Introduction

A critical proficiency for teachers internationally is the ability to integrate effective technology practices in the classroom (Crompton & Sykora, 2021; Kayaalp et al., 2022; Thohir et al., 2022; Xianhan et al., 2022). When preservice teachers (PSTs) graduate from an initial teacher education (ITE) programme, it is essential they are confident in their ability to meet the requirements and standards of their profession (Australian Institute for Teaching and School Leadership [AITSL], 2018; Blannin et al., 2021). In response, the United Nations Educational, Scientific and Cultural Organisation (UNESCO) developed the ICT Competency Framework for Teachers (ICT-CFT) (UNESCO, 2018). The ICT-CFT comprises competencies designed for preservice and in-service teacher training. The framework aims to transform education with a focus on teaching supported by technology (2018). In Australia, the ambitious Teaching Teachers for the Future (TTF) Project of 2011–2012 was developed to build the ICT education capacity of Australian teachers (Romeo et al., 2012). Recommendations derived from the TTF project assisted to create tangible outcomes and accountabilities in the Australian Curriculum and the Australian Professional Standards for Teachers (APST).

The graduate expectations required by the forementioned frameworks and standards have necessitated methods to categorise and measure PSTs’ confidence and competence. The technological pedagogical and content knowledge (TPACK) framework (Koehler & Mishra, 2009) was conceptualised to describe the combination of a teacher’s pedagogical content knowledge (PCK) with their technology knowledge and pedagogical use of technology (Admiraal et al., 2017; Wang et al., 2018). TPACK extends on the work of Shulman’s (1987) PCK framework. TPACK describes three different types of teacher knowledge required for effective integration of technology instruction: technological, pedagogical, and content (Koh, 2019; Yeh et al., 2021). Although technological knowledge (TK), content knowledge (CK), and pedagogical knowledge (PK) are distinct constructs, the TPACK framework involves the interaction of these constructs; that is, PCK amalgamated with technological content knowledge (TCK) and technological pedagogical knowledge (TPK) (Admiraal et al., 2017; Koh, 2019; Wang et al., 2018). Hence, the intention of the framework is to emphasise ‘the connections, interactions, affordances, and constraints between and among content, pedagogy, and technology’ (Wang et al., 2018 p. 235).

Utilising the TPACK framework, the TTF Project developed the TTF TPACK Survey which aimed to measure a change in PSTs’ TPACK after involvement in the project. The design of the survey was informed by earlier instruments created to measure TPACK and ICT use in teaching and learning (Albion et al., 2010; Jamieson-Proctor et al., 2007; Proctor et al., 2003). The TTF TPACK Survey instrument focused on the TPACK elements and PSTs’ self-perceptions of their confidence to use ICT and perceptions of usefulness of ICT in the classroom (Jamieson-Proctor et al., 2013). Whilst this survey has undergone an extensive evaluation process, the related confidence items do not indicate specific ICTs, which means that the PSTs were responding to confidence items on the use of ICTs as a classification.

Tondeur et al. (2017) utilised the ICT Competency Framework, developed by the Expertise NetWork at the Ghent University Association (2013), to identify the relevant items to include in a survey to measure PSTs’ ICT competencies. Tondeur et al. (2017) created a set of 19 essential competencies which focused on PSTs’ ability to educate students to become ICT competent, use ICT to support and strengthen learning, and organise the ICT learning environment. However, the survey does not explicitly state what the ICT items are. Whilst the PSTs may identify, for example, that they are able to support pupils to present information by means of ICT, there is no identification in the instrument of any specific technology, software, or skills in which they are claiming to be confident. Brianza et al. (2022), Mishra (2019), and Sofyan et al. (2023) recommend that further research be conducted into TPACK in specific contexts and settings. To this end, the aim of the current research is to investigate PSTs’ TPACK confidence in 10 specific categories of technologies using a self-audit survey and to improve their confidence to apply their knowledge contextually.

