1 Introduction

In this digital age, Information and Communication Technology (ICT) plays an increasingly important role in students’ education [1], since it offers a wide range of digital resources both to students and teachers in order to enhance the teaching and learning process [2]. Videos should be highlighted among such resources, as they help “to constitute and interpret meaningful mental activities, critical thinking and problem-solving” [3], p. 1]. Videos can be used through virtual reality [4, 5], or applications such as Genially, Prezi, Padlet, or Canva can be employed for content creation [6], with YouTube also being worth mentioning. This video platform has the highest number of users [7] and can be used for educational purposes [8, 9], which will be the focus of this study.

Due to the wide variety of digital resources available to teachers, Beetham and Sharpe [10] affirm the importance of teachers knowing how to use technology in an educational way, since perceived levels of user-friendliness and attitudes towards ICT influence its use [11]. However, teachers need adequate digital competence (DC), which is understood as the minimum set of skills required to work effectively with software tools, access the Internet, or carry out basic computer tasks [12]. Moreover, they need to achieve new literacy levels in digital competence [13], involving the mastery of ICT in professional contexts with good pedagogical judgement [14]. On the basis of these ideas, teachers are expected to know how to design digital content [15] and specifically be able to adapt it, when necessary, to student needs [16].

Translating this idea of a teacher’s skillset to the great number of educational resources that YouTube offers, educators must have adequate levels of digital competence to use this resource in various contexts. For example, it is important for teachers to have strategies for filtering educational videos [17], since they can be useful for illustrating abstract or difficult concepts. In this way, teachers can provide students with more visual and engaging learning experiences through YouTube [18], thereby also achieving an increase in student motivation [19]. Within the same context, teachers can share educational videos with students who want to deepen their understanding of certain topics or need more support in specific subjects, such as maths [20], music [21], or foreign language [22, 23], among other possibilities. Lastly, teachers can use YouTube to create and disseminate educational videos, which can be excellent teaching aids for working on academic knowledge and developing skills [24].

However, scientific literature continues to be quite scarce with respect to studies analysing levels of digital training that allow teachers to use YouTube as an educational resource. The majority of research on digital competence has, on the one hand, focused on the application of different frameworks, such as the DigCompEdu model [25], the TPACK model [26], or the PEAT model, which is currently being developed within the framework of the “Developing ICT in teacher education” (DICTE) Erasmus + Project. On the other hand, it has also mainly involved specific studies by researchers focusing on the local contexts where they work [27, 28]. It has been observed that there are hardly any valid and reliable instruments to assess teachers’ skills for using YouTube as an educational resource [29], with the emphasis being on instruments that measure different variables of the Technology Acceptance Model (TAM) [30] on YouTube [31]. Moreover, although the influence of different variables (gender and educational stage) on the digital skills of educational professionals has been thoroughly examined [32,33,34], these analyses have not specifically focused on YouTube, which is the main contribution of this study.

Taking this context in account, the research questions are as follows: What level of digital competence do teachers have in using digital resources for finding information, communicating information and creating multimedia content at different educational stages? Are there differences in teachers’ digital competence levels according to gender at each educational stage?

2 Related studies

Although there is little research on teachers’ digital competence levels, specifically for using YouTube, there are different studies related to the subject of study.

From a general perspective, regarding self-perceived levels of digital competence for using YouTube, Guillén-Gámez et al. [35] analysed the digital skills of 81 preschool, primary, and secondary teachers in Madrid (Spain) for using 2.0 tools including YouTube. The results revealed that the highest levels of self-perceived digital competence were linked with YouTube, which was the resource that teachers were most familiar with and that had the greatest educational application in the classroom. Furthermore, being female was a moderately negative predictor of digital competence levels, whereas educational stage did not predict digital skills. On the other hand, Ogirima et al. [36] found very positive scores about potential uses for YouTube and its user-friendliness for teaching and learning activities, with no significant differences according to gender among the 200 pre-service basic education teachers from Nigeria that made up the sample. Along the same lines, Buzzetto-More [37] also found that gender did not influence the perceived value of YouTube among 225 university students, although female participants scored higher, positively rating its implementation in the education process. There were also no significant gender differences in YouTube use among the 30 teachers with learning disabilities in Khasawneh’s [38] study, although female participants used it more often.

