Factors influencing the digital competence of students in basic vocational education training

RESEARCH Open Access © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 https://doi.org/10.1186/s40461-025-00198-0 *Correspondence: Ane Zubizarreta Pagaldai [email protected] 1Mondragon University, Mondragón, Spain 2Swiss Federal University for Vocational Education and Training, Zollikofen, Switzerland 3University of Lleida, Lleida, Spain Factors influencing the digital competence of students in basic vocational education training AneZubizarreta Pagaldai1*, AlbertoCattaneo2, AinaraImaz Agirre1 and Victoria I.Marín3 Introduction Digitalisation has had a great impact on society and this is why the 21 st century is known as the technology era (Zhao et al. 2021). The labour market has been changed by digitalisation and the advancements of Industry 4.0, requiring education systems to adapt their offerings to align with its evolving demands (Wild and Schulze Heuling 2020). Thus, new skills and competences are needed due to the growing importance of technology (Findeisen and Wild 2022). Digital competence (DC) is one of the key competences for lifelong learning (European Commission 2019) since it enables individuals to play an active role in all areas of society (González-Martínez et al. 2018). Notwithstanding the assumption that young people possess sufficient digital skills, studies reveal that they do not necessarily possess the appropriate digital knowledge often attributed Empirical Research in Vocational Education and Training Abstract Digital competence (DC) is essential in order for individuals to actively participate in all sectors of society. While high school students’ DC has been studied extensively, few studies have examined DC in vocational education and training (VET). To address this gap, a study in the context of the Basque Autonomous Community was carried out with the objectives of measuring Basic VET students’ DC perceptions and investigating the factors that have an impact on Basic VET students’ DC. Descriptive and multilevel analyses were conducted. The results indicate a need to focus on enhancing Basic VET students’ DC, as these skills are critical for their professional development. Furthermore, school predictors were found to be the strongest predictors of students’ DC, followed by attitude toward information and communication technologies and the frequency of technology use for school-related and personal purposes. This study provides knowledge that could help educational institutions identify areas for further improvement in fostering students’ DC, and provides policy makers with a comprehensive understanding of the present situation of VET students’ DC. Keywords Students’ digital competence, Vocational education and training, School digital capacity, Digital transformation Page 2 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 to them (Kirschner and De Bruyckere 2017). In several studies it has been concluded that young people do not possess an optimal level of DC, despite having grown up in a digital technology context (Estanyol et al. 2023; Gallardo Echenique et al. 2015; Kennedy et al. 2009; Valverde-Crespo et al. 2020). Education is essential for the progress of society (Kovalchuk et al. 2023) and vocational education and training (VET) plays a crucial role in meeting the needs of the labour market and society (Cattaneo et al. 2022). In order to significantly impact businesses and individuals, VET should prepare young people (Basque Government 2022a; Cedefop 2023a), providing environments in which students develop their digital skills (Subrahmanyam 2022). Digital transformation has become a priority in Europe (European Education and Culture Executive Agency 2019) and the need to work on DC has been integrated into educational policy in VET (Cedefop 2023a). Basic VET is one of the Basque Autonomous Community (BAC), designed to reduce early school leaving (Basque Government 2015, 2016; Cedefop 2023b). Nevertheless, basic VET students’ DC has rarely been studied (Aguilar de la Rosa, 2022). Given that the development of Industry 4.0 is reshaping work structures and making DC a core requirement across all sectors (Indrawan and Lay 2019; Cedefop 2023a), further research on this group is highly relevant. Thus, the present study analyses Basic VET students’ perceptions of DC in the Basque Autonomous Community (BAC) as well as the personal and contextual factors that influence those perceptions. It offers insights that can provide educational institutions with knowledge that will allow them to identify areas for enhancing students’ DC and equips policymakers with a thorough understanding of VET students’ digital landscape. To achieve this, first, the theoretical framework is presented to contextualise the study. The subsequent section details the methodological approach adopted. The results are then reported and further examined in the discussion. Finally, the conclusions highlight the main contributions, implications, and avenues for future research. Theoretical framework Digital competence DC is a key competence for lifelong learning, defined as “confident, critical and responsible use of, and engagement with, digital