INTRODUCTION

The Technological Pedagogical Technological Content Knowledge (TPACK) model is one of the most recognised major theoretical-methodological frameworks for the assessment of teacher competence levels. The integrated combination of its dimensions includes the effective implementation of technology during the teaching process and the teaching-learning process, especially influential in the period of the COVID-19 health crisis (Manokore & Kuntz, 2022). As can be seen in Figure 1, this model provides a coherent integrated alignment of the basic levels of digital competence in teaching, guiding decision-making and promoting digital literacy.

Despite the abundant international scientific literature available on their potential in teacher professional development from different curricular disciplines, classroom practices (Anderson & Kyzar, 2022; Kartal & Çınar, 2022; Sun et al, 2022; Tan & Chen, 2022) and teaching-learning spaces (Assis & Vieira-Santos, 2021; Ortega-Sánchez & Gómez-Trigueros, 2020), there is still a need to evaluate their effects in the field of initial teacher education (Aktaş & Özmen, 2020; Ismaeel & Aktas, 2022; Widyasari et al., 2022). In this line, its usefulness and impact on the professional development of in-service teachers through the improvement of knowledge, self-efficacy, digital competence and skills for technology integration in the classroom has been demonstrated (Chen & Cao, 2022; Oda et al., 2020).

Studies aimed at analysing its impact on the operational and effective use of technology from the specific field of teacher training are recent (Schmidt-Crawford et al., 2021), inseparable from the other components of the TPACK model (Ortiz-Colón et al., 2020), and few studies consider its impact in terms of the level of teacher training of future teachers and active teachers (Castéra et al., 2020; Cheng & Xie, 2018). Despite the usefulness of the analysis of perceived self-efficacy in the success and performance of teachers and students in the quality of teaching-learning processes, there is little research focused on prospective teachers’ beliefs and perceptions of their self-efficacy in the multidimensional framework of the TPACK model (Alpaslan et al., 2021; Diamah et al., 2022; Nazari et al., 2019; Tondeur et al., 2019; Wang et al., 2020), particularly in the social sciences (Ciriza-Mendívil et al., 2022). Similarly, “although [practising] teachers’ beliefs about TPACK influence [the] incorporation of technology in the classroom, few studies analyse them” (Liu, 2022, p. 305). Indeed, “the use of TPACK-based training to promote trainee teachers’ perceptions of TPACK remains limited” (Diamah et al., 2022, p. 3).

Technological inclusion, for pedagogical purposes, for the acquisition and development of digital competences from an integrated perspective has been one of the most significant teaching and research concerns in the field of education ( Cabero & Martínez, 2019). In this sense, the TPACK model has been proposed as an appropriate framework for teacher professional development, based on the coherent interrelation between curricular content, didactics and technology in specific contexts. Part of its complexity lies in this last element. Indeed, the “context makes the TPACK model unique, located in a time and place, idiosyncratic, adaptive, specific and different for each teacher; hence the difficulty generated by its measurement” (Jiménez & Cabero, 2021, p. 7).

The benefits of TPACK need to be complemented by specifying the factors that influence its development (Huang et al., 2020). From this perspective, the gender effect is presented as one of its fundamental components (Castéra et al., 2020). According to Long et al. (2020), divergences according to this factor could be due to “different attitudes of male and female teachers towards the use of technology and perceived self-confidence in teaching. Female teachers tend to have higher PK but lower TK than male teachers, while male teachers tend to have higher self-efficacy in using technology” (p. 5). Similarly, gender and level of teacher training in ICT were recorded as influential personal factors in Greek Primary School teachers’ perceptions of their TPACK skills (Roussinos & Jimoyiannis, 2019). The same study pointed to the relevance of the educational context.

