This study employs a quantitative correlational design to examine the relationship between digital literacy, teacher motivation, and TPACK among elementary school teachers. We selected the correlational method to identify patterns and relationships between these key variables without manipulating the study environment.. This approach is particularly suitable for assessing how teachers’ competencies and motivational factors influence their ability to integrate technology into teaching.

The study sample consists of 98 elementary school teachers who are active members of the teacher community in Balikpapan, Indonesia. A saturated sampling technique was used, meaning all teachers in the population meeting the inclusion criteria were included in the study. This method was chosen to ensure a comprehensive representation of teachers actively engaged in professional learning communities.

This research involves the collection of quantitative data on digital literacy, motivation, and teachers' TPACK. Digital literacy and motivation were assessed through a questionnaire utilizing a semantic differential scale, while TPACK was measured using a multiple-choice test. The indicators for the digital literacy variable were synthesized from several prior studies (Arvianto et al., 2023)(Rusydiyah et al., 2020)(Sánchez-Cruzado et al., 2021)(Siero, 2017). The indicators and sub-indicators for teacher motivation in performing professional duties were derived from the works of (Appova & Arbaugh, 2018)(Greenier et al., 2023)(Yalçınkaya et al., 2021). Similarly, the TPACK indicators were synthesized from studies by (Kaplon-Schilis & Lyublinskaya, 2020)(Li et al., 2022)(Singh & Malik, 2022). The appendix provides a detailed blueprint of the instruments.

We tested the validity and reliability of each variable using Jamovi software. The analysis confirmed that all items were both valid and reliable. A comprehensive summary of the validity and reliability results can be found in Table 1.

Item-rest correlation values above 0.7 are generally considered indicative of good validity, demonstrating that each item consistently contributes to the construct being measured. For the digital literacy variable, all items show strong correlations, ranging from 0.766 to 0.911, indicating high validity. tems 10, 11, and 12 exhibit a very strong contribution to the instrument, with correlation values close to or exceeding 0.9. These results affirm that the instrument is highly valid for assessing teachers' digital literacy.

In the self-development motivation variable, items 8, 10, 11, and 12 exhibit strong correlations. Items with moderate correlations, including items 1, 2, 6, 7, 13, and 14, display correlation values ranging from 0.764 to 0.834, which are within an acceptable range. Items 3, 4, and 5, which demonstrate low correlations, contribute less to the overall construct. While the instrument overall exhibits excellent validity, items 4 and 5 may require further examination to ensure they effectively measure teacher motivation in alignment with the intended construct.

For the TPACK variable, items 11, 12, 13, and 14 demonstrate high correlations, with item-rest correlation values exceeding 0.9, indicating these items are particularly robust in measuring teachers' TPACK. Strong correlations, ranging from 0.834 to 0.884, demonstrate the consistency of items 6, 7, 8, 9, and 10 with the assessed construct. Item 1, with a moderate correlation of 0.730, contributes less in comparison to the other items but remains within the acceptable range. The result suggests that while item 1 plays a role in measuring TPACK, it is not as strong as the other items. Overall, the TPACK instrument demonstrates high validity, with strong item-rest correlations for most items. Notably, items 11, 12, 13, and 14 exhibit particularly strong contributions, whereas item 1, while less robust, remains valid.

The results of the reliability test, using Cronbach’s α, are presented in Table 2.

The Cronbach's α value for the Digital Literacy scale is 0.972, indicating very high reliability. Similarly, the Self-Development Motivation scale demonstrates a high reliability coefficient of 0.960, as does the TPACK scale, with a value of 0.974. Cronbach's α assesses the internal consistency of a scale, with values close to 1 signifying that the items within the scale are highly consistent in measuring the same construct, whether related to digital literacy, self-development motivation, or TPACK.

Based on the research problem, this study employs several data analysis techniques, including the following:

a. Assumption checks, including normality, heteroscedasticity, and linearity tests, were conducted before hypothesis testing to validate the statistical models.

b. Hypothesis Testing: Hypothesis testing is conducted in two main stagesn.

Hypothesis 1

H0 : There is no relationship between digital literacy and the TPACK of elementary school teachers.

H₁ : There is a relationship between digital literacy and the TPACK of elementary school teachers.

Hypothesis 2

H0 : There is no relationship between motivation and the TPACK of elementary school teachers.

H₁ : There is a relationship between _motivation and the TPACK of elementary school teachers.

An analysis conducted on a sample of 98 elementary school teachers in Balikpapan, East Kalimantan, Indonesia, yielded a distribution of digital literacy levels among teachers, as illustrated in Figure 2.

