Analyzing the Availability of TPACK Framework Dimensions Among Elementary Mathematics Teachers: A Survey-Based Study on Demographic Variables

Abstract

This study sought to explore the extent to which the dimensions of the TPACK framework are present among mathematics teachers at the elementary level from their perspective. The study’s goals were accomplished through the use of a descriptive approach, and a questionnaire was distributed to 107 mathematics teachers in the eastern region of Saudi Arabia to collect data. The results indicated that the dimensions of the TPACK framework were highly present among the participants. The highest level was observed in the dimension of Pedagogical Content Knowledge (PCK) at 78.1%, while the lowest was in the Technological Content Knowledge (TCK) dimension at 68.2%. The findings also revealed no statistically significant differences based on gender or education sector. However, statistically significant differences were found in favor of teachers with higher academic qualifications (postgraduate studies) and more years of teaching experience. The study recommends continuing efforts to enhance teacher training programs dealing with integrating technology into teaching, drawing on global experiences in applying the TPACK framework.

1. Introduction

In the era of ongoing technological revolutions and artificial intelligence, the roles, responsibilities, and tasks of in-service teachers have evolved significantly. Teachers must now acquire technological skills, cultivate digital literacy, and prepare to address the ever-changing challenges in the educational environment. They must also stay informed about new strategies and teaching methods, requiring a deeper understanding of how to optimally integrate technology to facilitate teaching and learning (Gorev & Gurevich-Leibman, 2015).

Teachers need new competencies to fulfill their roles in the 21st century. This necessity has led to efforts emphasizing professional development for teachers, both pre-service and in-service, particularly in integrating technology as a modern educational innovation to address many teaching challenges. One such effort is the Technological Pedagogical Content Knowledge (TPACK) framework introduced by Mishra and Koehler (2006). This framework serves as a guide for teachers in selecting and integrating pedagogical and technological knowledge with the subject content they teach.

In 2006, Mishra and Koehler expanded on their earlier research by introducing TPACK, which highlights the need for understanding Information and Communication Technology (ICT) as a foundation for effective teaching. It emphasizes the dynamic interaction between content knowledge, pedagogical knowledge, and technological knowledge. Based on this framework, researchers like Voogt et al. (2012) have recommended integrating TPACK into teacher preparation curricula to equip teachers with ICT experiences and enhance teaching effectiveness while providing a supportive learning environment.

Wilton and Brett (2019) stressed the importance of teachers in the digital age possessing the ability to effectively integrate technology into the educational process. They pointed out that many teachers lack the necessary knowledge and skills for this. In-service training programs often focus on operating and using technology rather than on its effective application in classrooms to improve teaching practices and achieve desired outcomes. Canbazoglu Bilici et al. (2016) highlighted that the underperformance of mathematics teachers in integrating technology during service may be due to inadequate pre-service preparation programs. This insufficiency has led to negative attitudes toward technology use, extending into their in-service teaching practices. Canbazoglu Bilici et al. (2016) employed a TPACK-based instructional approach to enhance teachers’ teaching performance and equip them with skills for effective technology integration. The study recommended focusing teacher preparation programs on developing technological-pedagogical knowledge.

Similarly, Kirikçilar and Yildiz (2018) found that middle school mathematics teachers face difficulties in integrating their pedagogical knowledge with technology when designing computer-supported teaching activities due to deficiencies in TPACK competencies. Their study emphasized training mathematics teachers on TPACK competencies within classrooms to improve their ability to use various educational software in teaching.

Research (e.g., Kafyulilo & Fisser, 2019; Srisawasdi, 2014) underscores the importance of training in-service teachers to integratively employ technology in mathematics teaching. The TPACK framework is considered fundamental to effective teaching with technology, as it requires an understanding of educational technologies for delivering specific content and explaining its various concepts using technological tools. The TPACK framework provides a structured approach for teachers to merge technological, pedagogical, and content knowledge, ensuring meaningful learning experiences for students. At the elementary level, where students are still developing core cognitive and problem-solving skills, the effective integration of technology can enhance engagement and conceptual understanding. Research has highlighted the importance of teacher training in fostering TPACK competencies (Alamri, 2019; Jang & Tsai, 2012; Omar, 2018; Patahuddin et al., 2016), as many teachers struggle with incorporating technology beyond basic usage (Canbazoglu Bilici et al., 2016; Kirikçilar & Yildiz, 2018).

The focus on Saudi Arabian elementary teachers adds a significant dimension to the international discourse on TPACK. The country has witnessed substantial investments in educational technology as part of Vision 2030, aiming to modernize its educational sector and enhance digital literacy among students (Vision 2030, n.d.). Understanding how elementary teachers in Saudi Arabia perceive and implement TPACK provides valuable insights into the challenges and opportunities within this unique educational context. Given the cultural and structural differences in teacher preparation programs worldwide, examining the development of TPACK in Saudi Arabia contributes to the broader understanding of how technological integration varies across different educational systems.

This research is important because it addresses a critical gap in understanding how elementary mathematics teachers perceive and apply the TPACK framework—a model essential for effective technology integration in education. As digital tools become integral to teaching, especially in alignment with national education reforms like Saudi Arabia’s Vision 2030, it is vital to assess whether teachers possess the knowledge to integrate content, pedagogy, and technology. Most existing studies focus on secondary or pre-service teachers, leaving a lack of data on elementary educators. By examining how demographic factors relate to TPACK dimensions, this study provides evidence to guide targeted professional development, inform policy decisions, and support instructional improvements at the foundational level of education.

To achieve the study’s objectives, the following research questions were formulated:

1. To what extent do the dimensions of the TPACK framework exist among elementary mathematics teachers from their perspective?

2. Are there statistically significant differences in the average responses of elementary mathematics teachers regarding the extent of TPACK framework dimensions due to demographic variables (gender, academic qualification, teaching experience, and education sector)?

1.1. Theoretical Framework

Shulman (1986) sought to frame the knowledge and tools teachers need to teach specific content. These efforts resulted in what is known as the Pedagogical Content Knowledge (PCK) framework. Shulman emphasized that successful teaching requires teachers to understand pedagogical methods and approaches tailored to their field of expertise.

Shulman’s framework viewed educational technology as tools that facilitate teaching. Effective teaching is the outcome of the interaction between pedagogical knowledge, content knowledge, and these tools. Figure 1 illustrates the PCK framework.

With the advent of e-learning, Koehler et al. (2004) expanded the scope of technological knowledge required by teachers. They argued that when teachers are given the opportunity to design their lessons electronically, they inherently develop their technological knowledge and gain a broader understanding of how content, pedagogy, and technology influence one another. Their research clarified the concept of using e-learning lessons that integrate content, pedagogy, and technology as a means to develop teachers’ knowledge across these core areas.

In the era of technological advancements, successful teachers are ones who can effectively integrate technology into their teaching in a pedagogically sound manner based on learning and teaching theories. Today, it is essential for teachers to adapt and embed technology into their instructional content in a way that aligns with pedagogical principles. Mishra and Koehler (2006) expanded Shulman’s framework by introducing a third domain: technology, not merely as a supporting tool for teaching but as an independent area of knowledge.

After five years of continuous research focusing on preparing higher education teachers across disciplines to become professionals in the educational process, Mishra and Koehler (2006) developed the Technological Pedagogical Content Knowledge (TPACK) Framework. This framework highlights the essential competencies teachers need to integrate technology into education effectively.

