Artistic, Digital, and Pedagogical Competence in Language Teacher Education: Generating Educational Videos and Innovative Teaching Practices

Abstract

This study analyses the development of digital and artistic competence among pre-service language teachers within the framework of a teaching innovation project (2018–2024) at the University of Oviedo. It not only explores student teachers’ perceptions of the proposal’s pedagogical usefulness but also seeks to determine whether statistically significant differences exist between participating master and undergraduate students. The research adopts a mixed-methods approach: the qualitative component is based on the European DigCompEdu framework, while the quantitative component employed an ad hoc questionnaire analysed using SPSS (v.22), including descriptive analysis, Levene’s test to assess equality of variances and Student’s t-test to identify potential significant differences according to the master–undergraduate variable. The results indicate, on the one hand, that this initiative successfully strengthens five of the six areas defined in the European framework, and on the other, that there is an overall high level of satisfaction, reflected in the high scores obtained in the competences examined in this study: artistic, digital and pedagogical. These findings underscore the value of integrating innovative, video-based strategies into teacher education programmes to support the development of key competences required for 21st-century teaching.

1. Introduction

1.1. Digital, Artistic, and Pedagogical Competences

Addressing digital competence in initial teacher education is particularly relevant (UNESCO, 2020), as future teachers need to learn how to use analyses conducted through the European DigCompEdu framework and quantitative questionnaires to enhance, innovate and facilitate teaching and learning processes (Flores-Lueg & Roig, 2016). However, studies such as that by Castañeda et al. (2018) reveal that the current provision is often insufficient and fails to meet the demands of contemporary society. In response, the past decade has seen growing emphasis on methodologies, projects and activities aimed at fostering digital competence (Chiu et al., 2024; García & Trigueros, 2021).

Another key area addressed in this study is artistic competence, which—as García-Esteban (2022) highlights—not only improves academic performance but also fosters creativity, imagination and collaboration. According to García-Sampedro et al. (2024b), a strong connection exists between artistic and digital competence in the context of this project.

Artistic competence encompasses a set of capacities that enable individuals to communicate through the codes and languages of creative expression. It also involves the ability to engage critically with cultural expressions and to appreciate diversity and freedom of expression (Giráldez, 2007). This competence provides notable cognitive and emotional benefits, as students develop not only technical skills but also socio-emotional abilities that are essential for personal well-being and holistic development (Concha-Huarcaya et al., 2024). Research by Meneses and Valencia (2023) confirms that engagement with the arts enhances creativity, critical thinking and problem-solving skills, while also strengthening empathy and interpersonal relationships (Bajardi, 2016). Although artistic activities are not the only route to fostering these abilities, they represent a particularly effective and integrative one.

In this regard, other authors argue that it is also necessary to develop pre-service teachers’ ability to use and convey knowledge in disciplined, critical, and creative ways, including the capacity to make effective use of technology (Chirinos et al., 2020), which is essential for their pedagogical competence. Such competence, in turn, provides a framework through which learners may foster broader skills, including critical thinking, problem-solving and communication (Martín-Párraga et al., 2023).

1.2. Technology and Teaching Integration Models

Several frameworks have been developed to integrate technology into the teaching process (Cabero-Almenara et al., 2020). One of the most widely used is the SAMR model, designed to evaluate how technology reshapes teaching practices and tasks, rather than learning outcomes directly (Puentedura, 2010). The model distinguishes four levels grouped into two phases, as shown in Figure 1, created by Cáceres-Nakiche et al. (2024). While it has sometimes been associated with Bloom’s taxonomy (Paulauskaite-Taraseviciene et al., 2022), its primary focus is on the pedagogical use of tools for teaching.

Another influential framework is the Technological, Pedagogical, and Content Knowledge (TPACK), developed by Mishra and Koehler (2006). Rather than being a teaching tool, the TPACK framework provides a descriptive model to analyse how teachers combine content, pedagogy, and technology in educational contexts (Akturk & Ozturk, 2019; Atun & Usta, 2019; Chen & Jang, 2019). It is considered particularly useful in initial teacher education, as it provides a conceptual structure to guide pre-service teachers in understanding and reflecting on the competences required to integrate technology into their teaching practice (Aziz et al., 2022; Trigueros & Aldecoa, 2021).

This model integrates three domains of knowledge: content (CK), pedagogy (PK), and technology (TK). The interaction among these domains gives rise to four types of knowledge: Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK), each representing essential knowledge for effective teaching. This framework also includes contextual knowledge (XK), that is, an understanding of the institutional and social environment in which teaching takes place (Mishra, 2019). These types of knowledge are graphically represented as overlapping circles, as shown in Figure 2.

Despite the model’s success, several limitations have been identified in its structure, particularly regarding its somewhat vague definitions and limited practical applicability (Willermark, 2018; Falloon, 2020). For this reason, it is often complemented or substituted by the European DigCompEdu framework, which provides more concrete and actionable concepts for addressing teachers’ digital competence (Spante et al., 2018).

The European Framework for the Digital Competence of Educators (DigCompEdu) is an initiative of the European Commission’s Joint Research Centre (JRC). It provides a standard reference for assessing and developing teachers’ digital competence across all levels of education (Redecker, 2017).

