PIL-SMART refers to Project–Inquiry Learning integrating Science, Mathematics, Art, Religion, and Technology. In this abbreviation, “PIL” represents Project–Inquiry Learning, while “SMART” represents Science, Mathematics, Art, Religion, and Technology. The term should be written consistently as PIL-SMART throughout the article. In the present study, PIL-SMART is conceptualized as an interdisciplinary framework for organizing project and inquiry processes within early childhood learning activities that integrate scientific exploration, mathematical reasoning, artistic expression, religious or moral values, and age-appropriate uses of technology (Lev et al., 2020; Siraj-Blatchford, 2016).

PIL-SMART draws on constructivist perspectives that regard children as active participants in developing understanding through interaction, exploration, and reflection. Within this framework, children are not positioned merely as recipients of information. They are encouraged to observe phenomena, formulate questions, make predictions, undertake simple investigations, discuss findings, produce representations or products, and reflect on the meaning of their experiences. Teachers facilitate these processes by organizing the learning environment, providing scaffolding, introducing relevant materials, and encouraging children to communicate their emerging explanations (Odell & Kennedy, 2020; Strawhacker et al., 2020).

Project–Inquiry Learning constitutes the pedagogical foundation of PIL-SMART. Project learning allows children to engage with a meaningful topic or problem through interconnected activities over a particular period, whereas inquiry provides the investigative processes through which children formulate questions and develop evidence-based explanations. Combining the two approaches is intended to prevent projects from becoming activities focused only on producing products without sufficient investigation. It also prevents inquiry from becoming a series of isolated experiments that are disconnected from a broader learning purpose. Thus, project and inquiry processes operate as mutually reinforcing components of the model (Nimmo & Park, 2009; Wight et al., 2016).

The SMART components provide the interdisciplinary content of the framework. Science involves observing, predicting, experimenting, and explaining natural phenomena. Mathematics includes classification, comparison, counting, measurement, pattern recognition, and spatial reasoning. Art allows children to express their understanding through drawing, movement, construction, music, or other symbolic forms. Religion refers to age-appropriate moral and spiritual values, including gratitude, responsibility, care, and respect for living things and the environment. Technology is used as a supporting tool for observation, documentation, information access, communication, or the creation of simple learning products rather than as a replacement for direct experience (Daniele et al., 2025; Ramulumo, 2025).

The learning sequence of PIL-SMART consists of five interconnected stages. First, the teacher introduces a contextual phenomenon or problem that is familiar and meaningful to children. Second, children formulate questions and predictions through guided discussion. Third, children undertake project-based investigation by observing, comparing, experimenting, measuring, documenting, or collecting relevant information. Fourth, children integrate their findings through scientific explanation, mathematical representation, artistic expression, moral or religious reflection, and appropriate technological support. Fifth, children communicate their products or findings and reflect on the learning process with teachers and peers. These stages may be adapted according to children’s developmental characteristics, classroom conditions, available resources, and the topic being investigated.

For example, a project concerning plant growth may involve children observing seeds and plants, asking what plants need to grow, comparing plant height, documenting changes, drawing plant development, and discussing responsibility for caring for living things. Science is represented through observation and investigation, mathematics through measurement and comparison, art through visual representation, religion through gratitude and responsibility, and technology through photographic documentation or age-appropriate digital presentation. The example demonstrates that the SMART components are not separate subjects but complementary dimensions integrated within one project–inquiry learning experience.

In this study, teachers’ perceptions of PIL-SMART were operationalized through indicators concerning the model’s perceived relevance, clarity, feasibility, interdisciplinary integration, compatibility with children’s characteristics, support for active exploration, and potential contribution to classroom learning. Accordingly, the PIL-SMART construct represents teachers’ evaluations of the model’s implementation rather than a direct measure of implementation fidelity. This distinction is important because favourable perceptions do not necessarily demonstrate that all components of the model were implemented consistently in classroom practice.