Mishra and Koehler (2006) emphasized that genuine technological integration requires understanding the relationships between the three core knowledge domains. Effective teaching involves creating new concepts derived from the interplay between content, pedagogy, and technology, as outlined in the TPACK framework (Jimoyiannis, 2010).

The TPACK framework represents the complex interaction of three primary forms of knowledge: Content Knowledge (CK), Pedagogical Knowledge (PK), and Technological Knowledge (TK). Figure 2 illustrates the TPACK framework.

The dimensions of the TPACK framework are defined as follows:

1. TK: Teachers’ knowledge of how to use ICT devices, software, and related peripherals.

2. PK: Teachers’ understanding of teaching and learning methods, practices, objectives, and values.

3. CK: Teachers’ mastery of the structure of subject matter (concepts, theories, principles, etc.), independent of teaching methods.

4. TCK: Teachers’ understanding of how technology influences and is influenced by content and how they interact. Operationally in this study, it refers to mathematics teachers’ ability to integrate their content knowledge with technology, selecting and utilizing it to present mathematical content in an engaging and simplified way that captures students’ attention.

5. PCK: The integration of content and teaching methods to organize, adapt, and represent specific aspects of a subject for effective teaching. Operationally in this study, it refers to mathematics teachers’ integration of their mastery of mathematical content with knowledge of pedagogy, effective teaching skills, and modern methods, enabling them to plan, execute, and evaluate mathematics lessons professionally to enhance students’ performance.

6. TPK: Knowledge of how to use technology to enhance pedagogical strategies’ effectiveness in achieving goals, regardless of the subject or content structure. Operationally in this study, it refers to mathematics teachers’ integration of teaching skills and knowledge of effective teaching methods with technology applications, selecting and employing them to facilitate students’ use of technology and improve knowledge representation.

7. TPACK: Teachers’ perceptions of how certain technologies can alter instruction and learning when applied in particular ways. It includes knowledge of pedagogical aspects and constraints of technological tools designed for appropriate strategies.

Mishra and Koehler (2006) define TPACK as a framework for integrating technology with content and pedagogy. They emphasize that developing quality content requires the deliberate integration of the three core knowledge domains: technology, pedagogy, and content. They argue that no single technological solution applies universally to all teachers, subjects, or teaching methods. Understanding the intricate connections between pedagogy, material, and technology is essential for effective teaching.

It is worth mentioning that the Technological Pedagogical Content Knowledge (TPACK) framework, developed by Mishra and Koehler (2006), provides a comprehensive model for understanding the integration of technology in teaching. TPACK extends Shulman’s (1986) concept of Pedagogical Content Knowledge (PCK) by introducing technological knowledge as a crucial component. This model identifies the intersection of three core knowledge domains: Content Knowledge (CK), Pedagogical Knowledge (PK), and Technological Knowledge (TK), leading to the integrated knowledge required for effective technology-enhanced teaching.

One critical distinction that must be clarified is that the constituent constructs of TPACK—TK, TPK, TCK, and PCK—are necessary but not sufficient conditions for TPACK. As Niess et al. (2009) emphasize, TPACK is a distinct construct that emerges only at the full intersection of these knowledge domains. Thus, the assumption that possessing TCK or PCK independently contributes to TPACK is a misinterpretation. Instead, TPACK represents a holistic integration where technology, pedagogy, and content dynamically interact.

Niess et al. (2009) further developed a model outlining levels of TPACK development, identifying progressive stages through which teachers advance as they integrate technology into their instructional practice. Lyublinskaya and Kaplon-Schilis (2022) expanded on this framework, proposing a rubric for assessing classroom applications of TPACK at different levels. These perspectives inform our study’s interpretation of teachers’ self-reported knowledge and its implications for classroom technology integration.

According to this study’s operational definition, TPACK is a framework that combines the content, pedagogy, strategies, and technological expertise of elementary school math teachers to give them the tools, techniques, and knowledge required for effective instruction.

Maintaining technology as a separate knowledge domain can create challenges. However, when teachers understand the TPACK framework, they are able to successfully use technology in their lessons’ pedagogy and content. This integration enhances students’ learning outcomes. Teachers need to combine their knowledge of content and pedagogy with a growing focus on technology. They must learn to merge these domains to create effective learning environments (Mailizar et al., 2021).

1.2. Literature Review

There are several obstacles to overcome before instructors can successfully use and integrate technology in the classroom, and these obstacles have a big influence on how well they teach. Wilton and Brett (2019) highlighted the limited proficiency of teachers in this area and emphasized the need for programs to support the development of teachers’ TPACK competencies during in-service training. Similarly, Alamri (2019) identified weaknesses in teacher preparation programs related to technology and inadequate in-service training for teachers in this domain. Alizadehjamal et al. (2020) found that while some mathematics teachers view technology as a valuable educational tool, they express concerns about its inappropriate interference in understanding mathematical concepts. Consequently, they are hesitant to use technology in their classrooms, allowing its use only in informal learning activities.

Given the growing global demand for integrating technological tools and resources across educational levels, it is essential to examine the extent to which TPACK framework dimensions are present among mathematics teachers. The use of technology in the classroom can increase students’ motivation and level of engagement in mathematics.

A review of the educational literature on this topic, which focuses on analyzing the availability of TPACK dimensions among teachers, reveals that most studies employed a descriptive methodology (Alghimlas, 2022; Al-Otaibi, 2022; Al-Qattan, 2023; Hill & Uribe-Florez, 2020; Mehawed, 2021; Shaqour & Alsaadi, 2015). Questionnaires were the primary tool used in these studies (Alodail, 2024; Al-Otaibi, 2022; Al-Qattan, 2023; Alshammari, 2020; Alshehri, 2012; Hill & Uribe-Florez, 2020; Mailizar et al., 2021; Mehawed, 2021; Ozudogru & Ozudogru, 2019; Shaqour & Alsaadi, 2015). The samples varied significantly. Some studies focused on pre-service teachers (Al-Qattan, 2023; Erdogan & Sahin, 2010), while others examined in-service teachers in middle schools (Alshehri, 2012; Hill & Uribe-Florez, 2020) and high schools (Al-Otaibi, 2022; Alshehri, 2012; Hill & Uribe-Florez, 2020). Additionally, some studies explored TPACK dimensions among faculty members (Alshammari, 2020; Shaqour & Alsaadi, 2015). Notably, limited attention has been given to elementary school teachers, underscoring the need to assess TPACK dimensions at this level. To address this gap, the present study will adopt a descriptive methodology and collect data using a questionnaire to examine the extent to which the TPACK framework is present among elementary mathematics teachers.

Studies have yielded varied results regarding the availability of TPACK framework dimensions among teachers. Some indicate a high level of availability (Alodail, 2024; Al-Otaibi, 2022; Al-Qattan, 2023; Alshammari, 2020; Alshehri, 2012), while others report moderate levels (Alghimlas, 2022; Shaqour & Alsaadi, 2015), and some highlight low levels of availability (Mehawed, 2021). This underscores the importance of continuing to examine TPACK dimensions among teachers due to its critical role in facilitating the effective use of technology in classrooms.