The Framework is organised into six competence areas, comprising 22 specific skills related to digital education. These areas are: Professional Engagement, focusing on the use of digital tools for professional communication, collaboration among teachers and continuous professional development; Digital Resources, which addresses the creation, adaptation and sharing of digital educational materials; Teaching and Learning, involving the management and integration of digital technologies in teaching processes; Assessment, that is, the use of digital technologies to enhance the assessment of learning; Empowering Learners, relating to inclusion, personalisation and encouraging active student participation through digital tools; and Facilitating Learners’ Digital Competence, aimed at supporting the development of learners’ digital literacy, covering aspects such as safety, creativity and digital citizenship (Redecker, 2017). It should be noted, however, that a new version of the framework (DigCompEdu 3.0) is currently under development and expected to be released shortly, which may introduce updated competence descriptors.

Each of these six Competence Areas includes six progression levels, ranging from A1 to C2. This allows both pre-service and in-service teachers to self-assess their own competences while also serving as a valuable tool to evaluate whether innovation proposals linked to these areas genuinely achieve their intended purpose. Below is the outline of the competences and sub-competences defined in the DigCompEdu Framework as shown in Figure 3.

1.3. Innovative Learning Proposal: Artistic, Digital and Pedagogical Strategies in Language Teacher Education

The innovative learning proposal Artistic, Digital and Pedagogical Strategies in Language Teacher Education (hereafter ADPS), evaluated in this study, is one of the several projects undertaken within the framework of the University of Oviedo’s Teaching Innovation Project Call. Its initial aim was to showcase and disseminate educational materials created by future teachers in video format. To support this, students were given the necessary tools, and a dedicated website was created to host and share the videos. These materials—primarily intended for learners in early years (ages 3–5), primary (ages 6–12) and secondary education (ages 12–18)—were recorded using mobile devices and promote resource sharing and pedagogical exchange among students and faculty across teacher education programs. Within this collaborative framework, university students, guided by their instructors, produced didactic videos that were shared through subject-specific YouTube channels embedded on the project’s website, specifically designed to support this initiative.

Subsequent phases of the project built on the original proposal by inviting primary and secondary schools to join the digital platform and contribute new educational content. As a result, participating schools also created and shared video-based resources, fostering meaningful interactions among pre-service teachers, in-service educators—from both university and school settings—and pupils (García-Sampedro et al., 2023; Torralba-Burrial & García-Sampedro, 2022).

The ADPS proposal was launched in 2018. According to the project’s official website, participation expanded considerably during the COVID-19 pandemic, when teachers developed a broader range of digital teaching resources, particularly videos, and adapted their strategies to online or hybrid formats. The website served as a central hub for disseminating the educational content created by participants.

This paper presents the results of an evaluation of the proposal and pre-service teachers’ perceptions of its implementation at the Faculty of Teacher Training and Education of the University of Oviedo. The initiative aimed to train future teachers in designing and producing educational videos.

1.4. Objectives

On the one hand, the study applies the DigCompEdu framework (European Commission) to evaluate the ADPS proposal and to assess the extent to which it meets the professional needs of future language teachers. On the other hand, it draws on data collected from students enrolled in the Bachelor’s Degree in Early Childhood Education and the Bachelor’s Degree in Primary Education programmes, as well as participants in the Master in Secondary Education, all of whom completed an ad hoc questionnaire following their participation in the project. This data collection aimed to measure pre-service teachers’ satisfaction with the proposal, to evaluate their perceptions of its contribution to the development of three key competence areas—artistic, digital and pedagogical—and finally to determine whether statistically significant differences exist in their evaluations based on whether they are enrolled in undergraduate or master programs.

2. Materials and Methods

For this study, a mixed-methods approach was selected to align with the established objectives (Creswell, 2024). It is not conceived as simply adding quantitative and qualitative elements but as a strategic integration (Ramírez-Montoya & Lugo-Ocando, 2020) designed to connect the results of both. This mixed paradigm provides a more holistic foundation for knowledge, offering researchers a distinct perspective when conducting these studies (Ramírez-Montoya & Lugo-Ocando, 2020).

The study was divided into two clearly defined phases. Each phase employed a different methodological approach: a qualitative approach for analysing the proposal ADPS, and a quantitative approach for examining the perceptions of pre-service teacher education students who participated in it.

The DigCompEdu model was selected to analyse this proposal because, among other things, it was explicitly designed to assess teacher education programmes, educational initiatives and policies that address the needs of 21st-century society (Caena & Redecker, 2019). In addition to supporting the integration of technology into teaching, the DigCompEdu model was also applied as a standard reference for assessing and developing teachers’ digital competence (Redecker, 2017).

The ADPS proposal examined in this study was implemented between 2018 and 2024 as part of a broader innovation project at the University of Oviedo. It is embedded within a larger research initiative that used an ad hoc questionnaire completed by university language students for data collection. The quantitative questionnaire began with demographic questions and comprised 32 items, grouped into four blocks of eight items each: Satisfaction, Pedagogical Usefulness, Cooperative Competence, Digital Competence, Artistic Competence and Communicative Competence. Responses were measured using a five-point Likert scale, where 1 represented the lowest level of agreement and five the highest.

2.1. Participants

The study involved 466 students enrolled in various courses within the area of Language and Literature Didactics (Spanish, Asturian, and English), offered in both the Master Degree in Secondary Education (specialization in English Language Teaching) and the Bachelor’s Degree in Primary and Infant Education. The participating courses included Teaching and Learning; English from the master programme and the bachelor’s programme; Didactics of Literature, Didactics of the Asturian Language II, Didactic Training for the Language Classroom; English II, English for the Bilingual Classroom, and Storytelling, as well as the Songs and Games Workshop for the English Classroom. All students were enrolled in the Faculty of Teacher Training and Education at the University of Oviedo. Of the 466 participants, 77 identified themselves as men (16.52%) and 389 as women (83.48%).

