Science and Mathematics Teachers’ Integration of TPACK in STEM Subjects in Qatar: A Structural Equation Modeling Study

Abstract

This study aimed to explore how secondary school science and mathematics teachers in Qatar integrate Technological Pedagogical Content Knowledge (TPACK) into their teaching practices. The study examined the relationships between the subcomponents of TPACK using structural equation modeling (SEM), complemented by an analysis of additional categorical variables. A total of 245 science and mathematics teachers from Qatar participated in the research. The model’s findings showed that the internal components—technological knowledge, pedagogical knowledge, and content knowledge—had a significant and positive direct effect on the external factors: technological pedagogical knowledge, technological content knowledge, and pedagogical content knowledge. However, these internal components did not directly impact TPACK itself. Together, these variables accounted for 77% of the variance in TPACK. Among the findings, Technological Content Knowledge (TCK) emerged as one of the most influential variables affecting TPACK, emphasizing its importance in teachers’ TPACK integration. On the other hand, it was found that Pedagogical Content Knowledge (PCK) did not have a direct and significant effect on TPACK. Categorical variables like certificates and postgraduate education significantly impact TPACK and its subcomponents, while gender, field of study, and teaching experience do not. This finding underscores the importance of structured training and postgraduate education in enhancing TPACK skills for science and mathematics teachers. Participation in technology-based certification programs and postgraduate studies in STEM is crucial for their TPACK development in teaching STEM subjects. Future studies could explore the long-term impact of structured, technology-based training programs on enhancing STEM teachers’ TPACK development and assess how this improvement influences student learning outcomes in science and mathematics classrooms. This would provide deeper insights into the effectiveness of such programs and their potential to transform teaching practices and student achievement in STEM education.

1. Introduction

Educational researchers and policy makers highlight the key role of teachers in solving the world’s increasingly complex problems and integrating technology in education. In particular, they are expected to have the necessary infrastructure and skills in the use of digital media, to explore pedagogical strategies using technology, and to apply them in the classroom [1]. They are also expected to be learners and designers who can follow current scientific and technological developments and use them actively when necessary. Therefore, it is important to improve the knowledge and skills of teachers in order that they use technology more effectively in both classroom and out-of-classroom teaching environments in the face of these developments [2]. For effective teaching in the teaching profession, Shulman [3] proposed PCK. PCK refers to a teacher’s ability to blend pedagogical knowledge with content knowledge, making the material understandable and accessible to students [3]. Building on this concept, PCK was later expanded to incorporate technology, resulting in the TPACK framework. This expanded model has been widely recognized as a crucial framework of teacher expertise in technology-integrated classrooms [4]. TPACK integrates Technological Knowledge alongside pedagogical and content knowledge, ensuring that teachers can effectively use technology to enhance teaching and learning processes.

Lessons in which technology is integrated positively reflect on students’ interest and motivation and provide a better understanding of the subject [5]. As such, it is a critical for science teachers, especially STEM field teachers, to have technological knowledge and skills [6]. Although teachers in STEM fields are generally more inclined to use technology effectively [2], many still face challenges with full integration into their teaching practices [7]. Specifically, teachers often demonstrate deficiencies in integrating technology into instruction in meaningful ways [2]. To address this, field-specific training tailored to the unique demands of technology use in STEM education is necessary [2,5,6]. Consequently, continuous research, targeted professional development, and strategic investments are essential across all dimensions of education to enhance STEM teaching [7]. Focusing on the professional development of STEM teachers, particularly in integrating technology effectively, is therefore crucial. Furthermore, the STEM approach requires some higher-order thinking and application skills in teachers such as design thinking, technology use, and engineering skills [6].

2. Theoretical Framework

2.1. What Is the TPACK

Technological Pedagogical Content Knowledge (TPACK) represents an evolving framework that equips teachers with the necessary skills to effectively integrate technology into their teaching practices, both within and beyond the classroom (Figure 1) [4,8]. While not entirely new, TPACK has attracted significant attention over the past decade as educators and researchers recognize the importance of blending technology with pedagogical and content knowledge [7,8,9]. The framework includes seven sub-dimensions, but it is often emphasized for its focus on the integration of technology into teaching strategies, as noted by researchers such as Koehler and Mishra [10]. A review of the literature reveals that much of the research on TPACK tends to prioritize the technological dimension, often highlighting its central role in shaping modern teaching approaches [4,5,6,7,8,9,10].

It is not easy to use technology effectively in teaching within the context of a specific discipline. As Sari et al. [11] stated, it is challenging to align the right technology with the right subject at the right time and at an appropriate level, especially for teachers in STEM disciplines. To effectively integrate technology, STEM teachers must have a solid understanding of STEM content knowledge, technology, and pedagogy. In this context, three main components blend and intersect to form TPACK [12]. Considering that TPACK is one of the expectations of the teaching profession in the 21st century, it is important to understand its sub-dimensions. The approach has seven sub-dimensions (Table 1).

The TPACK framework was designed with a theoretical basis to ensure the appropriate integration of technology in teaching and learning to achieve quality education [8,10]. Later, the theory was adapted to integrate technology into learning and teaching and then into teaching through in-service training and teacher education programs for pre-service teachers and teachers [13]. In the TPACK model, it is emphasized that the TK, CK, and PCK subcomponents are not independent of each other; on the contrary, they are interconnected, and similarly, TCK, TPK, and PCK knowledge should be handled in an interactive manner [6].

2.2. TPACK in the Context STEM Teachers

The STEM approach is closely linked to real-world problems, reflecting the interdisciplinary nature of the STEM fields it encompasses [6]. A deep understanding of STEM disciplines and the ability to integrate this knowledge to design solutions to complex problems is recognized as a core competency by most societies [2,6,14]. Technology in STEM education is not only a tool but also a key discipline in its own right [2,4]. It plays a dual role both as a medium to facilitate learning and as a subject that requires specific knowledge and skills [5,7,8]. In the context of rapidly developing technologies, teachers must possess TPACK to effectively integrate technology into their teaching practices, ensuring that it is not merely used as a tool but understood and taught as an integral part of STEM education [15]. Parker et al. [16] emphasized the connection between teachers’ TPACK and STEM, arguing that these fields require integration. Morales et al. [17] further highlighted the importance of the TPACK framework in helping teachers better understand STEM education and enhancing its knowledge foundation.

