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Classroom Leadership Practices and Data-Driven Culture

Implementation as Predictors of Instructional Innovation Adaptability

among Secondary Teachers

Eddie P. Trases

, James L. Paglinawan, Ph.D

Department of Education, Valencia National High School, Philippines

Department of Professional Education, Central Mindanao University, Philippines

DOI:

https://dx.doi.org/10.47772/IJRISS.2025.910000767

Received: 08 November; Accepted: 13 November 2025; Published: 24 November 2025

ABSTRACT

This study examined the predictive relationship between classroom leadership practices, data-driven culture

implementation, and instructional innovation adaptability among secondary teachers. Methods: A cross-

sectional design was employed to collect data from 300 secondary teachers through a comprehensive survey.

Reliability analysis confirmed excellent internal consistency for all scales (CLP: α = .985, DDC: α = .989, IIA:

α = .986). Multiple linear regression analysis was conducted to test the predictive model. Results: The regression

model was significant and explained a substantial portion of the variance in instructional innovation adaptability.

Data-Driven Culture (DDC) was a strong and highly significant positive predictor (β = 0.525, *p* < .001). At

the same time, Classroom Leadership Practices (CLP) was a positive but marginally significant predictor (β =

0.091, *p* < .10). The resulting regression equation was: IIA = 1.300 + 0.097(CLP) + 0.534(DDC). Moderate

to strong positive correlations were found among all variables (r = .356 to .571, *p* < .001). Conclusions: The

findings indicate that while both factors are relevant, a Data-Driven Culture is a decisively stronger predictor of

teachers' capacity for instructional innovation than Classroom Leadership. Educational institutions should

prioritize developing robust data systems and a supportive data culture as a primary strategy to enhance

instructional adaptability.

Keywords: classroom leadership, data-driven culture, instructional innovation, teacher adaptability, multiple

regression, secondary education

INTRODUCTION

Background of the Study

The contemporary educational landscape is characterized by unprecedented complexity and rapid

transformation, requiring teachers to exhibit sophisticated levels of adaptability and innovation in their

instructional practices (Darling-Hammond, 2020; Fullan, 2021). The concept of instructional innovation

adaptability encompasses teachers' ability to modify their teaching strategies, integrate new technologies, and

continuously refine their practice in response to changing educational demands (Voogt & Knezek, 2021). In an

era marked by rapid knowledge expansion, this adaptability represents a cornerstone of educational quality and

equity (OECD, 2023).

Problem Statement

While both Classroom Leadership Practices (CLP) and a Data-Driven Culture (DDC) are theorized to support

teacher innovation (De Guia, 2023; Ding et al., 2025), a critical gap exists in understanding

their relative importance. Educational leaders often invest in professional development for classroom leadership

or in data systems without robust evidence of which factor is a more powerful driver of teacher adaptability. This

lack of clarity can lead to the misallocation of scarce resources. Therefore, the core problem is the absence of an

empirical model that quantifies and directly compares the unique predictive influence of CLP and DDC on

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Instructional Innovation Adaptability, guiding strategic decision-making.

Research Objectives

This study aims to:

1. Determine the individual predictive power of Classroom Leadership Practices and Data-Driven Culture on

Instructional Innovation Adaptability.

2. Compare the relative strength of these two factors to identify the more critical lever for fostering teacher

adaptability.

3. Provide evidence-based recommendations for educational leaders on how to prioritize resources most

effectively to enhance teacher innovation.

Research Questions

1. To what extent do Classroom Leadership Practices predict Instructional Innovation Adaptability among

teachers?

2. To what extent does Data-Driven Culture predict Instructional Innovation Adaptability among teachers?

3. Which of the two factors—Classroom Leadership Practices or Data-Driven Culture—is a stronger

predictor of Instructional Innovation Adaptability?

THEORETICAL FRAMEWORK

This study is grounded in three interconnected theoretical perspectives:

• Transformational Leadership Theory (Bass & Riggio, 2020) informs the concept of Classroom

Leadership Practices (CLP), suggesting that teachers who inspire and motivate can foster environments

conducive to change.

• Data-Informed Decision-Making Theory (Mandinach & Schildkamp, 2021) underpins Data-Driven

Culture (DDC), positing that evidence-based practice enables precise and effective instructional adaptation.

