Linking Instructional Leadership and Differentiated Instruction: Examining the Mediating Role of Teacher Agency

Differentiated Instruction

Differentiated instruction refers to a pedagogical approach through which teachers systematically adapt instruction to address students’ individual learning needs (Dixon et al., 2014; Tomlinson, 2017). This approach rejects standardized teaching and acknowledges the diverse backgrounds that students bring to classrooms. Numerous studies have demonstrated that teachers’ instructional practices exert substantial influence on student learning outcomes (Hattie, 2008). As such, educators must plan meticulously to meet diverse learning requirements (Subban, 2006). Differentiated instruction requires teachers to customize curriculum content, process, and learning tasks while also ensuring that students meaningfully apply newly acquired knowledge (Tomlinson, 2017; Tomlinson & Imbeau, 2023). Because classrooms emphasizing DI require high-quality instruction, teachers bear several responsibilities, including using materials suitable for different learning styles and designing varied learning activities (Y. L. Goddard et al., 2019).

Implementing differentiated instruction has been shown to yield favorable outcomes, including for students with learning disabilities (Tobin & McInnes, 2008). When students are engaged in lessons through appropriately adapted instruction, they are less likely to exhibit disruptive behavior, and they readily demonstrate their learning potential when they feel their efforts are recognized (Tomlinson & Imbeau, 2023). In this study, differentiated instruction is conceptualized using the two-dimensional framework developed by Roy et al. (2013). The first dimension, instructional adaptations, refers to the strategies teachers employ to align instructional methods with students’ capabilities. The second dimension, monitoring academic progress, concerns the systematic assessment practices used to track students’ learning trajectories.

Inclusive education, a globally endorsed pedagogical philosophy, provides an important contextual foundation for understanding differentiated instruction. It asserts that all students learn together in general education settings. Inclusive education emphasizes not only physical integration but also the transformation of school culture, policies, and instructional practices to eliminate barriers to participation and achievement. International frameworks such as the Salamanca Statement and the UN Convention on the Rights of Persons with Disabilities highlight the responsibility of schools to create equitable and accessible learning environments for all students (UNESCO, 1994; United Nations, 2006).

Within this broader framework, differentiated instruction serves as a key mechanism for operationalizing inclusive education principles. While inclusive education defines the why and where of equitable learning, differentiated instruction defines the how. Through formative assessment, flexible grouping, and the intentional adaptation of content, process, product, and learning environment, teachers can design multiple pathways that respond to learner diversity rather than adhering to a one-size-fits-all model (Griful-Freixenet et al., 2021; Subban, 2006).

Teacher Agency

Agency refers to the ability to effect change within the context of school improvement literature (Durrant & Holden, 2005). Teacher agency denotes the conviction that educators possess the capacity to bring about change within educational institutions through their own competencies (Biesta & Tedder, 2007; Emirbayer & Mische, 1998; Priestley et al., 2015; Xue et al., 2023). Several factors underpin teacher agency, including personal beliefs, instructional strategies, and adaptability to evolving educational environments (Cheng et al., 2014). Key characteristics of agency involve teachers collaborating with peers, enhancing educational quality, exchanging professional knowledge and experiences (Biesta & Tedder, 2007). Consequently, teacher agency entails taking responsibility for professional actions and effectively addressing emerging challenges (Fullan, 1993).

Teacher agency is also a key driver of differentiated instruction, as it enables teachers to adapt their practices to meet students’ diverse learning needs (Bellibaş et al., 2021). Teachers with a strong sense of agency perceive differentiated instruction as an opportunity to enhance their professional competence and improve learning outcomes (Alazmi & Hammad, 2023).

The literature identifies several perspectives on teacher agency. Structural approaches emphasize institutional and organizational conditions that either enable or constrain agency (Priestley et al., 2015), while sociocultural approaches highlight how teachers’ interactions and social contexts shape their capacity to act (Ahearn, 2019). Psychological approaches, on the other hand, underscore individual factors such as beliefs, motivation, and self-efficacy as central determinants of agency (Liu et al., 2016; Xue et al., 2023).