When examining teachers’ integration of technology into subject areas, school cultures as well as the education system and policies of the country should be taken into account [18]. However, the sub-dimensions that constitute TPACK have a multi-layered structure, making it quite complex [19]. Nelson et al. [20] showed that teachers’ TPACK varies according to the field of teaching and is influenced by teaching experience, school support, and knowledge of technology. In this context, we need research on what STEM teachers understand about STEM education, how they implement it in schools, and the factors—guided by their TPACK framework—that shape or inform their practices, as TPACK helps science and mathematics teachers integrate STEM by effectively combining content knowledge, pedagogy, and technology to create meaningful learning experiences.

2.3. SEM in TPACK Modeling Studies

In the literature, it is possible to find many studies in which TPACK and its subcomponents are analyzed through structural equation modeling (SEM). Research studies have made significant contributions to illuminating the complex relationships between CK, TK, and PK in TPACK [21,22] alongside research focusing on the relationships between TCK, TPK, and PCK [23]. It is also possible to find many studies that construct models testing the relationships between all components. [12,24]. In addition, structural equation modeling (SEM) has focused on a certain dynamic of TPACK [25], examining the effectiveness of the use of technology in a mathematics course through modeling the sub-dynamic of contextual knowledge. Sowfan et al. [26] and Chuang et al. [27] examined the effect of TPACK and its subcomponents on the integration of technology.

Although numerous studies have explored teachers’ TPACK competencies, no research has specifically examined the TPACK understanding of STEM teachers working in secondary schools in Qatar. Especially considering that the relationships between the subcomponents that make up TPACK are tightly interconnected and multi-layered, it is thought that revealing the relationships between them with structural equation modeling will fill the gap in the literature. Although comprehensive studies such as those by Susanti et al. [28] and Yang et al. [29] have detailed the interaction between TPACK and its subcomponents, there remains a need to explore how demographic variables influence STEM teachers’ integration of TPACK. Additionally, it is crucial to examine the specific TPACK knowledge required by science and mathematics teachers for effective integrated STEM education [30]. In addition to this, the inclusion of numerous variables in the model and revealing the relationship with TPACK and its subcomponents will provide valuable contributions to the literature, as well as adding a different dimension to the research.

2.4. Research Question

The main objective of this survey study is to explore the relationship between TPACK and its sub-dimensions, aiming to assess the ability of STEM teachers in secondary schools in Qatar to effectively apply TPACK in their teaching practices. In this context, the question and sub-questions of the research are as follows.

• 1.. How do TPACK and its components affect each other?

  1. 1.1.. What is the effect, if any, of TK, PK and CK on STEM teachers’ TPACK?

  2. 1.2.. What is the effect, if any, of TK on STEM teachers’ TPK and PCK?

  3. 1.3.. What is the effect, if any, of PK on STEM teachers’ TCK, TPK, and PCK?

  4. 1.4.. What is the effect, if any, of CK on STEM teachers’ TPK and PCK?

  5. 1.5.. What is the effect, if any, of TPK, TCK, and PCK on STEM teachers’ TPACK?

2.. How do the demographic variables of gender, minor degree, and major degree in STEM disciplines effect TPACK and its sub-dimensions?

Figure 2 shows a structural model designed in accordance with the TPACK framework, proposed by [3].

3. Materials and Methods

Relational survey modeling was used in this study. According to [31], survey models are procedures that are applied to the whole population or a certain segment of the population and carried out to define their attitudes, opinions, and behaviors surrounding a subject.

3.1. Participants

The study involved 245 STEM teachers, specializing in subjects such as physics, chemistry, biology, mathematics, science, technology, earth science, and engineering, who were working in secondary schools throughout Qatar. A convenience sampling method, as defined by [32], was used to reach a diverse group of teachers with varied educational backgrounds and experiences. The sample was drawn from schools across different cities that represent all the educational sectors in Qatar to ensure geographic representation. The focus was on reaching a significant number of science and mathematics teachers, as these subjects are critical to the study’s aims of understanding STEM integration in secondary education. The sample size of 245 teachers reflects those who responded positively to the invitation, providing a robust data set for meaningful analysis while ensuring representation from across the country. Before data collection, participants were thoroughly informed about the study’s objectives; provided with clear instructions on how to complete the survey; and briefed on privacy, confidentiality measures, and their right to voluntarily participate. Ethical approval was secured from the relevant institutional review board, and informed consent was obtained from all participants. The demographic details of the participants are outlined in Table 2.

Of the 245 STEM teachers participating in the study, 121 (49.4%) were female, and 124 (50.6%) were male. While 108 (44.1%) of the sample group had a minor degree in STEM disciplines, 137 (55.1%) did not have a minor degree in STEM. Those who did not have a certificate in education were 135 (55.1%), while those who did have any certificate were 110 (44.9%). The professional experience of the participating teachers in Qatar schools varied significantly. Among them, 53 teachers (21.6%) had less than 5 years of experience, 88 teachers (35.9%) had between 5 and 10 years of experience, and 104 teachers (42.4%) had more than 10 years of experience. Regarding the STEM fields represented by the sample, 96 teachers (39.2%) were from mathematics, 59 (24.1%) from chemistry, 48 (19.6%) from biology, 30 (12.2%) from physics, 5 (2%) from engineering, 3 (1.2%) from computer science, and 2 (0.8%) from earth sciences and other disciplines.

