Fostering Teachers’ Professional Capital: The Effect of Professional Learning Community Dimensions in Secondary Schools of Eastern Ethiopia

Abstract

This study examined how dimensions of professional learning communities (PLCs) impact teachers’ professional capital in eastern Ethiopia, using a quantitative correlational design with 406 teachers from 29 secondary schools. Results showed that shared and supportive leadership, collective learning, and supportive relationships are pivotal for teachers’ development, explaining 77% of variance. The findings provide evidence that key PLC dimensions enhance teachers’ professional capital in resource-limited schools, offering practical insights for leaders and policymakers to strengthen teacher development and educational quality. The study recommends prioritizing interventions that promote these PLC aspects to improve educational outcomes.

Keywords

professional capital learning communities professional learning professional development

Teaching has become an increasingly complex and demanding profession in today’s knowledge-driven society, requiring educators to prepare students for a rapidly changing world. This complexity arises from the dynamic interplay between pedagogy and context, compelling teachers to make nuanced, context-specific decisions daily (Smith, 2017). Yet many teachers feel marginalized and disempowered within rigid educational systems (Smith, 2017). To address these challenges and strengthen the teaching profession, Hargreaves and Fullan (2012) advocate for a “professional capital” approach—a strategy embraced by high-performing education systems worldwide (Herzog, 2016). This approach emphasizes the long-term development of human, social, and decisional capital to build collective expertise and enhance educational quality (Herzog, 2016).

Professional capital, as conceptualized by Hargreaves and Fullan (2012), integrates three critical elements: human capital (individual teachers’ skills and expertise), social capital (trust, collaboration, and shared practices among teachers), and decisional capital (teachers’ capacity for informed, context-sensitive decision-making). Together, these elements reinforce the idea that teaching quality is not merely an individual endeavor but a collective enterprise built through continuous collaboration and shared learning (Darling-Hammond, 2017; Hargreaves & Fullan, 2012, 2013). Investing in professional capital fosters environments where teachers engage in meaningful professional growth, collaboratively address instructional challenges, and make well-informed decisions that advance student learning outcomes (Fullan, 2016; Hargreaves & Fullan, 2012; Harris & Jones, 2017).

One of the most effective mechanisms for cultivating professional capital is the Professional Learning Communities (PLCs). Rooted in organizational learning theory (Senge, 1990), PLCs are collaborative groups of educators who work together regularly to enhance teaching practices and improve student learning outcomes (Hord, 1997; Stoll et al., 2006; Vescio et al., 2008). PLCs promote continuous professional inquiry, shared decision-making, and reflective practice, making them instrumental in fostering collective responsibility and a culture of sustained improvement (Bolam et al., 2005; DuFour & Eaker, 1998). Notably, PLCs serve as platforms for building both human and social capital, supporting teachers’ professional growth and contributing to systemic improvements in school culture and instructional quality (Hargreaves & Fullan, 2012; Harris & Jones, 2017).

This study adopts the conceptual framework of PLCs articulated by Huffman and Hipp (2003) and Kruse et al. (1994), which identifies five essential dimensions: shared and supportive leadership, shared values and vision, collective learning and application, shared personal practice, and supportive conditions (relationships and structures) (Dogan et al., 2017; Domingo-Segovia et al., 2020; Olivier & Hipp, 2010; Pandian et al., 2022). Each of these dimensions is critical in shaping collaborative cultures and professional growth. For instance, shared and supportive leadership distributes leadership responsibilities, empowering teachers (Hipp & Huffman, 2010; Howard, 2022); shared values and vision align teaching goals; and collective learning and shared personal practice foster reflective inquiry and peer feedback (Hipp & Huffman, 2010; Howard, 2022). Supportive conditions further sustain collaboration by providing the structural and relational environment needed for PLCs to thrive (Dogan et al., 2017).

Theoretically, the link between PLCs and teachers’ professional capital is well-grounded in adult learning theory (Knowles et al., 2005; Mezirow, 1991), social cognitive theory (Bandura, 1988), sociocultural theory (Vygotsky, 1978), organizational learning theory (Argyris & Schön, 1978; Senge, 1990), and professional capital theory (Hargreaves & Fullan, 2012). These frameworks highlight how PLCs foster self-directed, collaborative, and reflective professional learning, strengthening teachers’ human, social, and decisional capital.

In Ethiopia, policy frameworks—including the Education and Training Policy (MoE, 1994), the Continuous Professional Development (CPD) framework (MoE, 2009), and subsequent initiatives such as GEQIP and the Education Development Roadmap (MoE, 2018)—have underscored the role of PLCs in teacher development. Despite these commitments, research reveals persistent challenges such as resource shortages, weak systemic support, and informal PLC practices (Taddese & Rao, 2021; Tuli & Bekele, 2020). These challenges reflect a broader need to understand which PLC dimensions most effectively enhance teachers’ professional capital within the Ethiopian context and beyond.