technologies for learning, at work, and for participation in society” (European Commission 2019, p. 10). According to Larraz (2013), it involves the ability to mobilise information, technological, multimedia, and communication literacies to process information, share knowledge, and solve problems. Information literacy involves the capacity to articulate it, locate it, evaluate it, organise it, transform it into knowledge and communicate it appropriately in a given context. Technological literacy refers to the ability to process data in several formats, adequately and effectively adapting it to the public and the context. This implies a technical mastery of the organisation and management of technical devices. Multimedia literacy entails analysing and creating multimodal digital messages from a critical perspective. Finally, communication literacy enables safe, ethical, and responsible interaction and collaboration in digital environments, forming the basis of digital citizenship. Hence, the integration of these four literacies facilitates individuals to make informed decisions across all areas of the learning ecosystem, whether personal, professional, or social. Given this interrelated nature of DC, an instrument called INCOTIC 2.0 was Page 3 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 developed as its assessment requires an instrument capable of capturing its constituent literacies (González-Martínez et al. 2018). Research using the INCOTIC 2.0 instrument at the university level reveals key patterns in students’ self-perceived DC across its four core literacies. Findings show a common trend of high multimedia literacy but low technological literacy (Llopis Nebot et al. 2018; Sánchez-Caballé et al. 2019). However, other studies report high information and low communication literacy instead, with the indicator related to publication under the Creative Commons license yielding the lowest score (Henríquez Coronel et al. 2018). These differences likely reflect varying academic contexts and disciplinary demands. Additionally, the use of digital technologies for leisure had the highest scores (Henríquez Coronel et al. 2018; Sánchez-Caballé et al. 2019), higher than the scores for the use of these technologies for school-related purposes; this is consistent with the results of previous studies (Gisbert Cervera et al. 2016; Larraz 2013). Furthermore, the results showed that students have a highly positive attitude toward technology (Sánchez-Caballé et al. 2019), also in line with previous analyses (Kivunja 2015; Prendes et al. 2010). To the best of our knowledge, no prior studies have employed the INCOTIC 2.0 instrument to investigate DC at non-university educational levels. Its theoretical robustness, derived from a close alignment with Larraz’s (2013) multidimensional definition of DC, provides a solid foundation for this application. Given that these dimensions of DC are transferable across educational contexts, the questionnaire is suitable for the VET context, where the curriculum is explicitly designed to bridge the gap between education and labour market demands (Wild and Schulze Heuling 2020). Therefore, the INCOTIC 2.0 provides a robust framework to assess VET students’ digital readiness for labour market demands, as its four core literacies are crucial for developing the holistic skill set needed to navigate complex challenges and make informed decisions in all areas of life. To this end, the present study analyses VET students’ perceptions of DC and the factors that impact it. Students’ DC in VET The progress of society relies on how education develops and VET is one of the most important pillars that lead to successful economic development (Kovalchuk et al. 2023). It prepares future qualified workers who are aligned with the needs and requirements of the labour market by promoting training in order to achieve a high level of professional competency (Kovalchuk et al. 2023). The rapid development of Industry 4.0 is reshaping work structures, presenting significant challenges for adaptation (Indrawan and Lay 2019); in response, DC is becoming fundamental for nearly all occupations (Cedefop 2023a). Despite the fact that 90% of jobs in all sectors require some form of digital skills and that these required skills will shift from basic to advanced in the future, 35% of European workers still lack these skills (European Commission 2020). To address this situation, Europe has established a goal for a minimum of 80% of the population to possess basic digital skills by 2030 (European Commission 2022a). Therefore, the development of students’ DC is an important aspect of their training (Kovalchuk et al. 2023). This is why Basic VET students must be digitally competent and fully able to make appropriate use of digital technologies (Moreno Guerrero et al. 2019). However, each student has their own specific digital characteristics depending on their prior educational experience (Moreno Guerrero et al. 2019). According to Aguilar de la Rosa (2022), Page 4 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 VET students’ DC and digital technology use have not been frequently studied, and the few studies in his review demonstrate that VET