Teacher agency also encompasses a social justice and equity dimension, empowering educators to recognize and address diverse student needs, advocate for inclusive practices, and challenge structural inequities in education (Nindya et al., 2023; Priestley et al., 2015). From this perspective, teacher agency extends beyond professional autonomy to include an ethical responsibility to ensure equitable access to learning opportunities for all students.

In this study, teacher agency provides the theoretical lens for understanding how principal instructional leadership influences differentiated instruction. Differentiated instruction requires teachers to respond to students’ varying abilities, backgrounds, and learning styles (Tomlinson, 2017). Accordingly, this study adopts Liu et al.’s (2016) four-dimensional model, which conceptualizes teacher agency as a combination of personal beliefs, professional engagement, and proactive actions.

The first dimension, learning effectiveness, reflects teachers’ beliefs about their motivation, abilities, and perseverance as learners in peer-to-peer learning environments. The second, teaching effectiveness, captures teachers’ confidence in working with diverse students and achieving instructional goals. The third, optimism, represents teachers’ positive expectations about collaboration and future success. Finally, constructive participation describes teachers’ willingness to engage in learning and expand their professional influence within the school (Alazmi & Hammad, 2023; Bellibaş et al., 2021; Leijen et al., 2019; Nindya et al., 2023; Xue et al., 2023).

Teachers with strong agency are better positioned to adapt instructional strategies, address individual learning needs, and foster inclusive teaching practices. In sum, framing the study through the lens of teacher agency underscores its dual function: enabling teachers’ professional growth and advancing equity in student learning, thus providing a robust theoretical foundation for examining the direct and indirect effects of principal instructional leadership on differentiated instruction.

Principal Instructional Leadership

Principal instructional leadership is widely recognized as a key approach in research on school improvement (Marks & Printy, 2003). At the center of principal instructional leadership are school principals who aim to create a quality school environment to facilitate teaching and learning processes (Hallinger & Wang, 2015). Since principal instructional leadership focuses on defining the school’s mission, setting goals for student achievement, and monitoring progress (Hallinger & Lee, 2013), it also encourages differentiated instructional practices (Printy et al., 2009). Principal instructional leadership also supports teaching effectiveness which is an important feature of teacher agency, through principals’ self-efficacy beliefs (Hallinger & Heck, 2010).

In this study, the Principal Instructional Management Rating Scale (PIMRS) developed by Hallinger and Wang (2015) was taken as a reference. PIMRS encompasses three dimensions: Defining the School Mission, Managing the Instructional Program, and Developing the School Learning Climate. Defining the School Mission consists of two components: frames the school’s goals and communicates the school’s goals. Managing the Instructional Program consists of three components: coordinates the curriculum, supervises and evaluates instruction, and monitors student progress. Lastly, Developing the School Learning Climate includes the components of protects instructional time, provides incentives for teachers, provides incentives for learning, promotes professional development, and maintains high visibility.

Overview of the Literature Review

Prior literature suggests that principals influence classroom practices both directly by guiding instructional improvement, and indirectly by shaping teacher beliefs. At the same time, teacher agency has emerged as a key mechanism through which teachers enact learner-responsive practices such as differentiated instruction. Although these constructs have been examined separately, the literature has not sufficiently integrated them into a coherent model explaining how principal instructional leadership may foster differentiated instruction through teacher agency. Drawing on these theoretical and empirical foundations, the following subsections synthesize the relevant evidence and lead to the development of the study’s hypotheses.