Regarding the specific skills to search for and select information, Burke et al. [39] analysed 24 university professors’ perceptions about using YouTube for educational purposes in the health sector. According to the results, teachers who use YouTube consider it to be an effective, yet mainly supplementary resource for improving class content, and it is used more by female than male participants. On the other hand, Szeto and Cheng [40] focused on the perceptions of 33 pre-service preschool, primary, and secondary teachers regarding YouTube use during their professional internships. Considering the fact that 100% had used YouTube during their internships, it should be noted that the main purpose was to search for and choose up-to-date content, with none of the participants including other functions such as the social aspect or creating and disseminating their own videos.

As for interaction possibilities with other users through YouTube, Alkhudaydi’s [41] study with 109 university professors in Saudi Arabia stands out for revealing an increase in collaboration thanks to YouTube’s social potential. The creation and dissemination of one’s own content was studied by Tello and Ruiz [42], and YouTube was used by 18% of the 38 people surveyed about publishing content, since they had the necessary digital skills to do so.

With regard to the contributions of the present study, which have previously been justified at the end of the introduction, the research objectives are as follows:

  • O.1 To know teachers’ self-perceived levels of digital competence for using YouTube as an educational resource (finding information, communicating information, and creating multimedia content) at each educational stage.

  • O.2 To analyse whether there are significant differences in the level of digital competence of teachers according to gender, for each educational stage

3 Methods

3.1 Design

In order to achieve the purposes of the study, a quantitative method was used, specifically a non-experimental design through survey. After data collection, descriptive and inferential analyses were carried out according to gender for each educational stage.

3.2 Sample

Non-probability sampling was intentionally used. Data collection was carried out by contacting schools via email and providing them with a link to Google Forms. The total sample consisted of 2157 in-service teachers from all over Spain. Specifically, Table 1 shows the sample distribution according to gender, where information is collected on the number of teachers by educational stage, the percentages of female and male teachers in theses stages, as well as their age and the corresponding standard deviation.

Table 1 Sample distribution
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3.3 Instrument

To understand teachers’ digital skills related to using YouTube for educational purposes, an instrument developed by Guillén-Gámez et al. [29] was used. The instrument was based on three latent factors, with a total of 13 items. These items were created with a 7-point Likert scale where each value was associated with a concrete digital ability (1- I am unable to do it; 2- I cannot do it without help; 3- I can do it alone with great difficulty; 4- I can do it alone with difficulty; 5- I can do it alone with ease; 6- I can do it alone with great ease; and 7- I know how to teach it to others). Depending on the classification of these values, a low digital competence is interpreted with values in the range from 1 to 3, a medium competence with values close to the value 4, and a high digital competence with values between 5 and 7.

The dimensions were as follows:

  • DC-I (digital competence for finding information). The 3 items in this dimension focused on issues regarding searching for and selecting information through YouTube videos.

  • DC-C (digital competence for communicating information). The 5 items analysed teachers’ skills for sharing educational video information and interacting with other platform users.

  • DC-CC (digital competence for creating multimedia content). This dimension, with a total of 5 items, focused on teachers’ digital skills for creating educational audio-visual material for learning the academic content in the curriculum.

The description of the items which make up the three dimensions of the instrument appears in Table 2.

Table 2 Description of instrument items
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Regarding the psychometric properties of the instrument, the authors tested the construct validity with two types of techniques: exploratory factor analysis (EFA) with the IBM SPSS V24 software and confirmatory factor analysis (CFA) with the AMOS V.24 software.

In the EFA, the latent factor that accounted for the highest percentage of the true score (57.78%) was the Communication factor (DC-C). The latent factor with the second highest percentage of variance (11.78%) was Content Creation (DC-CC). The last factor, which obtained the lowest percentage of variation (5.86%), was Information (DC-I). The instrument as a whole explained 75.41% of the variance of the true scores. Moreover, both the Kaiser Meyer Olkin adequacy test (KMO = 0.950) and Bartlett’s test of sphericity were used to check whether the items were adequate to their corresponding latent factors, as well as the suitability of the sample size (χ2 = 33,379.971; df = 136; sig < 0.05) [43, 44].