students show lower DC perception levels than students in other stages of higher education. In another study on German VET, it was found that students do not have problems navigating the Internet but do experience difficulties when searching for information and reading Web content (Burchert et al. 2013). The DC of students in cooperative higher education and VET was compared in a study (Wild and Schulze Heuling 2020), which found that the DC level of cooperative higher education students was more advanced than that of VET students. Furthermore, several studies indicate that there is a higher affinity for using digital technologies for private purposes rather than for professional or educational reasons (Aguilar de la Rosa, 2022; Burchert et al. 2013). Regarding the frequency of technology use, the literature shows that VET students lack DC from a pedagogical perspective, although they possess these competences from a technological perspective (Moreno Guerrero et al. 2019). However, Moreno Guerrero’s analysis is based primarily on the use of digital technology for different purposes rather than on analysing the ability to manage the four literacies, which better aligns with the definition of DC (Larraz 2013) adopted here. Thus, the present study focuses on analysing Basic VET students’ DC based on the four literacies rather than merely studying the frequency of use of digital technologies. Factors influencing DC DC encompasses a range of factors, at both the contextual and personal levels, that are essential for effectively navigating the digital landscape. These factors have been extensively studied in the context of educators, with significant research focusing on teachers (Cattaneo et al. 2022; Lucas et al. 2021). However, when examining students, the situation is notably different. The school context plays a crucial role in shaping students’ DC. Nevertheless, there is a noticeable gap in research examining exactly how this context influences the development of students’ DC. Education should help learners to develop their DC (European Commission 2020). Therefore, in terms of skills development, research and knowledge generation, the educational system must adapt to the needs of the digital revolution (Basque Government 2022b). Considering this transformation scenario, DC must be established as a fundamental element in order to guarantee opportunities for digital technologies in teaching and learning environments (Basque Government 2022b). For a successful digital transformation in education, schools’ digital capacity must be increased in order to support the effective integration of technology (Costa et al. 2021). Digital capacity is defined as the effective integration of technology into teaching and learning processes through the school’s cultural environment, policies, infrastructure and students’ and teachers’ DC (Costa et al. 2021). With the objective of helping schools to determine their degree of digital capacity, the SELFIE tool was designed (European Commission 2022b). The tool provides schools with a holistic view of organisational strategies for the school’s digital transformation, taking 8 areas into account (European Commission 2022b): Leadership, Collaboration and networking, Infrastructure and equipment, Continuing professional development, Pedagogy – support and resources, Pedagogy – implementation in the classroom, Assessment practices, and Student digital competence. Page 5 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 Beyond contextual influences, it is crucial to consider the personal factors influencing DC (González-Martínez et al. 2018), as they provide critical insights into the individual differences that explain why students with similar access to technology and training often exhibit varying levels of proficiency. Several factors have been analysed at different educational levels, including gender, previous educational experience, the frequency of use of digital technologies, attitudes toward ICTs, age, and educational and professional qualifications. In terms of gender, while some studies carried out in secondary education have demonstrated that males have a greater perception of DC (Fuster-Rico et al. 2025; Niño-Cortés et al. 2023), others conclude that females have a better perception of this competence (Fraillon et al. 2014). Another study at the same educational level found statistically significant gender differences only in communicative literacy, with boys perceiving themselves as more proficient than girls (Verdú-Pina et al. 2024). Regarding students’ previous educational experiences, a study with lower-secondary students concluded that prior academic achievement is one of the most important predictors of students’ DC (Hatlevik et al. 2015). Additionally, in studies on the use of digital technologies in higher education, it was concluded that students’ DC perception was related to their frequency of use of ICTs (Esteve Mon 2015; González-Martínez et al. 2018) as well as to their attitudes toward ICTs (Browne 2009; Edmunds et al. 2012; González-Martínez et al. 2018; Sang et al. 2010). On the other hand, some studies in higher education revealed that students’ attitudes toward the use of ICTs are