While studies have examined the conceptualization and psychometric testing of PLCs in other countries (Dogan et al., 2017; Howard, 2022; Pandian et al., 2022; Soares et al., 2020), limited empirical research exists on how specific PLC dimensions influence teachers’ professional capital development in Ethiopian schools. Although theoretical links between PLCs and professional capital are well-established (Bandura, 1988; Hargreaves & Fullan, 2012; Knowles et al., 2005; Mezirow, 1991; Senge, 1990; Vygotsky, 1978), there is scant empirical evidence on the collective impact of shared and supportive leadership, shared values and vision, collective learning and application, shared personal practice, and supportive conditions on teachers’ professional capital.

Therefore, this study investigates the effects of these five PLC dimensions on teachers’ professional capital development in secondary schools in eastern Ethiopia, using a structural equation modeling approach. The findings have broader implications: they can guide policymakers, school leaders, and educators in designing effective PLC frameworks not only in Ethiopia but also in other low-resource or developing contexts. By identifying the PLC components that strongly drive professional capital development this study can inform scalable, evidence-based strategies for fostering collaborative, empowered, and high-performing teaching workforces worldwide. Thus, the study tested the following five research hypotheses:

The shared and supportive leadership has a significant direct influence on teachers’ professional capital (PC) development.

The shared values and vision has a significant direct influence on teachers’ PC development.

The collective learning and application has a significant direct influence on teachers’ PC development.

The shared personal practice has a significant direct influence on teachers’ PC development.

The supportive conditions (relationships and structures) have a significant direct influence on teachers’ PC development.

Literature Review

Establishing effective PLCs is crucial for fostering the growth of teachers’ professional capital, a multidimensional framework that strengthens individual and collective teaching expertise. The literature consistently underscores that high-quality PLCs serve as foundational mechanisms for developing professional capital within schools (Hargreaves & Fullan, 2012; Harris & Jones, 2017).

This study’s conceptual framework builds on established PLC models (e.g., Huffman & Hipp, 2003; Kruse et al., 1994), which identify five key dimensions: collective learning and application, shared personal practice, shared values and vision, shared and supportive leadership, and supportive conditions—both relational and structural (Dogan et al., 2017; Domingo-Segovia et al., 2020; Olivier & Hipp, 2010; Pandian et al., 2022). These dimensions are recognized as essential to creating collaborative professional environments where teachers can develop their expertise, confidence, and collective responsibility.

Collaborative PLCs—both within individual schools and across networks of schools—have proven effective in advancing teachers’ professional capacity and enhancing professional capital (Harris & Jones, 2017). Harris and Jones (2017) highlight a strong and positive correlation between the quality of PLCs and the growth of teachers’ professional capital, particularly in developing the collective ability to collaborate, share accountability, and engage in joint problem-solving. This reinforces the notion that professional capital, especially social capital, is cultivated through intentional efforts to build trust, collegiality, and shared ownership of instructional improvements (Hargreaves & Fullan, 2012).

Recent studies add depth to this understanding. For instance, Damkuviene et al. (2023) demonstrate that PLCs with cooperative learning structures significantly enhance professional capital by fostering collegial feedback, professional advice-sharing and mutual recognition of expertise. Nolan and Molla (2017) similarly find that teachers’ engagement in sustained, collaborative professional learning directly contributes to strengthening professional capital within schools.

The broader implication for practice and policy is that education systems aiming to improve teacher quality and student outcomes must prioritize the creation of robust PLCs. As Fullan et al. (2015) note, enhancing professional capital system-wide requires a compelling, inclusive vision that values continuous learning and aligns with the aspirations of educators, school leaders, and society at large. Policies that support structured collaboration, shared leadership, and professional autonomy are critical to sustaining high-functioning PLCs.

Effective PLCs not only advance social capital but also play a key role in developing human capital—enhancing teachers’ expertise, pedagogical knowledge, empathy, and relational skills (Hargreaves & Fullan, 2012; Sell, 2015). These findings highlight the dual impact of PLCs: while human capital focuses on the individual teacher’s growth, social capital emphasizes the collective dynamics that enable shared success. Importantly, Sell (2015) underscores that PLCs that promote peer-to-peer learning, teamwork, and shared responsibility are central to building cohesive, empowered teaching communities.

Evidence also points to broader educational benefits. Antinluoma et al. (2018) show that effective PLCs are associated with enhanced teacher professionalism, well-being, and improved student learning outcomes, underscoring their systemic importance. Cansoy and Parlar (2017) further illustrate the strong positive correlation between PLC dimensions and teacher professionalism, revealing that shared and supportive leadership, shared values and vision, collective learning and application, shared personal practice, and supportive conditions collectively explain 25% of the variance in teacher professionalism.