Table 1. Sociodemographic Data.

	Master’s Students	%	Undergraduate Students	%
Men	15	25.42%	62	15.23%
Women	44	74.58%	345	84.77%
Total	59		407

shows demographic data.

2.2. Instrument

The following section describes the instruments used in this study. Drawing on the DigCompEdu Framework, a checklist was developed that includes its 22 defined competences. This instrument was designed to evaluate whether the proposal meets the requirements set by the European Commission and supports the integration of technology into teaching and learning. It was chosen because, as noted earlier, it is particularly suitable for conducting content analyses of documents by recording observed characteristics and patterns. In addition, checklist implementations can be beneficial for indicating the likely pedagogical quality of online learning materials in higher education (Hosie et al., 2005). As Hernández-Sampieri and Mendoza-Torres (2018) point out, the checklist can be considered a qualitative data collection instrument, as it enables direct recording from which subsequent reports can be produced.

To conduct this study, university lecturers designed an ad hoc quantitative questionnaire. It was based on a review of relevant literature and aligned with the project’s objectives and variables. To ensure its validity, a panel of seven experts from participating universities reviewed the instrument. The final version included one block of demographic items and three additional blocks (each with eight items) addressing Artistic Competence, Digital Competence, and Pedagogical Usefulness. Responses were recorded on a five-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree).

Table 2. Blocks, number of items and response type (ad hoc instrument).

Blocks	Number of Items	Response Type
Artistic Competence	8	Likert scale, where 1 is the lowest score and 5 is the highest
Digital Competence	8
Pedagogical Usefulness	8
Satisfaction	8

includes blocks, the number of items and the type of response.

The reliability of the questionnaire was assessed using Cronbach’s alpha. The resulting coefficient (0.963) is considered excellent according to established thresholds (Shrestha, 2021), confirming the instrument’s suitability for future research.

2.3. Procedure

The checklist was designed and completed by the researchers involved in the proposal to assess its alignment with the DigCompEdu Framework. Given that DigCompEdu is the official European reference for teachers’ digital competence, and considering the study’s context within European higher education, it was considered the most appropriate framework for analysis. This approach can serve as a reference for pre-service teachers, enabling them to evaluate their own competences and determine the suitability of the teaching proposals they design and implement in their future classrooms.

The questionnaires were completed online via the Virtual Campus. They were answered in class after students had participated in the innovative learning proposal, ADPS. Clear instructions were provided on the study’s aim, completion guidelines, the voluntary nature of participation, data collection and management procedures, and assurances of anonymity. Although participation was voluntary, no student refused to complete the questionnaire, resulting in a 100% response rate.

2.4. Data Analysis

The proposal was analysed using the DigCompEdu six main areas as categories, and its 22 competences as subcategories. The data collected through the questionnaires were examined using IBM SPSS Statistics (v.22) for Windows. Questionnaire responses were rated on a five-point Likert scale (1 = lowest, 5 = highest). Descriptive statistics were calculated, including means, standard deviations, skewness, kurtosis and range.

To address the research objectives, inferential analysis was conducted through Student’s t-tests to determine whether statistically significant differences existed between undergraduate and Master’s students (Rubio & Berlanga, 2012). Levene’s test was applied to assess the equality of variances across groups. The significance level was set at p = 0.05. All written observations were coded independently by two researchers, who first identified recurrent ideas and then grouped them into broader categories aligned with the study’s objectives. The resulting categories were subsequently synthesised into the simplified paragraphs presented in the Results Section. Inter-coder agreement was checked, and discrepancies were resolved through discussion until consensus was reached, thus ensuring the reliability of the analysis.

3. Results

The following section presents the results obtained from the qualitative analysis conducted using the checklist, which categorizes the six primary Competence Areas and 22 competences as subcategories. Although the instrument was initially based on categorical responses (Yes/No/Sometimes), the results were aggregated thematically rather than presented as raw frequencies. For this reason, the analysis does not reproduce a table of percentages per item but instead synthesises the data into broader categories, as shown in the following sections.

3.1. Qualitative Analysis

• Area 1: Professional Engagement

  1.1.

  Organisational communication: By adopting collaborative methods, the proposal seeks to improve communication practices with students, families and external organisations. It focuses on developing shared strategies for planning and coordination that enhance the overall flow of information.

  1.2.

  Professional Collaboration: Digital tools are integrated within the proposal to promote joint work among educators, encouraging the co-construction of knowledge, the exchange of professional experiences and the collective development of innovative teaching practices.

  1.3.

  Reflective Practice: By incorporating a tailored questionnaire, the proposal creates opportunities for participants to engage in critical reflection, evaluate their digital teaching approaches and enhance their own pedagogical practices.

  1.4.

  Digital Continuous Professional Development: The proposal integrates digital materials to support the ongoing professional learning of pre-service teachers.

• Area 2: Digital Resources

  2.1.

  Creating and modifying digital content: As part of its approach, openly licensed and approved digital resources are adapted and repurposed, while new educational materials are developed collaboratively. Care is taken to ensure that their design and intended use reflect specific learning objectives, the educational context, chosen pedagogical methods and the characteristics of the learner group.

  2.2.

  Managing, protecting and sharing digital resources: Digital content is organised and made accessible to students, families and fellow educators. Sensitive information is handled securely, with strict adherence to privacy and copyright requirements. It further encourages the adoption and creation of openly licensed educational materials, emphasising correct attribution and promoting open educational practices.