Science literacy is understood as the ability of individuals to understand scientific concepts and processes and apply them in explaining phenomena, interpreting evidence, and making responsible decisions in daily life (Aunillah, 2024; Rai et al., 2025). Science literacy is also interpreted as the active involvement of individuals in scientific issues based on scientific reasoning and evidence. The National Research Council emphasizes the importance of integrating conceptual knowledge and scientific practice, such as observing, questioning, investigating, and interpreting data (Chandra et al., 2023; Hong et al., 2019). In addition, science literacy includes critical thinking and problem-solving skills in social and technological contexts (Liana et al., 2024; Thiel, 2025).

In the context of early childhood education, science literacy can be developed from an early age through hands-on experience that is appropriate to the child’s developmental stage (Fitriani, 2024). Science literacy is also seen as a means to shape social attitudes, values, and responsibilities, including concern for the environment (Agal & Seal, 2025). Thus, science literacy is not only oriented towards mastering concepts, but also on developing process skills, scientific attitudes, and social awareness holistically.

Environmental literacy is understood as the ability of individuals to understand environmental systems and problems and show attitudes and behaviors that are responsible for preserving nature. Environmental literacy encompasses a combination of ecological knowledge, awareness, and skills in making decisions related to the environment (Isa et al., 2021). In addition, environmental literacy emphasizes the integration of knowledge, thinking skills, caring attitudes, and pro-environmental behaviors as the core of its formation (Masykuroh et al., 2024).

Environmental literacy is part of education for sustainable development that emphasizes the formation of value and responsibility towards nature from an early age (Guerrero Fernández et al., 2022). Its development takes place effectively through direct experience and real interaction with the surrounding environment. In early childhood, environmental literacy is built through habituation, play activities, and strengthening attitudes that respect nature in daily life (Feng & Zhang, 2023; J. Li et al., 2025). Therefore, environmental literacy focuses not only on conceptual understanding, but also on the formation of sustainable attitudes and behaviors as a whole.

The conceptual framework of this study illustrates the structural relationships among the PIL-SMART learning model, science literacy, and environmental literacy in early childhood education, as presented in Figure 1. The framework explains that teachers’ perceptions of the implementation of the PIL-SMART model are assumed to influence the development of children’s science literacy and environmental literacy. In addition, science literacy is also hypothesized to contribute to environmental literacy development. The framework was developed based on theories of inquiry learning, project-based learning, and literacy development in early childhood education. Therefore, Figure 1 serves as the basis for testing the relationships among variables through the Structural Equation Modeling (SEM) analysis used in this study.

This study employed a quantitative cross-sectional survey design to examine the associations among teachers’ perceptions of PIL-SMART, teacher-rated science literacy, and teacher-rated environmental literacy in early childhood education. A cross-sectional design was selected because all variables were measured at one point in time using structured questionnaire responses. Accordingly, the structural relationships identified in this study were interpreted as statistical associations rather than causal effects.

The study was conducted in Bekasi City, West Java Province, Indonesia. Participants were recruited from 125 early childhood education institutions located in the city. Purposive sampling was employed to select respondents who met the study criteria. Eligible respondents were teachers who were actively teaching in an early childhood education institution and had experience implementing science- and environment-related learning activities. Participation was voluntary, and teachers who did not meet these criteria or did not provide informed consent were not included.

A total of 200 early childhood education teachers participated in the study. The sample size was reported transparently in relation to the complexity of the final Structural Equation Modeling model, which contained three latent constructs, 28 retained indicators, and 87 freely estimated parameters. Rather than relying solely on a universal respondent-to-parameter rule, model estimation was evaluated by examining convergence, standardized factor loadings, improper solutions, residual variances, and multiple model-fit indices. Nevertheless, the sample size and non-probability sampling method were recognized as limitations when interpreting the stability and generalizability of the results.

Data were collected using a structured questionnaire administered directly or through an online survey platform. The questionnaire measured three constructs: teachers’ perceptions of PIL-SMART, teachers’ ratings of children’s science literacy, and teachers’ ratings of children’s environmental literacy. Therefore, science and environmental literacy were not assessed through direct tests or systematic observations of children. Before completing the questionnaire, participants received information concerning the study objectives, participation procedures, voluntary nature of participation, confidentiality protections, and their right to withdraw without penalty.