Although it is expected that PSTs are informed, equipped, and willing to integrate information, communication, digital, and robotics technologies into their teaching (Blannin et al., 2021), they feel ill-prepared (Redmond & Lock, 2019; Wilson, 2021). Consequently, they steer away from or resist the integration and engagement of technology in their planning (Dinc, 2019). Such trepidation is due to a lack of knowledge and skills, negative experience, attitudes and confidence (Best, 2017; Wilson, 2021). With these issues being present, PSTs require extra support during their studies to use technologies for teaching and learning purposes (Blannin et al., 2021; Wilson, 2021).

Effective support and preparation of PSTs to integrate a range of educational technologies requires innovative course design that includes hands-on activities, authentic assessments, collaboration, and the flexibility to personalise their learning (Martin et al., 2020; Xianhan et al., 2022). To assist PSTs in the personalisation of their learning, this study presents a self-audit TPACK confidence survey designed by the researchers to provide finer-grained insight into PST confidence in a range of specific educational technologies and ICT pedagogies. The self-audit survey instrument measured first-year PSTs’ TPACK confidence pre- and post-course in 10 specific categories of technologies used for teaching and learning (Appendix 1). The PSTs in this study used the self-audit as a needs-assessment which guided their self-directed learning outcomes. To this end, the research question was the following: To what extent does a self-audit survey assess and modify PSTs’ confidence to apply their TPACK in specific categories of technologies?

Background

A previous study with first-year PSTs (Martin et al., 2020) used an innovative course design that aimed to enhance their TPACK confidence. The innovative course design included an online technology skills audit and assessment tasks comprising action plans, SMART goals, personal learning networking, and peer teaching. The course design was found to be effective in developing the PSTs’ ability to support their future students’ use of technologies. The findings were based on a 12-item confidence survey conducted pre- and post-course. In the discussion of the findings, Martin et al. (2020) made several proposals for improving education technologies coursework to support the development of PSTs’ TPACK. One suggestion was that PSTs’ TPACK confidence could be further developed by providing experiences for them to demonstrate knowledge of a range of specific technologies used for teaching and learning in creative ways.

Of particular interest to the present research was the online technology skills audit category of the course which is identified in this paper as the self-audit survey. The survey design was informed by a range of sources outlined in Table 1. The survey statements, the number of items, and the wording of the Likert scale response options evolved over time and changed from an agreement scale to a confidence scale in the current survey. The survey underwent an iterative process of design and implementation over a 4-year period, resulting in a 100-item self-audit survey instrument. After validation using Rasch analysisFootnote 1 (Linacre, 2022), the final self-audit survey instrument contained 77 items and was the final survey instrument utilised in the findings described herein.

Table 1 Key sources used in developing the self-audit survey
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Methodology

This paper focuses on the development and use of a self-audit survey designed to guide PSTs’ self-directed learning and assessments in 10 specific categories of technologies, and to improve their confidence to apply their knowledge. The self-audit was completed by first-year PSTs from an Australian university. The goal was to increase their awareness of the range of technology skills which are expected of graduate teachers. The PSTs used the self-audit survey results to identify their areas of strength and areas in need of further development. They then used the information provided by the survey results to develop and action their professional learning plans. The professional learning plans, submitted as the final assessment in the education technologies course, included achievements and timelines for the development of personal learning networks, technological skills and peer teaching aimed at developing the PSTs’ confidence to apply specific elements of their TPACK.

Designing the items in the self-audit

TPACK

The TPACK framework informed the design of the self-audit survey items in 10 specific educational technologies categories (Table 2). It was deemed important when designing the items that they reflected mainly technological skills, technological knowledge (TK), content knowledge (CK), and pedagogical knowledge (PK). Yet there were also some items which utilised the integrated domains of technological content knowledge (TCK), technological pedagogical knowledge (TPK), and pedagogical content knowledge (PCK). This strategy was used to raise PSTs’ awareness of the complexities of integrating technology in teaching and learning and the broad range of technological content and skills required for the profession. Scherer and Teo (2019) suggest that when PSTs perceive TPACK to be useful in improving their performance as a teacher, it becomes a robust predictor of their intention to use technology in their future teaching and learning.

