INTRODUCTION

Within the framework of digital education and open science, the communication of scientific knowledge through digital social networks (DSN) has emerged as a key competence for teachers. Nevertheless, teacher training in this area continues to face both conceptual and methodological challenges. In this context, this study is aligned with the Digital Teaching Competence Framework 2.2 (INTEF, 2022), particularly in its areas 2 (Digital content) and 6 (Developing students' digital competence), because of its focus on the creation, dissemination and critical analysis of digital content. Alongside Ferrés and Piscitelli’s (2012) media literacy model, these two frameworks underpin the design and validation of two constructs for assessing teachers' media competence (TMC). The factor structure of the indicators and their applicability in training contexts are analysed.

Teachers' media competence in the context of digital social networks

Digital competence and TMC are complementary and fundamental concepts in the current educational context (Marcelo-Martínez et al., 2025). The former focuses on the pedagogical use of technological tools to improve teaching and learning (Cabero-Almenara et al., 2020), while the latter emphasises critical media literacy and its impact on knowledge construction (Ferrés & Piscitelli, 2012). Both are essential for science outreach in DSN, where teachers act as creators and mediators of academic knowledge (Buzón et al., 2019).

Scientific dissemination in DSN has become a key element in bringing academic knowledge closer to society, broadening the impact of research and fostering interaction with different audiences (Valencia-Oliveros & Martín-Gutiérrez, 2024). Increasing digitisation and the rise of platforms such as Twitter/X (Vásquez-Rocca, 2024), Instagram (Romero Rodríguez et al., 2019), Facebook (Verstappen & Opgenhaffen, 2024) and YouTube (Marchal et al., 2025) have generated new opportunities to communicate science beyond traditional academic circles. However, their use among researchers remains limited, highlighting the need to strengthen training in digital communication (Said-Hung et al., 2024).

In a context of increasing misinformation, these platforms not only facilitate the dissemination of knowledge, but also enable interaction with the public and adaptation to new digital formats (Martin-Neira et al., 2023). Despite their potential in education and social sciences, the challenge lies in balancing the accessibility of the message without losing scientific rigour, especially in networks such as LinkedIn and Instagram, where content must be brief and attractive (Gallego et al., 2023).

LinkedIn has established itself as a key platform for scientific dissemination, differentiating itself from other DSNs by its professional approach and its ability to connect researchers with academic, business and education communities (Knight, 2019). Its algorithm favours the visibility of scientific content, enabling the dissemination of articles, preprints and research findings, as well as interaction in specialised communities (Baruffaldi et al., 2017). Recent studies have identified LinkedIn as the social network most used by academics to share their scientific output, surpassing platforms such as ResearchGate and Academia.edu, due to its ability to amplify the impact of knowledge and facilitate transfer between academia and industry (Huang et al., 2019; Knight, 2019; Ordóñez Castillo et al., 2024; Said-Hung et al., 2024; Tolbert, 2024). However, barriers to their widespread adoption persist, such as the lack of training in digital communication and the scarce institutional recognition of these activities (Argüello-Gutiérrez & Moreno-López, 2024). Despite these limitations, LinkedIn offers an environment conducive to the construction of a digital academic identity, the dissemination of knowledge and the generation of collaborative networks, which highlights the need to promote institutional strategies that encourage its effective use in scientific dissemination.

Previous research proposes general models of TMC (Gümüş & Kukul, 2023; Jiang & Yu, 2024; Vergili & Kara, 2024) but has not structured it in science outreach in DSN. Although they identify media skills for educational and scientific purposes, they do not establish a specific construct to serve as a basis for teacher training. Therefore, it responds to this gap by means of an integrative proposal that articulates the Spanish Framework for the Digital Competence of Teachers, hereafter referred to by its Spanish acronym, MRCDD 2.2 (INTEF, 2022) and the media literacy model of Ferrés and Piscitelli (2012).

Integration allows for a comprehensive analysis of TMC in science popularisation. In this regard, several recent systematic reviews support such convergence. Claro et al. (2024) stress the importance of consolidating the operationalisation of teachers' digital competence within normative frameworks such as MRCDD 2.2, while Olaya Guerrero et al. (2025) emphasise the fundamental role of institutional strategies in teacher training in digital competence. In the field of media literacy, Fuentes Cancell et al. (2021) highlight that effective use of DSN in higher education requires not only technological skills, but also critical thinking to assess the impact and credibility of media.