The research procedure consisted of four stages. First, questionnaire indicators were developed based on a review of literature concerning integrated learning models, science literacy, and environmental literacy in early childhood education. Second, the content validity and clarity of the indicators were examined through consultation with experts in early childhood education and educational evaluation. Third, the questionnaire was distributed to eligible teachers from participating early childhood education institutions. Fourth, the collected data were screened, coded, and analysed using reliability analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. Negatively worded items were reverse-coded before analysis so that higher scores consistently represented higher levels of the respective constructs.

Following the measurement-model evaluation, SL6 was excluded because it demonstrated a standardized factor loading of 0.330 and did not adequately represent the science literacy construct. EL9_R was excluded because its inclusion resulted in a non-positive-definite sample covariance matrix and prevented stable model estimation. The final model therefore retained 28 indicators: ten indicators for PIL-SMART, nine indicators for science literacy, and nine indicators for environmental literacy. All item-retention and removal decisions were based on a combination of statistical evidence, model-estimation diagnostics, and theoretical considerations.

Prior to data collection, ethical approval was obtained from the Ethical Clearance Commission of the Research and Community Service Institute (LPPM), Universitas Panca Sakti Bekasi, Indonesia, under Ethical Clearance Certificate No. 001/LPPM.ECC/PSUB/V/2026. All participants were informed about the study objectives, voluntary participation, anonymity, confidentiality, and their right to decline or discontinue participation. Verbal informed consent was obtained before questionnaire administration, and the verbal consent procedure was included in the protocol approved by the institutional ethics committee. This procedure was used because the study involved minimal-risk questionnaire participation, did not collect sensitive personal data, and did not involve intervention or direct assessment of children. Participant agreement was documented by the data-collection team before access to the questionnaire was provided.

The study involved 200 early childhood education teachers from 125 PAUD institutions in Bekasi City, West Java Province, Indonesia. Respondent characteristics were examined in terms of gender, age, educational attainment, and teaching experience. These characteristics provide contextual information for interpreting teachers’ perceptions of PIL-SMART and their ratings of children’s science and environmental literacy.

Most respondents were women, reflecting the predominantly female composition of the teaching workforce in the participating early childhood education institutions. In terms of age, most respondents were between 25 and 40 years old. However, gender and age were treated only as demographic characteristics and were not interpreted as indicators of competence or the quality of PIL-SMART implementation.

Regarding educational attainment, most respondents held a bachelor’s degree in early childhood education or another relevant educational field. Some respondents had also participated in professional-development activities related to innovative, science-based, environmental, or contextual learning. These educational and professional experiences provided relevant background for responding to questionnaire items concerning integrated early childhood learning.

Most respondents had between five and ten years of teaching experience, while the remaining participants had either shorter or longer teaching experience. This distribution indicates that the study involved teachers with varying levels of professional experience. Nevertheless, teaching experience alone does not demonstrate that respondents had implemented all components of PIL-SMART consistently. The data therefore represent teachers’ perceptions of the framework and their ratings of children’s literacy-related abilities rather than direct evidence of implementation fidelity.

The data analysis in this study was carried out using Structural Equation Modeling (SEM) with the help of Jamovi software through the SEMLj module. The SEM method is used because it is able to analyze the complex relationships between several latent variables simultaneously. In addition, SEM also allows researchers to test measurement models and structural models simultaneously. This analysis was used to determine the relationship between teachers’ perception of the Pil-Smart learning model and science literacy and environmental literacy in early childhood.

The research model consists of three latent constructs, namely Pil-Smart, science literacy, and environmental literacy. The Pil-Smart construct is measured using ten indicators that describe teachers’ perceptions of the implementation of the learning model. The construct of science literacy is also measured using ten indicators that describe children’s ability to understand simple science concepts. Meanwhile, the construct of environmental literacy is measured using ten indicators related to children’s awareness and behavior towards the surrounding environment. All indicators are measured using a five-point Likert scale.