As for the CFA, the instrument displayed an adequate factor structure based on the thresholds recommended by Bentler [45], Fornell and Larcker [46], Hair et al. [47], and Hu and Bentler [48]. The recommended thresholds as well as the coefficients obtained by the authors for each index analysed are shown in Table 3: CMIN/DF (minimum discrepancy), CFI (Comparative Fit Index), TLI (Tucker-Lewis coefficient), IFI (Incremental Fit Index), NFI (Normed fit index), and RMSEA (Root Mean Square Error of Approximation). Additionally, the authors tested the convergent and discriminant validity of the instrument through the Average Variance Extracted (AVE) and the Maximum Shared Variance (MSV).

Table 3 Construct, discriminant, and convergent validity as well as reliability of the instrument
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Lastly, the instrument had a satisfactory internal consistency through the values obtained with Cronbach’s Alpha, composite reliability (CR), and McDonald’s Omega coefficients.

3.4 Procedure and data analysis

For the collection of information, the instrument was distributed online (Google Forms). For its distribution, the researchers sent emails to preschool, primary, secondary, adult education and VET. The e-mail explained the purpose of the study and requested the free and voluntary participation of teachers. Along with the instrument, questions of a sociodemographic nature were included, such as educational stage, age or gender. The sample, as previously stated, was non-probabilistic and intentional, selecting all the correctly completed records to make up the study sample.

To meet the proposed objectives, the authors carried out the following analysis:

  • First, a descriptive analysis of each of the instrument's items and instrument´s dimensions was conducted, for each educational stage (Table 1 and Fig. 1). For this, different values for the measurement of central tendency and dispersion were used, such as the mean (ítems and dimensions) and standard deviation (dimensions).

  • Secondly, an inferential analysis was conducted to know the existence of significant differences in teachers’ digital skills according to gender for each educational stage. For this, the data were first checked for normality. The Kolmogorov–Smirnov goodness-of-fit test confirmed that the distribution was not normal in any dimension nor for either gender at a level of significance less than 0.05: DC-I (female, KS = 0.133, df = 1527; male, KS = 0.151, df = 630); DC-C (female, KS = 0.169, df = 1527; male, KS = 0.204, df = 630); and DC-CC (female, KS = 0.142, df = 1527; male, KS = 0.115, df = 630). Nevertheless, Srivastava [49] affirms that non-normality would not have a serious effect on data distribution in large samples (in our case, female = 1527 and male = 630) in order to use parametric techniques. Therefore, two tests were used. The first was Levene’s test (Snedecor’s F) to test whether the variances of the distribution of the three quantitative variables in the different compared groups are homogeneous (homoscedasticity criterion). The second was the student’s t-test to compare the means of the two independent groups (gender). Comparisons according to gender have also been carried out through non-parametric techniques (Mann–Whitney) with the aim of testing whether the effects produced in both techniques are similar. As for significance, Cohen [50] interprets the magnitude of the effect size as follows: a value below 0.4 constitutes a small effect, between 0.5 and 0.7 is medium, and above 0.8 is large.

Fig. 1
figure 1

Digital competence for using YouTube at each educational stage

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4 Results

4.1 Digital competences of the teacher on YouTube, for each educational stage

Table 4 shows the scores for each item, which are grouped by dimension (DC-I, DC-C, DC-CC) and educational stage of the teachers (Preschool, Primary, Secondary, VET, Adult). Regarding the information dimension, the teachers overall reported being able to do the different described actions by themselves with ease. The use of search filters (DC-12) and the ability to discern the suitability of YouTube content for proposed activities (DC-14) stand out. These elements are carried out very easily by VET (5.97 in both items) and secondary education teachers (DC-I2, 5.90; DC-I4, 5.95). These scores drop for using the explore function (DC-I3), especially in VET (4.98), and also affects teachers in the rest of the educational stages. As for the communication dimension, teachers reported being able to carry out the majority of the actions by themselves and with ease. Although there was some difficulty with participating in live chats on YouTube (DC-C5), especially for preschool (4.69) and secondary (4.83) teachers, the rest of the actions were carried out correctly and independently. It is worth noting the abilities of VET (6.15) and adult education teachers (6.00) to react to video content (like/dislike) (DC-C4); VET abilities (6.01) to share videos on YouTube (DC-C6); and adult education teachers’ abilities (6.04) to subscribe to a YouTube channel and activate alerts for new content (DC-C2). The lowest level of digital competence was found in the content creation dimension, with teachers being able to execute the majority of the items on their own but with great difficulty. However, there were some actions where teachers had greater skills and were able to do them alone, although with difficulty. This was the case for cutting and adding transitions and text to videos (DC-CC5), especially for primary teachers (3.90) and adult education teachers (3.90). This also occurred with knowing how to add subtitles to videos (DC-CC7), particularly for primary teachers (3.71) and VET (3.70). Nevertheless, there are actions that reflected great difficulty, such as putting the Creative Commons CC BY license on videos (DC-CC8), where preschool teachers (2.83) and adult education teachers (2.84) were closest to needing to ask for help to do so.