Considering its complexity, context has recently been proposed as an eighth domain (contextual knowledge, XK) about teachers’ knowledge of technological resources and educational policies ( Mishra, 2019). However, for this dimension we propose to consider the contextual factors derived from the socio-demographic, cultural or identity characteristics of the educational environments in which the TPACK model is developed. In this line, the present research has a twofold objective: on the one hand, it seeks to validate the psychometric properties of the TPACK-ES scale in the Spanish context; and on the other hand, to analyse the perceptions of self-efficacy in the model of Primary and Secondary Education teachers in initial training. In relation to this second objective, it formulates four hypotheses, based on the suggestion of Chai et al. (2016) on the examination of the effect of teacher gender on the domains of the TPACK model, the results of Roussinos and Jimoyiannis (2019) and Castéra et al. (2020), and the study of the educational level proposed by Ibrohim et al. (2022) and Long et al. (2020). We complete these hypotheses with the analysis of the potential interaction of both variables.

1. H₁₍₁₎ The dimensional values of the TPACK model are higher in male than in female future teachers.
2. H₁₍₂₎ The dimensional values of the TPACK model are higher in females than in males future teachers.
3. H₁₍₃₎ There are statistically significant differences in the values obtained in the self-perception of efficacy in the TPACK domains and the level of education of the prospective teacher.
4. H₁₍₄₎ There is an interaction between the factors gender and university studies in the perceived self-efficacy of future teachers on the dimensions of the TPACK model.

METHOD

Participants

Participants were selected by means of purposive or convenience sampling, in accordance with the research team’s possibilities of access to the field of study and according to their degree of suitability to the objectives formulated. A total of 303 students of Primary and Compulsory Secondary Education (68% female and 32% male) with a mean age of 23.12 ± 3.21 years, enrolled in a university in the north of Spain, agreed to participate in the research. With a margin of error of 5% and a confidence level of 95%, the sample is considered significant with respect to the total population from which it is drawn (N = 1265) (Otzen & Manterola, 2017) (Table 1).

Instrument

Consistent with the TPACK model as the most widespread and frequent measurement method, both in terms of its effectiveness and cost-effectiveness, the self-report questionnaire was selected as the data collection instrument. In all cases, the development of the scales has evidenced the expected factor structure (Kadioglu-Akbulut et al., 2020; Schmidt-Crawford et al., 2020).

The applied instrument (TPACK-ES) consists of 32 items distributed in 7 dimensional blocks or TPACK domains [TK-Technological Knowledge (4 items), CK-Content Knowledge (4 items), PK-Pedagogical Knowledge (7 items), PCK-Pedagogical Content Knowledge (3 items), TCK-Technological Content Knowledge (3 items), TPACK-Technological Pedagogical Content Knowledge (9 items) and TPACK-Technological Pedagogical Content Knowledge (2 items), TCK-Technological Content Knowledge (3 items), TPK-Technological Pedagogical Content Knowledge (9 items) and TPACK-Technological Pedagogical Content Knowledge (2 items)], measured on a five-point Likert-type scale, where 1 corresponded to strongly disagree and 5 to strongly agree.

The applied instrument was adapted according to the criteria developed by Schmidt-Crawford et al. (2009, 2020), so the theoretical dimensions of the TPACK were known. In order to determine the goodness of fit, structure and stability of the instrument for the data collection of this research, tests of validity, reliability and between-group invariance were conducted by applying the methods of Confirmatory Factor Analysis (CFA), Cronbach’s alpha and McDonald’s omega, and factorial invariance analysis, respectively, from the study population.

In order to generalise the results and the final CFA model, we estimated and diagnosed the model by analysing the total data, as the “recommendation to split the sample leads to a reduction of the initial sample size, which is not irrelevant considering that most studies use smaller sample sizes than those required by the general rule” (Fernández-Hernández et al., 2022, p. 39).

Internal consistency validity

The estimation of the internal consistency reliability of the items of the measuring instrument was calculated using Cronbach’s alpha coefficient (α) and McDonald’s omega method (w ); the justification for incorporating the latter method resided in its usefulness to “try to mitigate the inadequate use of Cronbach’s alpha when its statistical assumptions are not met” (Frías-Navarro & Pascual-Soler, 2022, p. 5). Firstly, the reliability indicator was calculated for the entire scale and, secondly, for each of the proposed factors or constructs. The coefficients returned indicate their adequacy and proximity to those obtained in factor distributions that, from differential structural scales, are based on the TPACK theoretical model (Table 2).