These insights have wide-ranging implications beyond the Ethiopian context. In diverse educational settings, particularly in low-resource and developing regions, establishing and sustaining effective PLCs can serve as a transformative strategy for capacity building. Policymakers and school leaders globally can draw from this evidence to design PLCs frameworks that prioritize collaboration, continuous learning, and shared leadership—cornerstones for improving teacher professionalism and student achievement across contexts.

Conceptual Model of the Study

In this study, we propose and test a conceptual model informed by the above literature, focusing on the Ethiopian secondary school context. Figure 1 presents the conceptual model, depicting the hypothesized relationships between the five PLC dimensions and teachers’ professional capital. The conceptual model illustrates the dimensions of PLCs contributing to teachers’ professional capital development, depicted as the central outcome. It includes five key components: shared and supportive leadership, shared values and vision, collective learning and application, shared personal practice, and supportive conditions (relationships and structures), each connected to the central concept with labeled arrows (H1 to H5) representing hypothesized relationships. These elements suggest that effective leadership, a unified vision, collaborative learning, shared teaching practices, and supportive environments collectively enhance the professional capital of teachers, which encompasses their skills, knowledge, and collaborative capacity within the educational setting.

Figure 1.

Conceptual model of the study

We examined this model using structural equation modeling (SEM), assessing its goodness-of-fit with standard model fit indices. The regression paths from each PLC dimension to teachers’ professional capital, as illustrated in Figure 1, reflect the hypotheses tested in our study.

Method

Design of the Study

This study employed a quantitative correlational research design (Creswell, 2015) to investigate the relationships between perceived professional learning communities (PLC) dimensions and aspects of teacher professional capital (PC). The choice of a correlational design was driven by the aim to establish a well-fitting measurement model using factor analysis (EFA and CFA), and to examine the direct influence of the five PLC dimensions (shared and supportive leadership, shared values and vision, collective learning and application, shared personal practice, and supportive conditions—relationships and structures) on teachers’ professional capital development using structural equation modeling (SEM), which are advanced correlational statistical techniques in educational research (Creswell, 2015). Following the predictive purpose of correlational design (Creswell, 2015), this study used structural equation modeling to determine PLC dimensions that significantly predicted teachers’ professional capital in secondary schools in eastern Ethiopia.

Participants

The study sample consisted of 406 teachers (239 males, 59%; 167 females, 41%) from 29 randomly selected secondary schools (grades 9–12) in eastern Ethiopia, drawn from a target population of 3,350. A three-phase random sampling procedure was employed: first, 10 woreda districts were randomly selected from six zonal administrations; second, 29 secondary schools were chosen; and finally, 406 teachers were recruited.

Measures

Professional Capital Scale and Measurement Model Fit

The perceived teacher professional capital was measured using an adapted version of Hargreaves and Fullan’s (2012) teacher professional capital survey, contextualized to align with Ethiopian teachers’ standards. Professional capital was conceptualized as a second-order construct with three first-order factors: human capital, social capital, and decisional capital, using a 5-point scale (1 = “strongly disagree” to 5 = “strongly agree”). High scores indicate higher professional capital. Data suitability for factor analysis was confirmed by a KMO of 0.95 and a significant Bartlett’s test (χ2₍₇₀₃₎ = 9,934.821, p < .001). Exploratory factor analysis (EFA) using promax oblique rotation yielded three factors (social capital, human capital, and decisional capital) with eigenvalues over 1.0, explaining 50% of variance, retaining 38 items after removing five with loadings below 0.40.

A first-order confirmatory factor analysis (CFA) in AMOS 23 tested the three-factor measurement model. Initial fit was suboptimal (χ2/df = 3.414, CFI = 0.833, TLI = 0.823, SRMR = 0.0527, RMSEA = 0.077), so items with low loadings were removed, and error terms within constructs were correlated. The refined model, with 24 items and standardized factor loadings of β = 0.69 to 0.82 (p < .001), achieved fit: χ2/df = 2.669, CFI = 0.931, TLI = 0.922, SRMR = 0.0417, RMSEA = 0.064. Subsequent second-order CFA for the overall professional capital construct confirmed acceptable fit (χ2/df = 2.669, CFI = 0.931, TLI = 0.922, SRMR = 0.0417, RMSEA = 0.064), with loadings from the second-order construct to the three first-order factors ranging from β = 0.74 to 0.95 (p < .001).

Professional Learning Community Scale and Measurement Model Fit

The Professional Learning Community Assessment-Revised (PLCA-R) scale (Olivier & Hipp, 2010) was adapted for Ethiopian secondary schools, as it was its validation in various national contexts (Domingo-Segovia et al., 2020; Dogan et al., 2017; Teague, 2012), covering five dimensions relevant to the context: shared leadership, shared values, collective learning, personal practice, and supportive conditions (relationships and structures), rated on a 5-point scale. Higher scores indicate stronger PLC dimensions. The KMO was 0.94, and Bartlett’s test was significant (χ2 (1,081) = 14,653.417, p < .001), affirming factorability. EFA yielded six factors—shared leadership, shared values, collective learning, shared practice, supportive conditions (structures), and supportive conditions (relationships)—which accounted for 59% of variance.