Table 4 Teachers' digital skills (arithmetic mean per item) for each educational stage
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The overall level of teachers’ self-perceived digital competence for using YouTube as a teaching resource (the mean of all the items that make up each dimension of the instrument) at each educational stage can be seen in Fig. 1. In general, the teachers have medium–low digital skills for audio-visual content creation (DC-CC) in all educational stages with similar scores (Preschool = 3.23 ± 1.92; Primary = 3.51 ± 1.91; Secondary = 3.46 ± 1.95; VET = 3.51 ± 1.91; Adult = 3.29 ± 1.98). However, the opposite was found in DC-I and DC-C, where teachers had similar and higher scores.

4.2 Analysis of the digital competences of teachers between genders, for each educational stage

Figure 2 shows the observed level of digital competence for using YouTube to search for information and educational content (DC-I). Male teachers score higher than women in all educational stages, with a greater difference in secondary education and VET stages.

Fig. 2
figure 2

Teachers’ digital competence for finding information on YouTube (DC-I) according to gender and educational stage

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Table 5 shows the statistical contrast to test for significant differences between genders in both of the previously described stages with the greatest differences (secondary and VET). The results are found using both statistical techniques (Student’s t-test and Mann–Whitney), with similar effect sizes in the small range.

Table 5 Statistical contrast between genders for DC-I
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Digital competence levels for sharing educational videos and interacting with other users on YouTube (DC-C) can be seen in Fig. 3. High scores are obtained in both genders. Specially, the difference is greater in the early stages of education, and less in the higher stages.

Fig. 3
figure 3

Teachers’ digital competence for sharing information on YouTube and interacting with other users (DC-C) according to gender and educational stage

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Table 6 shows statistically significant differences between genders at the initial educational stages (preschool, primary, and secondary education), whereas at higher stages (adult and VET) no differences were found. The statistical techniques reveal significant differences. Although the effect sizes are similar in the primary and secondary education stages, the effect size was different for preschool, ranging from a small to a large effect.

Table 6 Statistical contrast between genders for DC-C
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Digital competence levels for creating educational and audio-visual material for YouTube (DC-CC) can be seen in Fig. 4. In general, the levels were medium–low for both genders. The differences are minimal between genders for preschool and adult education, with greater differences in the rest of the stages (primary, secondary, and VET).

Fig. 4
figure 4

Teachers’ digital competence for creating educational content on YouTube (DC-CC) according to gender and educational stage

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Table 7 shows the statistically significant differences found according to gender in primary, secondary, and VET, with effect sizes between small and medium in both statistical techniques.

Table 7 Statistical contrast between genders for DC-CC
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5 Discussion

Knowing how to use technology from a pedagogical perspective is a key skill for teachers [10]. Among the different resources available, YouTube offers a wide range of videos with many educational possibilities [9], in addition to allowing for interaction and content creation for dissemination. For this reason, teachers must have digital skills for carrying out teaching and learning processes that improve the understanding of content through YouTube, which also influence motivation [19] due to the benefits of the audio-visual format [3]. Next, the discussion of the results is presented based on the objectives of the study.