Table 2 Technology skills self-audit categories
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The self-audit survey items were organised from simple isolating domains (PK, CK, TK) to more complex, part-integrating domains (PCK, TPK, TCK) to a fully integrated TPACK domain (Thohir et al., 2022) (Table 3). Items in the survey were selected to cover the range of TPACK categories that are expected of teachers to be operational users of technologies (AITSL, 2011; UNESCO, 2018).

Table 3 Self-audit item examples linked to TPACK domains
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Australian professional standards for teachers (APST)

In Australia, ITE programme accreditation requires graduates to evidence their ability to meet national standards identified in the APST at a graduate level. There are seven standards grouped into three domains of teaching: professional knowledge, professional practice, and professional engagement (AITSL, 2018). Guided by the TPACK framework, ICT teacher expectations were embedded into the APST (Romeo et al., 2012). Standard 2.6 states that PSTs need to use ICT in teaching ‘to expand curriculum learning opportunities for students’ (p. 13). Standards 3.4 and 4.5, respectively, require PSTs to demonstrate knowledge of a range of ICT resources ‘that engage students in their learning’ (p. 14) and ‘demonstrate an understanding of the ‘safe, responsible and ethical use of ICT in learning and teaching’ (AITSL, 2018, p. 17).

When designing the self-audit survey items, it was essential to ensure that the standards which relate to ICT teacher expectations were included. Survey items aligned to standards 2.6 and 3.4 are included in all the audit categories. Standard 4.5 is evidenced in a range of self-audit categories, for example, in the Email skills category, survey item 1 is I can identify SPAM emails, in the World-wide web skills category, item 7 is I can explain internet safety, and in the ICT and technologies pedagogy category, item 5 is I can explain laws/codes of ethics associated with using ICT and other technologies safely, respectfully, and responsibly.

Australian curriculum

Technology is a learning area of the Australian Curriculum comprising two subjects: Design and Technologies and Digital Technologies (Australian Curriculum Assessment and Reporting Authority [ACARA], 2023b). The Digital Technologies subject aims to teach students how to create digital solutions with a focus on students developing computational thinking skills. Examples of Digital Technologies learning activities include designing user interfaces, understanding data, cyber security, robotics, algorithms, and data acquisition. In the Design and Technologies subject, students use design thinking, technologies, and design processes and skills to generate and produce real-world solutions (2023b).

When designing the self-audit items, the researchers utilised the Technologies curriculum language and content to ensure alignment between the curriculum expectations and the competencies in the survey. This was particularly relevant in the coding and robotics skills category as digital technologies and robotics are relatively new to the curriculum and many first-year PSTs may not have been exposed to these technologies when they were at school (Martin et al., 2020; Wang, 2023).

The self-audit survey also included items which align to the General Capabilities, in particular Digital Literacy (ACARA, 2023a). Digital Literacy as a General Capability is integrated and taught across all learning areas in the curriculum with a focus on practising digital safety and wellbeing, investigating, creating and exchanging, and managing and operating technologies (2023a). It was also important to ensure that the self-audit items included knowledge and skills in a range of common technologies taught in schools, for example, email, as well as common technological skills utilised in the profession as a teacher—for example, using a digital camera.

UNESCO ICT competency framework for teachers (ICT-CFT)

With the intention to guide training of PSTs and in-service teachers to integrate ICT in schools and classrooms, UNESCO (2018) developed the ICT-CFT. The framework was designed in response to international expectations that ‘teachers have the competencies to integrate ICT in their professional practice to ensure the equity and quality of learning’ (2018, p. 1). Version 3 of the framework addresses recent technological advances in educational technologies.

The ICT-CFT was used as a cross reference to ensure that the self-audit categories and survey items reflected recent international technological and pedagogical developments (UNESCO, 2018). The example activities in the framework provided a good starting point for the identification of a range of technologies used in education. For instance, the ICT-CFT example activities include the following: technological skills such as using a browser to access websites, using an email account to send and reply to emails, internet safety, and assistive technologies.