In summary, the complementarity between the MRCDD 2.2 and Ferrés and Piscitelli's model justifies their integration to assess TMC in scientific dissemination in DSN, consolidating a robust framework for teacher training in digital contexts. This combination makes it possible to consolidate a solid model for teacher training in digital environments, aligned with the current demands of scholarly communication.

Based on these references, this study addresses the need to strengthen the participation of teachers in the communication and dissemination of knowledge through DSN, contributing to the consolidation of their academic and professional impact (Hidalgo & Hidalgo, 2024; Jiang & Yu, 2024). To this end, the design and validation of two specific constructs is proposed through a theoretical and empirical approach. The findings will allow structuring an evaluation model applicable to both teacher training and future research on media literacy and scientific dissemination in digital environments.

Research problem and objectives

In this context, the study poses the following problem: How is TMC structured for science popularisation in DSN, with a special focus on LinkedIn, and what are the implications for teacher education? It also examines the need to incorporate this competence into initial and in-service teacher education programmes.

To answer this question, the study aims to achieve the following specific objectives: (1) to design and validate two constructs that define the TMC for scientific dissemination in DSN and LinkedIn; (2) to analyse the factorial structure of these constructs; (3) to assess the reliability and validity of the indicators that compose them; and (4) to identify the pedagogical implications derived from the model for teacher training in digital contexts.

METHODS

Research design

This study follows an exploratory approach based on conceptual adequacy and structural validation of constructs (Hernández-Sampieri & Mendoza, 2018). The design is structured in three phases: (1) theoretical validation and expert evaluation of the indicators; (2) analysis of the factorial structure of the constructs; and (3) evaluation of the reliability and validity of the defined indicators.

Instruments

Two instruments were designed to assess teachers' media competence in science communication: one focused on DSN and the other on LinkedIn. Both are based on the MRCDD 2.2 and the media literacy model of Ferrés and Piscitelli (2012).

The DSN instrument covers six dimensions (language, technology, interaction, production and dissemination, ideology and values, and aesthetics) with 34 indicators, while the LinkedIn instrument, with the same structure, includes 28 indicators adapted to dissemination in professional environments. Although they share a conceptual basis, the DSN instrument assesses disclosure in general platforms, while the LinkedIn instrument focuses on communication strategies within a professional network. Both instruments were validated through expert judgement and exploratory factor analysis. Reliability was high in both cases, with internal consistency coefficients above α = 0.85 and ω = 0.87 (α = 0.91 and ω = 0.93 for DSN; α = 0.89 and ω = 0.90 for LinkedIn), ensuring their relevance and stability.

The MRCDD 2.2 is selected for its alignment with DigCompEdu and its contribution to the European Education Area, while Ferrés and Piscitelli's (2012) model is a reference in media literacy and widely cited in the scientific literature. While there are several models of competences and media literacy such as Buckingham (2003), UNESCO (Grizzle et al., 2021), Hobbs (2010) and Area and Guarro (2012), Ferrés and Piscitelli's approach stands out for its comprehensive approach and its impact on academia.

The MRCDD 2.2 structures teachers' digital competence in five areas: professional engagement, teaching and learning, assessment and feedback, student empowerment and development of digital competence. For its part, Ferrés and Piscitelli's (2012) model defines media competence in six dimensions, allowing for a comprehensive analysis of TMC in science communication (Table 1).

The instruments developed - one for dissemination in DSN and another specifically for LinkedIn - respond to complementary phases in the process of modelling teachers’ media competence. They do not constitute a single consolidated instrument, but rather differentiated approaches adapted to different contexts of science communication, aimed at validating specific constructs for the future development of a comprehensive model.

Procedure

The theoretical validation of the constructs was carried out by means of a literature review and expert judgement (September 2024 to January 2025), ensuring their alignment with the MRCDD 2.2 and the model of Ferrés and Piscitelli (2012). To this end, a digital questionnaire was designed in which 30 experts assessed the clarity, relevance and coherence of the indicators. The process guaranteed anonymity, confidentiality and compliance with ethical criteria. The selection of experts with extensive experience allowed for a robust validation of the content, the results of which are detailed in the data analysis section.