• Complex Adaptive Systems Theory (Mason, 2020) provides the overarching lens, framing schools as

dynamic ecosystems where multiple factors interact to influence a teacher's adaptive capacity.

This framework enables the examination of whether teacher-driven leadership, a system-wide data culture, or a

combination of both is more critical for navigating educational complexity.

LITERATURE REVIEW

The Evolution of Classroom Leadership

Contemporary perspectives view classroom leadership as a multidimensional construct encompassing

pedagogical expertise, relationship building, and facilitating change (Danielson, 2021; Hallinger, 2020). Recent

research by Garcia (2023) demonstrated that instructional leadership practices are correlated with improved

classroom strategies, suggesting they create environments conducive to innovation. Similarly, Ma et al. (2025)

found that transformational leadership approaches can impact teachers' capacity to adopt innovative methods.

The literature thus posits a positive, though not yet quantified, link between CLP and adaptability.

Data-Driven Cultures in Educational Settings

The implementation of data-driven cultures has emerged as a critical factor in educational improvement

(Mandinach & Schildkamp, 2021; Schildkamp, 2023). Effective data culture requires not only access to data but

also systematic training in data interpretation, collaborative analysis structures, and leadership support (HMH

Culture, 2024; Datnow et al., 2021). LSU Online (2020) found that data-driven approaches enable teachers to

make more targeted instructional adjustments, increasing teacher confidence and precision. This body of work

suggests that DDC provides the empirical foundation and collaborative structures necessary for informed

innovation.

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The Research Gap and This Study's Contribution

Despite substantial research on classroom leadership and data-driven practices as separate constructs, their

combined and comparative predictive relationship with instructional innovation adaptability remains

inadequately explored. Most existing studies have examined these factors in isolation (Prenger et al., 2021). This

study addresses a significant gap by directly testing and comparing the predictive power of these models within

a single framework. It moves beyond asking if these factors are related to adaptability to determining which is a

stronger predictor, thereby providing actionable evidence for educational leaders seeking to build more

innovative and adaptable teaching teams.

The Integration of Leadership and Data Practices

Recent literature suggests a synergistic relationship between leadership practices and data-driven

implementation (Prenger et al., 2021; Van Gasse et al., 2023). The integration of effective classroom leadership

with robust data systems creates an environment where instructional innovation can thrive. Studies indicate that

when teachers receive both leadership support and access to meaningful data, their capacity for instructional

adaptation increases substantially.

Marsh and Farrell (2022) identified specific mechanisms through which this integration occurs: leadership that

models data use, collaborative structures for data analysis, and professional development that connects

leadership competencies with data literacy. Their research demonstrated that schools achieving this integration

showed significantly higher levels of instructional innovation and student achievement growth.

CONCEPTUAL FRAMEWORK

Based on this comprehensive review of literature, Figure 1 presents the conceptual framework guiding this study.

The framework posits that classroom leadership practices and data-driven culture implementation serve as

independent predictors of instructional innovation adaptability, with potential interactive effects between the two

predictor variables.

Figure 1: Conceptual Framework of Predictors of Instructional Innovation Adaptability

Based on this framework and the literature, the following hypotheses are formulated:

• H1: Classroom Leadership Practices have a significant positive effect on Instructional Innovation

Adaptability.

• H2: Data-Driven Culture has a significant positive effect on Instructional Innovation Adaptability.

METHOD

Research Design

This study employed a quantitative, cross-sectional correlational design to examine the predictive

relationships between classroom leadership practices (CLP), data-driven culture implementation (DDC), and

instructional innovation adaptability (IIA). The design was selected to measure these variables across a diverse

sample of secondary teachers at a single point in time, allowing for the analysis of both bivariate correlations

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and multivariate predictions (Creswell & Creswell, 2023). While this design does not establish causality, it is

effective for identifying the strength and direction of relationships, providing a foundation for testing theories

and practical applications (Cohen et al., 2023).

Participants and Sampling Procedures

Sampling Strategy

A purposive sampling technique was used to recruit 300 secondary school teachers from various public and

private schools in the National Capital Region of the Philippines. This non-probability sampling method was

chosen to ensure the inclusion of participants who were information-rich and directly relevant to the research

problem—specifically, licensed professional teachers currently engaged in full-time classroom teaching at the

junior or senior high school level.