3.2. Data Tools

In developing the questionnaire for this research, Schmidt’s measurement tool [33], originally designed to assess preservice teachers’ TPACK, was modified to better suit the context of science and mathematics teachers in STEM education and project-based learning (PBL). These modifications were crucial for aligning the instrument with the study’s specific needs. The original tool did not adequately address the subject matter and pedagogical approaches central to this research, making the adjustments essential to ensure relevance and precision in measuring TPACK for this particular group of teachers.

To enhance the questionnaire’s alignment with the study’s focus on STEM education and project-based learning (PBL), both the content and phrasing were carefully adapted. In the (CK) dimension, for instance, the original item “I have sufficient knowledge about mathematics” was broadened to “I have sufficient knowledge about STEM topics” to encompass a wider range of subjects. Likewise, in the (TK) dimension, “I have the technical skills I need to use technology” was revised to “I have the technical skills to effectively use computers in designing learning activities related to STEM”, ensuring a better fit with the PBL approach.

In the (TPK) category, further changes were made to reflect the integration of technology in STEM instruction. For example, the original statement “I can choose technologies that enhance students’ learning for a lesson” was adapted to “I facilitate students’ use of technology to independently research STEM topics”. Additionally, “I can choose technologies that enhance the content for a lesson” was reworded to “I use technology to introduce students to real-world STEM applications”.

To ensure the validity of the adapted instrument in a non-Western educational context, a bilingual science educator translated the questionnaire, and three Arabic-speaking science education experts reviewed it. A pilot test with 120 teachers helped assess the clarity and relevance of the instrument. Cronbach’s alpha was used to evaluate the reliability of the questionnaire, and based on the feedback and analysis, further revisions were made to optimize the tool for use in the full study.

The internal consistency of the TPACK subscales was assessed using Cronbach’s alpha coefficient, and the results indicated satisfactory reliability across all dimensions. The (CK) subscale showed a coefficient of 0.890, reflecting strong internal consistency. The (PK) subscale had a coefficient of 0.846, while the TK subscale demonstrated a coefficient of 0.796. Additionally, the PCK subscale achieved a coefficient of 0.835, and the TPK subscale yielded a coefficient of 0.875. The TCK subscale had a coefficient of 0.889, and the TPCK subscale displayed a coefficient of 0.881. Overall, the scale demonstrated high internal consistency, with a Cronbach’s alpha coefficient of 0.873, ensuring the reliability of the instrument for further analysis.

The principal components required for the construct validity and reliability of the questionnaire were determined by factor analysis. Exploratory factor analysis (EFA) on TPACK subcomponents revealed clear and separate patterns. The CK subscale has a strong factor loading between 0.903 and 0.961. It is a single component explaining 88.361% of the variance and showed that Content Knowledge is effectively measured in TPACK. Similarly, the PK subcomponent had strong factor loading, ranging from 0.877 to 0.956, effectively representing the pedagogical knowledge domain. It showed a single component explaining 87.680% of the variance.

The TK subcomponent explains 79.777% of the variance. It is a single component, and the factor loadings are between 0.843 and 0.943. This finding is evidence that technical knowledge is effectively measured across TPACK. The PCK subcomponent consisted of a single component explaining 82.51% of the variance. Factor loadings were set between 0.875 and 0.941. This finding can be said to be evidence that Pedagogical Content Knowledge is measured accurately in TPACK. The TCK subcomponent explained 88.717% of the variance and was a unicomponent. Factor loadings were between 0.923 and 0.959. This is evidence that Technical Content Knowledge is measured effectively in TPACK. The TPK subcomponent explains 88.267% of the variance. It has a single component, and the factor loadings are between 0.912 and 0.954. This finding shows that technical knowledge is measured precisely in TPACK. Finally, the TPCK subcomponent explained 81.160% of the variance. The factor loadings of this subcomponent, which is a single component, are between 0.875 and 0.935. This proves that the integrated structure of TPACK is measured effectively.

As a result of the factor analysis, it was observed that each TPACK subcomponent indicates the presence of a single factor. In parallel with [33], the findings confirm that the questionnaire is reliable and valid in measuring STEM teachers’ TPACK self-efficacy in STEM and PBL contexts [34]. It can thus be predicted that the modified tool may be used as a powerful measurement tool in future studies in similar teaching environments.

3.3. Data Analysis

The standardized regression values of the items in the scale and the fit of the model were used to test the construct validity. The construct of the model was verified using the Pearson product moment, and the existence of significant correlations between TPACK and its subcomponents was determined. The model shown in Figure 2 was then fitted in AMOS 19. Path coefficients were analyzed in order to test the research questions in the model whose fit indices reached a sufficient level.

4. Results

A structural equation analysis was conducted to examine the relationships between the subcomponents of the TPACK scale—including TK, CK, PK, PCK, TCK, and TPK—in relation to the overall TPACK scale. The developed model was tested using structural equation modeling (SEM), where six exogenous variables (TK, CK, PK, PCK, TCK, and TPK) were specified as predictors, and TPACK was set as the endogenous variable. This approach allowed for a comprehensive analysis of how each subcomponent contributes to the overarching TPACK construct.

As shown in Table 3, the fit indices for the constructed model demonstrate a strong fit overall. Most of the fit values fall within the range of excellent fit, with the exception of the RMSEA value, which falls within the acceptable fit range. This indicates that, while not perfect, the model sufficiently represents the data, and the relationships among the variables are well captured by the structural equation model. No modifications were made to improve the fit indices.

Significant positive correlations were found at p < 0.01 level in the context of sub-dimensions of TPACK (Table 4). Based on this result, it shows that the relationships between sub-dimensions can be further investigated with SEM. The correlations between TPACK and its sub-dimensions are above 0.70. This shows that the relationship is strong and indicates a high level of association between TPACK and its sub-dimensions. This shows that the relationship is [38]. When the results are analyzed, it is seen that the highest correlations are between PK and PCK (0.92) and between CK and PK (0.91) and TK (0.84). In contrast, the lowest correlations were observed between TPACK and CK (0.73) and PCK (0.75) and between CK and TPACK (0.73). These findings are similar to some studies in the literature [13,39]. The emphasis on “teaching” in the items belonging to the CK dimension may be related to the teaching pedagogies mentioned in the PK dimension.