• Area 3: Teaching and Learning

  3.1.

  Teaching: Digital tools and resources are integrated into teaching to improve the impact of instructional activities. It includes careful coordination of technology-enhanced sessions and supports the exploration and adoption of innovative formats and pedagogical strategies.

  3.2.

  Guidance: The proposal integrates digital technologies and services to improve interaction with learners during classes and in other learning contexts. It supports personalised guidance that is timely and targeted and promotes the development of new and creative ways to offer adequate learner support.

  3.3.

  Collaborative learning: By embedding digital technologies into learning activities, the approach aims to facilitate meaningful student interaction and collective problem-solving. It promotes the use of these tools in group tasks to strengthen communication, encourage shared understanding and support the joint construction of knowledge.

  3.4.

  Self-regulated learning: Supporting learners’ capacity for self-regulated learning is a key aspect of the approach, with digital technologies integrated to help them plan, monitor and reflect on their progress. It also facilitates the documentation of learning evidence, the exchange of ideas and the development of creative solutions.

• Area 4: Assessment

  4.1.

  Assessment strategies: While it does not incorporate digital technologies for formative or summative assessment, the approach broadens the range of assessment formats and ensures they are well-suited to diverse learning needs.

  4.2.

  Analysing evidence: The approach involves working with learner-produced videos through processes of selection, critical analysis and interpretation to support and inform teaching practice.

  4.3.

  Feedback and Planning: Although it does not employ digital technologies to deliver immediate, targeted feedback, the approach focuses on adapting teaching strategies to offer personalised support informed by digital evidence. It also helps learners and families interpret this evidence effectively for educational decision-making.

• Area 5: Empowering Learners

  5.1.

  Accessibility and inclusion: The approach enhances accessibility for learners with hearing impairments by offering videos that include subtitles in multiple languages, making resources and activities more inclusive. Nevertheless, only a limited number of these videos provide audio descriptions to support learners with visual disabilities.

  5.2.

  Differentiation and personalisation: By integrating digital technologies, the proposal supports differentiated learning by enabling students to progress at their own pace and level while pursuing personalised pathways and goals.

  5.3.

  Actively engaging learners: The approach utilizes digital technologies to foster active and creative student engagement with subject matter. It also supports teaching strategies that build transversal skills, critical thinking and creative expression. In addition, it encourages learning in real-world contexts with hands-on activities, scientific inquiry and complex problem-solving.

• Area 6: Facilitating Learners’ Digital Competence

  6.1.

  Information and media literacy: While the approach includes learning activities, it does not incorporate formal assignments or assessments. Students are expected to search for information and resources in digital environments, organising, processing and interpreting this material. They also compare and evaluate the credibility and reliability of information and its sources with a critical perspective.

  6.2.

  Digital communication and collaboration: The approach offers learning activities but excludes assignments or assessments that aim to develop the practical and responsible use of digital technologies for communication, collaboration and civic engagement.

  6.3.

  Digital content creation: Learners are expected to express themselves digitally and modify or create content in various formats; however, there is no explicit instruction on applying copyright or licensing requirements or on acknowledging licensed content appropriately. The approach includes learning activities; however, it lacks assignments and formal assessments.

  6.4.

  Responsible use: While the proposal addresses risk management and promotes the safe and responsible use of digital technologies, it lacks specific measures to support students’ physical, psychological and social well-being during their engagement with these tools.

  6.5.

  Digital problem solving: The proposal offers learning activities but omits assignments or assessments that help students address technical challenges and transfer their technological knowledge creatively to unfamiliar situations.

3.2. Questionnaire Results

This section presents the descriptive statistics outlined below: Mean; Standard Deviation; Statistic; Skewness, Kurtosis and Range, followed by the global mean scores for each of the three blocks and the key results. Findings are first reported overall and then broken down by academic level (undergraduate vs. Master’s students). Student’s t-tests and Levene’s tests were applied to identify statistically significant differences where applicable.

Table 3. Descriptive statistics.

	M	SD	Skewness	Kurtosis	Range
	Stat.	Stat.	Stat.	Stat.
1.	4.14	0.891	−1.042	1.195	4
2.	4.27	0.891	−1.230	1.335	4
3.	4.31	0.904	−1.500	2.301	4
4.	4.15	0.969	−1.254	1.411	4
5.	4.16	0.941	−1.171	1.197	4
6.	4.16	0.924	−1.031	0.716	4
7.	4.20	0.932	−1.217	1.344	4
8.	4.12	0.958	−1.020	0.660	4
9.	4.18	0.967	−1.041	0.495	4
10.	3.96	1.054	−0.938	0.454	4
11.	4.03	1.069	−1.044	0.599	4
12.	4.23	0.977	−1.275	1.144	4
13.	4.11	1.012	−1.063	0.608	4
14.	3.27	1.374	−0.306	−1.107	4
15.	4.11	0.971	−1.122	1.078	4
16.	3.91	1.093	−0.897	0.215	4
17.	4.12	0.882	−1.015	0.956	4
18.	4.11	0.914	−1.038	1.033	4
19.	3.86	1.127	−0.724	−0.258	4
20.	4.10	0.919	−0.939	0.591	4
21.	4.11	0.939	−0.944	0.460	4
22.	3.85	1.041	−0.727	0.005	4
23.	3.71	1.148	−0.672	−0.287	4
24.	4.24	0.888	−1.244	1.576	4
25.	4.16	0.041	0.885	−1.132	4
26.	3.98	0.047	1.026	−0.986	4
27.	4.06	0.048	1.031	−1.029	4
28.	4.35	0.039	0.838	−1.318	4
29.	4.42	0.038	0.815	−1.446	4
30.	4.31	0.046	0.997	−1.531	4
31.	3.96	0.051	1.104	−0.942	4
32.	3.91	0.049	1.071	−0.805	4

Note: M = Mean; SD = Standard Deviation; Stat. = Statistic.

shows the descriptive statistics of the analysed items.