Participant Characteristics

The final sample of 300 teachers provided a diverse cross-section of the target population. The sample

included representation across:

• Teaching Levels: Junior High School (JHS) and Senior High School (SHS)

• Subject Specializations: Core subjects (Mathematics, Science, English, Filipino) and applied tracks

• Years of Teaching Experience: Ranging from early-career teachers (1-5 years) to veteran educators (20+

years)

This strategic approach to sampling enhanced the ecological validity of the study, supporting the

transferability of the findings to similar educational contexts.

Data Collection and Instruments

Primary data were collected using a comprehensive survey comprising three validated scales. All items were

measured on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).

1. Classroom Leadership Practices (CLP) Scale: A 40-item instrument measuring dimensions such as

vision and goal setting, instructional support, and monitoring and feedback. The scale demonstrated

excellent reliability in this study (Cronbach's α = .985).

2. Data-Driven Culture (DDC) Implementation Scale: A 40-item instrument assessing data use in

instruction, collaboration, and sharing, and data-driven decision-making practices. The scale showed

excellent reliability (Cronbach's α = .989).

3. Instructional Innovation Adaptability (IIA) Scale: A 30-item instrument measuring dimensions

including openness to change, innovative teaching practice, and collaboration for continuous learning.

This scale also demonstrated excellent reliability (Cronbach's α = .986).

Ethical Considerations

The study adhered to rigorous ethical standards throughout its implementation. Prior to data collection, ethical

approval was obtained from the relevant institutional review board. The following protocols were strictly

observed:

• Informed Consent: All participants were provided with a detailed information sheet outlining the study's

purpose, procedures, potential risks, and benefits. Written informed consent was obtained from each

teacher prior to their participation.

• Confidentiality and Anonymity: To protect participant privacy, all collected data were anonymized.

Identifying information was removed during data encoding, and each participant was assigned a unique

code. Data were stored on a secure, password-protected server, accessible only to the research team.

• Voluntary Participation and Right to Withdraw: Participants were explicitly informed that their

participation was entirely voluntary and that they could withdraw from the study at any point without any

negative consequences.

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• Data Utilization: Participants were assured that the data would be used solely for this academic research

and would be reported in aggregate form to ensure no individual could be identified.

Data Analysis and Statistical Tools

Data screening and assumption testing were conducted first, handling missing data via maximum likelihood

estimation. The analysis was performed using SPSS and R software. The specific analytical techniques were

chosen to directly address each research objective, as detailed in the table below.

Table 1: Data Analysis Plan Aligned with Research Objectives

Research Objective

Analysis Technique

Purpose

Statistical Tool

1. To describe the

sample and variables

Descriptive Statistics

(Mean, SD) &

Reliability Analysis

(Cronbach's α)

To characterize the sample and confirm

the internal consistency of all

measurement scales.

SPSS / R

2. To examine

relationships between

variables

Bivariate Correlation

(Pearson's r)

To assess the strength and direction of

the linear relationships between CLP,

DDC, and IIA.

SPSS / R

3. To determine and

compare the

predictive power of

CLP and DDC on IIA

Multiple Linear

Regression

To test the unique contribution of each

predictor (CLP and DDC) to the

outcome variable (IIA) and compare

their relative importance using

standardized coefficients (Beta, β).

SPSS / R

The multiple regression model was specified as: IIA = β₀ + β₁(CLP) + β₂(DDC) + ε, where β₀ is the intercept,

β₁ and β₂ are the regression coefficients, and ε is the error term. Key assumptions of linearity, homoscedasticity,

independence of errors, and absence of multicollinearity were checked and met prior to the analysis.

RESULTS AND DISCUSSION

This section presents the findings of the statistical analyses conducted to examine the relationships between

classroom leadership practices, data-driven culture, and instructional innovation adaptability. The presentation

of results follows the sequence of the data analysis plan, beginning with descriptive statistics and scale reliability,

followed by bivariate correlations to explore relationships between variables. The core of this section is a

multiple regression analysis, which was performed to determine the extent to which the predictor variables

uniquely explain variance in instructional innovation adaptability. The results are then discussed in relation to

the research questions and the existing literature, leading to an exploration of their theoretical and practical

implications.