The education literature does not confirm a consensus on the influence of gender on the TPACK framework among teachers. Regarding TPACK dimensions, a number of research found no statistically significant differences between male and female teachers (Alodail, 2024; Al-Otaibi, 2022; Alshammari, 2020; Mehawed, 2021; Ozudogru & Ozudogru, 2019; Shaqour & Alsaadi, 2015). Others, however, reported differences favoring male teachers (Alghimlas, 2022; Erdogan & Sahin, 2010; Mailizar et al., 2021), while some studies noted better availability among female teachers (Al-Qattan, 2023). This calls for further investigation into the effect of gender on TPACK dimensions among mathematics teachers. Accordingly, the following hypothesis is proposed:

H₀₁: 

There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on gender.

Similarly, the literature does not establish agreement on the impact of academic qualification on the TPACK framework. Many studies reported no significant differences in teachers’ TPACK competencies based on their qualifications (Alodail, 2024; Al-Otaibi, 2022; Al-Qattan, 2023; Ozudogru & Ozudogru, 2019). In contrast, other studies found differences in availability levels based on teachers’ academic qualifications (Alghimlas, 2022; Mailizar et al., 2021; Shaqour & Alsaadi, 2015). This highlights the need to study the effect of academic qualifications on TPACK dimensions among mathematics teachers. Accordingly, the following hypothesis is proposed:

H₀₂: 

There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on academic qualification.

Furthermore, studies differ on whether teaching experience influences the availability of TPACK dimensions. Some studies reported no significant differences based on teaching experience (Alodail, 2024; Al-Otaibi, 2022; Alshammari, 2020; Mailizar et al., 2021; Ozudogru & Ozudogru, 2019), while others noted differences favoring teachers with more years of experience (Hill & Uribe-Florez, 2020). This calls for examining how teaching experience affects TPACK dimensions in primary school mathematics teachers. Accordingly, the following hypothesis is proposed:

H₀₃: 

There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on teaching experience.

Finally, the current study will also examine the effect of the type of education sector (public or private) on the availability of TPACK dimensions among elementary mathematics teachers. Accordingly, the following hypothesis is proposed:

H₀₄: 

There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on the type of education sector.

2. Materials and Methods

The research used a descriptive survey approach, which is well-suited for the current research. This method relies on describing the studied phenomenon as it exists in reality and quantifying it (Obeidat et al., 2021). Using a questionnaire, the study described the level of availability of TPACK framework dimensions among elementary mathematics teachers in Saudi Arabia from their perspectives.

2.1. Population and Sample

The research population consisted of all elementary mathematics teachers working in the Eastern Province of Saudi Arabia during the first semester of the 2024–2025 academic year. To ensure representation across key demographic variables—gender, academic qualification, teaching experience, and education sector—we used a stratified random sampling technique.

To implement this technique properly, we first obtained data from local education departments in Dammam, Khobar, and Qatif. These data provided the total number of elementary mathematics teachers in each city, along with distributions for the four demographic variables used as stratification criteria. For example, education office records showed that approximately 70% of elementary mathematics teachers in the region were male and 30% were female. Similarly, they showed that about 60% of the teachers held graduate degrees, and 40% held undergraduate degrees. Public school teachers made up approximately 70% of the total, while private school teachers accounted for the remaining 30%. These proportions were used to define the stratum sizes in our sample.

After determining the proportions, we calculated the required number of participants in each stratum relative to the total sample size of 107. For instance, since 60% of the population had graduate degrees, 64 teachers in our sample were selected from this group. We then used simple random sampling within each stratum to select specific individuals. This process ensured that the sample reflected the population’s structure, thereby improving the accuracy and generalizability of the results.

This detailed stratification process reduced potential sampling bias and ensured that key subgroups were adequately represented in the study, enhancing the validity of comparisons based on demographic characteristics.

Table 1. Research sample distribution according to demographic factors.

Variable	Classification	N	Percentage (%)
Gender	male	77	72
	female	30	28
	total	107	100
Academic qualification	undergraduate	41	38.3
	graduate	66	61.7
	total	107	100
Teaching experience (y)	y < 5	56	52.3
	5 ≤ y ≤ 10	15	14
	10 < y	36	33.6
	total	107	100
Education sector	public	63	58.9
	private	44	41.1
	total	107	100

presents the distribution of participants based on the study’s four demographic variables: gender, academic qualification, teaching experience, and education sector.

Table 1 demonstrates that there are more male participants than female participants (male = 72%, female = 28%). The majority of participants hold graduate degrees (N = 66). Most participants have less than five years of teaching experience (N = 56), and the majority teach in public schools (N = 63).

2.2. Research Instrument

The researchers created the first draft of a survey to gauge elementary school math instructors’ familiarity with the TPACK framework dimensions. This was based on educational literature, including studies by Erdogan and Sahin (2010), Alshehri (2012), Alshammari (2020), and Al-Qattan (2023).

The questionnaire consisted of two sections: (1) Demographic data: This section collected general demographic information, including gender, academic qualification, years of teaching experience, and type of educational sector. (2) Likert scale items: This section included a five-point Likert scale (very high, high, medium, low, very low). Participants rated the level of availability of TPACK framework dimensions, which were categorized into four dimensions: TCK contains 5 items, TPK contains 12 items, PCK contains 10 items, and TPACK contains 5 items. The maximum possible scores for the subscale sums are determined as follows:

TCK: $5\times 5=25$ (5 items, 5-point Likert scale)

TPK: $12\times 5=60$ (12 items, 5-point Likert scale)

PCK: $10\times 5=50$ (10 items, 5-point Likert scale)

TPACK: $5\times 5=25$ (5 items, 5-point Likert scale)

To ensure the linguistic clarity and reliability of the questionnaire, it was tested on a pilot sample of 28 teachers in the Eastern Province who were not part of the main study sample. This process included reviewing language clarity and statement precision and calculating the validity and reliability of the questionnaire.

2.2.1. Validity

To ensure the validity of the survey instrument, we employed multiple validation methods. Face validity was initially established through expert review, examined content validity, and studied construct validity.

1. Face Validity: Before distributing the questionnaire to the pilot sample, it was reviewed by 10 experts specializing in mathematics education at Saudi universities. The experts evaluated the relevance, clarity, and linguistic accuracy of the items, as well as the overall alignment of the questionnaire with the study objectives. Based on their feedback, items were modified or removed as necessary, resulting in a final version of 32 items.

Table 2. The level of availability and the response range.

Levels	Range
Very high	5.00–4.20
High	4.19–3.40
Moderate	3.39–2.60
Low	2.59–1.80
Very low	1.79–1.00

presents the mean scores for the availability of the TPACK framework among teachers. The following scale is used to interpret the scores: 5.00–4.20 shows very high availability, 4.19–3.40 shows high availability, 3.39–2.60 shows moderate availability, 2.59–1.80 shows low availability, and 1.79–1.00 shows very low availability. These levels indicate the extent of TPACK framework availability among elementary school teachers.

To address interpretive ambiguity, we followed a response range categorization model used in prior TPACK surveys (e.g., Alshehri, 2012), defining five bands: Very High (4.20–5.00), High (3.40–4.19), Moderate (2.60–3.39), Low (1.80–2.59), and Very Low (1.00–1.79) (see Table 2). These bands correspond to the Likert scale anchors and allow us to classify the mean scores into conceptually meaningful categories of perceived availability. For example, a mean of 3.70 represents a “High” level of availability, meaning respondents generally agree that this knowledge domain is present in their teaching practice.