Starting with teachers’ self-perceived levels of digital training for using YouTube (finding information, communicating information and creating multimedia content) at each educational stage, the results in general were excellent. The teachers positively evaluated their ability to implement this resource in educational contexts, which coincides with different studies [35,36,37]. It should be noted that the teachers’ scores at different educational stages, for both the items and dimensions, are similar, thus aligning with the findings of Guillén-Gámez et al. [35], whose study demonstrated that educational stage is not a predictor of digital competence levels for using digital resources.

Regarding the dimensions of the instrument, it is necessary to examine the overall scores for each educational stage and the impact of gender on these results, considering the possibility of significant differences between female and male participants. It should be noted that in all dimensions and educational stages, male teachers always had higher scores than their female counterparts, with significant differences in some cases. This partially aligns with Ogirima et al.’s [36] study, where male participants had more positive perceptions, although no significant differences were found. However, the results contradict findings of various studies [37,38,39], where in addition to not finding significant differences, women used YouTube more than men.

Looking into the ability to search for and filter videos that are pedagogically suitable for class, teachers had high scores at all educational stages. These results confirm that teachers are capable of selecting pedagogically suitable audio-visual content, as seen in a study by Burke et al. [39]. They also coincide with the evaluations of such skills in Szeto and Cheng’s [40] study, which included preschool, primary, and secondary education. It should be noted that in this dimension, gender differences were significant in secondary and VET.

As for teachers’ abilities to interact through YouTube, there were positive evaluations in all stages, similar to Alkhudaydi’s [41] findings, where this platform was seen to improve collaboration and student–teacher relationships. Significant differences were found according to gender at the initial educational stages (preschool, primary, and secondary) in favour of the male participants.

With regard to skills for audio-visual content creation, the teachers at different educational stages gave this dimension medium–low scores. This aligns with Szeto and Cheng’s [40] study, where pre-service preschool, primary, and secondary teachers did not consider the purpose of creating their own YouTube videos. However, Tello and Ruiz [42] highlight that 18% of the respondents in their study used YouTube to publish content, a number that ought to be improved through digital competence training. The scores of male participants were significantly higher in primary, secondary, and VET.

6 Conclusions

In a society characterised by mass Internet access, digital platforms such as YouTube open the door to all kinds of content, including educational content that can be a valuable tool to complement classroom learning [8]. Because of this, teachers must develop digital skills that enable them to find and select relevant existing content by evaluating the quality of it [17]. They must also be able to communicate and interact with other users, thereby taking advantage of YouTube’s social factor [41], protecting their privacy, and navigating the platform safely, critically, and effectively. Moreover, the possibility of creating educational content that meets the needs and interests of each teacher, in addition to being able to disseminate it [42] is an attractive option for students that prefer digital media. For these reasons, it is essential to understand teachers’ digital skills in order to detect areas for improvement and implement them in teacher training.

Among teachers at the different educational stages, it is worth noting that teachers’ perceptions of their digital competence for using YouTube are greater in terms of searching for and selecting information as well as sharing videos and interacting with other platform users. On the other hand, teachers report lower skills for creating and disseminating their own audio-visual content for their classes. Considering the gender variable, male participants had better perceptions of their digital competence for using YouTube in all dimensions and educational stages analysed. It should be noted that there were significant differences in secondary education in all dimensions, possibly due to the impact of different specialisations and the use that can be made of YouTube. However, there were no significant differences in adult education in any of the dimensions, which may be due to the flexible nature of this educational stage and the interaction between its participants. There were significant differences between scores in other stages, such as in secondary and VET in DC-I (searching for and selecting information), preschool, primary, and secondary education in DC-C (sharing videos and interacting with users), and primary, secondary, and VET in DC-CC (audio-visual content creation and dissemination).

Regarding the limitations a, using a self-perception instrument can lead to a fragmented view, as it only reflects the digital skills that participants believe they have and not what they truly know how to do. Also, when defining the study variables, only educational stage and gender were considered.

About future lines of research, pre-test/post-test studies on the impact of digital competence training for using YouTube could be carried out through practical tasks. Another aspect would be creating a sample of pre-service teachers to analyse their digital skills when they start higher education, so that policies and curricular actions could be implemented to reduce the digital divide in their training. Also, in future research, should be included other factors that may be relevant for this topic, such as years of experience, activity levels as a YouTube user, or skill levels for designing digital resources.