Teachers for the future (TTF) project

The TTF Project was commissioned by the Australian government in 2011–2012 to change how educational ICT was promoted and modelled in ITE across Australia (Jamieson-Proctor et al., 2013). The project aimed to develop, support, and increase PSTs’ ability to use a range of digital technologies effectively in the classroom and build educational ICT resources that would be readily available to PSTs, in-service teachers, and teacher educators (Romeo et al., 2012). The TTF Project utilised sources such as the Australian Curriculum, the APST, and the TPACK framework to underpin its resource and curriculum development (2012).

One of the resources created by the TTF Project was the ICT Elaborations for Graduate Teachers (AITSL, 2011). The development of this resource was led by AITSL in conjunction with the Australian Council for Computers in Education. The document provides ICT elaborations for graduate standards in the APST to encourage ITE programmes, teacher educators, and PSTs to incorporate educational ICT as a fundamental element in teacher education (Romeo et al., 2012). The document and its ICT elaborations were used in the development of the items in the self-audit, mainly in the ICT and technologies pedagogy category. The ICT elaborations provided alignment between the APST and the self-audit survey items.

Development of the self-audit output report

The self-audit results report provided to the PSTs was informed by a teacher ICT skills audit developed by the Western Australian Government (Department of Education & Training Western Australia, 2006). The Government’s self-audit comprised an ICT teacher survey (self-reporting on their knowledge and skills), online ICT competence test (testing ICT knowledge and skills), and concurrent validity (statistically validating the ICT teacher survey against the online ICT competence test). The evaluation was conducted with 1,500 in-service teachers working in government schools in Western Australia.

In the current study, the self-audit survey immediately returned a report to the PSTs which they could quickly analyse to identify their areas of strength and areas for improvement to guide their self-directed learning. The online survey tool provided participants with a PDF of their results, showing as a percentage total for each category (Fig. 1). These results were immediately emailed to participants so that they could view their results on-screen in the content of an email. The PDF of their results could be downloaded and stored for further analysis, used to justify items included in their professional learning plans, and kept for future pre- and post-course analysis of their confidence to apply TPACK.

Fig. 1
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Pre-course PST skills audit results example from the coding and robotics skills category

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The validated self-audit survey

To complete the self-audit, PSTs needed to provide a response to every item in each of the 10 categories in the survey. The Likert scale response options utilised a 4-point scale: not confident at all, slightly confident, moderately confident, and very confident. The self-audit was accessed online in Jotform, which was the online survey platform chosen for the self-audit as it had the capacity to send participants an instant summary of their results via email. Instant access to results meant the PSTs could immediately use the results to make judgements about their TPACK confidence in a range of specific educational technologies.

The results emailed to the PSTs indicated the level of confidence they selected for each item in the survey with a total percentage of confidence for each self-audit category (Fig. 1). The percentage calculation is derived from the sum of the item response scores in each category (0, 1, 2, or 3 for ‘not confident at all’ to ‘very confident’) divided by the number of items in the category.

These results were used by the PSTs to guide their self-directed learning outcomes and to identify and plan their professional learning needs. This involved creating plans and timelines to engage in targeted opportunities to work on their priority learning areas, improving on areas of moderate confidence, and identifying ways in which they could support their colleagues in the areas in which they were very confident.

Methods

This research was approved by the university’s Human Research Ethics Committee and conducted in accordance with all required ethics protocols. Convenience sampling was employed to recruit the PSTs (n = 296) who were enrolled in their first-year education technologies course as part of a 4-year Bachelor of Education degree or a Certificate in Primary Education at an Australian university. The data set comprises PST responses from two consecutive iterations of the education technologies course. Table 4 summarises the participant demographics.