Table 1 Descriptive Statistics and Scale Reliability for Study Variables (N = 300)

Scale: All items were measured on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).

Variable

Mean (M)

Standard Deviation (SD)

Skewness

Kurtosis

Classroom Leadership Practices (CLP)

3.49

0.729

-0.341

0.007

Data-Driven Culture (DDC)

3.50

0.761

-0.287

-0.102

Instructional Innovation Adaptability (IIA)

3.51

0.774

-0.197

-0.141

The descriptive statistics indicate that teachers reported moderately high levels of all three constructs, with

mean scores for CLP (3.49), DDC (3.50), and IIA (3.51) all falling well above the midpoint (3.0) of the 5-point

scale. This suggests a positive baseline where teachers, on average, agree with statements reflecting effective

leadership, data-driven practices, and innovation adaptability.

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The similar standard deviations (all approximately 0.73-0.77) indicate a consistent degree of spread in the

responses across all variables. The fact that the data is not tightly clustered shows meaningful variability among

teachers, which is necessary for detecting relationships in subsequent correlation and regression analyses.

The values for skewness and kurtosis for all variables are within the acceptable range of ±2, indicating that the

data for these constructs approximates a normal distribution. This satisfies a key assumption for the parametric

statistical tests (like multiple regression) used later in the analysis.

Reliability Analysis

All measurement instruments demonstrated excellent internal consistency, with Cronbach's alpha coefficients

exceeding conventional thresholds for research instruments (α > .90). The high reliability coefficients support

the psychometric quality of the scales and enhance confidence in the subsequent analyses.

• Classroom Leadership Practices Scale: The overall reliability was α = .985, with subscale reliabilities

ranging from α = .941 to α = .953. These values indicate exceptional internal consistency across all

leadership dimensions.

• Data-Driven Culture Implementation Scale: The scale demonstrated an α of .989 reliability, with

subscale coefficients ranging from α = .938 to α = .949. The consistent high reliability across domains

supports the robustness of this measurement.

• Instructional Innovation Adaptability Scale: This scale demonstrated an α = 0.986 reliability, with

subscale values ranging from α = 0.927 to α = 0.935. The strong internal consistency validates the scale's

utility for measuring teacher adaptability.

Correlation Analysis

Bivariate correlations revealed strong positive relationships among all study variables (see Table 3). Classroom

leadership practices and data-driven culture implementation were strongly correlated (r = 0.503, p < 0.001),

suggesting a substantial overlap while maintaining discriminant validity. Both predictor variables demonstrated

significant correlations with instructional innovation adaptability, with DDC (r = 0.571, p < 0.001) showing a

stronger relationship than CLP (r = 0.356, p < 0.001). This pattern indicates that teachers who report higher

levels of classroom leadership and data-driven practices also tend to report greater instructional innovation

adaptability, providing preliminary support for the hypothesized predictive model.

Table 2: Correlation Matrix for Study Variables

Variable

1. CLP

2. DDC

3. IIA

1. CLP

—

2. DDC

.503**

—

3. IIA

.356**

.571**

—

*Note: ** p < .001*

Multiple Regression Analysis

To address the research questions regarding the predictive power of Classroom Leadership Practices (CLP) and

Data-Driven Culture (DDC) on Instructional Innovation Adaptability (IIA), a multiple regression analysis was

conducted. The results are presented in Table 3.

Table 3: Multiple Regression Analysis Predicting Instructional Innovation Adaptability

Predictor

Unstandardized

Coefficient (B)

Standard Error (SE)

Standardized

Coefficient (β)

t-value

p-value

Constant

1.300

0.082

6.435

<.001

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Classroom Leadership

0.097

0.058

0.091

1.665

<.10

Data-Driven Culture.

0.534

0.056

0.525

9.571

<.001

The analysis produced a statistically significant model. The resulting regression equation is:

IIA = 1.300 + 0.097(CLP) + 0.534(DDC)

The results provide clear answers to the research questions:

1. To what extent do Classroom Leadership Practices predict IIA? CLP demonstrates a positive but modest

and only marginally significant predictive relationship with IIA (β = 0.091, p < .10). For each one-point

increase in CLP, IIA increases by 0.097 points, controlling for DDC.