The analysis of the digital literacy data reveals that respondents' digital literacy skills are categorized into three levels: low, medium, and high. As illustrated in Figure 2, the majority of respondents (74%) fall within the medium digital literacy category. In contrast, 15% of respondents exhibit low digital literacy, indicating limited proficiency in the effective use of digital technology. Only 11% of respondents demonstrate high digital literacy, reflecting a small portion of the population with advanced competence in utilizing digital tools. These results highlight a notable disparity in digital literacy, with most respondents positioned at a medium level and relatively few achieving higher proficiency.

Figure 3 presents an analysis of the self-development motivation of elementary school teachers in Balikpapan.

As shown in Figure 3, respondents' self-development motivation is classified into three categories: low, medium, and high. The majority, 73%, exhibit a medium level of motivation. A smaller proportion, 14%, falls into the low category, indicating limited inclination toward self-improvement. Only 13% of respondents demonstrate a high level of self-development motivation. These results suggest that overall, the respondents' motivation predominantly resides in the medium range, with a relatively equal distribution between the low and high categories. This disparity highlights the need for targeted interventions to enhance motivation, particularly among teachers with lower levels of self-development motivation.

The analysis of TPACK among elementary school teachers in Balikpapan is presented in Figure 4.

Figure 4 categorizes the percentage distribution of respondents' Technological Pedagogical Content Knowledge (TPACK) into three levels. The majority, comprising 71%, fall within the moderate TPACK category. Approximately 14% demonstrate low TPACK, highlighting limitations in their ability to integrate technology, pedagogy, and content effectively in the learning process. In contrast, only 15% of respondents exhibit a high level of TPACK, indicating that a small segment possesses a strong aptitude for integrating these three critical elements. These results imply that while a significant portion of respondents demonstrates adequate competence, there is ample opportunity for further improvement in merging technology with effective teaching practices.

To investigate the relationship between the variables, hypothesis testing was conducted, preceded by assumption tests, including a normality test and a heteroskedasticity test. The results of the normality test are presented in Table 3.

The Kolmogorov-Smirnov test results, as shown in Table 3, confirm that the data are normally distributed, with p-values exceeding the 0.05 threshold, thus satisfying the assumption of normality. The subsequent assumption test, the heteroskedasticity test, is detailed in Table 4.

Table 4 indicates that the Goldfeld-Quandt test returns a p-value greater than 0.05, suggesting no significant evidence of heteroskedasticity. This conclusion implies that the assumption of homoscedasticity, or constant error variance, holds in the tested model. The Goldfeld-Quandt test is widely recognized as a reliable method for detecting heteroskedasticity, a condition where residual variance is inconsistent. Failure to account for heteroskedasticity can result in inefficient coefficient estimates and erroneous standard errors, ultimately leading to incorrect inferences. Following these tests, a linearity assessment was performed, with the Q-Q plot presented in Figure 5.

Figure 5 illustrates that the residuals exhibit a largely normal distribution, with only slight deviations in the tails. These minor deviations can be considered negligible, thereby confirming that the assumption of linearity holds. Therefore, we can employ parametric statistics, specifically linear regression, for further analysis. This validation allows the researcher to confidently proceed with a simple linear regression, the results of which are presented in Table 5.

Table 5 presents an R² value of 0.735, indicating that 73.5% of the variance in digital literacy is accounted for by variations in TPACK. The p-value, less than 0.001, confirms the statistical significance of this relationship, implying that TPACK is a robust predictor of digital literacy. Furthermore, the correlation coefficient (R) of 0.857 reflects a very strong positive relationship between the two variables. The F-statistic of 274 further substantiates the model's overall significance in predicting TPACK, providing compelling evidence of the substantial influence of digital literacy on it. The regression equation, based on these results, is detailed in Table 6.

The regression equation derived from Table 6 is as follows:

Where:

This equation suggests that for each unit increase in digital literacy, the TPACK score increases by 0.633 units, assuming all other factors are held constant. The predictor is highly significant (p < .001) and exhibits a strong positive relationship with TPACK, as evidenced by the standardized estimate of 0.857. In conclusion, digital literacy emerges as a significant and influential predictor of TPACK in this study.

Hypothesis testing was conducted to explore the relationship between the variables, following the completion of assumption tests, including assessments of normality and heteroskedasticity. The results of the normality test are displayed in Table 7.

The Kolmogorov-Smirnov test results indicate that the assumption of normality is satisfied (p-value > 0.05). Subsequently, the heteroskedasticity test was performed, and the findings are presented in Table 8.

As shown in Table 8, the Goldfeld-Quandt test revealed no significant evidence of heteroskedasticity (p-value > 0.05), indicating that the data is homoscedastic. A linearity test was then conducted using a Q-Q lot, with the results depicted in Figure 6.

Figure 6 reveals slight deviations from normality, particularly in the lower-left and upper-right tails; however, these deviations are minor and do not violate the assumption of normality for the purposes of this study. As a result, hypothesis testing can proceed with parametric statistical methods, specifically linear regression analysis. The outcomes of the linear regression are summarized in Table 7.