The SEM analysis stage is carried out through several steps. The first step is to test the measurement model to determine the validity of the construct through the analysis of the loading factor on each indicator. The second step is the evaluation of the feasibility of the model using various goodness of fit indices such as Chi-square, RMSEA, SRMR, CFI, and TLI. The third step is to test the structural model to find out the relationships between latent variables using the values of the path coefficient and the level of significance.

In addition, the analysis also includes testing the coefficient of determination (R²) to find out the extent to which the dependent variables can be explained by the research model. The results of the SEM analysis were then interpreted to explain the relationship between the Pil-Smart learning model and science literacy and environmental literacy in early childhood. The entire analysis process is carried out using the latest version of Jamovi software equipped with the SEMLj module. Thus, the analysis method used is able to provide comprehensive results in explaining the relationship between research variables.

The final Structural Equation Model was estimated using Maximum Likelihood estimation with the NLMINB optimization method. The model included 200 observations, three latent constructs, 28 retained indicators, and 87 freely estimated parameters. The estimation process converged successfully after 42 iterations and produced no negative latent variances or other improper solutions. The final model was overidentified, with 347 degrees of freedom, and was therefore not a saturated model.

The chi-square test for the final model was statistically significant, χ²(347) = 408, p = 0.014. However, the chi-square statistic is sensitive to sample size and model complexity and should therefore be interpreted together with other model-fit indices. The relative chi-square value was 1.18, which was below the commonly applied threshold of 3.00 and indicated an acceptable correspondence between the hypothesized model and the observed covariance matrix.

The incremental and residual-based fit indices provided additional support for the model. The Comparative Fit Index was 0.980, and the Tucker–Lewis Index was 0.978. Both values exceeded the recommended threshold of 0.90 and the more stringent threshold of 0.95. The Root Mean Square Error of Approximation was 0.030, with a 95% confidence interval ranging from 0.014 to 0.041, while the Standardized Root Mean Square Residual was 0.049. These values were below the recommended maximum of 0.08 and indicated a low level of approximation and standardized residual error.

Some secondary incremental indices were lower than 0.90, including the Normed Fit Index of 0.880 and the Relative Fit Index of 0.869. Nevertheless, the principal fit indices—CFI, TLI, RMSEA, and SRMR—consistently supported the adequacy of the final model. Accordingly, the model was considered sufficiently fitted for interpreting the measurement and structural relationships. The results were not described as a “perfect fit,” because model evaluation was based on the combined evidence from multiple indices rather than a single fit statistic.

To examine whether all indicators could be explained by one general factor, the hypothesized model was compared with an alternative single-factor model. The single-factor model demonstrated poor fit, χ²(350) = 940, p < 0.001, CFI = 0.805, TLI = 0.789, RMSEA = 0.092, and SRMR = 0.081. In comparison, the final three-construct model produced substantially better fit, supporting the empirical distinction among teachers’ perceptions of PIL-SMART, teacher-rated science literacy, and teacher-rated environmental literacy.

Table 1 presents the results of the model fit evaluation obtained from the Structural Equation Modeling (SEM) analysis.

The RMSEA value presented in Table 2 was 0.030, with a 95% confidence interval ranging from 0.014 to 0.041. This value was below the recommended threshold of 0.08, indicating a low level of approximation error. The SRMR value was 0.049, which also satisfied the acceptable criterion of less than 0.08. As shown in Table 3, the Comparative Fit Index (CFI) was 0.980 and the Tucker–Lewis Index (TLI) was 0.978. Both values exceeded the recommended threshold of 0.90 and the more stringent criterion of 0.95, providing strong support for the fit of the final model. Although the NFI (0.880) and RFI (0.869) were marginally below 0.90, the combined evidence from the relative chi-square (χ²/df = 1.18), CFI, TLI, RMSEA, and SRMR indicated that the final three-construct model adequately represented the observed data and was appropriate for interpreting the structural relationships.

Table 2 and Table 3 present the goodness-of-fit indices used to evaluate the suitability of the SEM model.