The overall mean for Artistic Competence was M = 4.19 (SD = 0.042), followed by Satisfaction M = 4.13 (SD = 0.1960), Pedagogical Usefulness (M = 3.99; SD = 0.1733) and Digital Competence (M = 3.98; SD = 0.288). Students rated the proposal positively, particularly in Artistic Competence. The top-rated items were: 29, I consider it useful to implement these types of tasks among students (M = 4.42); 28, I feel capable of implementing these tasks as a teacher (M = 4.35); 3, Understanding the aesthetic value in audiovisual communication (M = 4.31). The lowest were: 14, Creating a YouTube channel (M = 3.27); 23, Use of space (M = 3.71); 22, Time organisation (M = 3.85).

A total of 71.87% of items (23/32) scored above 4, showing broad student agreement across the two blocks analysed. Differences between master and undergraduate students were explored by comparing the mean scores per item and block.

Table 4. Mean scores by level of study (Master or Undergraduate).

		Mean	SD
1.	Master	4.15	0.887
	Undergraduate	4.14	0.893
2.	Master	4.44	0.836
	Undergraduate	4.24	0.897
3.	Master	4.44	0.794
	Undergraduate	4.29	0.919
4.	Master	4.27	0.980
	Undergraduate	4.13	0.968
5.	Master	4.19	0.919
	Undergraduate	4.16	0.945
6.	Master	4.14	0.937
	Undergraduate	4.16	0.923
7.	Master	4.42	0.814
	Undergraduate	4.17	0.944
8.	Master	4.20	0.906
	Undergraduate	4.11	0.965
9.	Master	4.10	0.029
	Undergraduate	4.19	0.959
10.	Master	3.90	1.155
	Undergraduate	3.97	1.039
11.	Master	4.08	1.164
	Undergraduate	4.03	1.056
12.	Master	4.36	1.030
	Undergraduate	4.21	0.969
13.	Master	4.12	1.052
	Undergraduate	4.10	1.007
14.	Master	2.81	1.444
	Undergraduate	3.34	1.353
15.	Master	4.08	1.022
	Undergraduate	4.11	0.965
16.	Master	3.81	1.196
	Undergraduate	3.93	1.078
17.	Master	4.00	0.891
	Undergraduate	4.14	0.881
18.	Master	4.08	0.877
	Undergraduate	4.12	0.920
19.	Master	3.68	1.319
	Undergraduate	3.88	1.096
20.	Master	4.00	0.928
	Undergraduate	4.12	0.918
21.	Master	3.93	1.096
	Undergraduate	4.14	0.913
22.	Master	3.73	1.080
	Undergraduate	3.87	1.036
23.	Master	3.53	1.120
	Undergraduate	3.73	1.150
24.	Master	4.27	0.997
	Undergraduate	4.23	0.872
25.	Master	4.34	0.779
	Undergraduate	4.13	0.899
26.	Master	4.03	1.017
	Undergraduate	3.97	1.032
27.	Master	4.08	1.005
	Undergraduate	4.06	1.039
28.	Master	4.36	0.804
	Undergraduate	4.34	0.845
29.	Master	4.51	0.751
	Undergraduate	4.41	0.825
30.	Master	4.46	1.023
	Undergraduate	4.29	0.994
31.	Master	3.95	1.136
	Undergraduate	3.96	1.104
32.	Master	3.98	1.075
	Undergraduate	3.90	1.072

SD = Standard Deviation.

presents the averages obtained from master and undergraduate students for each item.

In the Artistic Competence block, Master’s students reported a mean level of agreement of M = 4.27 (SD = 0.1319), while undergraduates reported M = 4.17 (SD = 0.0602). In Digital Competence, the reported means were M = 3.87 (SD = 0.4721) for master and M = 3.99 (SD = 0.2784) for undergraduates. For Pedagogical Usefulness, reported means were M = 3.89 and M = 4.02, respectively. In the Satisfaction block, Master’s students reported M = 4.20 (SD = 0.2272), while undergraduates reported M = 4.12 (SD = 0.1928).

In Digital Competence, both groups scored below 4, suggesting a moderate view of its usefulness. By contrast, Artistic Competence received scores above 4, indicating high perceived value, as is also the case with the Satisfaction block. The most significant difference between groups also appeared in Digital Competence, where undergraduate students showed slightly greater agreement, reflecting a more favourable overall assessment.

As previously noted, results in three of the four blocks (Artistic Competence, Digital Competence, and Pedagogical Usefulness) suggest that undergraduate students expressed slightly more positive perceptions, while in the Satisfaction block, Master’s students reported marginally higher values. This pattern in the first three blocks is reflected in the number of items with mean values above 4: 18 for Master’s students and 23 for undergraduates.

The highest-rated items among Master’s students were: 29, I consider it useful to implement these types of tasks among students (M = 4.51; SD = 0.751); 30, I would recommend maintaining this activity for the next course (M = 4.46; SD = 1.023); 2, Creating personal artistic products (M = 4.44; SD = 0.836). In contrast, the lowest were: 14, Creating a YouTube channel (M = 2.81; SD = 1.444); 19, Values and multicultural content (M = 3.68; SD = 1.319); 23, Use of space (M = 3.53; SD = 1.120).