Teacher Agency and Differentiated Instruction

Teacher agency plays a pivotal role in promoting differentiated instruction, as it empowers teachers to adapt their teaching methods to meet students’ diverse learning needs (Bellibaş et al., 2021; Leijen, et al., 2019). Furthermore, teachers with a strong sense of agency contribute to their professional development by investing time and effort in implementing differentiated instruction (Alazmi & Hammad, 2023; Biesta & Tedder, 2007). This research uses Liu et al., 2016’s four-dimensional teacher agency model. Teachers with strong agency are open to professional growth and consistently update their teaching practices to support students’ diverse learning needs (Li & Ruppar, 2020; Xue et al., 2023). Teachers with high agency are more likely to engage in reflective practice, adapt their teaching strategies, and implement differentiated instruction that meets students’ diverse needs (Bellibaş et al., 2021; Leijen et al., 2019). Evidence indicates that agency motivates teachers to invest effort in modifying content, processes, and assessments to optimize learning outcomes for all students. Based on these arguments, we hypothesize that:

Principal Instructional Leadership and Teacher Agency

Over recent decades, school principals have shifted from administrative management to a more dynamic role in shaping educational practice. This evolution has renewed interest in principal instructional leadership, emphasizing the principal’s role in guiding and supporting teaching and learning (Hallinger, 2005). At the same time, teacher agency has grown, highlighting the significance of letting educators make choices that influence their professional practice and student results (Biesta et al., 2015). Principal instructional leadership is how school leaders enhance teaching and learning (Leithwood et al., 2020). Teacher agency is their capacity to actively and constructively direct their professional growth and student learning which is essential for successful schooling (Lasky, 2005). By providing a supportive and collaborative atmosphere, a clear vision, and professional development, principals may encourage teachers to take ownership of their work and improve student learning. Research has shown that principals who provide clear guidance enhance teachers’ sense of agency (Hallinger & Heck, 2010; Leithwood et al., 2020). This suggests that strong instructional leadership not only sets goals but also empowers teachers to take ownership of their professional growth. Based on these arguments, we hypothesize that:

Principal Instructional Leadership and Differentiated Instruction

On the other hand, the impact of principal instructional leadership on differentiated teaching is significant. Previous research on the effects of principal instructional leadership on teaching techniques primarily serves as the basis for forecasting this effect (Bellibaş et al., 2021; Y. L. Goddard et al., 2019; Hallinger, 2011; Leithwood et al., 2020). Evidence indicates the direct impact of principal instructional leadership on differantiated instructional practices (Hammad et al., 2024). Principals’ instructional leadership has been linked to improvements in classroom practices, including differentiated instruction (Y. L. Goddard et al., 2019; Hammad et al., 2024). Based on these arguments, we hypothesize that:

Indirect Pathway From Principal Instructional Leadership to Differentiated Instruction

Building on established theoretical and empirical evidence above, prior studies suggest that principals exert an indirect influence on differentiated instruction by enhancing teacher agency. Teachers who experience greater professional autonomy and self-confidence are more inclined to employ flexible and adaptive instructional strategies (Bellibaş et al., 2022; Cong-Lem, 2021). This mediating mechanism aligns with ecological perspectives that emphasize the role of leadership in shaping teacher behaviors through enabling conditions rather than direct control. Based on these arguments, we hypothesize that:

Sampling and Data Collection

The study utilized data collected from primary and secondary school teachers across Türkiye. Convenience sampling was used in this research owing to practical constraints related to time, cost, and participant availability. Prior to administering the survey, this study was approved by the ethics committee of the authors’ institution. The data were collected via online survey methods. The study’s population consisted of all teachers working in Türkiye’s primary and secondary schools throughout the academic year 2022 to 2023. The total number of teachers is 672.503, allocated as follows: 261.473 in public primary schools, 38.494 in private primary schools, 329.863 in public secondary schools, and 42.673 in private secondary schools. It was decided to poll at least 384 instructors to obtain a 0.95 reliability and a 0.05 margin of error (Cochran, 1977). Upon receiving approval from the Ministry of National Education (MoNE), the principals of the selected elementary and secondary schools were formally notified about the purpose of the study. Following this, the link to the online survey was distributed through the schools to the participating teachers. A total of 706 valid responses were obtained over a 3-month period. The participants were predominantly female (n = 481, 68%) and had attained a bachelor’s degree (n = 593, 84%). The mean teaching experience was 15.12 years (SD = 8.83), and 418 (59.2%) teachers were employed in secondary schools. These demographic characteristics align with national distributions across Türkiye (MoNE, 2023).