Initial CFA for the six-factor model indicated poor fit (χ2/df = 3.456, CFI = 0.824, TLI = 0.813, RMSEA = 0.078, SRMR = 0.053). Model refinement involved removing low-loading items and correlating error terms, resulting in 32 items and fit indices (χ2/df = 2.719, CFI = 0.92, TLI = 0.91, RMSEA = 0.065, SRMR = 0.044) with factor loadings from β = 0.70 to 0.86 (p < .001). A second-order CFA confirmed the six-factor structure of PLC with acceptable fit (χ2/df = 2.770, CFI = 0.913, TLI = 0.904, RMSEA = 0.066, SRMR = 0.0498), with second-order factor loadings from β = 0.52 to 0.867 (p < .001).

Construct Measurement Reliability and Validity

Reliability and validity were assessed for all PC and PLC constructs. The Cronbach’s alpha values for the PC dimensions—human, social, and decisional capital—ranged from 0.81 to 0.95, demonstrating high internal consistency. Composite reliability (CR) and maximal reliability (MR) values for each construct were also strong, ranging from 0.81 to 0.95. Convergent validity was confirmed, with each construct’s average variance extracted (AVE) between 0.51 and 0.55, exceeding the 0.5 threshold (Hair et al., 2019). Discriminant validity was supported as the square root of AVE for each construct (0.71–0.74) exceeded correlations with other constructs (0.55–0.70), and AVE was greater than the mean shared variance (MSV), establishing construct distinctiveness.

For PLC constructs, Cronbach’s alpha values ranged from 0.86 to 0.92, and CR and MR values were between 0.87 and 0.92, indicating high reliability. Convergent validity was demonstrated with AVE values from 0.56 to 0.65. Discriminant validity was also confirmed, as the square root of AVE for each construct (0.75–0.81) exceeded correlations with other constructs (0.37–0.76), and AVE was greater than MSV. These results confirm the strong reliability and validity of the PC and PLC measurement constructs used in the study.

Results

Preliminary Data Analysis and Descriptive Statistics

In our preliminary data analysis, we thoroughly examined all cases and items to detect any missing values, multivariate outliers, influential scores, or unengaged responses. We assessed unengaged responses by evaluating the standard deviation of responses, with a threshold of 0.25 and lower values indicating unengaged responses (Collier, 2020), but all standard deviation scores were above 0.5, indicating no unengaged responses in the data. For missing values, we applied the rule of eliminating cases with more than 10% missing data (Hair et al., 2019), resulting in only six cases with less than 2.8% randomly missed values, which were addressed through mean imputation. To identify multivariate outliers, we used the significance level threshold of .001 based on Mahalanobis distance (Hair et al., 2019), and found that all significance levels of Mahalanobis distances were greater than or equal to 0.001, indicating an absence of outlier scores (Table 1).

Table 1.

Scale Reliabilities, AVE, and Inter-construct Correlations for PC Constructs

Second-order latent constructs	First-order latent factors	# Of items	α	CR	AVE	MSV	MR
Professional capital (#items = 24 α = .95)	Decisional capital	4	.81	.83	.55	.49	.84
	Human capital	4	.83	.81	.51	.50	.81
	Social capital	16	.95	.95	.55	.50	.95
Professional learning community (#items = 32 α = .96)	Supportive conditions - relationships	5	.86	.87	.56	.54	.87
	Shared and supportive leadership	5	.89	.89	.62	.58	.90
	Shared values and vision	7	.92	.92	.61	.58	.92
	Collective learning and application	4	.87	.87	.63	.57	.88
	Shared personal practice	5	.87	.87	.58	.57	.87
	Supportive conditions-structures	6	92	.92	.65	.28	.92

Furthermore, we evaluated the adequacy of the data for factor analysis and SEM by testing multivariate assumptions. The inter-factor correlations estimated ranged between r = .34 and r = .73 (see Table 2), which were less than 0.85 and indicating no multicollinearity concern (Collier, 2020). Regarding distribution normality, skewness values ranged from −0.78 to −0.27 and kurtosis values ranged from −0.27 to 0.90, both falling within acceptable ranges (−2 to +2 for skewness and −10 to +10 for kurtosis) (Collier, 2020), confirming the normal distribution of the data. To address common method bias (CMB) before SEM analysis, we employed the common latent factor method. The comparison between the CFA model with a common latent component and one without it showed a nonsignificant difference, indicating the absence of common method bias in SEM analysis.

Table 2.