It is important to note that “availability” in this context does not imply objective measurement of competence but rather the extent to which teachers believe each dimension is integrated into their instructional work. Therefore, we caution against overinterpreting minor numerical differences and emphasize the interpretive value of categorical thresholds as a framework for understanding perceived TPACK integration.

2. Content Validity: Content validity was assessed by a panel of three experts specializing in mathematics education and TPACK. The panel reviewed whether the survey comprehensively captured all dimensions of the TPACK framework. Additionally, given that the study participants were elementary teachers (generalists rather than subject specialists), we ensured that the panel included experts with elementary-level teaching experience. This consideration strengthened the relevance of the instrument for the target population.

3. Construct Validity: To ensure the construct validity of the questionnaire used in this study, principal component analysis (PCA) was conducted. Given the theoretical overlap between dimensions such as TCK, TPK, PCK, and TPACK, Promax rotation (an oblique rotation method) was employed instead of Varimax. This decision was based on the assumption that these components are interrelated, as established in the TPACK literature.

Exploratory factor analysis (EFA) was also conducted to assess the factor structure of the instrument. The Kaiser-Meyer-Olkin (KMO) value was 0.812, and Bartlett’s test of sphericity was significant (χ² = 1256.734, p < 0.001), indicating that the data were suitable for factor analysis.

The Promax-rotated solution yielded a clear and interpretable factor structure that aligned with the TPACK framework. The items loaded onto distinct but correlated factors corresponding to the primary TPACK dimensions. This confirmed the theoretical expectation that knowledge domains in TPACK are not orthogonal but intersect and influence each other.

The extracted factors accounted for a substantial proportion of the variance, and the pattern matrix showed strong and interpretable item loadings. These findings reinforce the construct validity of the instrument and its appropriateness for assessing the perceived availability of TPACK dimensions among elementary mathematics teachers.

Table 3. Pattern Matrix for TPACK Questionnaire Items (Promax Rotation).

Item No.	TPACK Dimension	Factor Loading
1	TCK	0.78
2	TCK	0.75
3	TCK	0.83
4	TCK	0.70
5	TCK	0.66
6	TPK	0.86
7	TPK	0.74
8	TPK	0.61
9	TPK	0.78
10	TPK	0.81
11	TPK	0.76
12	TPK	0.72
13	TPK	0.77
14	TPK	0.80
15	TPK	0.89
16	TPK	0.87
17	TPK	0.85
18	PCK	0.81
19	PCK	0.88
20	PCK	0.85
21	PCK	0.83
22	PCK	0.82
23	PCK	0.74
24	PCK	0.76
25	PCK	0.84
26	PCK	0.75
27	PCK	0.80
28	TPACK	0.71
29	TPACK	0.959
30	TPACK	0.85
31	TPACK	0.82
32	TPACK	0.60

presents the pattern matrix from the Promax-rotated exploratory factor analysis. The factor loadings reflect strong associations between each item and its designated dimension. The absence of high cross-loadings supports the discriminant validity of the constructs, while the use of oblique rotation acknowledges the theoretical interdependence of the TPACK components.

2.2.2. Reliability

The reliability of the questionnaire was assessed using two methods:

1. Cronbach’s Alpha method: Cronbach’s alpha coefficient was used to measure the reliability of each dimension and the overall questionnaire.

Table 4. Reliability using Cronbach’s Alpha.

Dimensions	# Items	Coefficient
TCK	5	0.908
TPK	12	0.971
PCK	10	0.977
TPACK	5	0.818
Whole Total	32	0.988

presents the Cronbach’s Alpha coefficients for each dimension and the total questionnaire.

Table 4 shows that the reliability coefficients for the questionnaire dimensions ranged between 0.818 and 0.977, with an overall reliability coefficient of 0.988 for the total scale. These high reliability values indicate strong confidence in the results obtained when applying the questionnaire to measure the availability of TPACK framework dimensions among elementary mathematics teachers. While the high Cronbach’s alpha values (ranging from 0.818 to 0.977) indicate strong internal consistency, values above 0.95—particularly those observed in the TPK (α = 0.971) and PCK (α = 0.977) dimensions—may also suggest item redundancy (Tavakol & Dennick, 2011). This level of consistency could imply that certain items may be too similar or overlapping in content, potentially reducing the conceptual breadth of the constructs measured.

To examine this possibility, we conducted item-total correlation analysis and exploratory factor analysis with Promax rotation. The results confirmed strong and distinct loadings of items onto their respective dimensions, with minimal cross-loading (see Table 3). Inter-item correlations within each subscale were reviewed and found to range between 0.689 and 0.959, suggesting a high degree of coherence without clear evidence of redundancy across most items.

2. Split-half method: The split-half method was also used to calculate reliability through Spearman–Brown and Guttman coefficients.

Table 5. Reliability coefficients for the questionnaire dimensions using the split-half method.

Dimensions	# Items	Spearman–Brown	Guttman Split Half
TCK	5	0.872	0.867
TPK	12	0.957	0.957
PCK	10	0.954	0.954
TPACK	5	0.954	0.807
Whole Total	32	0.971	0.970

presents the reliability coefficients for each dimension and the overall questionnaire using the split-half method.

Table 5 indicates that the reliability coefficients for each dimension of the questionnaire ranged between 0.872 and 0.957 using the Spearman–Brown coefficient and between 0.867 and 0.957 using the Guttman Split Half coefficient. We applied random splitting as well as an odd-even method to divide the items, ensuring that different forms of item separation did not significantly alter reliability estimates. These high reliability values enhance confidence in the results obtained when the questionnaire is applied to measure the availability of TPACK framework dimensions among elementary mathematics teachers. Thus, the validity and reliability of the questionnaire have been confirmed, ensuring its suitability for use with the main study sample.

2.2.3. Testing the Discrimination Ability of Questionnaire Items

The correlation coefficients between each item’s score and the overall score of its associated dimension were computed in order to assess the questionnaire items’ capacity for discriminating. Additionally, correlations were calculated between the total score of each dimension and both the other dimensions and the overall questionnaire score. The correlation coefficients between the scores of each item and the overall score of its associated dimension are shown in

Table 6. Coefficients of correlation between the scores of each item and the overall score.

TCK		TPK		PCK		TPACK
Items	Coefficient	Items	Coefficient	Items	Coefficient	Items	Coefficient
1	0.813 **	6	0.903 **	18	0.841 **	28	0.771 **
2	0.744 **	7	0.837 **	19	0.957 **	29	0.959 **
3	0.870 **	8	0.913 **	20	0.955 **	30	0.931 **
4	0.759 **	9	0.874 **	21	0.922 **	31	0.689 **
5	0.720 **	10	0.875 **	22	0.941 **	32	0.852 **
		11	0.824 **	23	0.862 **
		12	0.808 **	24	0.824 **
		13	0.806 **	25	0.923 **
		14	0.838 **	26	0.854 **
		15	0.944 **	27	0.893 **
		16	0.931 **
		17	0.914 **
Domain	0.892 **	Domain	0.992 **	Domain	0.975 **	Domain	0.973 **

** p < 0.01.

.