2. To what extent does Data-Driven Culture predict IIA? DDC is a strong and highly significant predictor

of IIA (β = 0.525, p < .001). For each one-point increase in DDC, IIA increases by 0.534 points, controlling

for CLP.

3. Which factor is a stronger predictor? Data-Driven Culture (β = 0.525) is a decisively stronger predictor

of Instructional Innovation Adaptability than Classroom Leadership Practices (β = 0.091).

DISCUSSION OF REGRESSION FINDINGS

The regression analysis confirms that while both factors are positively associated with adaptability, Data-Driven

Culture is the dominant driver. The strong predictive power of DDC suggests that systemic, cultural factors

embedded within the school environment—such as collaboration around data, data-driven decision-making, and

institutional support—are more critical for fostering innovation adaptability than individual classroom leadership

practices. The marginal significance of CLP indicates that its influence, while positive, is less robust and should

be interpreted with caution. In practical terms, efforts to enhance teacher innovation may yield a greater return

on investment by focusing on building a comprehensive, supportive data culture rather than focusing solely on

leadership techniques.

Summary of Key Findings

This study provides evidence that both classroom leadership practices and the implementation of data-driven

culture are predictors of instructional innovation adaptability among secondary teachers. The model explains a

significant portion of the variance in adaptability, underscoring the role these factors play.

The finding that Data-Driven Culture (DDC) demonstrated a significantly stronger predictive relationship (β

= 0.525, p < .001) compared to Classroom Leadership Practices (CLP) (β = 0.091, p < .10) suggests that

systematic, organizational data use is a more crucial lever for fostering instructional innovation than individual

leadership behaviors. This aligns with literature emphasizing the importance of evidence-based practice in

educational innovation (Mandinach & Schildkamp, 2021).

The moderate correlation between classroom leadership and data-driven culture (r = 0.503) suggests that these

constructs, while distinct, are related in educational environments. Teachers who exhibit stronger leadership

behaviors may be more likely to engage with data-driven practices, creating a complementary relationship that

supports instructional innovation.

Theoretical Implications

The findings have several important theoretical implications. First, they support an integrated theoretical

framework that connects leadership theory with data-informed decision-making models. However, the vast

difference in predictive power between DDC and CLP suggests that data culture may be a more central

component in a theory of teacher innovation capacity.

Second, the results challenge models of educational change that assume all factors are equally weighted. The

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apparent dominance of data-driven culture in this model underscores the need for theoretical frameworks that

not only include multiple factors but also account for their relative and potentially hierarchical influence on

outcomes.

Third, the findings contribute to complex adaptive systems theory by pinpointing a specific, actionable

organizational factor (data-driven culture) as a primary driver of teachers' adaptive capacity. This helps bridge

the gap between abstract theoretical concepts and practical strategies for educational improvement.

Practical Implications

These findings have several important implications for educational practice and policy:

Professional Development Design

School districts should prioritize professional development that robustly builds teachers' data literacy skills and

fosters a school-wide data culture. While classroom leadership remains valuable, the stronger returns on

innovation adaptability lie in enhancing data-driven practices. Specific focus should include:

• Data literacy training that enables teachers to derive actionable insights from multiple data sources for

instructional planning.

• Collaborative analysis protocols that institutionalize evidence-based decision-making within teams.

• Leadership development that specifically empowers teachers to lead data-informed initiatives and create

cultures of collective efficacy.

School Leadership Practices

Principals and instructional leaders should create organizational structures and allocate resources that primarily

support a data-driven culture. This includes:

• Establishing professional learning communities focused on data analysis and the implementation of

innovative strategies derived from that data.

• Protecting time for collaborative data analysis and instructional planning.

• Providing resources and support for teachers to experiment with innovative approaches informed by data.

• Modeling and celebrating effective data use to drive instructional improvement.

Policy Considerations

Educational policymakers should consider:

• Funding initiatives that support the development of user-friendly data systems and data coaching accessible

to classroom teachers.

• Revising school improvement frameworks to emphasize the foundational role of a data-driven culture in

fostering innovation.

• Supporting the integration of advanced data literacy and data culture development into both teacher

preparation and ongoing professional learning.

Limitations and Future Research Directions

Several limitations should be considered when interpreting these results. First, the cross-sectional design

prevents causal inferences. While the theoretical framework suggests that leadership and data practices influence

innovation, reverse causality or bidirectional relationships are possible.