Construct validity

In order to verify and accept the questionnaire and its factorial structure, the necessary analyses were carried out to assess its instrumental usefulness in the measurement of the competences associated with the TPACK model. Thus, based on the scientific literature and applied instruments related to self-efficacy in the theoretical and procedural dimensions of the model, we tested 7 hypotheses on latent variables (constructs), defined on the basis of 32 observed variables or indicators. Previous Exploratory Factor Analysis (EFA) guided the numerical and factorial distribution and the choice of the number of indicators for each latent variable.

Our purpose was to identify the relationship between these indicators and their constructs (factors), and between the constructs themselves, based on the resulting measures of goodness of fit. To this end, and within the framework of structural equation analysis using covariance structures, we conducted a Confirmatory Factor Analysis (CFA). The estimation method employed, consistent with the sample size, was maximum likelihood estimation, for which a sample size of around 200 observations is recommended (Bentler, 1989).

Hu and Bentler (1999) recommended that the model be fitted by paying attention to the RMSEA (Root Mean Square Error of Approximation) and SRMR (Standardised Root Mean Square Residual) indices, whose recommended values are < .08, and the CFI (Comparative Fit Index), whose optimal values are > .95 (Jöreskog & Sörbom, 1993; Schumacker & Lomax, 2015) (Figure 2).

Both the parsimony fit measure (χ²₍₄₄₃₎ = 868, p < .001, χ² / df = 1.95 [< 5]) and the overall absolute and comparative fits achieved can be considered satisfactory ( Table 3).

Discriminant validity

Empirical evidence of the existence of discriminant validity was obtained by applying the confidence interval test of the correlations (Anderson & Gerbing, 1988) between the seven factors that make up the theoretical model of Mishra and Koehler (2008). According to the correlation matrix (p < .001) and the confidence intervals obtained (Table 4) and, considering that none of them contains 1 at 95% confidence, the discriminant validity of the scale can be confirmed (Henseler et al., 2015).

Convergent validity

Finally, the convergent validity of each latent construct is analysed on the basis of the Average Variance Extracted (AVE) (Lévi & Varela, 2006). The coefficients returned show that the heptafactorial structure of the model presents sufficient evidence of convergent validity (TK [4 items]: AVE = 0.689; CK [4 items]: AVE = 0.648; PK [7 items]: AVE = 0.648; PCK [3 items]: AVE = 0.633; TCK [3 items]: AVE = 0.371; TPK [9 items]: AVE = 0.614); TPACK [2 items]: AVE = 0.547). It can be stated that more than 50% of the variance of each construct is due to its indicators and, therefore, its suitability for the empirical explanation of the latent constructs can be concluded.

Factor invariance analysis

Considering the lack of studies in Spanish teacher training contexts that have evaluated the stability of the structure of the measurement instruments based on the TPACK model among different subgroups, we analyse the factorial invariance of the TPACK-ES scale. With this analysis, we hope to verify the independence of its measurement properties from the characteristics of the groups being compared, to obtain empirical evidence of its stability, to check the absence of bias in the measurement and, therefore, to guarantee the validity of the results.

In order to find out what extent measurement invariance existed, we performed a multi-group structural equation modelling (SEM), based on the grouping variables gender and educational level. In this modelling, we included the mean structure for the three proposed models: Model 1 (configurational invariance), Model 2 (metric invariance) and Model 3 (scalar or strong invariance) (Rens van de Schoot & Hox, 2012).

The results returned (Table 5 and Table 6) indicate that the factor model can be applied to all comparison groups and that the factor loadings are equal across groups (p > .05). This circumstance makes possible the statistical comparison of structural relationships between latent variables between groups. It is also evident that the intercepts of the items are equal in the groups of interest (p > .05), which proves the usability of the scale for intergroup comparison of means. Given that the factor structure, factor loadings and intercepts can be considered invariant, the instrument meets the equivalence criteria (Abalo et al., 2006).