Descriptive Statistics and Correlation Coefficients

Variables	PC	SSL	SVV	CLA	SPP	SCR	SCS
Professional capital (PC)	1	.73^**	.64^**	.62^**	.60^**	.62^**	.41^**
Shared and supportive leadership (SSL)		1	.70^**	.62^**	.64^**	.65^**	.41^**
Shared values and vision (SVV)			1	.68^**	.65^**	.66^**	.39^**
Collective learning and application (CLA)				1	.66^**	.51^**	.43^**
Shared personal practice (SPP)					1	.59^**	.51^**
Supportive conditions for relationships (SCR)						1	.34^**
Supportive conditions for structures (SCS)							1
M	3.82	3.68	3.72	3.66	3.62	3.74	3.34
SD	.67	.89	.91	.84	.81	.88	.99
Skewness	−.78	−.73	−.64	−.72	−.67	−.48	−.27
Kurtosis	.90	.48	−.13	.73	.69	−.43	−.27
N	406	406	406	406	406	406	406

p < .001 (two-tailed).

The Pearson’s correlation coefficients of the latent variables, as shown in Table 2, revealed significant relationships between all variables at the p < .001 level, as theoretically expected. These coefficients were considered medium (i.e., r > 0.3) or large (i.e., r > 0.5) based on Cohen’s (1988) cut points. For instance, teachers’ professional capital exhibited large positive relationships with shared and supportive leadership (r₍₄₀₆₎ = .73), shared values and vision (r₍₄₀₆₎ = .64), collective learning and application (r₍₄₀₆₎ = .62), shared personal practice (r₍₄₀₆₎ = .60), supportive conditions for relationships (r₍₄₀₆₎ = .62), and a medium positive relationship with supportive conditions for structures (r₍₄₀₆₎ = .41).

Structural Equation Modeling

After confirming the goodness-of-fit and validity of the measurement model through CFA, we proceeded to construct an initial structural model with six factors predicting teacher professional capital using AMOS 23. This initial structural model demonstrated good fit to the data, with χ2/df = 2.61, CFI = 0.92, TLI = 0.90, SRMR = 0.044, and RMSEA = 0.063. However, we refined the original model by eliminating predictors that did not have statistically significant direct effects, aiming to create a more simplified structural model. The simplified structural model exhibited improved goodness-of-fit, with χ2/df = 2.55, CFI = 0.96, TLI = 0.95, SRMR = 0.036, and RMSEA = 0.062. This improvement was evident in several aspects: the chi-square value (χ2) decreased by 1,098.661, degrees of freedom decreased by 418, normed chi-square (χ2/df) decreased by 0.04, SRMR decreased by 0.008, RMSEA decreased by 0.001, CFI increased by 0.04, and TLI increased by 0.05 (refer to Table 3). Table 3 presents the goodness-of-fit indices criteria based on Byrne (2016) and compares the structural model fit indices between the initial structural model (Model 1) and the final simplified structural model (Model 2), along with the changes observed in fit indices.

Table 3.

Fit Indices for SEM Initial Model (Model 1) and Final Modified Model (Model 2)

Fit indices	Criteria	Model 1	Model 2	Change (∆)
1. Normed chi-square (χ2/df)	≤5.0	2.61	2.55	0.06
2. Comparative fit index (CFI)	≥0.90	0.92	0.96	0.04
3. Tucker–Lewis index (TLI)	≥0.90	0.90	0.95	0.05
4. Standardized root mean square residual (SRMR)	≤0.08	0.044	0.036	0.008
5. Root mean square error of approximation (RMSEA)	≤0.08	0.063 [0.059, 0.067]	0.062 [0.053,0.071]	0.001
6. Chi-square (χ2 )		1,384.012 (p < .001)	285.351 (p < .001)	1,098.661
7. Degree freedom (df)		530	112	418

The initial analysis of the six-predictor structural model indicated that the standardized (β = .02) and unstandardized (B = .01) regression coefficients demonstrated statistically nonsignificant direct effects of shared values and vision on teacher professional capital at p < .05, with t = 0.24, p = .807. Similarly, the standardized value of −0.003 and unstandardized regression coefficient of −0.002 indicated statistically nonsignificant direct effects of shared personal practice on teacher professional capital at p < .05, with t = −0.04, p = .972. Additionally, the standardized value of 0.03 and unstandardized regression coefficient of 0.02 showed statistically nonsignificant direct effects of supportive structural conditions on teacher professional capital at p < .05, with t = 0.71, p = .477 (refer to Table 4). Consequently, the simplified structural model (Model 2) was established by removing these three nonsignificant predictors of teacher professional capital, namely, shared values and vision, shared personal practice, and supportive structural conditions (see Figure 2).

Table 4.