Table 6 presents the correlation coefficients between each item and the total score of its associated dimension. All correlations are statistically significant at the 0.01 level, ranging from 0.689 to 0.959. This confirms that each item is strongly related to its respective dimension and supports the internal discrimination ability of the scale. The strongest correlations are observed in the PCK and TPK domains, with multiple items exceeding 0.90, suggesting strong conceptual cohesion within these subscales. The relatively lower—but still robust—correlations in TCK and TPACK indicate a more moderate but valid relationship to the latent construct.

Table 7. Correlation coefficients between the total score of each dimension, other dimensions, and the overall questionnaire score.

Dimensions	TCK	TPK	PCK	TPACK	Whole Total
TCK	-
TPK	0.880 **	-
PCK	0.812 **	0.948 **	-
TPACK	0.814 **	0.966 **	0.946 **	-
Whole Total	0.892 **	0.992 **	0.975 **	0.973 **	-

** p < 0.01.

presents the correlation coefficients between the total scores of each dimension, the other dimensions, and the overall questionnaire score.

Table 7 displays the correlation coefficients between the total scores of each dimension and the overall questionnaire score. All coefficients are also significant at the 0.01 level and range between 0.812 and 0.992. The strongest inter-dimensional correlation is between TPK and the total TPACK score (r = 0.992), reinforcing its central role in technology integration across pedagogical contexts. These results confirm strong internal coherence across the instrument and support its factorial structure. Moreover, the correlation pattern aligns with theoretical expectations that the TPACK dimensions are interrelated rather than orthogonal.

In addition, Table 6 demonstrates strong correlations among the various TPACK components. Similarly, Table 7 provides an overview of the correlation between the total TPACK score and its subcomponents, particularly between TPK and PCK (r = 0.948, p < 0.01), indicating that teachers with strong pedagogical content knowledge tend to exhibit higher technological pedagogical knowledge. This suggests that training efforts should emphasize reinforcing these interconnections to enhance overall teaching effectiveness. In Table 6, the highest correlation is observed between TPK and the overall TPACK framework (r = 0.992, p < 0.01), reinforcing the idea that effective technology integration is heavily dependent on pedagogical strategies. These findings suggest that professional development should prioritize pedagogically sound technological applications to maximize their impact on teaching outcomes.

It is worth mentioning that this study assesses elementary teachers’ perceptions of their TPACK knowledge rather than their actual proficiency. A self-report survey was used to capture teachers’ subjective evaluations of their knowledge and integration of technology in teaching. While self-reported data provide valuable insights, they are inherently limited in assessing actual competency. This limitation introduces a potential validity threat, as teachers may overestimate or underestimate their actual TPACK levels. This study must be interpreted as an examination of how teachers perceive their TPACK rather than an objective measurement of their actual knowledge.

3. Results and Discussion

To address the first research question: “To what extent do the dimensions of the TPACK framework exist among elementary mathematics teachers from their perspective?”, the mean, standard deviations, and levels of availability for the TPACK framework dimensions among elementary mathematics teachers were calculated (see

Table 8. Descriptive statistics for the availability of TPACK framework dimensions among participants.

Dimensions	Rank	M	SD	Availability Percentage 1	Availability Level
TCK	4	3.41	4.043	68.2%	High
TPK	2	3.79	9.727	75.8%	High
PCK	1	3.91	8.597	78.1%	High
TPACK	3	3.68	3.897	73.7%	High
Whole Total		3.70	24.67	74%	High

¹ “Availability Percentage” refers to the percentage representation of the mean score for each TPACK dimension relative to the maximum possible score on the Likert scale. Since each item was rated on a 5-point scale (1 = very low, 5 = very high), the percentage was calculated by dividing the observed mean by 5 and multiplying by 100.

).

Table 8 shows that the overall availability level of the TPACK framework among participants was high (M = 3.70, SD = 24.67, Availability Percentage = 74.0%). The results indicate that the mean scores for the availability of TPACK dimensions among elementary mathematics teachers ranged from 3.41 to 3.91, with availability percentages between 68.2% and 78.1%. The highest availability level was for the Pedagogical Content Knowledge (PCK) dimension (M = 3.91, SD = 8.597, Availability Percentage = 78.1%), followed by the TPK dimension (M = 3.79, SD = 9.727, Availability Percentage = 75.8%), the TPACK dimension (M = 3.68, SD = 3.897, Availability Percentage = 73.7%), and the TCK dimension (M = 3.41, SD = 4.043, Availability Percentage = 68.2%). The results indicate a high overall availability of TPACK dimensions among elementary mathematics teachers in Saudi Arabia, with PCK scoring the highest and TCK the lowest. This reflects a consistent pattern in the literature where pedagogical and content knowledge are often more developed than technological integration within content-specific domains (Canbazoglu Bilici et al., 2016; Kirikçilar & Yildiz, 2018). However, while our findings align with several regional studies (Al-Otaibi, 2022; Alodail, 2024), contrasting results have been reported internationally. For example, Mailizar et al. (2021) found relatively balanced TPACK dimensions in Indonesian mathematics teachers, which may reflect different national training priorities or availability of digital tools.

Our results indicate that elementary mathematics teachers perceive their TPACK levels to be high across multiple dimensions. However, given the inherent limitations of self-reported data, these findings should be interpreted with caution. The idea that teachers may not be fully aware of their own limitations raises the prospect that perceived TPACK levels and real classroom competencies are not entirely compatible.

This finding has important implications for professional development (PD) programs. Teachers may be less receptive to typical PD approaches that presume a skills shortage if they already feel their TPACK is strong. Instead, PD providers should design interventions that encourage self-reflection and experiential learning, allowing teachers to critically assess their actual competencies in real teaching scenarios. Structured workshops incorporating classroom simulations, peer observations, and video analysis could help bridge the gap between perceived and actual TPACK levels.

Even though our results indicate that elementary mathematics teachers report a high level of TPACK dimensions, a key insight from Niess et al.’s (2009) developmental model is that technology integration in the classroom does not directly equate to the presence of TPACK. Instead, teachers may operate at varying levels of TPACK proficiency, with different integration strategies reflecting distinct developmental stages. The strong presence of PCK (78.1%) suggests that teachers possess the pedagogical expertise to effectively teach mathematical content. However, the relatively lower levels of TCK (68.2%) indicate challenges in integrating technology within content-specific contexts. This aligns with prior research emphasizing that technology integration in mathematics requires targeted training and ongoing professional development (Canbazoglu Bilici et al., 2016).

These findings of the current study align with Alshehri (2012), which reported a high level of TPACK knowledge among mathematics teachers for grades 7–12 but differ in the ranking of knowledge dimensions. In Alshehri’s study, the highest was PCK, followed by TCK, and then TPK.

The current study also aligns with that of Shaqour and Alsaadi (2015), which found high levels of TPK knowledge but differs in the availability of the TPACK dimension, which was reported as moderate. It similarly aligns with Alghimlas (2022) regarding the high availability of TCK but differs in the TPACK dimension, which Alghimlas reported as moderate.

The results agree with Alshammari (2020), which showed high levels of TPACK among mathematics specialists at Hafr Al-Batin University. However, they differ in the knowledge ranking, where PCK was the highest, followed by TCK and then TPK.

The results are in line with those of Al-Otaibi (2022), who found that high school instructors had a high degree of TPACK availability. They also concur with Alodail (2024), who found that teachers were significantly prepared to use technology in the classroom using the TPACK framework.