Structural Equation Model

The model created to answer the first research question and sub-questions of the research is given in Figure 3.

Figure 3 illustrates that the relationships between TK, CK, PK, and TPACK are not statistically significant. Therefore, these paths do not have a measurable impact on TPACK in the current model. Similarly, the relationship between PCK and TPACK also shows no significant effect. To enhance clarity and model effectiveness, the model was revised and finalized, as depicted in Figure 4, and the research questions were addressed more accurately. Table 5 provides the path coefficients for the structural equation model, reflecting the refined relationships between the variables.

Table 5 shows a non-significant relationship between TK, CK, and PCK, as well as between PCK and TPACK. However, the other pathways in the model are significant. Based on these findings, the model was revised, as shown in Figure 4, to further explore the research questions.

In line with the first question of the study, analysis of Figure 4 shows the following.

• For the sub-question 1.1,

  TK, PK, and CK do not significantly affect STEM teachers’ TPACK. TK positively affects TPK (β = 0.50) and PCK (β = 0.67), both directly and significantly (p < 0.001).

• Regarding the sub-question 1.2,

  PK has a positive, direct, and significant effect on TCK (β = 0.16), TPK (β = 0.39), and PCK (β = 0.71) (p < 0.001).

• For sub-question 1.3,

  CK has a significant, direct effect on TCK (β = 0.14) and PCK (β = 0.23) (p < 0.001).

• For the sub-question 1.4,

  TCK (β = 0.47) and PCK (β = 0.44) have a significant, direct, and positive effect on TPACK (p < 0.001), with TCK being the most significant external variable influencing TPACK, followed by PCK. However, TPK did not have a significant effect on TPACK.

Additionally, in line with the second main research question, the direct and indirect effects of certain demographic variables on TPACK and its sub-dimensions were analyzed. The demographic information obtained from the participant teachers was added to the model created in the context of the second main research question of the study. These variables are certificate in education (CE), gender, minor degree (MinorD) in STEM, major degree (MajorD) in STEM, highest academic degree (HAD) and years of experience in Qatari school (YEQS). There are some prerequisites to analyzing these variables in SEM. The literature recommends the use of weighted least squares and robust maximum likelihood estimation methods in such studies [40]. It is also recommended not to use the normal covariance matrix or Pearson correlation matrix with such data [35]. The use of SmartPLS was deemed appropriate for the analysis of a model containing categorical variables with non-normal distribution. This is because the use of SmartPLS is recommended for path analysis of complex models that do not meet the conditions of normality in our field [41,42]. Demographic and also categorical variables were included in the final model. For the analysis, a series of transformations were performed on the data sets, and they were made ready for analysis (Figure 5).

In the constructed model, firstly, the significance and t-values were examined during the bootstrap step, and the test was repeated by constructing a new model by removing the inappropriate variables from the model.

As seen in Table 6, the effect of the gender variable on TPACK sub-variables was not significant. Again, the effect values of the minor degree (MinorD) in STEM, major degree (MajorD) in STEM, and teaching variables on TPACK and its sub-variables were not statistically significant. In addition, it was revealed that the effect of the HAD variable on the TK variable was not statistically significant (HAD → TK = 0.39, p > 0.05). In contrast to these findings, the effects of the CE (CE → CK = 0.04, CE → PK = 0.03. CE → TK = 0.04, p < 0.05, t > 1.6) and HAD (HAD → CK = 0.02, HAD → PK = 0.03, p < 0.05, t > 1.96) variables on the CK, PK, and TK subcomponents were significant. In this context, these categorical variables were included in the model, and the others were excluded. CE and HAD categorical variables were directly significant. The path analysis of the model constructed with the variables with significant p-values after the bootstrap test is given in Figure 6. As seen in Figure 6, the highest education degree and certificate in education had an effect on the subcomponents of TPACK.

The R² values for the endogenous latent variables of TK, CK, PK TCK, TPK, PCK, and TPACK were determined as TK = 0.13, CK = 0.16, PK = 0.15, TCK = 0.79, TPK= 0.82, PCK = 0.84 and TPACK = 0.78, respectively. In studies wherein abstract expressions such as human emotions and thoughts had attempted to be measured with the help of questionnaire questions, the R² value was generally not close to 1. Threshold values of 0.25, 0.50, and 0.70 are generally used to define weak, moderate, and strong coefficients of determination, respectively [43]. The size of R² values is important in determining the accuracy of the prediction. Table 7 gives the details of each weight of CFS and CE in the model.

As seen in Table 7, the categorical variable CE had a significant negative effect on CK, PK, and TK. A path coefficient of less than 0.10 indicates a small effect, around 0.30 represents a medium effect, and 0.50 or higher indicates a large effect [44]. In the study, path coefficients according to the standardized regression weights between variables were interpreted according to these criteria.

For CE → CK, the standardized parameter value for this structural relationship was β = −0.21, and the t value was t = 1.93 (p < 0.05). For CE → CK, the standardized parameter value for this structural relationship was β = −0.28, and the t value was t = 2.30 (p < 0.05). For CE → TK, the standardized parameter value for this structural relationship β = −0.22, and t = 1.95 (p < 0.05). Therefore, it can be concluded that the effect of CE on CK and TK is moderate. As CEnew → CE has no statistically significant effect on CE (p > 0.05), it indirectly affects CK and TK in favor of those who have a certificate in the field of education. As seen in Table 7, we observed that the categorical variable HAD has a significant positive effect on CK and PK.