Standardized and Unstandardized Coefficients for Direct Effects From Initial SEM Model Analysis on Professional Capital

Model	Predictor latent variables	Standardized Coefficient (β)	Unstandardized Coefficient (B)	SE	t	p-value
Initial model (model 1)	Shared and supportive leadership	.53	.39	.06	6.64	***
	Shared values and vision	.02	.01	.05	0.24	.807
	Collective learning and application	.21	.17	.06	2.80	.005
	Shared personal practice	−.003	−.002	.06	−0.04	.972
	Supportive conditions for relationships	.20	.14	.05	2.77	.006
	Supportive conditions for structures	.03	.02	.03	0.71	.477
	Squared Multiple Correlations (R ² )	.77
Final simplified model (model 2)	Shared and supportive leadership	.54	.39	.06	7.17	***
	Collective learning and application	.23	.18	.05	3.99	***
	Supportive conditions for relationships	0.2	.14	.05	3.15	.002
	Squared multiple correlations (R²)	.77

Figure 2.

Simplified Structural Model Predicting Teachers’ Perceived Professional Capital

Table 4 displays the standardized and unstandardized estimates illustrating the direct effects of the six dimensions of PLCs (shared and supportive leadership, shared values and vision, collective learning and application, shared personal practice, supportive conditions for relationships, and supportive conditions for structures) on teachers’ professional capital in the initial model (Model 1). It also outlines the direct effects of the three dimensions of PLCs (shared and supportive leadership, collective learning and application, and supportive conditions for relationships) that significantly predicted teacher professional capital in the final simplified structural model (Model 2) (also depicted in Figure 2).

The standardized regression results from the initial six-predictor structural model, presented in Table 4, revealed that shared and supportive leadership significantly and positively influenced teacher’ professional capital development (β = 0.53, t = 6.64), with an unstandardized coefficient (B) of 0.39. This effect remained significant in the final simplified structural model, with an improved standardized regression coefficient (β = 0.54, t = 7.17), both at p < .001. Based on Cohen’s (1988) operational definition of effect size boundaries, the ƒ² value of 0.435 showed a large direct influence of shared and supportive leadership on teacher’ professional capital. Similarly, both models showed a statistically significant and positive direct effect of collective learning and application on teacher’ professional capital development. In the initial structural model, the standardized regression coefficient (β) was 0.21, with an unstandardized regression coefficient (B) of 0.17 (t = 2.80, p = .005). This effect was slightly stronger in the final simplified structural model, with a standardized regression coefficient (β) of 0.23 and an unstandardized regression coefficient (B) of 0.18 (t = 3.99, p < .001). The Cohen’s ƒ2 value of 0.13 in the final simplified model indicated a small to medium direct influence of collective learning and application on teacher’ professional capital development.

Moreover, the Cohen’s ƒ² value of 0.13 showed small to medium direct influence (Cohen, 1988) of collective learning and application on teacher’ professional capital development. In a similar vein, the standardized regression coefficient (β) of 0.20 and unstandardized regression coefficient (B) of 0.14 indicated a statistically significant and positive direct effect of supportive conditions for relationships (t = 2.77) on teacher’ professional capital development at p = .006 in initial model and at p = .002 (t = 3.15) in the final simplified model. In the final simplified three-predictors structural model, the Cohen’s ƒ² value of 0.09 showed small to medium direct influence (Cohen, 1988) of supportive conditions for relationships on teacher’ professional capital. The Squared Multiple Correlations (R²) of 0.77 indicated that shared and supportive leadership, collective learning and application, and supportive conditions for relationships collectively explained 77% of the variance in teacher’ professional capital development. Furthermore, the standardized regression coefficient (β) of 0.89 and unstandardized regression coefficient (B) of 0.78 indicated a statistically significant and positive direct effect of PLC as a whole (t = 12.997) on teacher’ professional capital development at p < .001. The Squared Multiple Correlations (R²) of 0.79 indicated that the PLC as a whole explained 79% of the variance in teacher’ professional capital development.

Discussion

Effect of Overall PLCs on Professional Capital

This study found that Professional Learning Communities (PLCs) significantly predict teachers’ professional capital development, explaining 79% of the variance. This aligns with existing literature underscoring PLCs’ transformative role in fostering professional growth and improving educational quality (Bolam et al., 2005; Hargreaves & Fullan, 2012; Harris & Jones, 2017; Stoll et al., 2006; Vescio et al., 2008). PLCs provide structured collaborative frameworks that enable teachers to engage in collective learning, refine instructional practices, and share accountability for student success (Harris & Jones, 2017; Hipp & Huffman, 2010). Through these processes, teachers enhance their capacity to solve instructional challenges, implement effective strategies, and contribute to systemic school improvement (Bolam et al., 2005; DuFour et al., 2004). Research further highlights that PLCs promote professional capital by fostering reflective practices, peer observation, and collective accountability (Hipp & Huffman, 2010; Nolan & Molla, 2017; Sell, 2015). By creating collaborative environments, PLCs empower educators to develop pedagogical skills and improve teaching quality, ultimately enhancing student outcomes. Therefore, policymakers should prioritize funding and institutionalizing PLCs as integral components of teacher professional development programs.