Variables and Measures

We used the principal instructional management rating scale, teacher agency scale, and differentiated instruction scale within this study, as well as control variables such as school level, teachers’ gender, seniority, and educational status.

Predictor variable: Principal instructional management rating scale (PIMRS):Hallinger and Wang (2015) developed the scale as 3 dimensions (school mission, managing the instructional program, and developing the school learning climate) and 22 items. The Principal Instructional Management Rating Scale (PIMRS), adapted into Turkish by Bellibaş et al. (2016), was used in this study with 18 items. Example items include “Set specific annual school-wide goals” and “Participation in extracurricular activities at school.”

Mediator variable: Teacher Agency Scale (TAS): The Teacher Agency Scale (TAS), originally developed by Liu et al. (2016) as a four-dimensional construct (learning effectiveness, teaching effectiveness, optimism, and constructive engagement) consisting of 24 items, was employed in this study. The scale was adapted into Turkish by Bellibaş et al. (2019). Example items include: “If I work hard enough, I can continue to improve my teaching skills” and “I am mostly optimistic, even in times of uncertainty.”

Outcome variable: Differentiated Instruction Scale (DIS):Roy et al., 2013 developed the scale as bi-dimensional (Instructional adaptations, and academic progress monitoring) and 12 items. The authors adapted the scale into Turkish with 12 items for this study. According to Exploratory Factor Analysis (EFA), the factor loadings were between .58 and .81 and explained 68.19% of the total variance. One of the items is “I plan different assignments to match students’ abilities.” and “I analyse data about students’ academic progress.”

The scales are of the 5-point Likert type, with scores ranging from one (never agree) to five (completely agree). The reliability of the scales was evaluated by assessing the Average Variance Extracted (AVE), Composite Reliability (ω), and Cronbach alpha values (α; see Table 1). To ensure the requisite convergent validity, we followed the criteria set forth by Hu and Bentler, 1999, which stipulate that the AVE value must exceed 0.50 and the ω value must surpass .70. Consequently, we have provided the requisite reliability values for these measurement instruments.

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Table 1.

Fit Indices and Reliability Results.

Variables	Fit indices							Reliability values
	χ2	df	χ2/df	RMSEA	CFI	TLI	SRMR	α	ω	AVE
PIMRS	508.667	116	4.39	0.069	0.952	0.944	0.026	.98	.98	0.75
TAS	857.585	247	3.47	0.059	0.934	0.926	0.083	.97	.97	0.64
DIS	277.702	52	5.34	0.078	0.935	0.917	0.037	.94	.94	0.61

Note. PIMRS = Principal instructional management rating scale. TAS = Teacher agency scale; DIS = Differentiated Instruction Scale; χ² = Chi-square; df = Degrees of Freedom; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker Lewis Index; α = Cronbach alpha; ω = Composite Reliability; AVE = Average Variance Extracted.

Control variables (School level, teachers’ gender, seniority, educational status): In alignment with prior research indicating that organizational and teacher characteristics significantly influence educational practices (Özdemir et al., 2022; Smale-Jacobse et al., 2019). Several teacher demographic variables were included in the model as control variables to isolate the relationships between the primary constructs. Gender, education level, school level, and seniority were modeled as direct covariates of differentiated instruction. All control variables were entered simultaneously into the structural modelwe included school level, teachers’ gender, seniority, and educational status as control variables in our structural equation model. The categorical controls (school level and educational status) were dummy-coded, while seniority was treated as a continuous variable. Each control was specified as an exogenous variable, with direct paths drawn to the endogenous construct of differentiated instruction. The model was estimated in Mplus using maximum likelihood estimation, which partials out the variance associated with these controls. This treatment ensures that the estimated paths among the focal constructs reflect their unique relationships, independent of these potential confounds.