The RMSEA value shown in Table 2 was 0.002, which was below the recommended threshold of 0.08, indicating a very small approximation error. Furthermore, the SRMR value was 0.074, which also satisfied the acceptable criterion of less than 0.08. As shown in Table 3, the Comparative Fit Index (CFI) value was 0.991 and the Tucker-Lewis Index (TLI) value was 0.990. Both indices exceeded the recommended value of 0.90, indicating that the model had an excellent fit with the observed data. Overall, these findings confirmed that the proposed SEM model was feasible and suitable for structural analysis.

Table 4 presents the factor loading results for the measurement model used in this study.

The measurement-model results showed that all 28 retained indicators adequately represented their respective latent constructs, with standardized factor loadings ranging from 0.596 to 0.790. Within the PIL-SMART construct, TP5 demonstrated the highest standardized loading (β = 0.790), followed by TP7 (β = 0.789) and TP9 (β = 0.783), while all ten indicators had loadings above 0.70. For teacher-rated science literacy, the standardized loadings ranged from 0.596 to 0.745, with SL10 providing the strongest contribution (β = 0.745) and SL9 showing the lowest but still acceptable loading (β = 0.596). In the teacher-rated environmental literacy construct, EL10 demonstrated the highest loading (β = 0.741), whereas EL3 had the lowest loading (β = 0.685), and all retained indicators were statistically significant at p < 0.001. SL6 was excluded because its initial standardized loading was only 0.330, while EL9_R was excluded because its inclusion resulted in a non-positive-definite sample covariance matrix. Overall, the magnitude and statistical significance of the retained factor loadings supported the adequacy of the final measurement model for representing teachers’ perceptions of PIL-SMART and their ratings of children’s science and environmental literacy.

Figure 2 illustrates the structural relationships among Pil-Smart, science literacy, and environmental literacy in the SEM model.

Figure 2 illustrates the final measurement and structural model connecting teachers’ perceptions of PIL-SMART, teacher-rated science literacy, and teacher-rated environmental literacy. As shown in Figure 2, PIL-SMART was positively associated with science literacy (unstandardized estimate = 0.701; standardized β = 0.744; p < 0.001) and environmental literacy (unstandardized estimate = 0.238; standardized β = 0.266; p = 0.006). Science literacy was also positively associated with environmental literacy (unstandardized estimate = 0.489; standardized β = 0.517; p < 0.001). Therefore, H1, H2, and H3 were supported. The indirect association between PIL-SMART and environmental literacy through science literacy was statistically significant (unstandardized estimate = 0.343; standardized β = 0.384; p < 0.001), indicating partial mediation. The model retained 28 indicators, comprising ten PIL-SMART indicators, nine science-literacy indicators, and nine environmental-literacy indicators. Overall, Figure 2 provides visual support for the positive direct and indirect associations identified in the final structural model.

The structural-model results showed that teachers’ perceptions of PIL-SMART were positively and significantly associated with teacher-rated science literacy (β = 0.744, p < 0.001). This result indicates that more favourable teacher perceptions of PIL-SMART were associated with higher teacher ratings of children’s science literacy; therefore, H1 was supported. Teachers’ perceptions of PIL-SMART were also positively and significantly associated with teacher-rated environmental literacy (β = 0.266, p = 0.006), supporting H2. Although this direct relationship was weaker than the association between PIL-SMART and science literacy, it indicates that PIL-SMART perceptions retained a statistically significant relationship with environmental literacy after science literacy was included in the model.

Teacher-rated science literacy was positively and significantly associated with teacher-rated environmental literacy (β = 0.517, p < 0.001); therefore, H3 was supported. The indirect association between PIL-SMART and environmental literacy through science literacy was also statistically significant (β = 0.384, p < 0.001), while the total association was β = 0.651 (p < 0.001). Because both the direct and indirect paths were significant, teacher-rated science literacy demonstrated a partial mediating role in the association between teachers’ perceptions of PIL-SMART and teacher-rated environmental literacy. Overall, the final structural model presented in Figure 2 supported all three hypotheses and demonstrated positive relationships among the three latent constructs. However, these findings represent associations based on teachers’ questionnaire ratings and should not be interpreted as evidence of causal effects or direct improvements in children’s literacy.

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