The standardized parameter value for the structural relationship HAD → CK was β = 0.33, and the t value was t = 2.25 (p < 0.05), while the standardized parameter value for the structural relationship HAD → PK was β = 0.32, and the t value was t = 1.97 (p > 0.05). Therefore, it can be concluded that the effect of HAD (sub-dimensions: bachelor’s, master’s, doctoral) on CK and PK is moderate. When we analyzed in which direction the variable had an effect, we determined that HADdoc → HAD had a moderate positive effect (β = 0.31), and HADmast had a highly positive effect (β = 0.50). In short, it can be said that teachers with doctorate and master’s degrees positively affect the HAD variable (and thus the CK and PK subcomponents) more than those with other degrees.

Findings related to indirect effects of categorical variables are given in Table 8.

Participants who do not have a certificate in education indirectly affect PCK, TCK, TPK, and TPACK negatively and moderately (CE → PCK, β = −0.25, t = 2.15 p < 0.05 CE → TCK, β = −0.23, t = 2.18, p < 0.05, CE → TPK, β = −0.23, t = 2.15, p < 0.05, CE → TPACK, β = −0.05, t = 1.45 p < 0.05).

The HAD variable indirectly affects only PCK positively and moderately (HAD → PCK, β = −0.30, t = 2.18 p < 0.05).

5. Discussion

In this study, structural equation modeling (SEM) was employed to analyze the perceived TPACK pathways of 245 STEM teachers in Qatar, following the assumptions outlined in the model proposed by [3]. In this study, a model explaining TPACK with R² = 77 was constructed. When the literature is examined, the range determined in studies investigating the relationships between the subcomponents of TPACK is between 50% and 80% [45,46].

5.1. What Is the Effect, If Any, of TK, PK, and CK on STEM Teachers’ TPACK?

According to the model finalized after the tested model, TK, CK and PK do not significantly affect the TPACK of STEM teachers.

The fact that TK has no direct effect on TPACK aligns with Pierson [47], who found that experienced teachers struggle to transfer their pedagogical expertise into their lessons when they lack proficiency in technology. Santika et al. [48] reached a similar conclusion in their study with pre-service teachers. Competence in technology also enhances teachers’ self-efficacy in integrating it into their teaching [49], which is an important indicator of teachers’ progress in TPACK [50]. In addition to this, it should be noted that teachers’ inadequacies in technology, technological infrastructure, and support in the institutions they work in are important factors affecting teachers’ TPACK [51]. According to similar studies reported in the literature, teachers’ access to technological tools and support has a positive effect on their TPACK and efficient use of technology [52].

PK includes classroom management and assessment and evaluation knowledge as well as teaching strategies. Pedagogical knowledge, which includes the ability to use effective teaching strategies to prompt meaningful learning in the classroom, is also one of the basic skills of being a quality teacher [53]. A good teacher must possess both deep Content Knowledge, which enables them to thoroughly understand the subject matter they are teaching, and strong Pedagogical Knowledge, which equips them with the skills and strategies needed to effectively convey that content to students [54]. The lack of a significant effect of PCK on TPACK in this study may be due to teachers’ lack of in-depth pedagogical knowledge. This is perhaps in line with the conclusion that teachers who do not have deep Content Knowledge cannot use their pedagogical knowledge effectively [55]. The findings of this study align with previous research indicating that Technological Knowledge (TK) does not significantly affect TPACK [56,57]. Despite this, Angeli and Valanides [58] demonstrated that when teachers successfully integrate TK, Content Knowledge (CK), and Pedagogical Knowledge (PK), they develop new forms of knowledge. This highlights the transformative nature of TPACK, emphasizing the importance of meaningful and dynamic integration of various knowledge sources, rather than merely adding them in a linear fashion [59].

5.2. What Is the Effect, If Any, of TK on STEM Teachers’ TPK and PCK?

This study found that the TK sub-dimension had a significant positive impact on both TCK and TPK but not on TPACK itself. This relationship stands out as one of the most prominent effects in the model. To strengthen the positive effects of TCK and TPK on TPACK, teachers must gain experience not only with technology but also with its pedagogical applications. Specific strategies should be implemented to facilitate these experiences, particularly in fields like Information Technology [12]. For instance, teachers need to first learn what IT tools are and how they function before being given the opportunity to explore their application independently. This exploration phase is crucial as it allows teachers to internalize the tool within their specific discipline [60].

Recent research supports the notion that Technological Knowledge, Technological Content Knowledge, and Pedagogical Knowledge are essential competencies for teachers [61]. However, being familiar with technological tools does not necessarily mean that teachers can effectively apply them in educational settings. Thus, comprehensive professional development programs, including in-service training, are necessary to help teachers use technology in ways that enhance student learning. Quality professional development programs that focus on the integration of technology have been shown to boost teachers’ self-confidence and self-efficacy [62]. This suggests that sustained and well-structured training can enable teachers to better integrate technology into their pedagogical practices.

5.3. What Is the Effect, If Any, of PK on STEM Teachers’ TCK, TPK, and PCK?

It is important to emphasize that the model presented in the research demonstrates that PK positively influences both TPK and PCK. Furthermore, PK also has a significant positive effect on TCK, which represents the integration of technology with subject content. This finding suggests that teachers’ pedagogical expertise plays a key role not only in combining pedagogy and content but also in integrating technology into their instructional practices in ways that are pedagogically sound and contextually relevant. In other words, the meaningful integration of technological tools with subject matter and pedagogy is more impactful than merely frequent technology use by teachers, reinforcing the need for thoughtful and deliberate application of technology in STEM education [63].