The loading values of the scales were 0.792 to 0.916 for PIMRS, 0.670 to 0.870 for TAS and 0.703 to 0.850 for DIS. The overall fit indices and reliability results of the Confirmatory Factor Analysis (CFA) are presented in Table 1.

Table 1 presents the fit indices of the scales: χ²/df < 5; root mean square error of approximation (RMSEA) < 0.080; comparative fit index (CFI) > 0.900; Tucker Lewis index (TLI) > 0.900; and standardised root mean square residual (SRMR) < 0.050. To these values, we can say that the scales’ measurements model shows acceptable fit (Hu & Bentler, 1999). Additionally, to determine the reliability of the scales, we calculated the correlations of the items with each other, the total score of the scales and the item subtotal and Cronbach’s alpha reliability coefficients, and we determined that the scales are reliable (Tabachnick & Fidell, 2013).

Analytical Strategy

Structural Equation Modeling (SEM) was employed as the primary analytical technique in this study. SEM is particularly suitable as it allows for the simultaneous examination of multiple relationships among latent and observed variables, offering a more comprehensive understanding than traditional regression methods (Kline, 2023). This approach was chosen to test the hypothesized model linking principal instructional leadership, teacher agency, and differentiated instruction, while accounting for measurement error and the complexity of indirect effects.

Preliminary analyses included calculation of means, standard deviations, normality, and bivariate correlations for all scale variables using IBM SPSS 27. The results indicated that the standard deviation values for Differentiated Instruction (DI) and Teacher Agency (TA) were low, suggesting minimal variability in teacher responses. In contrast, Principal Instructional Leadership (PIL) showed higher variability, indicating that teachers’ perceptions of their principals were more diverse.

To test the hypothesized relationships, structural equation modeling (SEM) was employed using Mplus 8.11 (Muthén & Muthén, 2019). Although clustering of teachers within schools can sometimes affect predictor effects, prior research indicates that if the variation across schools is not the primary focus, standard analyses accounting for clustering are sufficient (McNeish et al., 2017). Therefore, the model analyzed the direct and indirect effects of PIL on DI, mediated by TA, while controlling for school-level, gender, seniority, and educational status.

Before testing the structural model, confirmatory factor analysis (CFA) was conducted in Mplus 8.11 to evaluate the measurement model (see Table 1). Second-order constructs, such as Principal Instructional Leadership, were modeled as latent variables reflected by their respective first-order dimensions (e.g., defining school mission, managing instructional program, and developing school learning climate), consistent with their conceptualization as multidimensional constructs. The suitability of the data for SEM was assessed by examining frequency distributions, outliers, multicollinearity, and Mahalanobis distances, ensuring that the assumptions of SEM were met.

Model fit was evaluated using multiple indices: the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), with recommended cutoff values of CFI/TLI ≥ 0.90 and RMSEA/SRMR ≤ 0.08 (Byrne, 2016; Hair et al., 2019; Hu & Bentler, 1999; Schumacker & Lomax, 2016).

Preliminary Analysis

Firstly, we computed the mean, standard deviation, kurtosis, skewness values of the variables and analyzed the relationships between the variables. Table 2 presents the results:

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Table 2.

Descriptive Statistics and Correlations Among Study Variables.