Second, the reliance on self-report measures may introduce social desirability bias. Future research should

incorporate multiple data sources, such as observational measures of classroom practices and administrative data

on data system usage.

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Third, the study focused specifically on secondary teachers, and the generalizability to other educational contexts

requires verification.

Several promising directions for future research emerge from these findings:

• Longitudinal or experimental studies to test the causal impact of data culture initiatives on innovation

adaptability.

• Qualitative investigations exploring the specific mechanisms through which data practices directly

influence a teacher's willingness and ability to innovate.

• Research on how school-level leadership moderates the relationship between data culture and teacher-

level innovation.

• Cross-cultural comparisons to examine the universal and context-specific aspects of these relationships.

CONCLUSION

This study establishes that the adaptability of secondary teachers to instructional innovation is a professional

capacity that can be fostered through specific organizational conditions and practices. The findings confirm that

both classroom leadership practices and data-driven culture implementation are predictors of instructional

innovation adaptability, with data-driven culture emerging as the decisively stronger factor.

The integrated yet imbalanced nature of these relationships suggests that educational improvement efforts should

take a focused approach, prioritizing the development of a robust, supportive data culture as the primary engine

for innovation. By making strategic investments in data systems, data literacy, and collaborative data use,

educational institutions can more effectively build resilient and innovative teaching forces capable of navigating

the complexities of contemporary education. As demands for educational transformation increase, this study

clarifies that empowering teachers with a strong data-driven culture is a critical strategy for enhancing

adaptability and, ultimately, improving student learning.

REFERENCES

1. A systematic review of research on educational leadership and management in South Africa: mapping

knowledge production in a developing society - Fingerprint - University of Johannesburg.

https://pure.uj.ac.za/en/publications/a-systematic-review-of-research-on-educational-leadership-and-

man/fingerprints/?sortBy=alphabetically

2. Albert, G., Kwong, G., & Ibrahim, N. (2023). Factors Influencing the Effectiveness of Professional

Virtual Communities in Public Service Efficiency.

3. Albert, M., & Scott, C. (2023). Co-Realizing COVID Co-Teaching Concerns: Recognizing Present

Challenges to Student Equity in Remote Education. Taboo, 21(2), 8–17.

4. Alzoraiki, M., Alzoraiki, M., & Almaamari, Q. (2023). Impact of Teachers' Commitment on the

Relationship between Transformational Leadership and Sustainable Teaching Performance.

Sustainability, 15(5), 4620.

5. European Conference on Knowledge Management, 1(), 24-32, R23, R29.

6. American Educational Research Association. (2023). Ethical standards of the American Educational

Research Association. AERA.

7. Arshi, A. S., Islam, S., Tamanna, A. Y., Sultana, S., & Ikram, S. B. (2024). Fintech for Sustainability in

Business and Economics Research: Trends and Future Agendas. https://doi.org/10.38157/bpr.v6i1.620

8. Bass, B. M., & Riggio, R. E. (2020). Transformational leadership (3rd ed.). Psychology Press.

9. Baker, H., Kaddumi, T. A., Nassar, M., & Muqattash, R. (2023). Impact of Financial Technology on the

Improvement of Banks' Financial Performance. Journal of Risk and Financial Management.

https://doi.org/10.3390/jrfm16040230

10. Bahri, Z., Muspawi, M., & Sofwan, M. (2025). The Influence of Principal's Authentic Leadership,

Responsible Management Education, and Teacher Performance Motivation on Continuing Education in

Public Junior High Schools in Jambi City. https://core.ac.uk/download/671666111.pdf

11. Batran, A., Melhem, R., & Shouli, M. (2025). Work engagement and its association with emotional

intelligence and demographic characteristics among nurses in Palestinian neonatal intensive care units.

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

Page 9402

www.rsisinternational.org

PLoS One, 20(9), e0332908.

12. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2023). Applied multiple regression/correlation analysis

for the behavioral sciences (4th ed.). Routledge.

13. Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods

approaches (6th ed.). Sage Publications.

14. Danielson, C. (2021). The Framework for Teaching: Evaluation Instrument. The Danielson Group.

15. Darling-Hammond, L. (2020). Accountability in teacher education. Action in Teacher Education, 42(1),

1–12.