Table 7 shows a correlation coefficient (R) of 0.866, indicating a very strong relationship between motivation and TPACK. The R² value of 0.751 suggests that approximately 75.1% of the variability in TPACK can be explained by self-development motivation. The Adjusted R² of 0.748, slightly lower than the R², accounts for the number of predictors and sample size, offering a more accurate estimate of the model's explanatory power. The F-statistic of 298 demonstrates the significance of the overall regression model, indicating that motivation significantly improves the prediction of TPACK compared to a model without predictors. The highly significant p-value (< 0.001) underscores the model's statistical relevance. The regression equation can be derived from the data provided in Table 8.

Based on the results in Table 8, the regression equation for TPACK and self-development motivation is as follows:

Where:

This equation implies that for every oneunit increase in self-development motivation, the TPACK score is expected to rise by 0.887 units, assuming all other factors remain constant. The predictor is highly significant (p < .001) and shows a strong positive relationship with TPACK, as evidenced by the standardized estimate of 0.866. In conclusion, self-development motivation is a powerful predictor of TPACK in this study.

The findings indicate a significant and positive relationship between digital literacy and elementary school teachers' TPACK in Balikpapan, Indonesia. Prior research corroborates this result, highlighting digital literacy as a critical factor in mastering TPACK. Teachers with high levels of digital literacy are better equipped to integrate technology, pedagogy, and content in their teaching practices (Fazilla et al., 2022)(Hasanah et al., 2023). A solid foundation in digital literacy enables teachers to effectively utilize information technology, thereby enhancing their capacity to merge various learning components. Consequently, enhancing digital literacy is pivotal in strengthening teachers' TPACK, especially given that many teachers demonstrate intermediate levels of digital literacy (Altun, 2019)(Falloon, 2020)(Su, 2023).

These findings reinforce previous research indicating that digital literacy is a fundamental component in fostering TPACK (Tondeur et al., 2020)(Xiangjun & Lei, 2024). Teachers with higher digital literacy are more likely to engage in technologysupported pedagogy, aligning with studies emphasizing digital literacy as a key enabler of TPACK development (Falloon, 2020). Hence, teacher capacity-building initiatives should prioritize training and professional development programs that emphasize multidimensional digital literacy (Botturi, 2019)(Susanti et al., 2024).

In parallel, the study demonstrates that self-development motivation significantly influences TPACK. Teacher motivation, characterized by attributes such as perseverance, curiosity, and a commitment to lifelong learning, is vital in cultivating TPACK competencies (Tanak, 2020). Highly motivated teachers are more likely to engage in professional development opportunities, thereby enhancing their ability to integrate technology with pedagogy and content (Yurtseven Avci et al., 2020). These insights highlight the importance of fostering teacher motivation to ensure active participation in development programs aimed at bolstering TPACK.

Further analysis reveals that the interaction between digital literacy and selfdevelopment motivation significantly contributes to improving teachers' TPACK (Muhaimin et al., 2019). Teachers who possess both strong digital literacy and high motivation are better positioned to incorporate technology effectively into their teaching practices. This finding underscores the importance of a comprehensive intervention strategy that simultaneously enhances digital literacy and motivates teachers to ensure optimal technology integration in education (Valtonen et al., 2020). This aligns with previous studies emphasizing motivation as a key factor in facilitating technology adoption in educational settings (Demirtaş & Mumcu, 2021).

Overall, the study confirms that digital literacy and self-development motivation are two critical factors in enhancing TPACK. These elements complement each other in shaping strong TPACK competencies, which ultimately contribute to improving the quality of learning in elementary schools (Chaidam & Poonputta, 2022)(Kumala et al., 2022). Therefore, educational policies and teacher development programs should integrate both aspects to create more effective learning environments that adapt to technological advancements (Fazilla et al., 2022)(Yumna et al., 2024).

In conclusion, it is imperative for educational institutions to design training programs that not only focus on enhancing digital literacy but also prioritize fostering teachers' motivation. Such programs should be structured to provide relevant, practical experiences and equip teachers with the tools to overcome challenges in integrating technology into teaching (Xiangjun & Lei, 2024). Furthermore, collaboration among teachers, curriculum developers, and education researchers is essential to devise innovative and effective strategies for TPACK development (Altun, 2019)(Ritonga et al., 2024).

In conclusion, it is imperative for educational institutions to design training programs that focus on enhancing digital literacy and prioritize fostering teachers' motivation. Such programs should be structured to provide relevant, practical experiences and equip teachers with the tools to overcome challenges in integrating technology into teaching (Xiangjun & Lei, 2024). Furthermore, collaboration among teachers, curriculum developers, and education researchers is essential to devise innovative and effective strategies for TPACK development.