Among undergraduate students, the top-rated items were: 29, I consider it useful to implement these types of tasks among students (M = 4.34; SD = 0.825); 28, I feel capable of implementing these tasks as a teacher (M = 4.34; SD = 0.845); 3, Understanding the aesthetic value in audiovisual communication (M = 4.29; SD = 0.919). In contrast, the lowest rated were 14, Creating a YouTube channel (M = 3.34; SD = 1.353); 23 (M = 3.73; SD = 1.150); 22, Time organisation (M = 3.87; SD = 1.036).

Table 5. Levene’s test and Student’s t-test for independent samples.

		Levene’s Test for Equality of Variances		t-Test for Equality of Means
		F	Sig.	t	df	Sig. (2-Tailed)	Mean Difference	Standard Error Difference	Confidence Interval of the Difference (95%)
									Lower	Upper
1.	EVa	0.002	0.966	0.140	464	0.889	0.017	0.124	−0.227	0.262
	EVna			0.141	76.052	0.888	0.017	0.124	−0.229	0.264
2.	EVa	1.046	0.307	1.593	464	0.112	0.197	0.124	−0.046	0.441
	EVna			1.679	78.675	0.097	0.197	0.118	−0.037	0.431
3.	EVa	1.570	0.211	1.158	464	0.247	0.146	0.126	−0.102	0.393
	EVna			1.291	82.267	0.200	0.146	0.113	−0.079	0.370
4.	EVa	0.125	0.724	1.044	464	0.297	0.141	0.135	−0.124	0.406
	EVna			1.035	75.349	0.304	0.141	0.136	−0.130	0.412
5.	EVa	0.073	0.786	0.204	464	0.839	0.027	0.131	−0.231	0.285
	EVna			0.208	76.903	0.836	0.027	0.128	−0.229	0.283
6.	EVa	0.007	0.934	−0.225	464	0.822	−0.029	0.129	−0.282	0.224
	EVna			−0.223	75.259	0.824	−0.029	0.130	−0.289	0.230
7.	EVa	1.167	0.281	1.945	464	0.052	0.252	0.129	−0.003	0.506
	EVna			2.174	82.401	0.033	0.252	0.116	0.021	0.482
8.	EVa	0.106	0.745	0.732	464	0.464	0.098	0.133	−0.165	0.360
	EVna			0.768	78.387	0.445	0.098	0.127	−0.156	0.351
9.	EVa	0.293	0.589	−0.667	464	0.505	−0.090	0.135	−0.355	0.175
	EVna			−0.633	73.350	0.529	−0.090	0.142	−0.373	0.193
10.	EVa	2.795	0.095	−0.475	464	0.635	−0.070	0.147	−0.358	0.219
	EVna			−0.439	72.271	0.662	−0.070	0.159	−0.387	0.247
11.	EVa	1.472	0.226	0.387	464	0.699	0.058	0.149	−0.235	0.351
	EVna			0.360	72.518	0.720	0.058	0.160	−0.262	0.377
12.	EVa	0.107	0.744	1.081	464	0.280	0.147	0.136	−0.120	0.415
	EVna			1.033	73.668	0.305	0.147	0.142	−0.137	0.431
13.	EVa	0.251	0.616	0.110	464	0.913	0.015	0.141	−0.262	0.293
	EVna			0.106	74.248	0.916	0.015	0.146	−0.275	0.306
14.	EVa	1.066	0.302	−2.752	464	0.006	−0.523	0.190	−0.897	−0.150
	EVna			−2.620	73.523	0.011	−0.523	0.200	−0.921	−0.125
15.	EVa	0.621	0.431	−0.191	464	0.849	−0.026	0.135	−0.292	0.240
	EVna			−0.183	73.784	0.856	−0.026	0.141	−0.308	0.256
16.	EVa	3.475	0.063	−0.740	464	0.460	−0.113	0.152	−0.412	0.187
	EVna			−0.685	72.325	0.496	−0.113	0.165	−0.441	0.215
17.	EVa	2.932	0.088	−1.140	464	0.255	−0.140	0.123	−0.382	0.101
	EVna			−1.130	75.401	0.262	−0.140	0.124	−0.387	0.107
18.	EVa	2.262	0.133	−0.241	464	0.809	−0.031	0.127	−0.281	0.220
	EVna			−0.250	77.710	0.803	−0.031	0.123	−0.275	0.214
19.	EVa	5.851	0.016	−1.317	464	0.189	−0.207	0.157	−0.515	0.102
	EVna			−1.147	70.091	0.255	−0.207	0.180	−0.566	0.153
20.	EVa	1.967	0.161	−0.921	464	0.358	−0.118	0.128	−0.370	0.134
	EVna			−0.913	75.397	0.364	−0.118	0.129	−0.375	0.139
21.	EVa	2.050	0.153	−1.572	464	0.117	−0.205	0.131	−0.462	0.051
	EVna			−1.372	70.139	0.175	−0.205	0.150	−0.504	0.093
22.	EVa	0.158	0.691	−0.989	464	0.323	−0.143	0.145	−0.428	0.142
	EVna			−0.958	74.298	0.341	−0.143	0.150	−0.442	0.155
23.	EVa	0.394	0.530	−1.310	464	0.191	−0.209	0.160	−0.523	0.105
	EVna			−1.337	76.854	0.185	−0.209	0.157	−0.521	0.102
24.	EVa	0.695	0.405	0.305	464	0.760	0.038	0.124	−0.206	0.281
	EVna			0.276	71.454	0.783	0.038	0.137	−0.235	0.311
25.	EVa	0.982	0.322	1.694	464	0.091	0.209	0.123	−0.033	0.451
	EVna			1.884	82.100	0.063	0.209	0.111	−0.012	0.429
26.	EVa	1.454	0.229	0.424	464	0.671	0.061	0.144	−0.221	0.343
	EVna			0.429	76.397	0.669	0.061	0.142	−0.222	0.343
27.	EVa	0.588	0.443	0.179	464	0.858	0.026	0.144	−0.258	0.309
	EVna			0.183	77.116	0.855	0.026	0.141	−0.254	0.306
28.	EVa	0.540	0.463	0.102	464	0.919	0.012	0.117	−0.218	0.242
	EVna			0.106	77.754	0.916	0.012	0.113	−0.213	0.236
29.	EVa	1.512	0.219	0.885	464	0.377	0.101	0.114	−0.123	0.324
	EVna			0.949	79.692	0.346	0.101	0.106	−0.110	0.312
30.	EVa	0.446	0.505	1.224	464	0.222	0.170	0.139	−0.103	0.443
	EVna			1.199	74.788	0.234	0.170	0.142	−0.113	0.453
31.	EVa	0.035	0.852	−0.091	464	0.928	−0.014	0.154	−0.317	0.289
	EVna			−0.089	74.755	0.930	−0.014	0.158	−0.328	0.300
32.	EVa	0.351	0.554	0.561	464	0.575	0.084	0.149	−0.210	0.377
	EVna			0.560	75.727	0.577	0.084	0.150	−0.214	0.382