PCK items measure teachers’ perceptions of pedagogical expertise with the aim of facilitating meaningful learning. PK directly and positively affects STEM teachers’ PCK. This is consistent with in the results of previous studies by Behrang and Mehdi [64], Khine et al. [65], and Schmid et al. [22], but in some other studies (such as those by Khine et al. [66] and Hao and Lee [67]), the effect of PK on PCK was statistically insignificant. The effect of PK on PCK is stronger than the effect of PK on TPK and TCK. This is due to the fact that there are numerically more physics, chemistry, biology and mathematics teachers than STEM teachers in Qatar, and teachers in these disciplines are more likely to perceive the common and mainstream forms of teaching knowledge emphasized in Shulman [2]’s formulation of PCK. To summarize, the low number of teachers in branches such as accountancy and computer science, which involve more use of technology, may have affected this result. LeBlanc [68] found that teachers have more educational background and experience in the areas of PCK, CK, and PCK than technological knowledge, so it should not be denied that their non-technological knowledge levels are higher.

5.4. What Is the Effect, If Any, of CK on STEM Teachers’ TPK and PCK?

Since CK had no effect on TCK, it was excluded from the model, which aligns with the findings of Khine et al. [65], where CK was shown to have a significant and positive effect on TPK and PCK. Several studies in the literature corroborate this result, particularly regarding the positive impact of CK on PCK [22,23,29]. However, in contrast, Hao and Lee [67] found no effect of PK on PCK. The current study also revealed that the effect of PK on PCK was stronger than its effect on TPK, which is consistent with the findings of Khine et al. [65]. Based on the literature, it can be argued that the PCK sub-dimension is closely related to teachers’ Content Knowledge and pedagogical skills. In this regard, it is crucial to recognize the importance of continually updating and enhancing STEM teachers’ expertise in both content and pedagogy and integrating these areas to strengthen their instructional capabilities [69].

5.5. What Is the Effect, If Any, of TPK, TCK, and PCK on STEM Teachers’ TPACK?

Another key finding of this study is that TPK, TCK, and PCK significantly and positively influence the TPACK competence of STEM teachers in Qatar. This is consistent with previous studies in the literature, which suggest that TPK, TCK, and PCK all contribute to the development of TPACK competencies [70,71]. The results indicate that as TPK, TCK, and PCK increase, so too does TPACK.

Research has underscored that TPACK training should be activity-based, especially in STEM education, where teachers must integrate technology, pedagogy, and subject-specific knowledge to produce outcomes through engineering design processes [14]. This synthesis is essential, and particular emphasis should be placed on using technology effectively within teachers’ respective fields.

For both pre-service and in-service training, it is crucial to focus on how teachers can use technology within their specific disciplines rather than only how they operate technological tools. Studies have shown that participation in seminars and workshops on technology integration enhances teachers’ TPACK and positively impacts the delivery of technology-supported lessons [4,72].

5.6. How Do the Demographic Variables of Gender, Minor Degree, and Major Degree in STEM Disciplines Effect on TPACK and Its Sub-Dimensions?

Within the scope of the second main research question, the finalized model was tested with various demographic variables: certificate in education (CE), gender, minor degree (MinorD) in STEM, major degree (MajorD) in STEM, highest academic degree (HAD), and years of experience in Qatari Schools (YEQS). Among these variables, CE and HAD had significant effects on TPACK and its subcomponents, while variables such as gender, minor degree, major degree in STEM, and years of experience did not significantly influence the outcomes. Notably, the lack of influence from years of experience was unexpected, as it might be assumed that longer teaching experience would contribute to more effective technology integration [73,74]. This finding suggests that the development of TPACK may be more closely linked to specific training and certification in technology use than to general teaching experience [75,76]. Further exploration of the impact of professional development and targeted training on experienced teachers’ TPACK skills could offer valuable insights [34].

These findings align with some studies in the literature that indicate similar results for variables such as gender [22,77,78], experience in schools [21,79], and major or minor degrees in STEM [34,54]. However, there are also studies that contradict these findings, especially regarding the influence of gender and STEM-related degrees. Variables that did not significantly affect the model were removed, and the effects of CE and HAD were reassessed, both directly and indirectly.

One of the notable findings of the study is that the majority of teachers who participated did not hold a certificate in education (CE), which directly or indirectly influenced their TPACK and its subcomponents (e.g., CK, PK, TK directly; TCK, TPK, PCK, and TPACK indirectly). This result is consistent with other studies in the literature, which emphasize that technology-supported certified training programs have a positive impact on TPACK and its subcomponents. As Bilici and Guler [80] assert, targeted training significantly enhances teachers’ TPACK competencies.

The study’s findings related to the highest academic degree (HAD) align with numerous studies, particularly regarding CK. For instance, studies by [81] and [82] indicate significant differences favoring those with graduate-level education, especially in the CK dimension. However, other studies report no effect of academic degrees on TPACK [78,79]. In the current study, HAD was found to have a direct positive effect on CK and PK and an indirect positive effect on PCK [83]. This suggests that postgraduate training, such as master’s or doctoral degrees, enhances teachers’ content knowledge, pedagogical knowledge, and pedagogical content knowledge [84,85]. On the other hand, having only a bachelor’s degree may limit the effective integration of technology in the classroom, especially in relation to the merging of technological and pedagogical content knowledge [86,87,88].

Interestingly, in this study, variables such as HADPhD or HADMaster did not significantly affect the technology-related subcomponents (e.g., TK, TCK, or TPK). Research by [89] indicates that while PCK increases most over time, TK and TPK tend to increase at a slower rate. This is an important area for future focus. As [90] noted, a lack of technological–pedagogical knowledge, insufficient pedagogical skills, and limited teaching experience can hinder the development of TPACK. Furthermore, there appears to be a general lack of training in TK, TPK, and TCK, particularly regarding the integration of content and pedagogy [34].

6. Conclusions and Implications

The literature provides limited insight into teachers’ ability to integrate technology into their teaching environments, and there is almost no modeling that accounts for different variables. More importantly, science and mathematics teachers need to equip their students with TPACK competencies by effectively integrating technology into their lessons. This approach will help students become not only technologically literate but also lifelong users and enthusiasts of technology. This study aimed to explore how science and mathematics teachers in Qatar perceive their ability to integrate technology into their STEM teaching and examined this ability across different variables.