16. Datnow, A., Greene, J. C., & Gannon-Slater, N. (2021). Data Use for Equity: Implications for Teaching

and Learning. Harvard Education Press.

17. Embracing Negative Feedback for Inclusive Leadership and Growth - SEE Change Happen: The

Inclusive Culture Experts. https://seechangehappen.co.uk/watch-archive/embracing-negative-feedback-

for-inclusive-leadership-and-growth/

18. Fullan, M. (2021). The right drivers for whole system success. Centre for Strategic Education.

19. García-Martínez, I., Molina-Fernández, E., & Ubago-Jiménez, J. (2020). School Principals in Spain:

Interplay of Leaders, Teachers and Context. Sustainability, 12(4), 1469.

20. Garcia, C. L. M. (2023). Leadership style of school heads in relation to teachers' classroom practices in

selected secondary schools.

21. International Journal for Research in Applied Science and Engineering Technology, 11(5), 123-135.

22. Gutierrez-Villalpando, T., Cárdenas-Cuevas, M., Pérez, L., Gutierrez, E., & Zavala, A. (2022). Lean

Tools implementation for scrap reduction in a production line - A Case study. IISE Annual Conference.

Proceedings, (), 1-6.

23. Hallinger, P. (2020). A systematic review of research on educational leadership and management in Asia.

Educational Management Administration & Leadership.

24. Hargreaves, A., & O'Connor, M. T. (2020). Collaborative professionalism: When teaching together

means learning for all. Corwin Press.

25. Harris, A. (2020). Teacher leadership and educational change. School Leadership & Management, 40(1),

1–4.

26. Harris, A., & Jones, M. (2022). COVID-19 and the future of educational change. Journal of Educational

Change, 23(2), 145–159.

27. Hill, T., Kelly, G., McQuillan, J., Phillips, M., Melson, M., & Blake, J. (2023). Mapping Teacher Informal

Advice Networks as a Tool for District Administrators: A Case Study. Science Educator, 29(2), 1–11.

28. HMH Culture. (2024). How to build a data-driven culture in schools. Educational Leadership Review,

25(2), 45–58.

29. Ikemoto, G. S., & Marsh, J. A. (2020). Cutting through the "data-driven" mantra: Different conceptions

of data-driven decision making. Teachers College Press.

30. Ivey, J. (2016). Taking It Home. Pediatric Nursing, 42(3), 137.

31. Isac, M., Isac, M., Sass, W., Boeve-de Pauw, J., De Maeyer, S., Schelfhout, W., & Claes, E. (2022).

Differences in Teachers' Professional Action Competence in Education for Sustainable Development:

The Importance of Teacher Co-Learning. Sustainability, 14(2), 767.

32. Jackson, B., Alaradi, M., Ling, J., & Lehna, C. (2016). Assessment of Home Safety in Children From

Kentuckiana. Pediatric Nursing, 42(3), 131–136.

33. Leithwood, K., Harris, A., & Hopkins, D. (2020). Seven strong claims about successful school leadership

revisited. School Leadership & Management, 40(1), 5–22.

34. López-Eugenio, K., Torres-Bermeo, P., & Del-Valle-Soto, C. (2025). Projection of Photovoltaic System

Adoption and Its Impact on a Distributed Power Grid Using Fuzzy Logic. Sustainability, 17(12), 5235.

35. LSU Online. (2020). How educators can use student data to drive instruction. Journal of Educational

Technology, 15(3), 78–92.

36. Ma, Z., Zhang, T., Jiang, J., & Wang, L. (2025). The Impact of Teachers' Transformational Leadership

on the Soft Skills of Chinese Secondary Vocational Students. International Journal of Educational

Management, 39(1), 112–128.

37. Mandinach, E. B., & Schildkamp, K. (2021). The Ethical Challenges of Using Data for Educational

Decision-Making. Studies in Educational Evaluation, 68, 100972.

38. Marsh, J. A., & Farrell, C. C. (2022). How leaders can support teachers with data-driven decision

INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

Page 9403

www.rsisinternational.org

making. Educational Management Administration & Leadership, 50(3), 456–474.

39. Martín, C. (2025). Insights into Organic Food Consumption in Tenerife (Spain): Examining Consumer

Profiles and Preferences. Sustainability, 17(5), 2123.