EVa = Equal variances assumed; EVna = Equal variances not assumed, EV = Equal variances; F = Levene’s F statistic; Sig. = significance level; t = test statistic; df = degrees of freedom.

presents the results of the Levene test and The Student t-test.

In this case, all Levene’s tests—except one—returned p-values above 0.05, indicating equal variances across items, except for item 19 (p = 0.16). For this item, variances could not be assumed equal, but the resulting p-value (0.255) suggests that academic level had no effect. In 31 of the 32 items, no significant differences were found.

Item 14 was the only one showing a statistically significant difference by academic level (p = 0.006, assuming equal variances). Master’s students expressed lower agreement than undergraduates.

Box-and-whisker plots are presented below to identify outliers and compare the response distribution for item 14 (Figure 4).

The chart supports the previous analysis and reveals a clear difference in the third quartile: undergraduate students expressed a more favourable view, with scores approaching 4.5 on the scale.

4. Discussion and Conclusions

The findings indicate that future language teachers perceived video creation as a meaningful and valuable learning experience, in line with previous studies that highlight its potential to foster autonomous learning, metacognitive awareness, and creativity (Zhang et al., 2006; Epps et al., 2021; Hawley & Allen, 2018).

The study aimed to analyse the ADPS proposal using the guidelines of the DigCompEdu Framework. It sought to determine whether the proposal addressed all the competencies recommended by the European Commission for integrating technology into teaching and to identify any strategies that might need redesign. Additionally, the study assessed student satisfaction with the proposal and its impact on developing students’ artistic and digital competencies. It also explored their perceptions of its pedagogical usefulness and investigated whether statistically significant differences existed between undergraduate and Master’s students.

The results confirm that the ADPS proposal supports most of the competencies in the DigCompEdu Framework, although there are limitations in Areas 4, 5, and 6. In Area 4 (Assessment), only one of the three competencies is addressed. In Area 5, the proposal needs to strengthen elements that improve the inclusion of students with visual impairments. For Area 6, greater attention should be given to measures that support students’ physical, psychological, and social well-being when using digital technologies.

At the same time, language student teachers were delighted with the proposed ADPS, which effectively supported the development of artistic, digital, and pedagogical competences, and was positively received overall. As for artistic competence, students rated its development particularly high (see Table 5), recognising its relevance in teacher education (Teshabayeva, 2021). Specifically, the most highly rated aspects within this block were understanding the aesthetic value of the videos produced (Tajiev, 2023), the opportunity to leave a personal imprint on each product (Teshabayeva, 2021), and the video production process itself. Conversely, lower ratings were given to the creation of a YouTube channel, along with issues related to space (Pérez-Norambuena et al., 2022) and time constraints (Aroca, 2021). Nonetheless, students’ responses still fell within the upper-mid range of the scale, further confirming the generally positive reception of the proposal.

Digital competence is a key element in teacher education, as it equips future educators to meet the pedagogical challenges (Carpenter et al., 2025) posed by today’s information society (Castañeda et al., 2018). The European Framework for the Digital Competence of Educators (Redecker & Punie, 2020) aims to support EU member states in promoting innovation and enhancing teachers’ digital skills, not only in higher education (Zhao et al., 2021a) but also in primary (Laakso et al., 2021) and secondary education (Diz-Otero et al., 2023). Based on studies such as Zhao et al. (2021b), which evaluated pre-service teachers’ creative use of digital tools—generally yielding modest results—it is noteworthy that, in the present study, students perceived that the implementation of this innovative learning proposal did indeed contribute to the development of their digital competence (García-Sampedro et al., 2024a). As Falloon (2020) and Sailer et al. (2021) observe, strengthening digital competence is essential for the effective integration of educational technologies (Flores-Tena et al., 2021).