The findings indicate that science and mathematics teachers in Qatar need further development in their TPACK skills. For this, they require more hands-on experiences with technology and more opportunities to apply it pedagogically. In the STEM context, technology serves as both a discipline and a bridge. The model presented in this research shows that the integration of technology with domain-specific pedagogical content is more critical than simply using technology frequently.

One of the most significant contributions of this study is that categorical variables such as certificates in education and postgraduate education significantly impact TPACK and its subcomponents, whereas variables such as gender, minor or major degree in STEM, and years of teaching experience do not have a notable effect. This finding fills a gap in the literature and highlights the importance of structured training and postgraduate education for enhancing TPACK skills. To improve science and mathematics teachers’ TPACK skills, it is essential for them to participate in technology-based certified training programs and pursue postgraduate education in their fields.

Furthermore, integrating technology into pedagogical practices is crucial. Teachers who are unable to do so will struggle to equip their students with 21st-century skills. In the digital age, a lack of TPACK competence within STEM education will negatively affect students’ preparedness for future STEM careers.

Therefore, it is vital to encourage educators to engage in continuous professional development focused on emerging technologies and related software. Additionally, schools and educational systems should support teachers in pursuing postgraduate education, such as master’s and doctoral degrees. Educational training, particularly technology-based certification programs and advanced degrees, should be prioritized in teacher development strategies to better prepare educators for the evolving demands of STEM education.

Lastly, future research should further explore the long-term impact of educational training programs on teachers’ TPACK development and student outcomes. Integrating ongoing training into professional development plans could be a key direction for enhancing the integration of technology in science and mathematics classrooms.

Author Contributions

Conceptualization, N.M., Z.S., M.Ç. and A.A.-T.; methodology, N.M., Z.S. and M.Ç.; software, N.M. and M.Ç.; validation, N.M. and M.Ç.; formal analysis, N.M. and M.Ç.; investigation, N.M., Z.S., M.Ç. and A.A.-T.; resources, N.M., Z.S. and A.A.-T.; data curation, N.M., Z.S. and A.A.-T.; writing—original draft preparation, N.M. and M.Ç.; writing—review and editing, N.M., Z.S. and M.Ç.; visualization, N.M. and M.Ç.; supervision, N.M. and Z.S.; project administration, N.M., Z.S. and A.A.-T.; funding acquisition, N.M., Z.S. and A.A.-T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Qatar University (protocol code QU-IRB 1697-EA/22, approved on 22 March 2022).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

Data supporting the findings and conclusions are available upon request from the corresponding author.

Acknowledgments

The authors would like to thank the Qatar National Research Fund for their generous support of this research through the National Priority Research Program. It is important to note that any opinions, findings, conclusions, or recommendations expressed in this article are solely those of the authors and do not necessarily reflect the views of the Qatar National Research Fund. QNRF has neither approved nor endorsed the content of this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Table 1

Subcomponents of the TPACK model.

Dimensions	Definitions
Content Knowledge (CK)	Information related to the subject area
Technology Knowledge (TK)	Technological knowledge of everything that makes life easier
Pedagogical Knowledge (PK)	General information about learning and teaching methods
Technological Pedagogical Knowledge (TPK)	Knowledge about how the use of technology affects teaching and learning strategies
Technological Content Knowledge (TCK)	Knowledge of presenting the subject with technology
Pedagogical Content Knowledge (PCK)	The pedagogical knowledge required for the teaching of a particular subject area
Technological Pedagogical Content Knowledge (TPCK)	Presenting a specific subject area by integrating knowledge of technology and pedagogy

Table 2

Descriptive information on the participants of the study.

Variables	Category	N	Mean
Gender	Female	121	49.4
	Male	124	50.6
Minor Degree in STEM (Minor D)	Yes	108	44.1
	No	137	55.9
Certificate in Education (CE)	Yes	110	44.9
	No	135	55.1
Years of Experience in Qatari School (YEQS)	<5 years	53	21.6
	5–10	88	35.9
	>10 years	104	42.4
Majors Degree in STEM (MD)	Biology	48	19.6
	Chemistry	59	24.1
	Computer Science	3	1.2
	Earth science	2	0.8
	Mathematics	96	39.2
	Engineering	5	2.0
	Physics	30	12.2
	others	2	0.8
Highest Academic Degree (HAD)	Vocational certification	4	1.6
	Bachelor’s degree	173	70.6
	High Diploma degree	32	13.1
	Master’s degree	32	13.1
	Doctorate	4	1.6

Table 3

Fit index.

Fit Indices	TPACK Fit Variables	Excellent Fit	Acceptable Fit
1 χ2/sd	2.79	0 ≤ χ2/sd ≤ 2	2 ≤ χ2 /sd ≤ 3
2 GFI	0.97	0.95 ≤ GFI ≤ 1.00	0.90 ≤ GFI ≤ 0.95
3 AGFI	0.91	0.90 ≤ AGFI ≤ 1.00	0.85 ≤ AGFI ≤ 0.90
3 CFI	0.99	0.95 ≤ CFI ≤ 1.00	0.90 ≤ CFI ≤ 0.95
3 NFI	0.99	0.95 ≤ NFI ≤ 1.00	0.90 ≤ NFI ≤ 0.95
3 NNFI (TLI)	0.98	0.95 ≤ NNFI (TLI) ≤ 1.00	0.90 ≤ NNFI (TLI) ≤ 0.95
3 IFI	0.99	0.95 ≤ IFI ≤ 1.00	0.90 ≤ IFI ≤ 0.95
4 RMSEA	0.08	00 ≤ RMSEA ≤ 0.05	0.05 ≤ RMSEA ≤ 0.08

^1,2 [35], ³ [36], ⁴ [37].

Table 4

Intercorrelations between constructs.