40. Mason, M. (2020). Complexity theory and the philosophy of education. Educational Philosophy and

Theory.

41. Matthiessen, L., & Youssef, A. (2025). Is Organic Food Consumption Associated with Other Sustainable

Food-Related Behaviors? Insights from a Survey in the Capital City of Poland. Nutrients, 17(13), 2113.

42. OECD. (2023). Teachers as Designers of Learning Environments: The Importance of Innovative

Pedagogies. OECD Publishing.

43. Moss, P. (2012). Exploring the Macro-Micro Dynamic in Data Use Practice. American Journal of

Education. https://doi.org/10.1086/663274

44. Parsons, D., Thomas, H., & Wishart, J. (2023). Exploring teacher adaptability through reflective

practice. Teaching and Teacher Education, 121, 103945.

45. Paz, M., Paz, M., Vasconcelos, C., & Vasconcelos, C. (2025). Scientific Literacy to Address

Sustainability: A Study on Deep-Sea Mining Education with Adolescents from a Social Care Institution.

Sustainability, 17(2), 688.

46. Prenger, R., Poortman, C. L., & Handelzalts, A. (2021). The effects of networked professional learning

communities. Journal of Teacher Education, 72(1), 103–117.

47. Rodríguez Moreno, S., Rojas, B., & Roca Morales, P. (2024). Mechanisms of change in compassion-

based programs for medical students. https://doi.org/10.1038/s41598-024-81557-x

48. Schildkamp, K. (2023). Data-based decision making in education: Challenges and opportunities.

Springer.

49. Schildkamp, K., Poortman, C. L., Luyten, H., & Ebbeler, J. (2020). Factors Influencing the Sustainability

of Data Use in Schools. School Effectiveness and School Improvement, 31(2), 145-168.

50. Spomocník: Jak využívat žákovská data pro zdokonalení výuky?.

https://clanky.rvp.cz/clanek/23204/JAK-VYUZIVAT-ZAKOVSKA-DATA-PRO-ZDOKONALENI-

VYUKY.html

51. Suazo-Flores, E., Kastberg, S. E., Grant, M. R., & Chapman, O. (2022).

52. Stouthart, T., & Van der Veen, J. (2023). Capturing Pedagogical Design Capacity of STEM Teacher

Candidates: Education for Sustainable Development through Socioscientific Issues. Sustainability,

15(14), 11055.Philosophical Underpinnings of Mathematics Teacher Educator\u27s Work.

https://doi.org/10.51272/pmena.44.2022

53. Tripon, C. (2022). Supporting Future Teachers to Promote Computational Thinking Skills in Teaching

STEM—A Case Study. Sustainability, 14(19), 12663.

54. Trust, T., & Whalen, J. (2021). Should teachers be trained in emergency remote teaching? Journal of

Technology and Teacher Education, 29(2), 145–162.

55. Varner, K., & Carlson, D. (2023). Editorial Introduction. Taboo, 21(2), 3–7.

56. Van Gasse, R., Vanlommel, K., Vanhoof, J., & Van Petegem, P. (2023). The Impact of Collaboration on

Teachers' Data Use. Educational Studies, 49(1), 67-85.

57. Vieira, M., Jenkins, S., & Ellerbee, M. (2020). Forming the Habit: A Case Study of Effective Practice in

Group Piano I. MTNA e-Journal, 11(4), 26–27.

58. Voogt, J., & Knezek, G. (2021). Theoretical perspectives on digital technologies in education. Routledge.

Week 2 Resources - ED-7012 - LibGuides at National University.

https://resources.nu.edu/c.php?g=1150197&p=8395660

59. Wishart, J. (2009). Ethical considerations in implementing mobile learning in the workplace.

https://doi.org/10.4018/jmbl.2009040105

60. York-Barr, J., & Duke, K. (2020). What do we know about teacher leadership? Findings from two decades

of scholarship. Review of Educational Research, 74(3), 255–316.

61. Zheng, X., Wu, Z., Luo, S., Peng, X., & Peng, X. (2022). A Job Demands–Resources Perspective on

Kindergarten Principals' Occupational Well-Being: The Role of Emotion Regulation. International

Journal of Environmental Research and Public Health, 19(22), 15030.