Effect of Shared and Supportive Leadership on Professional Capital

Shared and supportive leadership is crucial for developing teachers’ professional capital within PLCs. It enhances human, social, and decisional capital by fostering trust, collaboration, and professional dialogue (Damkuviene et al., 2023; Hargreaves & Fullan, 2012; Harris & Jones, 2017). Through distributed decision-making and mentorship, leaders empower teachers to refine instructional strategies, engage in reflective practices, and collaboratively address challenges (Bolam et al., 2005; Vescio et al., 2008). This approach nurtures mutual respect and collegial feedback, strengthening social capital and supporting sustainable professional growth (Huffman & Hipp, 2003; Leithwood et al., 2004).

Shared leadership also facilitates double-loop learning, enabling teachers to adapt practices to evolving classroom needs while maintaining collective accountability (Hargreaves & Fullan, 2012; Harris & Spillane, 2008). By equitably allocating resources and creating supportive conditions, school leaders promote mentorship, collaborative inquiry, and reflective dialogue, which enhance professional growth even in resource-constrained settings (Bolam et al., 2005; Hughes & Pickeral, 2013). Consequently, leadership training programs should prioritize distributive and collaborative models to strengthen teachers’ professional capital and improve educational outcomes (Brett, 2018; Sintayehu et al., 2021).

Effect of Collective Learning and Application on Professional Capital

Collective learning and application demonstrated a strong positive impact on professional capital, underscoring the value of collaborative learning environments where teachers actively share knowledge and apply new strategies. This aligns with studies highlighting the role of professional collaboration in developing teachers’ social capital and improving job satisfaction (Campbell et al., 2016; Hargreaves & Fullan, 2012; Ikoma, 2016; Tadesse & Sintayehu, 2022).

Teachers engaged in PLCs benefit from joint inquiry, networking beyond their schools, and sharing expertise, which builds their professional knowledge base and strengthens collective efficacy (Watts, 2018). Therefore, school leaders should institutionalize ongoing collaborative learning activities tailored to local educational challenges and leverage peer-driven professional development.

Effect of Supportive Conditions for Relationships on Professional Capital

In this study, supportive conditions for relationships were found to have a significant and positive direct impact on the development of professional capital, highlighting the importance of fostering supportive relational contexts within educational environments. This finding aligns with prior research demonstrating that effective PLCs provide essential infrastructure for professional capital development by creating platforms for collaboration, learning, and growth among teachers (Harris & Jones, 2017). Strong teacher learning communities enhance teacher growth, commitment, and ultimately lead to improved student outcomes (Antinluoma et al., 2018; Little, 2006). Well-functioning PLCs with established norms of collaboration are also linked to better teaching and learning outcomes (Talbert, 2010). Research further shows that supportive relationships among teachers and school leaders are positively associated with collective efficacy beliefs (Skaalvik & Skaalvik, 2019). Professional networks and collaborations, including advice and interdependent relationships, significantly influence teachers’ perceived collective efficacy and professional capital (Berebitsky & Salloum, 2017; Blatti et al., 2019). Therefore, school leaders should prioritize building trust and collegial relationships to strengthen social and professional networks that support teachers’ professional capital development.

Additionally, the model’s high explanatory power (R² = 0.77) indicates that shared and supportive leadership, collective learning and application, and supportive relational conditions collectively account for 77% of the variance in teachers’ professional capital. This underscores the pivotal roles these PLC factors play in shaping professional growth and is consistent with evidence highlighting their positive contributions to professional capital development (Campbell et al., 2016; Harris & Jones, 2017; Watts, 2018). Our findings further substantiate Cansoy and Parlar’s (2017) study, which reported that six PLC dimensions explained 25% of the variance in teacher professionalism. Moreover, evidence links PLCs to human capital development, as they help teachers build expertise, improve qualifications, and foster empathy and interpersonal skills (Hargreaves & Fullan, 2012; Sell, 2015). PLCs characterized by cooperative, multi-level, and multi-agent learning opportunities enhance collegial feedback, professional advice-sharing, and recognition of collaborative insights, strengthening social capital (Damkuviene et al., 2023). Furthermore, PLCs designed to advance professional capital provide opportunities for teachers to innovate in chosen areas (human capital), engage in inquiry and professional dialogue (social capital), and lead collaborative projects to improve teaching practices (decisional capital) (Campbell et al., 2016). Such environments promote both trust-based relationships and continuous professional learning (Smith, 2017), ultimately maximizing teachers’ growth and contributing to school improvement.