Within the Digital Competence block, students particularly valued the development of their digital skills through the production of educational videos (Fernández-Cruz & Rodríguez-Legendre, 2022). They also rated the use of video and photo editing tools positively, which allowed them to express their artistic values. In contrast, lower scores were given to the creation of a YouTube channel (Curran et al., 2020), the use of digital tools to promote critical thinking (Almerich et al., 2020), and the use of the Internet as a source of information (Agudo et al., 2021). These results suggest that both undergraduate and Master’s students are more interested in the creative and artistic potential of producing and editing video than in uploading it to a platform or using the web for informational purposes. This may be because young people interact with the Internet constantly (Admiraal et al., 2017) and no longer see it as an innovative educational tool (Fyfield, 2021).

Regarding the exploration of students’ perceptions of the pedagogical usefulness of the proposal, the results reveal a generally upbeat assessment of its implementation. This was the second most highly rated block, just after artistic competence. The findings indicate that pre-service teachers perceived video production as a valuable learning experience that contributed to their professional development (García-Sampedro et al., 2024a; García-Sampedro & González-Rúa, 2025). In a similar vein, Pereira et al. (2014) found, in a study with nursing students, that self-produced videos enhanced both curricular and transversal competences. More recently, Epps et al. (2021) conducted a systematic review of student-generated video practices, many of whose conclusions align closely with the present study (Del Valle-Ramón et al., 2020). However, aspects—such as space and time availability—received slightly lower ratings. This may be because project activities were conducted in classrooms where furniture could not be rearranged, which hindered group work and made it less comfortable (Pérez-Norambuena et al., 2022). As Aroca (2021) notes, university students often feel they lack sufficient time to complete tasks or projects, which could account for the lower ratings on this item.

Finally, this study also sought to determine whether there were significant differences in perception between undergraduate and Master’s students (Roselló et al., 2023; Tominc & Rožman, 2023). It is essential to note that the proposal was implemented in the same manner and using the same methodology in both programs. The results reveal a slight advantage in favour of undergraduates, who rated a higher number of items positively (18) compared to Master’s students (12). A similar pattern was observed in other studies, such as that by Seva-Larrosa et al. (2021).

Among Master’s students, the highest-rated items were related to the satisfaction with the proposal implementation, the creation of artistic products with a personal imprint (López, 2016), an appreciation for the aesthetic dimension of video production (Tajiev, 2023), and the development of communicative skills through digital tools (Tursunovich, 2023). Conversely, the lowest ratings were found in items from the digital competence and pedagogical usefulness blocks, particularly those concerning the creation of a YouTube channel (Torralba-Burrial & García-Sampedro, 2022), values and multicultural education (Rotary & Shonfeld, 2025), and the availability of adequate physical spaces (Kumpulainen et al., 2020).

For undergraduate students, the highest-rated items largely mirrored those of their master peers: A slightly higher score on the blocks in Digital Competence and Pedagogical Usefulness, understanding the importance of aesthetic value in audiovisual communication (Tajiev, 2023), creating personal artistic products (López, 2016) and evaluating the final video outcomes (García-Sampedro et al., 2024a). As in the master group, the lowest-rated items concerned the creation of a YouTube channel, and limitations related to space (Álvarez-Díaz et al., 2023; Pérez-Norambuena et al., 2022) and time (Aroca, 2021).

Thus, no statistically significant differences were observed between undergraduate and Master’s students across most items. The only exception was the YouTube channel item, which revealed a considerable difference: Master’s students expressed lower, whereas undergraduates rated it more positively. This may be explained by the fact that Master’s students are preparing to teach in secondary, upper secondary education or vocational training contexts, where learners are already highly familiar with digital tools and platforms such as YouTube (Lozano et al., 2020). As a result, future teachers in these contexts may place less value on learning how to create a channel and instead emphasize the creative and artistic dimensions of video production (Carson et al., 2018), as also observed in similar studies (Torralba-Burrial & García-Sampedro, 2022).

Master’s students rated the three competence blocks in the following order: artistic competence first, followed by digital competence, and lastly, pedagogical usefulness. Undergraduate students, however, viewed the project as particularly helpful in developing creative, pedagogical and digital tools (in this order). The differences between the two groups can be explained as follows. In the artistic competence block, scores were consistently high, approaching the top of the scale. For digital competence, the overall assessment was more moderate, though undergraduate students reported slightly higher levels of agreement. In the pedagogical block, undergraduate students score somewhat higher than Master’s students, in contrast to overall satisfaction with the implementation and development of the project, which is slightly higher among Master’s students. Some of the factors that help explain these differences relate to the workload faced by undergraduate students compared to those in the master programme. The latter, due to the structure of the degree itself, have shorter modules and therefore a lighter workload, which may account for their slightly higher overall satisfaction.

This study has some limitations that must be acknowledged. The DigCompEdu analysis revealed that digital technologies were not utilised for assessment or to provide specific and timely feedback to students. This was identified as an area for improvement in the current proposal and may also inform future research on the digitalisation of assessment. Another limitation concerns the difficulty of determining whether the specific subjects in which the project was implemented influenced students’ perceptions. Further research could also examine the impact of the videos created by undergraduate and Master’s students in the schools where they were used, as well as gather feedback from university faculty members who implemented the project.

Despite these limitations, the findings highlight the originality and significance of the study. The proposal demonstrates how innovative learning experiences can contribute to the development of artistic, digital and pedagogical competences, while also reinforcing the role of creativity as a key dimension that enhances digital competence. In this sense, creativity is not presented as a strict prerequisite but as a crucial enabler for meaningful and adaptive uses of digital technologies in teacher education.