Lastly, the findings align with Al-Omair and Alshahrani (2023), which reported a high level of TPACK dimensions among high school mathematics teachers, except for TPK, which was moderate. However, they differ in the ranking of dimensions, where PCK was the highest, followed by TPK.

Overall, these results can be summarized as follows: Among the four dimensions examined, Technological Content Knowledge (TCK) received the lowest mean score (M = 3.41, Availability Percentage = 68.2%), while Pedagogical Content Knowledge (PCK) was the highest. Although all dimensions were rated at a ‘high’ level, the relative position of TCK suggests that elementary mathematics teachers feel less confident or less experienced in integrating specific technological tools with mathematical content compared to their pedagogical or general technological skills. This interpretation aligns with previous findings by Canbazoglu Bilici et al. (2016) and Kirikçilar and Yildiz (2018), who reported that teachers often struggle to meaningfully connect technology with subject content in ways that enhance learning. Therefore, our results support the view that TCK may represent a persistent area of difficulty. While this does not imply a deficiency in absolute terms, it indicates a relative gap that could benefit from focused professional development designed to strengthen content-specific technology integration.

To address the second research question, “Are there statistically significant differences at the level of 0.05 in the average responses of elementary mathematics teachers regarding the extent of TPACK framework dimensions due to demographic variables (gender, academic qualification, teaching experience, and education sector)?”, the research hypotheses were tested.

The first hypothesis states, “There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on gender.”

Table 9. Results of the t-test based on gender.

Dimensions	Gender	N	M	SD	Std. Error	df	t	p
TCK	male	77	17.06	4.396	0.501	105	0.074	0.941
	female	30	17.00	3.017	0.551
TPK	male	77	45.10	11.084	1.263	105	0.681	0.497
	female	30	46.53	4.725	0.863
PCK	male	77	38.30	9.340	1.064	105	1.47	0.145
	female	30	41.00	6.006	1.096
TPACK	male	77	18.35	4.233	0.482	105	0.296	0.768
	female	30	18.60	2.920	0.533
Whole	male	77	118.82	27.659	3.152	105	0.811	0.419
	female	30	123.13	14.330	2.616

presents the results of the independent sample t-test conducted to examine differences between the mean scores of participants based on gender.

Table 9 shows no statistically significant differences at the 0.05 level between the mean responses of participants regarding the extent of the TPACK framework due to gender (male and female) (t = 0.811, p = 0.419).

The results also indicate no significant differences between male and female responses concerning the extent of TPACK dimensions among elementary mathematics teachers based on gender ($t_{TCK}$ = 0.074, $P_{TCK}$ = 0.941; $t_{TPK}$ = 0.681, $P_{TPK}$ = 0.497; $t_{PCK}$ = 1.47, $P_{PCK}$ = 0.145; $t_{TPACK}$ = 0.296, $P_{TPACK}$ = 0.768). This may be explained by the equal degrees of training and proficiency that male and female educators have in incorporating technology into mathematics classes.

These findings align with previous studies, including Shaqour and Alsaadi (2015), Alshammari (2020), Mailizar et al. (2021), Mehawed (2021), Al-Otaibi (2022), and Alodail (2024), which reported no significant differences in the availability of TPACK dimensions among mathematics teachers based on gender.

However, the results contrast with studies such as Erdogan and Sahin (2010), Ozudogru and Ozudogru (2019), and Alghimlas (2022), which found statistically significant differences in TPACK dimensions favoring male teachers. Additionally, the findings differ from Al-Qattan (2023), which revealed that female teachers exhibited a higher level of availability in TPACK dimensions. These inconsistencies may stem from cultural, institutional, or methodological differences, indicating that gender effects on TPACK are context-dependent and should not be generalized.

Although no statistically significant differences were found between male and female teachers across TPACK dimensions, the standard deviations (SDs) for the female group were consistently smaller for some dimensions (e.g., TCK and TPACK) and slightly larger for others (e.g., PCK and TPK). This suggests more variability in male teachers’ self-assessments on certain dimensions, while female teachers’ responses appear more concentrated. These differences in variability may point to more homogeneous experiences or perceptions among female teachers, though further qualitative or observational data would be needed to confirm this.

To test the validity of the second hypothesis, which states, “There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on academic qualification,” an independent samples t-test was conducted to examine the differences in mean scores among participants based on their academic qualifications (see

Table 10. Result from the t-tests for the research sample based on the variable of academic qualification.

Dimensions	Gender	N	M	SD	Std. Error	df	t	p
TCK	undergraduate	41	15.00	4.330	0.676	105	4.484	0.000 **
	graduate	66	18.32	3.292	0.405
TPK	undergraduate	41	40.20	11.487	1.794	105	4.912	0.000 **
	graduate	66	48.80	6.652	0.819
PCK	undergraduate	41	33.22	8.816	1.377	105	6.534	0.000 **
	graduate	66	42.68	6.152	0.757
TPACK	undergraduate	41	16.90	4.603	0.719	105	3.323	0.000 **
	graduate	66	19.36	3.062	0.377
Whole	undergraduate	41	105.32	28.203	4.405	105	5.490	0.000 **
	graduate	66	129.17	16.779	2.065

** p < 0.01.

).

Table 10 shows a statistically significant difference at the 0.05 level between the mean responses of participants regarding the extent of TPACK framework attributed to the academic qualification variable (undergraduate, graduate). The difference favors teachers holding graduate degrees (t = 5.490, p = 0.000).

The results also indicate statistically significant differences in participants’ responses about the availability of TPACK dimensions, favoring those with graduate qualifications ($t_{TCK}$ = 4.484, $P_{TCK}$ = 0.000; $t_{TPK}$ = 4.912, $P_{TPK}$ = 0.000; $t_{PCK}$ = 6.534, $P_{PCK}$ = 0.000; $t_{TPACK}$ = 3.323, $P_{TPACK}$ = 0.000). This may be attributed to the fact that obtaining higher academic qualifications enhances teachers’ cognitive and technological competencies, thereby increasing the extent of their integration of content knowledge, teaching skills, and technology.

This finding aligns with previous studies, including those by Shaqour and Alsaadi (2015), Mailizar et al. (2021), and Alghimlas (2022), which also reported statistically significant differences in the availability of TPACK dimensions based on academic qualifications, favoring those with postgraduate degrees in the field. However, not all international studies confirm this; for example, Ozudogru and Ozudogru (2019) found no significant differences by qualification. This discrepancy points to the need to consider how teacher education programs structure technological-pedagogical integration rather than assuming graduate education alone leads to higher TPACK.

While the t-tests revealed significant differences favoring teachers with graduate qualifications, it is worth noting that the SDs for undergraduate teachers were notably higher in all dimensions. This may indicate greater variability in preparation and confidence among this group, possibly reflecting inconsistencies in undergraduate training programs. The lower SDs among graduate-qualified teachers suggest more consistent experiences or perceptions, potentially due to more uniform exposure to technology-focused coursework.

To test the validity of the third hypothesis, which states: “There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on teaching experience,” a one-way ANOVA was conducted to examine the differences between the mean responses of participants based on teaching experience.

Table 11. Means and standard deviations of participants based on the variable of teaching experience.