Variables	M	SD	S	K	1	2	3	4	5	6	7
1. DI	4.13	0.64	−0.69	0.71	1	.60**	.42**	.05	−.03	−.16**	−.03
2. TA	4.06	0.65	−1.0	2.77		1	.31**	.03	−.04	−.11**	−.03
3. PIL	3.52	1.03	−0.48	−0.49			1	−.04	−.02	−.09*	−.09*
4. Gender	—	—	—	—				1	.01	−.15**	−.16**
5. Education	—	—	—	—					1	.10*	−.02
6. School level	—	—	—	—						1	−.13**
7. Seniority	15.12	8.83	0.41	−0.38		.60	.42	.05	−.03	−.16	1

Note. Gender (reference group is female), education (reference group is master’s/PhD degree), school level (reference group is middle). M = Mean; SD = standard deviation; S = skewness; K = kurtosis; DI = differentiated instruction; TA = teacher agency; PIL = principal instructional leadership.

*

p < .05. **p < .01.

Table 2 indicates that the means were relatively high for all variables, including PIL (M = 3.52, SD = 1.03), TA (M = 4.06, SD = 0.65), and DI (M = 4.13, SD = .64). The skewness and kurtosis values of the scales were as follows: DIS (−0.688, 0.710), PIMRS (−0.482, −0.486), and TAS (−0.999, 2.766). The analysis revealed that the DIS and PIMRS values fell within the normal distribution range of −1 and +1, thus fulfilling the requisite conditions. The findings, however, reveal that TAS does not fall within the expected range for normal distribution. Additionally, the significance of kurtosis and skewness values at the 0.05 level in the Mardia test further corroborates this conclusion. This result indicates that the data set does not satisfy the multiple normality assumption. Consequently, the MLR estimator was employed in all parameter estimations. Furthermore, given that the variance inflation factor is 1.105 (VIF < 3) and the tolerance index is 0.905 (TI > 0.10), and that the condition index is below 30, it can be concluded that there is no multicollinearity problem. According to Pearson correlation results, there are moderate, positive correlations between PIL and the other two main constructs: TA (r = .31, p < .01) and DI (r = .42, p < .01). Likewise, there is a moderate positive relationship between DI and TA (r = .60, p < .01).

Measurement Model: Validity and Reliability

To ensure the robustness of the constructs, the measurement model was evaluated for convergent and discriminant validity, as well as reliability. All factor loadings were above 0.60, indicating strong convergent validity. Average Variance Extracted (AVE) values were 0.55 for Principal Instructional Leadership (PIL), 0.61 for Teacher Agency (TA), and 0.58 for Differentiated Instruction (DI), exceeding the recommended threshold of 0.50.

Composite Reliability (CR) values ranged from 0.87 to 0.91, and Cronbach’s alpha values ranged from .85 to .90, demonstrating satisfactory internal consistency for all constructs. Discriminant validity was supported, as the square roots of AVE for each construct were greater than the correlations with other constructs, confirming that the latent variables represent distinct dimensions of the model. These results indicate that the measurement model is both reliable and valid, providing a solid foundation for testing the hypothesized structural relationships among principal instructional leadership, teacher agency, and differentiated instruction.

Structural Model and Hypotheses Testing

In the concluding phase of the study, a mediated SEM was constructed to evaluate the hypothesized direct and indirect effects of principal instructional leadership on differentiated instruction. The model incorporates teacher-reported principal instructional leadership as the independent variable, teacher agency as the mediating variable, differentiated instruction as the outcome variable, and gender, seniority, educational level, and school level as control variables.

The fit indices for the research model indicate a good model fit: χ² (df = 1521) = 3971.144, RMSEA = 0.048, SRMR = 0.048, CFI = 0.91, and TLI = 0.90 (see Figure 1). The χ²/df ratio is below 5, RMSEA and SRMR values are below 0.08, and CFI and TLI values exceed 0.90, indicating that the hypothesized model fits the data well.

The analysis (see Table 3) demonstrates a moderate and statistically significant relationship between teacher agency and differentiated instruction (β = .579, p < .001), providing full support for Hypothesis 2, which proposed that teacher agency would positively predict differentiated instruction. Moreover, principal instructional leadership is a significant predictor of both differentiated instruction (β = .261, p < .001) and teacher agency (β = .323, p < .001). These findings offer strong support for Hypothesis 1 and Hypothesis 3, indicating that principal instructional leadership directly influences both teacher agency and differentiated instruction.