Table 4 Participant demographics
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Results

Survey validity

The self-audit survey was employed pre- and post-course to measure PSTs’ TPACK confidence in specific categories of technologies with n = 296 respondents. Prior to validity testing, the pre- and post-surveys consisted of a total of 100 scale items aligned with 10 underlying construct categories (Table 5); each construct originally contained 10 scale items. After analysing each category’s items for fit using Rasch modelling (Linacre, 2022), 18 items were removed as a result of their fit statistics showing MNSQ INFIT or OUTFIT values less than .6 or greater than 1.4. Five additional items had suitable item fit statistics between .6 and 1.4; however, they had ZSTD scores > + 2.0 or < − 2.0, suggesting that these items did not contribute to the construct (Wright et al., 1994), leaving 77 scale items across the 10 categories.

Table 5 Descriptive statistics for confidence level scores pre- and post-course
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Survey results

The raw scores for survey items within each category were averaged for each of the 296 responses to produce pre- and post-course scores for each category. Table 5 shows pre- and post-course means for each category. A pre–post-increase in mean scores can be observed across all categories.

Normality tests using Shapiro–Wilks determined the scores were non-normally distributed (p < .05), so the data were treated as non-parametric in the analysis of pre- and post-course difference. The scores were tested pairwise with the Wilcoxon signed ranks test. Bonferroni adjustment (p < .05/10) was applied to correct for multiple comparisons, so the adjusted p-value was p < .005. Pre- and post-course comparison of response scores (Table 5) showed a significant (p < .001) increase in the PSTs’ self-evaluated confidence across all categories. The value r in Table 6 and Appendix 1 is the effect size, which is the Z value from the test divided by the square root of the total number of observations. r = .10– < .30 = small; r = .30– < .50 = medium; r ≥ .50 = large. The effect size ranged from medium to large (.42–.82) across the categories.

Table 6 Pre- and post-course comparison of underlying construct categories (Wilcoxon signed ranks, 2-tailed)
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All the validated scale items included in the survey (Appendix 1) received a significant pre- to post-course improvement in student TPACK confidence for this cohort of PST respondents. For this analysis, alpha was set to p < .05, and the significance of the results was p < .001 for all items.

Discussion

The research question posed for this study was the following: To what extent does a self-audit survey assess and modify PSTs’ confidence to apply their TPACK in specific categories of technologies? The research question is grounded in the necessity that PSTs are confident in their ability to meet the requirements and standards of their profession (AITSL, 2018; Blannin et al., 2021). The self-audit pre-course responses revealed that before the course intervention, the PSTs’ TPACK confidence level was generally lower across all categories (Tables 5 and 6). The post-course survey results showed that the PSTs’ confidence had improved significantly across all items in the survey with a medium to large effect size (p < .05, r = .42 to .82). The word processing skills category achieved the highest pre-course mean confidence response (\(\overline{x}\) = 3.73) (Table 5) and the smallest pre- to post-course difference due to the ceiling effect, but the magnitude of the effect was still medium (r = .42) (Table 6). In other words, there was not much room for improvement in this skill, presumably because the PSTs had acquired the skill in their schooling where word processing skills are often introduced, or in workplaces where the skills are often mandatory. The item in this category with the smallest effect pre- and post-course was I can change the page orientation from portrait to landscape (r = .22) and the highest effect was I can set up an automated table of contents for a document (r = .51), (Appendix 1, Table 10). However, it is important to note that the intervention still had a medium effect on the PSTs’ confidence in this word processing skills category (r = .42). Word processing skills were not directly taught as part of the course content, but students did have to use word processing in the assessments and in their other courses; thus, any improvement in this skill was self-directed by the PSTs.