Dimensions	Teaching Experience (y)	N	M	SD	Std. Error
TCK	y < 5	56	15.00	3.991	0.533
	5 ≤ y ≤ 10	15	18.40	1.404	0.363
	10 < y	36	19.67	3.024	0.504
	total	107	17.05	4.043	0.391
TPK	y < 5	56	40.50	10.274	1.373
	5 ≤ y ≤ 10	15	48.60	3.979	1.027
	10 < y	36	52.00	5.171	0.862
	total	107	45.50	9.727	0.940
PCK	y < 5	56	34.27	8.525	1.139
	5 ≤ y ≤ 10	15	44.67	6.966	1.799
	10 < y	36	44.17	3.629	0.605
	total	107	39.06	8.597	0.831
TPACK	y < 5	56	17.04	4.272	0.571
	5 ≤ y ≤ 10	15	19.00	3.402	0.878
	10 < y	36	20.33	2.390	0.398
	total	107	18.42	3.897	0.377
Whole Total	y < 5	56	106.80	25.676	3.431
	5 ≤ y ≤ 10	15	130.67	14.226	3.673
	10 < y	36	136.17	11.390	1.898
	total	107	120.03	24.668	2.385

presents the means and standard deviations of participants’ responses according to their years of teaching experience.

Table 11 indicates that the differences in the mean responses of participants based on the variable of teaching experience are relatively close regarding the extent of TPACK framework dimensions among elementary mathematics teachers. To determine whether these differences are statistically significant at the 0.05 level, a one-way ANOVA was conducted to calculate the F-value between these groups (refer to

Table 12. One-way ANOVA results based on the variable of teaching experience.

Dimensions	Variance	Sums of Squares	df	Mean Squares	F	p
TCK	between-groups	509.166	2	254.583	21.638	0.000 **
	within-groups	1223.600	104	11.765
	total	1732.766	106
TPK	between-groups	3065.148	2	1532.574	22.889	0.000 **
	within-groups	6963.600	104	66.958
	total	10,028.75	106
PCK	between-groups	2696.348	2	1348.174	27.292	0.000 **
	within-groups	5137.315	104	49.397
	total	7833.664	106
TPACK	between-groups	244.146	2	122.073	9.294	0.000 **
	within-groups	1365.929	104	13.134
	total	1610.075	106
Whole	between-groups	20,867.74	2	10,433.87	24.869	0.000 **
	within-groups	43,633.17	104	419.550
	total	64,500.95	106

** p < 0.01.

).

Table 12 confirms the presence of statistically significant differences at the 0.05 level in the level of TPACK framework dimensions based on the teaching experience variable, favoring those with greater years of experience (F = 24.869; p = 0.000). The ANOVA results showed significant group differences, but the descriptive statistics further reveal that variability in responses decreases with increased teaching experience. For example, the most experienced group (more than 10 years) had smaller SDs in key dimensions like TPK and PCK. This pattern supports the interpretation that experience contributes not only to higher self-reported competencies but also to more consistent perceptions of technological integration.

To clarify the significance of the differences in responses among elementary mathematics teachers regarding the level of TPACK dimensions, the Scheffe test was conducted to compare the categories of the research sample based on teaching experience across the questionnaire as a whole (refer to

Table 13. Inter-correlations for statistics anxiety and predictor variables.

Teaching Experience (y)	M	Mean Difference (I-J)
		y < 5	5 ≤ y ≤ 10	10 < y
y < 5	106.80	-
5 ≤ y ≤ 10	130.67	23.863 *	-
10 < y	136.17	29.363 *	5.500 *	-

* p < 0.05.

).

Table 13 indicates statistically significant differences among categories of teaching experience regarding the availability of TPACK framework dimensions among elementary mathematics teachers. The findings suggest that as teaching experience increases, the availability of TPACK dimensions also improves. Significant differences were identified among the groups:

• Teachers with less than 5 years of experience and those with 5 to 10 years of experience (Mean Difference = 23.863), favoring the latter group.
• Teachers with less than 5 years of experience and those with more than 10 years of experience (Mean Difference = 29.363), favoring the more experienced group.
• Teachers with 5 to 10 years of experience and those with more than 10 years of experience (Mean Difference = 5.500), favoring the group with greater experience.

This trend may be attributed to the increase in teachers’ subject-matter knowledge of mathematics as they gain more years of experience. Additionally, the modern educational environment necessitates the use of technology in teaching. With more experience, teachers are likely to enhance their scientific and technological competencies, thereby increasing the extent to which TPACK dimensions are applied in their teaching practices. Also, the analysis of teaching experience in Table 12 shows that more experienced teachers tend to exhibit significantly higher TPACK scores, particularly in PCK and TPK. This finding aligns with previous research emphasizing the role of experience in developing expertise in technology integration (Hill & Uribe-Florez, 2020).

The findings of the current study align with those of Hill and Uribe-Florez (2020), which indicate statistically significant differences in responses regarding the extent of TPACK framework dimensions based on teaching experience, favoring teachers with greater teaching experience. However, these results differ from the findings of Shaqour and Alsaadi (2015), Ozudogru and Ozudogru (2019), Alshammari (2020), Mailizar et al. (2021), Al-Otaibi (2022), and Alodail (2024), which found no significant differences attributable to teaching experience.

On the other hand, by incorporating Niess et al.’s (2009) levels of TPACK development into our analysis, we can better interpret these findings. Teachers with more years of experience and higher academic qualifications exhibit significantly higher levels of perceived TPACK, suggesting that professional development and academic exposure contribute to their confidence in integrating technology. However, the results also highlight the need for structured interventions to move teachers from fragmented technology use toward integrated and adaptive technology-supported instruction, as outlined in Lyublinskaya and Kaplon-Schilis’ (2022) rubric.

To test the fourth hypothesis, which states, “There is no statistically significant difference at the 0.05 level in the mean responses of elementary mathematics teachers on the TPACK dimensions questionnaire based on the type of education sector,” a t-test was conducted to examine differences in the mean scores of participants according to the type of education sector (see

Table 14. Result of the t-tests based on the type of education sector variable.

Dimensions	Education Sector	N	M	SD	Std. Error	df	t	p
TCK	public	63	17.54	4.589	0.578	105	1.518	0.132
	private	44	16.34	3.011	0.454
TPK	public	63	45.70	11.660	1.469	105	0.245	0.807
	private	44	45.23	6.088	0.918
PCK	public	63	38.86	9.576	1.206	105	0.285	0.776
	private	44	39.34	7.057	1.064
TPACK	public	63	17.94	4.351	0.548	105	1.547	0.125
	private	44	19.11	3.052	0.460
Whole	public	63	120.03	28.923	3.644	105	0.002	0.999
	private	44	120.02	17.143	2.584

).

Table 14 shows no statistically significant difference at the 0.05 level between the mean responses of participants regarding the extent of TPACK framework among elementary mathematics teachers in the Eastern Province of Saudi Arabia based on the type of education sector (public or private) (t = 0.002, p = 0.999).

The results also indicate no statistically significant differences in responses regarding the availability of TPACK framework dimensions among elementary mathematics teachers attributable to the type of education sector ($t_{TCK}$ = 1.518, $P_{TCK}$ = 0.132; $t_{TPK}$ = 0.245, $P_{TPK}$ = 0.807; $t_{PCK}$ = 0.285, $P_{PCK}$ = 0.776; $t_{TPACK}$ = 1.547, $P_{TPACK}$ = 0.125). This may be due to current conditions that have made integrating technology into teaching an essential element rather than a luxury in education. Consequently, both public and private school mathematics teachers exhibit similar proficiency in using technology for teaching.