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Table 3.

Structural Equation Model (SEM) Estimates, Standard Errors, and 95% Confidence Intervals for the Hypothesized Paths Among Principals’ Instructional Leadership (PIL), Teacher Agency (TA), and Differentiated Instruction (DI). Standardized Coefficients (β) Are Presented, With Significance Levels Indicated.

Model	Coefficient		95% Confidence interval		p
	β	SE	Lower	Upper
Direct effects
PIL → DI	.261	0.046	0.172	0.350	.000***
PIL → TA	.323	0.042	0.241	0.405	.000***
TA → DI	.579	0.050	0.482	0.677	.000***
Gender → DI	.023	0.032	−0.039	0.085	.470
Education level → DI	−.001	0.033	−0.065	0.063	.975
School level → DI	−.087	0.031	−0.148	−0.025	.006**
Seniority → DI	.006	0.032	−0.056	0.068	.854
Indirect effect
PIL → TA → DI	.187	0.032	0.124	0.250	.000***
Total effects
PIL → DI	.448	0.040	0.369	0.527	.000***

Note. Gender (reference group is female), education (reference group is master’s/PhD degree), school level (reference group is middle). DI = differentiated instruction; TA = teacher agency; PIL = principal instructional leadership. ***p < 0.001. **p < 0.01.

In addition to its direct effects, principal instructional leadership also exerts a significant indirect effect on differentiated instruction through teacher agency (β = .187, p < .001), confirming Hypothesis 4. The total effect of principal instructional leadership on differentiated instruction was also significant (β = .448, p < .001), with 95% confidence intervals (0.369, 0.527), indicating that teacher agency serves as a partial mediator in this relationship.

Figure 2 and Table 3 further reveal that the only control variable significantly influencing differentiated instruction is school type, with primary schools showing a small but significant advantage (β = −.087, p = .051). Finally, the model explains 51% of the total variance in differentiated instruction (R² = .510), indicating strong explanatory power.

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Figure 2.

Model results.

Interpretation of Findings

Our study’s first hypothesis was that teacher agency has a positive direct effect on differentiated instruction. The findings support this assumption, indicating that higher levels of teacher agency are linked to an increased likelihood of teachers adapting their differentiated instruction practices. Consequently, when teachers act intentionally in their professional roles, they adjust their instructional methods to provide more equitable learning opportunities. In this way, teacher agency enables the modification of instructional practices to meet diverse student needs. Our findings provide consistent evidence with the current literature that teacher agency is essential for the successful implimentation of differentiated instruction (Leijen et al., 2019; Nindya et al., 2023).

The second hypothesis of our our study was that principal instructional leadership has a positive direct effect on teacher agency. According to research, principal instructional leadership predicts teacher agency. When school principals practice strong instructional leadership by setting instructional goals, they create a school climate where teachers feel empowered to make pedagogical decisions (Hallinger, 2011; Leithwood & Louis, 2012). This highlights how school administrators may impact teachers’ instructional approaches and autonomy (Skaalvik & Skaalvik, 2014; York-Barr & Duke, 2004). Our findings provided empirical support for this assumption indicating that when school principals are better instructional managers, teachers are more likely to act as teacher agents (Biesta & Tedder, 2007). Moreover, teachers who think every student can thrive are more likely to participate in professional development and enhance their teaching (Frost, 2006). In other words, teachers who work under a strong instructional leader aim to enhance their teaching abilities throughout their careers (Blase & Blase, 1999; Liu et al., 2016).