Design and procedure

The present research belongs to the non-experimental cross-sectional studies (Ato et al., 2013). The instrument was administered to the participant sample in a single 30-minute session during the second semester of the 2021-2022 academic year, with prior communication of the purpose of the study and the confidentiality with which the data would be treated. Likewise, their consent was requested to use the responses obtained.

The study was conducted in accordance with the guidelines of the Declaration of Helsinki (Declaration of the World Medical Association), guaranteeing the ethical-philosophical commitment and unwavering respect for human dignity, privacy, physical and moral integrity, as well as the protection of personal data in the processing of the survey and throughout the research. Privacy regulations were complied with, taking into account the personal data protection code (Organic Law 3/2018), anonymity and informed consent. The study was also reviewed and approved by the Bioethics Committee of the university responsible for the research (IR 15/2018).

Data analysis

After applying the Kolmogorov-Smirnov hypothesis test and testing the absence of the expected normality in the variables and factors that make up the construct, their skewness and kurtosis values were calculated. Since the skewness results for each variable were below 2 (S = 0.260 - 1.898, SE = 0.140 - 0.141) and the kurtosis results were below 7 (K = 0.020 - 2.433, SE = 0.279 - 0.281), it can be considered that the distribution is close to normal (Curran et al., 1996). Likewise, for the fulfilment of this assumption in the application of parametric statistics, this tendency to normality was identified in each study variable and comparison group (gender) ( S = -0.984 _ - 1.456, SE = 0.169 - 0.245; K = 0.942 - 1.012, SE = 0.377 - 0.485), and in each scale factor and comparison group (gender) (S = -0.725 - 0.230, SE = 0.169 - 0.246; K = 0.634 - 1.003, SE = 0.377 - 0.488). Having identified the absence of equality of variances (homoscedasticity) in 6 of the 32 items of the scale and in its fifth factor (TCK), the Welch’s test, Student’s t-test for independent samples, was applied.

Once the assumption of homoscedasticity and descriptive normality (skewness and kurtosis) ( S = -0.23 _ - 0.712, SE = 0.140; K = 0.159 - 0.918, SE = 0.279 - 0.280) in the group distributions of the study variable was verified, we sought to test the existence of statistically significant differences according to this variable, identify the predictor variables and evaluate the joint effect or interaction of the categorical variables gender and level of study on the TPACK dimensional model. For this purpose, single-factor ANOVAs, 2x4 factorial ANOVAs and Bonferroni post- hoc analyses were carried out to determine the specific differential levels and their potential effects. Finally, the analyses are completed with effect sizes and the calculation of statistical power.

Checking for the absence of multivariate outliers was done by calculating the Mahalanobis distance. The results returned report p-values greater than .001 (p = .21 - .90) for all scale factors. Consequently, given the absence of missing cases and/or outliers, the application of value imputation techniques was not necessary. Furthermore, in order to identify possible neglected responses, we proceeded to study the individual reliability of the items (Huang & Wang, 2021) using the Partial Least Squares Analysis Method (PLS-SEM). The results obtained show that the items that make up the scale are reliable, as the loadings of their coefficients have values above 0.7 (0.832 - 0.912). Therefore, the presence of random responses, inattentive or with a possible insufficient effort to respond (IER [Insufficient Effort Responding] or CR [Careless Responding] is not identified, a circumstance that has motivated the non-application of bias controls.

For data processing, the statistical packages SPSS v.25, JASP 0.16.4.0, G*Power v.3.1.9.7. and SmartPLS v. 3.3 were used.

RESULTS

The group results report statistically higher mean levels in men in variables related to the TK [Technological Knowledge] dimension (ability to solve technical problems with technologies, ability to learn technology and update new technologies), CK [Content Knowledge] (sufficiency in content knowledge), PK [Pedagogical Knowledge] (familiarity with common misconceptions in students), TCK [Technological Content Knowledge] (technological training for teaching) and, fundamentally, with the TPK [Technological Pedagogical Knowledge] dimension (selection of technologies for learning, selection of technological resources for teaching, selection of technologies for teaching and learning, leadership in teaching support for the use of technologies and diverse methodologies, and representation of the usefulness of technologies in the teaching task). When the hypothesis reverses its premises (Group 1 [Men] < Group 2 [Women]), however, no statistically significant increases in value are evident in any variable or factor.