Data analysis

The sample was selected by purposive sampling, considering the trajectory in media literacy, educational technology or teacher training. Thirty specialists from universities in Spain, Portugal and Latin America participated, 70% with more than 10 years of experience. The inclusion criteria were: relevant publications or projects and experience in instrument validation. The sample, composed of 60% men and with a mean age of 45.2 years (σ = 7.1), provided disciplinary and geographical diversity, guaranteeing a comprehensive expert judgement. For validation, a digital questionnaire was applied to assess the clarity, relevance and coherence of the indicators, following ethical and confidentiality criteria.

In terms of professional background, 70% of the participants had more than 10 years of experience in university teaching and teacher training in digital environments. In addition, 20% were specialised in media literacy research, and 10% in the development of technologies applied to education. The experts were selected on the basis of their experience and background in the aforementioned fields, ensuring the representation of different academic and professional profiles. This approach allowed for a multidimensional evaluation of the instrument, guaranteeing its validity from a holistic and transdisciplinary perspective.

The sample of 30 experts used in this study is considered methodologically adequate for content validation, in line with the recommendations of the specialised literature on instrument development. Authors such as DiFazio et al. (2018) and Polit and Beck (2006) argue that between 5 and 30 experts are sufficient for this type of analysis, provided that thematic specialisation and professional diversity of the participants is guaranteed. Instead of opting for large samples with non-specialised participants, as in other similar studies, the quality and relevance of the expert judgement was prioritised by selecting professionals with a consolidated track record in media literacy, educational technology and teacher training. This choice responds to the need to validate highly specialised constructs with a rigorous theoretical approach. Robust statistical procedures such as the Content Validity Index (CVI) and the Fleiss Kappa concordance coefficient (Fleiss et al., 2003), which provide quantitative evidence of the level of agreement between evaluators, even in small but highly qualified samples, were also applied.

Based on expert feedback, adjustments were made to improve the accuracy of the instrument, redundancies were eliminated by merging indicators with conceptual overlap, and items on critical thinking and source verification were incorporated to strengthen media literacy. These adjustments guarantee the theoretical and methodological validity of the constructs, ensuring their alignment with reference models and their applicability in teacher education.

Exploratory Factor Analysis (EFA) using SPSS v. 29.0 allowed us to assess construct validity and identify the underlying structure of the indicators (López-Aguado & Gutiérrez-Provecho, 2019). The Kaiser-Meyer-Olkin sample adequacy criteria and Bartlett's test of sphericity were used. Using the Maximum Likelihood (ML) method with Varimax rotation, factors were identified to ensure their independence. The reliability of the instrument was assessed using Cronbach's Alpha (α) and McDonald's Omega (ω) internal consistency coefficients (Viladrich et al., 2017). Given its exploratory nature, the EFA was applied to a purposive sample of 30 experts. Although the results provide clues about the factor structure of the constructs, this sample does not allow for conclusive validation. Therefore, it will be necessary to replicate the analysis with samples larger than 200 university teachers in future studies.

As a visual complement to the quantitative analysis, heat maps were developed to graphically represent the intensity of the factor loadings between the indicators and the factors extracted in the EFAs. This resource facilitates the identification of empirical groupings and possible cross-loadings, reinforcing the interpretation of the results. It should be noted that, as this is a study based on EFA, neither structural equations nor latent model diagrams are presented, as these correspond to the confirmatory approach (CFA), which is specific to later phases of empirical validation.

RESULTS

Theoretical validation and concordance analysis

The TMC construct for scientific dissemination in DSN is structured according to the dimensions proposed by Ferrés and Piscitelli, (2012), describing its indicators and relationship with the MRCDD 2.2 (Table 2).

Once the indicators have been defined, their levels of achievement are established according to the MRCDD 2.2, and examples of performance are presented (Table 3).

The construct: TMC for scientific dissemination on LinkedIn was adapted to the specificity of LinkedIn, defining 28 indicators and levels of progression. Table 4 shows its 28 indicators.

Once the indicators were defined, their levels of achievement and examples of performance were established (Table 5).

Exploratory Factor Analysis

The suitability of the data for factor analysis was confirmed by KMO (0.85 and 0.83) and Bartlett's test of sphericity. All factor loadings exceeded 0.40, ensuring a significant contribution of the items to their respective items. The identified factors explained 78.2 % for the TMC construct in DSN (Table 6) and 78.8 % of the total variance in DSN and LinkedIn (Table 7), respectively.