The third hypothesis of our our study was that principal instructional leadership has a positive direct effect on differentiated instruction. The result of our analysis showed evidence that principal instructional leaderhip was a positive and a significant predictor of differantiatad instruction. It seems that principal instructional leadership practices are critical to developing support mechanisms for teachers to be effective in differentiated instruction (Sebastian & Allensworth, 2012; Tomlinson & Imbeau, 2023). This further demonstrates that the more school principals value instructional leadership, the more teachers are successful in differentiating instruction to improve students’ learning. One possible explanation for this finding is that principals who prioritize instructional leadership tend to provide ongoing professional development, resources, and feedback, empowering teachers to implement differentiated instruction effectively (Leithwood et al., 2008; Sebastian & Allensworth, 2012). Other studies also support these findings. For instance, Robinson et al., 2008 found that principal instuctional leadership practices focused on teaching and learning had a significant positive impact on students’ academic performance. Additionally, he highlighted that principals who consistently support instructional improvements cultivate a school culture that empowers teachers to differentiate their instructional approaches effectively (Day et al., 2016).

The final hypothesis framing this study was that principal instructional leadership has a positive indirect effect on differentiated instruction through teacher agency. This is consistent with earlier research that emphasizes the significance of leadership in creating a climate in which teachers feel empowered to take ownership of instructional choices (Leithwood & Jantzi, 2008). This suggests that the improvement in differentiated instruction among teachers did not result from principals’ direct involvement in instructional leadership practices. Rather, principal instructioanal leadership practices encouraged an increase in teacher agency, which in turn facilitated change in differantiated instruction. This result was consistent with the research, which concluded that a feeling of empowerment enhances teacher readiness to employ innovative ways in differentiated instruction (Priestley et al., 2015; Vangrieken et al., 2017).

The findings highlight that teacher agency plays a critical mediating role in the relationship between principal instructional leadership and differentiated instruction. This suggests that the influence of principals on instructional practices is not merely direct but occurs through empowering teachers to take ownership of their professional decisions. Theoretically, this supports ecological and agency-based frameworks in education, which posit that leadership creates the conditions for teachers to enact change rather than dictating practices (Biesta & Tedder, 2007; Priestley et al., 2015). Practically, these results imply that school leaders aiming to enhance differentiated instruction should focus on strategies that strengthen teacher autonomy, promote collaborative decision-making, and provide ongoing professional development, as these actions indirectly improve classroom instruction. By emphasizing teacher agency as a mediator, this study demonstrates that fostering a culture of empowerment may be as important as direct instructional guidance in achieving effective differentiated practices.

Limitations

This study has several limitations. First, the surveys were completed by educators themselves, which may introduce bias toward more favorable comments about their colleagues (Fowler, 2013). Second, while the sample size was nearly double the minimum required, the study did not adequately represent various regions across the country (Tabachnick & Fidell, 2013). Although significant findings regarding the relationship between principal instructional leaderhsip and differentiated instruction were produced, the cross-sectional design of the data limits the ability to establish causality, as the surveys were completed at a single point in time. Finally, future research could benefit from incorporating interviews with teachers to complement and compare with statistical results, thereby providing a richer understanding of the findings.

Conclusion

This study adds to the growing body of research on principal instructional leadership and differentiated education. According to the study, principal instructional leadership had an impact on differentiated instruction, but more so on teacher agency. This broader influence on teacher agency demonstrates that principal instructional leadership primarily motivates teachers to direct their own professional development, reflect, and innovate. Our model explains 51% of the variance in differentiated instruction, highlighting the significance of effective leadership that not only provides direct instruction but also fosters a culture of teacher empowerment and responsibility.

Our study also supports existing literature that emphasizes the indirect effect of leadership on instructional practices (Özdemir et al., 2023; Thoonen et al., 2011). This suggests that developing teacher agency is a critical mechanism through which leadership influences classroom instruction. Future research should look into how leadership practices affect student learning outcomes across cultural and educational contexts, especially in developing countries where empirical evidence on the topic is limited. As a result, school leaders must focus on promoting teacher agency through strong instructional leadership in order to significantly improve instructional quality. National policymakers should support such initiatives by creating leadership development programs and legislation that encourage principals to engage in instructional leadership activities.