Consequently, a higher self-perceived male dominance of technological knowledge and competence (M♂ = 3.83 - 4.03, SD = 0.71 - 0.95; M♀ = 3.51 - 3.74, SD = 0.84 - 0.93), and of knowledge about teaching and learning processes with technology (M♂ = 3.77 - 4.37, SD = 0.68 - 0.93; M♀ = 3.50 - 4.18, SD = 0.66 - 0.83) can be identified. However, these results record small to moderate effect sizes (d = 0.214 - 0.457) and low statistical power validity indices (1-β = .459 - .723). The mean values are close to 4, confirming an optimal degree of identification with the formulated competence statements. These levels find dimensional correspondence, consistently, with the TK factor (M♂ = 3.99, SD = 0.65; M♀ = 3.70, SD = 0.67, d = 0.439, 1-β = .673) and the TPK factor (M♂ = 3.96, SD = 0.52; M♀ = 3.83, SD = 0.48, d = 0.259, 1-β = .492) ( Table 7).

There are no significant factorial interactions between gender and education in any of the domains of the TPAK model [TK (F_{(3, 295)} = .602, p = .614), CK (F_{(3, 295)} = .602, p = .227), PK (F_{(3, 295)} = .786, p = .503), PCK (F_{(3, 293)} = .773, p = .510), TCK ( F_{(3, 293)} = 1.179, p = .318), TPK (F_{(3, 295)} = .334, p = .801) and TPACK (F_{(3, 293)} = .457, p = .712).

Although no statistically significant differences are identified as a function ofeducational background in the TK and CK dimensions (F_{(3, 295)} = .745, p = .526; F_{(3, 295)} = 1.462, p = .225), the remaining factors report their existence with moderate effect sizes (f = 0.263 - 0.351), except for the TPK domain with a small effect size (f = 0.175). Considering the values returned by statistical power (1-β = .762-.985) [greater than .80], the differences identified can be generalised to the population from which the data are drawn, except for the TPK domain with reduced statistical power (1-β = .617) (Table 8).

DISCUSSION AND CONCLUSIONS

The results of the validity, reliability and stability of the TPACK-ES scale were satisfactory, a circumstance that will allow its application in other Spanish teacher training contexts with similar characteristics. According to the results obtained, statistically significant differences are identified according to the gender of the future teachers, in the domains related to technological experimentation (TK), in the understanding of how teaching-learning can improve when certain technologies are used (TPK), and in other structuring variables of the TPACK model. These results are consistent with those obtained by Ortiz-Colón et al. (2020) for the first dimension (TK) in in-service primary school teachers in Andalusia (Spain) (n = 607), with those returned by Koh et al. (2010) in trainee teachers in both dimensions and with those obtained by Cetin-Berber and Erdem (2015) in the first dimension, and distinctive with respect to those provided by Leal and Rojas (2020) for Colombian trainee teachers (n = 274), which conclude the absence of significant differences according to the gender declared by the participants.

They are also consistent with Ibrohim et al.’s study (2022) of Indonesian science teachers’ perceptions of the TPACK model (n = 1,357). Their research identified significant differences according to the gender and educational level of the participants. Concurrent with the results of the present study, higher overall levels of men’s TPACK competence perception were observed. These same results are in line with the findings of the meta-analysis by Yanpar Yelken et al. (2019) and the study by Beri and Sharma (2019), which identified significant differences between the types of knowledge of the TPACK model as a function of gender. In the context of these differences, in the present research, technological knowledge (TK) and technological pedagogical knowledge (TPK) have small to moderate (TK) effect sizes that are larger in favour of males.