positive when they are useful to reach the objective and easy to use (Edmunds et al. 2012; Espuny Vidal et al. 2011). Findeisen and Wild (2022) analysed factors influencing commercial VET students’ DC perceptions, highlighting the positive impact of professional qualifications, the frequency of use in some learning opportunities (the use of devices to search for content/information on the Internet, the use of office programs, reading blogs or forums, and creating blog entries) and the frequency of use of instant messaging services. Conversely, online learning had a negative impact, suggesting it is not seen as relevant to DC development. However, the research was based on the commercial VET sector, and the conclusions cannot be extended to other VET fields. Thus, in the present research, additional factors that have an impact on Basic VET students’ DC will be analysed. To date, few studies have considered both personal and contextual factors when analysing students’ DC. Zhong (2011) found that the ICT penetration rate negatively impacts students’ digital skill perception, indicating that the presence of ICTs does not guarantee greater opportunities to use ICTs or to work on literacy. The history of using ICTs and ICT use at home and at school showed a positive impact on their digital skill perception, with their use of ICTs at home as the greatest predictor. Nevertheless, this previous study does not analyse the pedagogical use of ICTs or school digital implementation based on the factors that have an impact on students’ digital skills. Hippe and Jakubowski (2022) used SELFIE data to analyse differences in students’ DC across countries, schools, and individuals. The results revealed that at the student level, individual perceptions of infrastructure, pedagogy, and assessments were strongly related to their DC. At the school level, the aggregation of these student perceptions (school climate) showed an even stronger association with DC. In contrast, teacherreported indicators were not significant in explaining variations. At the country level, differences in DC were linked to perceived support for teacher pedagogy and access to Page 6 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 digital resources. Nevertheless, students’ DC was based on SELFIE results, and the focus was on measuring the impact of several predictors on the opportunities that students have to work on their DC in schools. To the best of our knowledge, no studies have yet investigated VET students’ DC in relation to personal and school (digital capacity) factors. Therefore, the present study also aims to identify the factors that have the strongest impact on students’ DC perception. The specific context of the study Against the international background provided so far, the Spanish VET system aims to train professionals for specific occupations and direct labour market entry (Aguilar de la Rosa, 2022). Basic VET is one of the VET levels offered in the BAC (Basque Government 2016) as it is in Spain. The aim of Basic VET is to promote student retention in the education system by offering other opportunities to obtain a Compulsory Secondary Education Graduation Certificate as well as a basic technician diploma (Basque Government 2015; Cedefop 2023b). Through Basic VET, students not only acquire professional qualifications but also develop essential skills (Moreno Guerrero et al. 2019), such as digital skills (Basque Government 2022a). As the demand for digital skills in the labour market is increasing, the need to work on DC has been integrated into VET educational policy (Cedefop 2023a). The Spanish VET modernisation plan includes a strategy to promote digitalisation for economic and social growth (Spanish Government 2020). Aligned with this, the 6th Basque VET plan aims to expand digital transformation and encourage the pedagogical use of digital technologies (Basque Government 2022a). Aims and research questions As indicated by the discussion above, no studies could be found on Basic VET students’ DC perceptions, as previous analyses were carried out with students at other educational levels (Henríquez Coronel et al. 2018; Llopis Nebot et al. 2018; Sánchez-Caballé et al. 2019). Additionally, the available research on the use of digital devices for other purposes (Moreno Guerrero et al. 2019) was based on a specific VET field and country (Findeisen and Wild 2022). Finally, we found no studies that consider the interactions among personal and school factors as they relate to students’ DC perceptions. Thus, the present study aims to measure Basic VET students’ perceived DC in the BAC, identify the effects of personal and school factors on it, and determine the strongest predictors of their DC. To do so, the following research questions were posed: RQ1: What are the attitudes toward ICTs, frequency of use of digital technologies and DC perceptions of Basic VET students in the BAC? RQ2: How do the school’s degree of digital capacity, the SELFIE areas, and the students’ personal characteristics influence Basic VET students’ DC perceptions? Method Sample The data employed in this study were obtained from BAC Basic VET schools. The sample consisted of 857 students from 21 