	CK	PK	PCK	TK	TPK	TCK	TPACK
CK	1
PK	0.91 **	1
PCK	0.88 **	0.92 **	1
TK	0.84 **	0.86 **	0.84 **	1
TPK	0.79 **	0.84 **	0.81 **	0.89 **	1
TCK	0.81 **	0.85 **	0.84 **	0.86 **	0.88 **	1
TPACK	0.73 **	0.78 **	0.75 **	0.82 **	0.86 **	0.86 **	1

N = 245, ** p < 0.001.

Table 5

Path coefficients of the TPACK structural equation model.

Research Questions	Path	Path Coefficient	Standard Error	Critical Ratio	Effect Result
Sub-question 1.1	TK → TPACK	N.S.			No
	CK → TPACK	N.S.			No
	PK → TPACK	N.S.			No
Sub-question 1.2	TK → TCK	0.50 ***	0.02	8.74	Yes
	TK → TPK	0.67 ***	0.04	12.47	Yes
Sub-question 1.3	PK → PCK	0.71 ***	0.05	12.14	Yes
	PK → TPK	0.39 ***	0.04	5.70	Yes
	PK → TCK	0.16 ***	0.02	7.41	Yes
Sub-question 1.4	CK → TPK	0.14 **	0.07	2.29	Yes
	CK → PCK	0.23 ***	0.09	3.99	Yes
Question 2	TCK → TPACK	0.47 ***	0.10	7.36	Yes
	TPK → TPACK	0.44 ***	0.06	6.93	Yes
	PCK → TPACK	N.S.			No

** p < 0.001, *** p < 0.0001, N.S.: Not Significant.

Table 6

Modeling of l variables (p ‘significance’ and t values) from the bootstrap test.

	Original Sample (O)	Standard Deviation (STDEV)	t Statistics(O/STDEV)	p Values
CE → CK	−0.16	0.14	1.97	0.04 *
CE → PK	−0.29	0.14	2.11	0.03 *
CE → TK	−0.22	0.14	1.95	0.04 *
CK → PCK	0.23	0.07	3.11	0.00 *
CK → TPK	−0.12	0.08	2.94	0.00 *
Gender → CK	0.23	0.15	1.55	0.12
Gender → PK	0.08	0.15	0.52	0.60
Gender → TK	0.16	0.15	1.07	0.28
HAD → CK	0.45	0.20	2.20	0.02 *
HAD → PK	0.44	0.20	2.16	0.03 *
HAD → TK	0.21	0.25	0.84	0.39
Major → CK	−0.25	0.57	0.45	0.65
Major → PK	−0.41	0.80	0.51	0.61
Major → TK	−0.54	0.66	0.81	0.41
Minor → CK	0.03	0.12	0.30	0.75
Minor → PK	0.04	0.12	0.37	0.71
Minor → TK	0.04	0.12	0.38	0.69
PK → PCK	0.71	0.07	9.37	0.00 *
PK → TCK	0.42	0.08	5.21	0.00 *
PK → TPK	0.38	0.09	4.25	0.00 *
TCK → TPACK	0.47	0.07	6.25	0.00 *
TK → TCK	0.50	0.08	6.26	0.00 *
TK → TPK	0.67	0.06	10.65	0.00 *
TPK → TPACK	0.44	0.07	5.88	0.00 *
** Teach → CK	0.19	0.22	0.87	0.38
** Teach → PK	0.18	0.22	0.81	0.41
** Teach → TK	0.30	0.34	0.90	0.36

* p < 0.05. ** Teach: Total years of experience in teaching; <5, 5–10, >15.

Table 7

Coefficients and p-values of variables and subcomponents in the final model.

	Path Coefficient	Standard Deviation (STDEV)	t Statistics (O/STDEV)	p Values
CE → CK	−0.21	0.12	1.93	0.04 *
CE → PK	−0.28	0.12	2.30	0.02 *
CE → TK	−0.22	0.12	1.95	0.04 *
CK → PCK	0.23	0.07	3.11	0.00 *
CK → TPK	−0.12	0.08	1.94	0.05 *
HAD → CK	0.33	0.18	2.25	0.03 *
HAD → PK	0.32	0.18	1.97	0.04 *
PK → PCK	0.71	0.07	9.37	0.00 *
PK → TCK	0.42	0.08	5.21	0.00 *
PK → TPK	0.38	0.09	4.25	0.00 *
TCK → TPACK	0.47	0.07	6.25	0.00 *
TK → TCK	0.50	0.08	6.26	0.00 *
TK → TPK	0.67	0.06	10.65	0.00 *
TPK → TPACK	0.44	0.07	5.88	0.00 *
CEnew → CE	1.0	0.00	n/a	n/a
** HADbach → HAD	−0.38	0.61	1.85	0.06
** HADdoc → HAD	0.31	0.55	3.11	0.00 *
** HADhigh → HAD	0.82	0.44	1.44	0.15
** HADmast → HAD	0.50	0.51	1.95	0.04 *
** HADvoc → HAD	0.21	n/a	1.77	0.07

* p < 0.05. ** HADbach: bachelor; HADdoc: doctoral; HADhigh: high school; HADmast: master; HADvoc: vocational high school.

Table 8

Indirect effect of categorical variables on TAPCK and its subcomponents.

	Path Coefficient	Standard Deviation (STDEV)	t Statistics (|O/STDEV|)	p Values
CE → PCK	−0.25	0.11	2.15	0.03 *
CE → TCK	−0.23	0.10	2.18	0.02 *
CE → TPACK	−0.21	0.09	2.15	0.03 *
CE → TPK	−0.23	0.10	2.15	0.03 *
CK → TPACK	−0.05	0.03	1.45	0.14
HAD → PCK	0.30	0.16	1.92	0.04 *
HAD → TCK	0.13	0.08	1.60	0.10
HAD → TPACK	0.10	0.06	1.54	0.12
HAD → TPK	0.08	0.06	1.34	0.18

* p < 0.05.

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