The absence of significant differences by school sector (public vs. private) suggests that Saudi Arabia’s policies on digital integration may be creating uniformity in teacher training and access to technology. In contrast, studies from countries with uneven resource distribution (e.g., Srisawasdi, 2014 in Thailand) have found sector-based disparities in TPACK availability. This reinforces the importance of national policy context in shaping technology use in schools.

Although no statistically significant differences were found between public and private school teachers, the SDs for the public sector group were higher across most dimensions. This may reflect more diverse conditions in public schools regarding access to technology, training, or institutional support, which could influence how teachers perceive their competencies. The relatively lower variability in the private sector group suggests more uniform working conditions or professional development offerings.

It is worth noting that the results of this study offer meaningful insights into the availability of the TPACK framework dimensions among elementary mathematics teachers and provide important implications for both theory and practice. In light of the literature reviewed, this study contributes to the existing body of knowledge in several key ways.

The study confirms previous findings that teachers with higher academic qualifications and more teaching experience demonstrate greater TPACK competencies (Shaqour & Alsaadi, 2015; Mailizar et al., 2021). This pattern underscores the importance of advanced education and professional development in equipping teachers to integrate technology effectively. Furthermore, the absence of significant differences in TPACK dimensions based on gender and education sector aligns with findings from studies such as Al-Otaibi (2022) and Alodail (2024). This suggests a degree of uniformity in TPACK competencies across these demographics, challenging earlier work that reported gender disparities (Erdogan & Sahin, 2010; Alghimlas, 2022).

However, the study diverges from some research by revealing statistically significant differences in TPACK dimensions based on academic qualifications, which were not observed in studies such as Ozudogru and Ozudogru (2019). These discrepancies suggest that contextual factors, such as the structure of professional development programs and educational policies in Saudi Arabia, may influence the relationship between qualifications and TPACK dimensions.

4. Conclusions

This study explored the extent to which the dimensions of the TPACK framework are present among elementary mathematics teachers in Saudi Arabia and examined the influence of demographic variables such as gender, academic qualification, teaching experience, and education sector. The findings revealed a high overall availability of TPACK dimensions, with Pedagogical Content Knowledge (PCK) being the most prevalent and Technological Content Knowledge (TCK) the least. Significant differences were found based on academic qualifications and teaching experience, favoring teachers with higher qualifications and more years of experience, while no differences were observed based on gender or education sector. This study extends the existing literature by providing a nuanced understanding of TPACK dimensions among elementary mathematics teachers in Saudi Arabia. It confirms the high prevalence of PCK and TPK dimensions while highlighting gaps in TCK. These findings have significant implications for teacher training programs, suggesting a need for targeted strategies to enhance teachers’ technological competencies in content-specific contexts. Schools and educational institutions should offer professional development workshops that emphasize active learning, collaborative lesson planning, and real-world applications of TPACK principles. Mentorship programs and professional learning communities can support peer learning and knowledge exchange, enabling less experienced educators to benefit from the expertise of those with high TPACK competency. Teacher training programs should embed TPACK-focused courses, requiring pre-service teachers to demonstrate competency in designing and implementing technology-enhanced lesson plans. Schools must also provide access to modern digital tools, platforms, and training materials, along with ongoing technical support to build teachers’ confidence in integrating technology. By building on these insights, future research can explore the effectiveness of tailored professional development initiatives in addressing these gaps and further advancing the integration of technology in education.

5. Implications and Contributions

One of the novel contributions of this study is its focus on elementary mathematics teachers, a group that has received limited attention in prior research. By identifying the high availability of PCK and TPK dimensions, this study highlights strengths in teachers’ pedagogical knowledge and the integration of technology in teaching practices. These strengths can serve as a foundation for further development, particularly in enhancing TCK competencies. Addressing this gap is critical to enabling teachers to leverage technology effectively to simplify complex mathematical concepts and foster student engagement.

This study’s findings highlight the high availability of TPACK competencies among elementary mathematics teachers in Saudi Arabia. However, variations exist across different dimensions, particularly in the integration of technological content knowledge (TCK). These results underscore the need for targeted professional development programs that not only equip teachers with digital tools but also train them in their effective pedagogical application. By comparing these insights with international studies, future research can identify best practices and strategies to further enhance teacher preparation and technology integration in diverse educational settings.

The identification of TCK as the lowest-rated dimension—even in perceived terms—signals that teachers feel less confident in integrating technology with mathematical content. While these are self-reported perceptions, they highlight a recognized need among teachers themselves, which is a valid and actionable starting point for PD design. Our proposed hands-on workshops and collaborative planning sessions are not generic recommendations but directly address this self-identified gap. These formats allow teachers to engage with content-specific tools (e.g., dynamic geometry software, data visualization platforms) in context, providing targeted practice in connecting technology to mathematical concepts.

The findings also underscore the importance of tailoring professional development programs to address specific deficiencies in TPACK dimensions. Programs should emphasize practical strategies for integrating technology into content areas, drawing on successful global practices. For example, reflective discussions, peer feedback, and exposure to classroom video cases can help surface and reshape beliefs that may hinder or support meaningful TCK development. Additionally, professional development programs should incorporate structured support for advancing teachers along this developmental continuum, ensuring that technology use in mathematics instruction is both meaningful and pedagogically sound.

Also, we highlight that future professional development should be diagnostic and adaptive, beginning with structured self-assessments or belief inventories to help teachers examine both their competencies and their convictions. This layered approach is more likely to produce durable shifts in classroom practice than technical training alone.

Future studies should employ observational tools, such as the TPACK Levels Rubric (Lyublinskaya & Kaplon-Schilis, 2022), to assess how teachers operationalize their reported TPACK knowledge in the classroom.

Given Niess et al.’s (2009) emphasis on TPACK as a developmental construct, longitudinal studies tracking teachers’ progression through TPACK levels would provide deeper insights into how knowledge translates into practice.

Future refinements of the instrument should address the potential redundancy indicated by the high internal consistency scores. Although current analyses support the dimensional validity of the scale, conducting a confirmatory factor analysis and item reduction could improve its efficiency and psychometric rigor.

Finally, based on the subgroup analyses, we offer the following targeted recommendations:

i. TCK-focused professional development: Teachers with less teaching experience and undergraduate qualifications showed significantly lower scores in TCK. We recommend that professional development programs prioritize content-specific technological integration, such as workshops on mathematical modeling tools (e.g., GeoGebra and Desmos) and visual representation platforms. These should include classroom-based simulations and follow-up coaching.

ii. Scaffolded PD for early-career teachers: Given the performance gap based on years of experience, novice teachers would benefit from mentoring programs, lesson study models, and co-teaching strategies that help them observe and apply TPACK principles, particularly in TCK and TPK domains.

iii. Differentiated training based on academic background: Graduate-qualified teachers reported higher TPACK across all dimensions. PD programs should differentiate content intensity and progression based on academic level, ensuring foundational support for those with undergraduate degrees while offering advanced, integrative modules for more academically prepared teachers.

iv. Use of diagnostic tools: To personalize training, schools and education offices should consider using TPACK self-assessment inventories to identify individual teacher needs and customize PD plans accordingly.

These targeted recommendations support the development of evidence-based, differentiated professional learning pathways that align with teacher demographics and existing capacity levels.