schools, and data collection was carried out during April and May of 2023. The distribution of students across the VET work areas in the study was as follows: 28.4% in industry and manufacturing, 25.7% in sport and wellbeing, 13.2% in hospitality, tourism and commerce, 11.9% in technology and innovation, Page 7 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 9% in transport and automotive, 3.9% in administration and management, 3% in hospitality, tourism and commerce, 2.8% in agriculture, fisheries and the rural environment, and 2.1% in arts, design and communication. The gender distribution of the participants was as follows: 32.7% female, 59.6% male, 3.4% non-binary, and 4.3% preferred not to answer (Appendix A). Schools were assigned numbers in place of names to maintain anonymity. Instruments INCOTIC: measuring students’ DC perception McDonald’s omega analysis was conducted in order to determine the reliability of the INCOTIC 2.0 instrument (González-Martínez et al. 2018) with Basic VET students. The results indicate that the instrument can be considered reliable, with all subscales having a McDonald’s omega value ranging from 0.807 to 0.855 (Table1). The questionnaire consisted of five sections: demographic information, technological profile, DC perception, attitude toward ICTs, and technoethics. The students’ demographic information, such as gender, type of studies and school, were collected in the first section of the questionnaire. All other items were answered on a 5-point Likert scale. Four items gathered the students’ technological profile responses on a scale from 1 (“I completely disagree”) to 5 (“I strongly agree”). Two of these items were related to the students’ frequency of use of digital technologies for leisure or school-related purposes (e.g., “I am a person who uses digital tools a lot in my personal life”) and two were related to their need for telephone and Internet services (e.g., “I couldn’t live without my mobile phone”). A total of 19 items measured the subjects’ DC perceptions, with responses on a scale from 1 (“I do not know how to do this”) to 5 (“I can do this without difficulty”); these were divided into four dimensions: Information (e.g., “Saving all links to classwork information in an orderly manner”), Technology (e.g., “Connect to the most secure Wi-Fi network available”), Multimedia (e.g., “Realize when I’m being tricked by a multimedia message”) and Communication (e.g., “Work collaboratively on a shared document in the cloud”). Nine items gathered information on the students’ attitudes toward ICTs (e.g., “They help me learn autonomously”) and five items measured their technoethics (e.g., “I’m concerned about my security and privacy on the Internet”) on a scale from 1 (“I completely disagree”) to 5 (“I strongly agree”). Table 1 General indicators of INCOTIC 2.0 Scale reliability statistics McDonald’s Scale 0.846 Item Reliability Statistics Technological profile 0.855 Digital competence 1. Information literacy 0.807 2. Technological literacy 0.807 3. Multimedia literacy 0.810 4. Communication literacy 0.822 Attitude toward ICTs 0.818 Technoethics 0.835 Page 8 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 SELFIE: measuring the digital capacity of schools McDonald’s omega analysis was conducted (Table2) in order to determine the reliability of the SELFIE tool (European Commission 2022b). The results indicate that the instrument can be considered reliable, with all subscales having a McDonald’s omega value ranging from 0.942 to 0.952 (Table2). All items employed a 5-point Likert scale, with responses ranging from 1 (“I completely disagree/I do not do this at all”) to 5 (“Strongly agree/I do this very well”), including an option for “Not Applicable” (N/A). The survey was completed by each school’s management team members, teachers and students in order to gather information on their experience of digital implementation in their schools (European Commission 2022b). Management team members and teachers completed a 31-item questionnaire encompassing Leadership (e.g., “At our school, we have a digital strategy”), Collaboration and networking (e.g., “At our school, we use digital technologies when collaborating with other institutions”), Infrastructure and equipment (e.g., “At our center, there are digital devices for teaching use”), Continuing professional development (e.g., “Our management team discusses digital technologies with us in relation to continuing professional training needs related to education”), Pedagogy in terms of support and resources (e.g., “Our teachers seek online educational resources”), Pedagogy related to classroom implementation (e.g., “I use digital technologies in my teaching method to adapt to the needs of each student”), Assessment practices (e.g., “Our teachers use digital technologies to assess students’ abilities”), and Student digital competence (e.g., “At our school, students learn how to act safely on the Internet”). Students, on the other hand, completed an 18-item questionnaire focusing on Infrastructure and equipment (e.g., “At our school, I have access to the Internet to learn”), Pedagogy related to classroom implementation (e.g., “At our school, when I use