The three categories exhibiting the most substantial change in PSTs’ confidence were coding and robotics skills, ICT and technologies pedagogy, and common technologies used in schools. The magnitude of the change in PSTs’ confidence in these categories was large (effect sizes of r > .8). The category common technologies used in schools had the second lowest pre-course mean confidence response (\(\overline{x}\) = 2.09) (Table 5) and a large pre- to post-course difference indicated by a large effect size (r = .82) (Table 6). In this category, the item I can use a webmail calendar to schedule and remind me of appointments and events had the least change in confidence pre- and post-course; however, this change had a medium effect size (r = .53), (Appendix 1, Table 16). Like the word processing skills category, it could be assumed that PSTs had been exposed to this technology at school, or in the workplace. During the course, PSTs were encouraged to put the actions from their plans into their webmail calendar, developing their contextual knowledge about how the technology can be used in educational environments (Brianza et al., 2022). The course content did not specifically teach students webmail calendar skills, which indicates that an increase in confidence in this skill was self-directed by PSTs. In contrast, the item from the coding and robotics skills category I can program in Scratch Jnr and Scratch 3.0 had a large effect size (r = .82). These digital technology coding programs were taught in the coursework and were also delivered in peer teaching sessions presented for assessment. It can therefore be concluded that the course content and design impacted positively on the development of PSTs’ confidence in this area.

The ICT and technologies pedagogy category achieved the third lowest mean pre-course confidence response (\(\overline{x}\) = 2.51) (Table 5) with a substantial pre- to post-course mean difference and a large effect size (r = .81) (Table 6). All the items in this category showed a significant difference in confidence pre- and post-course with large effect sizes (Appendix 1, Table 15). The item with the lowest effect size in this category was I can use ICT in administrative tasks like creating classroom newsletters for parents (r = .62). While the course content did not include the creation of classroom newsletters, the PSTs responded that they were confident in using technologies for these types of administrative tasks, so likely already developed relevant skills. The largest difference in confidence was for the item I can use the SAMR model to select how ICT and other technologies will be used in teaching strategies to engage students in their learning (r = .85). The course content and assessment focused on the SAMR model (Puentedura, 2013a) and pedagogies for teaching with technology (Puentedura, 2013b). One of the assessments required PSTs to select a technology and to teach their peers how to use the technology in a student-directed, hands-on activity. During the course, PSTs also learned how to develop personal learning networks to access professional learning outside of the usual university channels, for example, LinkedIn Learning and social media networks. These results suggest that the innovative course design (Martin et al., 2020) has developed PSTs’ confidence in this category. According to Koh (2019), TPACK programs which focus on pedagogical approaches with the integration of technology are more successful in the development of TPACK in teachers.

Coding and robotics skills had the lowest pre-course mean confidence response (\(\overline{x}\) = 1.29) (Table 5) and was the category in which PSTs’ confidence was most improved (r = .82) (Table 6); all the items in the category had large effect sizes ranging from .73 to .80 (Appendix 1, Table 14). This strong improvement most likely occurred due to many PSTs not having learnt about coding or robotics at school, but they were explicitly taught this content in the course. Coding and robotics skills were only made compulsory in schools in 2015, after the endorsement of the national Digital Technologies curriculum (ACARA, 2023b). Kayaalp et al. (2022) identified that a barrier to confidence with technologies is access to equipment. During the course, students were provided with robotics equipment and participated in hands-on coding and robotics activities in tutorials. They were provided with online learning materials to support the development of this knowledge and these skills. Many PSTs identified coding and robotics in their professional learning plans, thus choosing to learn and then present the knowledge and skills in peer teaching sessions using technologies such as Bee-Bot, Dash robot, Pro-Bot, LEGO WeDo 2.0, Kodu Game Lab, Minecraft edu and Code.org. The PSTs’ choice to present peer teaching sessions on coding and robotics demonstrates their increased confidence in coding and robotics. These survey results are supported by findings that indicate the importance of teachers having high levels of confidence and competency, as self-efficacy has been found to be strongly linked with a teacher’s motivation to successfully integrate ICT into the classroom (Martin et al., 2020; Tezci, 2011).