Nonsignificant PLC Dimensions

This study found that three PLC components—shared values and vision, shared personal practice, and supportive structural conditions—did not significantly predict teachers’ professional capital. A possible explanation is the conceptual overlap these dimensions share with stronger predictors, such as shared and supportive leadership. Effective leadership inherently fosters shared vision and values by engaging teachers in decision-making and encouraging ownership of professional growth (Harris & Jones, 2017; Hipp & Huffman, 2010). It also facilitates shared personal practice by ensuring structural supports within PLCs (Dogan et al., 2017; Huffman & Hipp, 2003; Pandian et al., 2022) and promoting reflective dialogue that helps teachers refine instructional strategies (Bolam et al., 2005; Vescio et al., 2008). Leaders who create safe, collaborative environments provide teachers with time, space, and encouragement to analyze challenges collectively and share professional insights, fostering trust, mutual respect, and continuous professional growth (Damkuviene et al., 2023; Hord, 1997; Howard, 2022). When constructs overlap, their unique contributions may diminish, rendering them statistically nonsignificant (Hair et al., 2019).

Additionally, these dimensions may exert indirect effects on professional capital, operating through mediators such as leadership or collective learning (Vescio et al., 2008). For instance, shared values may underpin leadership practices rather than directly influencing individual or collective professional capital. Likewise, shared personal practice often requires more mature PLCs where collaboration is routine, limiting its effect in emerging PLC contexts. Structural conditions, though necessary, are insufficient on their own and function more as supports for other components like relationships and collective learning (Stoll et al., 2006).

Measurement-related factors may also contribute. Variability in the operationalization of constructs across schools, inconsistent implementation of PLC practices, or limitations in measurement tools may weaken their observed predictive power (Bryk et al., 1999). Future research should therefore examine the indirect effects of these dimensions and their interplay with leadership and collective learning, explore PLC–professional capital dynamics longitudinally, and test intervention models to assess causal relationships. Studies should also consider contextual variables such as school culture, socioeconomic conditions, and geographic location, which may moderate the influence of PLC dimensions on teachers’ professional capital.

Limitations and Directions for Future Research

While our study makes significant contributions to the field of teacher education and professional learning and development (PLD), it is essential to acknowledge some key limitations. First, the focus on secondary schools in eastern Ethiopia may limit the generalizability of findings to other regions or educational contexts. The specific cultural, socioeconomic, and resource constraints in this setting may not fully represent other populations. Second, the correlational, cross-sectional design restricts the ability to infer causality between PLC dimensions and teachers’ professional capital. Third, reliance on self-reported data introduces potential social desirability bias, which may affect the reliability of responses. Finally, the study accounts for 77% of the variance in professional capital, leaving 23% unexplained, potentially due to unexamined variables such as school type, teacher experience, or gender.

Thus, future studies can address these limitations through several approaches. First, longitudinal research designs could explore the dynamic relationships between PLC dimensions and PC development over time, providing insights into causal pathways. Second, mediation and moderation models should be investigated to understand how contextual factors (e.g., school culture, socioeconomic conditions, or geographic location) influence the impact of PLC components on PC. Third, employing a canonical correlation model to examine the effects of PLC dimensions on individual PC components (human, social, and decisional capital) rather than treating PC as a second-order factor could reveal specific relationships and their relative contributions. Finally, incorporating mixed methods, including observational and qualitative measures, could enhance reliability and provide a deeper understanding of how PLCs foster professional capital across diverse educational contexts.

Implications for Practice and Policy

The study highlights the need for school leaders and policymakers to prioritize the development of PLCs to enhance teachers’ professional capital. Schools should foster shared and supportive leadership that builds trust, encourages collaboration, and promotes reflective dialogue. Professional development programs must provide structured, ongoing opportunities for collective learning and practical application. Adequate resources should be allocated to institutionalize PLCs, particularly in resource-limited contexts, to build sustainable collaborative cultures. Leadership training should focus on developing distributed and collaborative leadership skills. Strengthening relational conditions—through mentoring, peer networks, and collegial feedback—will further support professional growth, enhance collective efficacy, and improve teaching quality and student outcomes. By institutionalizing PLCs as a core strategy within school improvement frameworks, educational stakeholders can cultivate professional capital that drives long-term improvements in both teaching quality and student outcomes.

Conclusion

This study underscores the transformative potential of PLCs in strengthening teachers’ professional capital. By highlighting the pivotal roles of shared and supportive leadership, collective learning and application, and supportive relational conditions, the findings demonstrate that PLCs not only enhance individual teacher growth but also foster a collaborative culture essential for sustained school improvement. The strong predictive power of these PLC components signals clear pathways for practice and policy: investing in effective PLC structures and leadership development can yield significant dividends in teaching quality and student outcomes. Ultimately, this research affirms that cultivating robust PLCs is a strategic lever for educational advancement, particularly in resource-constrained and complex educational contexts.

Footnotes

ORCID iDs

Sintayehu Belay

Wondiferaw Dejene

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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