technology, I participate more”), and Student digital competence (e.g., “At our school, I learn how to determine whether the information I find on the Internet is reliable and correct”). This tailored approach ensured that the information collected reflected the school’s digital capacity, incorporating the direct participation of each group involved in the implementation of digitalisation. Data collection Data were collected through online surveys. For the SELFIE questionnaire, direct links were provided by the SELFIE coordinator of each school to management team members, teachers and students, and each profile had its own link. Once the surveys were completed, SELFIE reports were requested from all schools. Regarding INCOTIC 2.0, a Table 2 General indicators of SELFIE Scale reliability statistics McDonald’s Scale 0.952 Item Reliability Statistics Leadership 0.949 Collaboration and networking 0.945 Infrastructure and equipment 0.948 Continuing professional development 0.951 Pedagogy: support and resources 0.945 Pedagogy: implementation in the classroom 0.943 Assessment practices 0.943 Student digital competence 0.942 Page 9 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 direct link was provided to students. For both surveys, informed consent was obtained from all participants, and confidentiality and anonymity were maintained throughout the research process. This study was conducted always considering the ethical principles of the Declaration of Helsinki and the American Psychological Association. Analysis All analyses in this study were conducted using R (version 4.4.0). First, descriptive statistics were generated to explain the means and standard deviations for the scores of students’ attitudes toward ICTs, frequency of use of digital technologies for personal and school-related purposes, and DC perceptions. Additionally, considering that students (first level) are nested within schools (second level), multilevel linear modelings (MLMs) were conducted to answer RQ2. In the MLM analysis, all schools with at least 10 student participants were included, as this was deemed an appropriate number following Schmitz et al.‘s (2022) study. In order to determine whether an MLM analysis was necessary, an unconditional model (null model) was estimated to test whether differences in students’ DC perceptions could be found at the individual and school levels. Analysis of variance (ANOVA) was used to compare a single-level model. Furthermore, the intraclass correlation (ICC) for students’ DC perceptions was computed in order to determine what proportion of the total variance in those perceptions is attributable to differences among schools. Next, grand mean centering was applied to center the predictors, which made it possible to determine the average effect of student and school characteristics. After that, random intercept MLM analyses were conducted. Two different models were created to answer RQ2. However, in order to confirm that these two models were the appropriate ones, different models were compared (including and excluding different predictors) using ANOVA and considering the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and log-likelihood (logLik). For the first model, the level 1 predictors were attitudes toward ICTs and the use of digital technologies for personal and school-related purposes, and the level 2 predictor was the school’s degree of digital capacity, considering the mean of all SELFIE areas. In order to determine which SELFIE areas were significant predictors of the students’ DC perceptions, a breakdown of the areas was made. For the second model, the level 1 predictors were the same as those for the first model but, after comparing different models (Appendix B), only three SELFIE areas were considered appropriate level 2 predictors: Pedagogy (support and resources), Pedagogy (implementation in the classroom) and Assessment practices. These areas are related to the integration of digital technologies in teaching and learning processes, thus it is expected that they would have a direct impact on students. Finally, 95% confidence intervals were calculated to determine the precision of the results. Results Descriptive analyses To address RQ1, descriptive statistics—including means and standard deviations—were calculated to analyse the means and standard deviations of students’ frequency of use of digital technologies for personal and school-related purposes and DC perception. With respect to the frequency of use of digital technology, higher scores were obtained for personal use (M = 3.86; SD = 1.24) than for school-related use (M = 2.89; SD = 1.32). Page 16 of 16Zubizarreta Pagaldai et al. Empirical Research in Vocational Education and Training (2025) 17:19 Gisbert Cervera M, González Martínez J, Esteve Mon FM (2016) Competencia digital y competencia digital docente: Una panorámica sobre El Estado de La cuestión. Revista Interuniversitaria De Investigación En Tecnología Educativa 74–83. h t t p s : / / d o i . o r g / 1 0 . 6 0 1 8 / r i i t e 2 0 1 6 / 2 5 7 6 3 1 González-Martínez J, M Esteve-Mon F, Larraz Rada V, Espuny Vidal C, Gisbert Cervera M (2018) INCOTIC 2.0. 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