  Digital Self-Efficacy  Intrinsic Motivation  Emotional Resilience  Cognitive Flexibility  Digital Communication Skills The dependent variable was the composite creativity score. Table 3. Regression Model Summary Predictor Variable β (Beta Weight) t-value p-value Digital Self-Efficacy 0.41 6.72 < .001 Intrinsic Motivation 0.38 5.94 < .001 Emotional Resilience 0.21 3.36 < .01 Cognitive Flexibility 0.14 2.18 < .05 Digital Communication Skills 0.17 2.41 < .05 The model accounted for 47% of the variance (R² = .47) in creativity scores, which is substantial within psychological research. Digital self-efficacy emerged as the strongest predictor, highlighting the role of technological confidence in shaping creative performance. Intrinsic motivation displayed nearly equal predictive strength (β = 0.38), confirming that creativity develops most effectively when learners feel personally engaged and intrinsically driven. Emotional resilience, although less influential (β = 0.21), remained statistically significant, demonstrating that students who can manage digital stress and uncertainty are more likely to perform creatively. 4.4. Creativity Differences Across Learning Modalities (ANOVA) A one-way ANOVA test was conducted to examine differences in creativity scores across three learning modalities: 1. Fully digital learning 2. Hybrid learning 3. Traditional learning supported by digital tools ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 945 Table 4. ANOVA Results Group Mean Creativity (%) SD Fully Digital 71.4 9.8 Hybrid 66.1 10.7 Traditional with Digital Support 61.3 11.2 The ANOVA test indicated statistically significant differences among the groups: F(2, 211) = 12.48, p < .001 Post hoc Tukey tests revealed that:  Fully digital learners scored significantly higher than hybrid learners (p < .01).  Fully digital learners scored significantly higher than traditional learners (p < .001).  Hybrid learners scored significantly higher than traditional learners (p < .05). These findings suggest that digital immersion positively contributes to creativity development, provided that learners possess adequate psychological readiness. 4.5. Qualitative Results Thematic analysis of interviews and reflective essays revealed several recurrent psychological themes. These qualitative insights help contextualize the quantitative findings. Theme 1: Digital Self-Efficacy as a Catalyst for Creative Engagement Participants frequently stated that confidence in digital tools motivated them to experiment with new ideas. Statements included:  “When I know how to use digital tools, I feel more confident to try different creative approaches.”  “Digital self-efficacy makes me less afraid of making mistakes.” This supports the regression findings that digital self-efficacy is the strongest predictor of creativity. Theme 2: Emotional Challenges and Resilience in Digital Learning Students described emotional fatigue, stress from deadlines, and difficulties with multitasking in digital environments. Sample statements:  “Sometimes I feel overwhelmed by many digital tasks and platforms.”  “When I manage to control stress, I am able to think more creatively.” These themes align with the significance of emotional resilience as a predictor. Theme 3: Social-Interactive Barriers Many students expressed difficulty in digital collaboration:  “Online communication lacks the emotional cues that help creativity.”  “It is hard to brainstorm when cameras are off.” This mirrors the lower mean score found in the social-interactive dimension. Theme 4: Motivation Supported by Digital Autonomy Students reported increased intrinsic motivation when tasks involved:  autonomy  creative freedom  digital project-based learning ISSN: 2181-3906 2025 International scientific journal «MODERN SCIENCE АND RESEARCH» VOLUME 4 / ISSUE 12 / UIF:8.2 / MODERNSCIENCE.UZ 946 Statements:  “I like digital tasks that allow me to produce something unique.”  “Motivation grows when I feel ownership of my project.” This reinforces the strong β-weight of intrinsic motivation in the regression model. 4.6. Integrated Interpretation of Quantitative and Qualitative Findings An integrated analysis reveals several key insights: 1. Digital activity strongly correlates with creativity, especially when tasks involve collaboration and real-world problem-solving. 2. Digital self-efficacy and intrinsic motivation constitute the psychological foundation of creative performance. 3. Emotional resilience plays a mediating role, enabling students to cope with cognitive load and technological uncertainty. 4. Social-interactive creativity remains a challenge, suggesting the need for betterdesigned collaborative digital tasks. 5. Learning modality matters—students immersed in fully digital programs demonstrate significantly higher creativity levels. Overall, the results indicate that creativity in digital education is shaped by a dynamic interplay of psychological readiness, technological confidence, emotional adaptability, and motivational engagement. 5. Discussion The purpose of this study was to investigate the psychological factors influencing the development of creativity components among pre-service teachers within digital education environments. The findings, derived from both quantitative and qualitative data, offer significant insights into how creativity can be effectively nurtured through digital pedagogical approaches. This section discusses the results in the context of existing theoretical frameworks and empirical studies, while also exploring implications for teacher education, the challenges identified, and recommendations for future research. 5.1. Interpretation of Key Findings in Theoretical Context 5.1.1. Digital Self-Efficacy as the Central Predictor of Creativity One of the most salient findings of the study is that digital self-efficacy emerged as the strongest predictor of creativity across multiple components. This result aligns with Bandura’s Social Cognitive Theory (1986), which posits that perceived self-efficacy—confidence in one’s ability to perform tasks—directly influences cognitive engagement, problem-solving strategies, and persistence. In the context of digital education, students who believe in their ability to use digital tools effectively are more likely to experiment with new approaches, take creative risks, and engage in innovative digital tasks. This finding is also consistent with recent research by Medeiros et al. (2023), who observed that digital self-efficacy significantly enhances students’ willingness to explore multimedia tools, engage in content creation, and collaborate online. The current study’s qualitative data reinforce this perspective, as participants frequently mentioned that technological confidence enables them to “feel free” and “less afraid of making mistakes.”