Most of the PSTs in the study were in the 18 to 25-year-old age range (78%), graduating from high school within the past 8 years. A large subset of these younger students had no previous tertiary qualification (63%). It is a common assumption that younger PSTs are competent users of technologies (Milutinović, 2022). However, as they may be confident in the use of common technologies, younger PSTs are unaware of the type and scope of educational technologies used in the teaching profession (Chen et al., 2010; Milutinović, 2022). PSTs can become skilled in educational technologies once they are aware of them, have access to them, actively seek out opportunities to learn them, and practise using them in authentic situations (Davies, 2011). The results suggest that when provided with equipment and given opportunities to engage with a variety of technologies, whilst concurrently developing pedagogical and content knowledge, PSTs experience a statistically significant increase in their confidence levels. These findings are in accordance with the conclusions reached in Kayaalp et al. (2022).

Implications and recommendations

The self-audit survey has utility as a needs analysis tool for PSTs. The self-audit results were used by PSTs in this study to develop their confidence and guide their self-directed learning. From the self-audit results, they could view their confidence level across a range of technologies via 10 categories, consisting of multiple individual technologies and pedagogies, allowing for an in-depth analysis. The results assisted PSTs in making professional decisions regarding which technologies they needed to learn more about in each of the categories. Presenting PSTs with a greater range of technologies that are utilised in the teaching profession has the effect of normalising these technology items. Joa et al. (2022) suggest that when technologies are perceived to be typical or normal, individuals are easily motivated to follow the social norms and adopt technologies. The fine-grained items presented in the self-audit survey also provided the PSTs with insight into a range of educational technologies and pedagogies used in schools, thus normalising the use of these tools within an education context and potentially motivating their confidence to apply them in their future teaching practice.

Moreover, the collection of PSTs’ self-audit survey results has utility as a needs analysis tool for teacher educators. The results could assist teacher educators to modify their course content to better target the learning needs of their own PSTs. In other words, the self-audit results could be used by teacher educators to make decisions to adapt, adopt, or reject the items and categories in the self-audit when planning future coursework. Teacher educators should also regularly review the categories and items in the self-audit, due to the speed of development of technologies in education.

Future research could also target the extent to which teacher educators in tertiary education could utilise the findings of the self-audit survey. This would enable them to identify which technological and pedagogical concepts and skills are useful, should be revised, or reconsidered in course development. Moreover, teacher educators could use the results to determine if students’ confidence improves over the duration of an education technology course, or over the duration of an ITE programme. The results could also be used to tailor course content to meet the changing learning needs of each PST cohort. Over time, as students enter ITE programmes with a varying range of information, communication, digital, and robotics skills and pedagogies, and as these new educational technology knowledge requirements change, the self-audit tool can assist in tracking PSTs’ various learning needs.

Limitations

In the future development of the survey, an identified need from the survey validation process is to revise some of the survey items to be more fine-grained. For example, in response to the survey prompt I can name computer categories and their functions, the term ‘computer categories’ could be replaced with the names of the most important categories that teachers are required to teach, for example, random access memory (RAM) and central processing unit (CPU).

The speed of technological change, including the rapid evolution of artificial intelligence (AI) and other emerging technologies, carries significant implications for the future utility of the self-audit survey. As certain software, hardware and applications featured in the self-audit survey become obsolete, it will be imperative to incorporate new technological categories and items to ensure the survey’s continued relevance and currency.

Conclusion

The utilisation of a self-audit survey to assess and modify PST’s confidence to apply their TPACK within specific categories of technologies unveils valuable insights into their readiness for effective technology integration in the classroom. The results of the study suggest that by utilising a validated self-audit survey in combination with a creative course design using action plans, SMART goals, personal learning networking, and peer teaching (Martin et al., 2020), it is possible to enhance PSTs’ confidence in applying their TPACK with specific technologies. The findings indicate that the implementation of the self-audit survey promoted the PSTs’ self-directed learning, fostered agency, and positively influenced the perception of their own TPACK confidence. When PSTs realise their areas of strength and areas that need improvement, this knowledge can empower them to develop specific professional learning plans and seek professional learning solutions. Our findings support previous research which suggests that once PSTs self-actualise their TPACK confidence and gain proficiency with these domains, they are likely to integrate technologies into their classroom lessons.