As for content knowledge, technological content knowledge and pedagogical knowledge, where differences are identified in some of their variables (CK-V5, TCK-V19 and PK-V14), the analysis of Yanpar Yelken et al. (2019) observes full factorial differences; however, partially concurrently with the present research, small effect sizes in favour of men are recorded in all three domains. In line with the research of Gebhardt et al. (2019) and in the context of this research, it can be stated that men express a higher level of digital competence confidence and thus in the use of ICT and its application in learning than women. This digital gender gap seems to contrast with other recent studies in the field of teacher education ( Pozas & Letzel, 2021).

In relation to the influence of educational level on perceptions of TPACK self-efficacy, significant differences are found in 5 (PK, PCK, TCK, TPK and TPACK) of the 7 dimensions of the model, with mean scores close to 4 in favour of men in practically all the educational stages. However, no increase in these scores is identified as a function of the future teacher’s educational progress. These results are consistent with those of Lee and Lee (2020) and Ibrohim et al. (2022), who identified the influence of educational level on in-service teachers’ perception of TPACK; however, they differ in their descriptive trends, as higher scores were observed in teachers with a Master’s degree in all domains. The results are in line with the findings of Long et al. (2020), who found significant differences according to the educational level of Chinese primary school teachers (n = 159), except for the TK and TPK dimensions, where males had higher scores than females. Similarly, these levels of perceived self-efficacy of prospective teachers contrast with those obtained in other geographical areas, as evidenced by the research results of Al-Abdullatif (2019) for Saudi teacher trainees and Wang et al. (2020) for Chinese prospective teachers.

Contextual factors such as learning styles, cultural, socio-economic (Ali & Hawk, 2022) and identity characteristics, or the influence of teachers’ emotions (Huang et al., 2022) need to be considered in the articulation and design of teaching-learning activities based on the TPACK model. Consequently, it is necessary to diagnose and fill in the gaps in the development and innovation of this model in order to ensure its effective implementation. In this regard, we agree with Napitupulu and Sebayang (2022) in identifying the limitations in moving towards heutagogical and cyber-agogical learning designs related to TPACK-based learning, conditioned, among other factors, by the lack of access to studies on the impact of the model on learning and sufficient infrastructure support for its proper development.

The results show the influence of contextual factors on the processes of technology integration and on the assessment of perceived self-efficacy in digital competences by prospective teachers. Context, which includes factors linked to personal identity (gender) and prior education (Morgan et al., 2022), is indeed a complex and interrelated multi-layered structure. In this sense, the TPACK model is not reduced to a set of integrated knowledge and competences, but is also context- oriented, which in turn impacts on the aims of the model itself (Kulaksız & Karaca, 2022). Thus, the existence of a gender digital divide continues to be identified and, consequently, the need for more specific training in digital competence for teachers (Gisbert-Cervera et al., 2022), which considers the incorporation of contextual components in the design, implementation and evaluation of the TPACK model in teacher education.

Limitations

Despite the type of sampling, design (case study) and, therefore, the limited sample size, the results of the present research may be useful for the implementation of teacher training programmes on the operational and effective implementation of the TPACK model. In addition, the drawbacks of administering the instrument as a self-report should be considered, especially in relation to the potential biases produced by social desirability and assent (De las Cuevas & González de Rivera, 1992). In this line, future work should triangulate the results obtained in this research with the application of techniques such as on-site observation, semi- structured and in-depth interviews, and the focus group, in order to understand the conceptions, teaching actions and didactic positions of trainee teachers on the integration of educational technology in their teacher training plans.

Finally, the cross-sectional design of this research suggests the need for further longitudinal studies (Lachner et al., 2021) capable of analysing, through different temporal measures, possible changes in the perception of future teachers’ competence self-efficacy after the design, implementation and evaluation of specific teacher training programmes based on the TPACK model.

FUNDING

This research has been funded by the research project La naturaleza de I-STEM (NoSTEM) para la formación ciudadana (PID2020-118010RB-I00) [State Agency for Research - Ministry of Science and Innovation].

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Received: 03 September 2022
Accepted: 06 February 2023
Published: 13 June 2023