In order to facilitate the visual interpretation of the factor structure resulting from the EFA, a heat map was elaborated representing the intensity of the factor loadings of the 34 indicators on the five empirically identified factors. This representation allows us to observe clustering patterns, identify cross-loaded items and verify the internal consistency of the proposed model. Figure 1 shows the heat map of the factor loadings corresponding to the TMC construct for DSN. This representation allows visualising the intensity of the saturations and the internal consistency of the factors, facilitating the identification of clustering and possible cross-loadings. Its interpretation complements the EFA results and provides graphical evidence of the model's consistency.

As can be seen in the graph, most of the indicators show high loadings on a single factor, which reinforces the validity of the proposed empirical structure. However, some items show relevant loadings on more than one dimension, suggesting a possible interdependence between the components of the model, especially between the dimensions of interaction, technology and production and diffusion. Table 7, corresponding to the EFA of the second construct, focusing on teachers' media competence for scientific dissemination on LinkedIn, is presented below.

Although the TMC theoretical model for science outreach in DSN was designed with six dimensions (Language, Technology, Interaction, Production and Dissemination, Ideology and Values, Aesthetics), the EFA revealed a different empirical structure. This suggests that some theoretical dimensions may have been grouped into the same factor in practice, reflecting the interdependence between them. In particular, dimensions such as Interaction, Technology and Production and Dissemination clustered together, indicating that technological use directly influences the way knowledge is interacted with and disseminated.

To complement the matrix of factor loadings obtained in the EFA of the second construct, a heat map (Figure 2) was produced showing the concentration of high loadings on specific factors. This visual analysis reinforces the preliminary validity of the model and allows observing the empirical consistency of the indicators.

The heat map for the second construct reveals, as in the previous case, a clear concentration of high loadings around specific factors, indicating a good empirical definition of the proposed dimensions. The consistency of the results obtained in both exploratory factor analyses supports the preliminary structural validity of the instruments developed. This empirical evidence strengthens the applicability of the model in training contexts and provides a solid basis for future research into the assessment of teachers' media competence in DSN.

Reliability assessment

Reliability analysis confirmed the internal consistency of the instrument, with coefficients of α ≥ 0.70 and ω ≥ 0.70 in all dimensions.

In the first construct (TMC for science outreach on DSN), α values ranged between 0.84 and 0.89, with an overall coefficient of 0.91, while ω ranged between 0.86 and 0.91, reaching an overall value of 0.93. For the second construct (TMC for science outreach on LinkedIn), the coefficients per dimension showed a similar trend, with an overall α of 0.89 and an overall ω of 0.90. These values indicate a high internal consistency across dimensions, which reinforces the stability of the model (Table 8).

The item-total correlation analysis showed coefficients above 0.30 for all indicators, which guarantees their adequate discrimination within each dimension. No items were identified with critical values suggesting their elimination or modification. This confirms that each item contributes significantly to the measurement of the construct to which it belongs.

The EFA results showed a reduction from the initial six theoretical dimensions to five factors in DSN and four in LinkedIn. This grouping suggests conceptual integrations, particularly between the interaction, technology and production/dissemination dimensions, which tend to converge empirically. This pattern could be explained by the functional convergence that characterises teaching practice in digital environments, where networked interaction, strategic use of technology and content creation develop in an interrelated way. Moreover, the professional and academic nature of platforms such as LinkedIn seems to enhance this integration, blurring the boundaries between dimensions theoretically envisaged. These findings advance the empirical understanding of teachers' media competence, providing a basis for its critical interpretation and possible application in teacher education and scientific dissemination. Theoretical and practical implications are discussed below.

DISCUSSION AND CONCLUSIONS

The results obtained offer an empirical approach to the structure of the TMC for scientific dissemination in DSN, differentiating between the generalist use of digital platforms and scholarly communication in professional environments such as LinkedIn. The reduction of the initial theoretical dimensions and the observed groupings suggest the need to reconsider certain conceptual overlaps, in line with digital teaching practice and the functional integration of interaction, technology and content production. The high reliability indices and preliminary factorial consistency support the theoretical relevance of the instrument, in line with the MRCDD 2.2 and media literacy frameworks (Ferrés & Piscitelli, 2012). These findings are in line with previous studies that highlight the importance of strengthening TMC in academic contexts (Cabero-Almenara et al., 2020; Suelves et al., 2022).

These findings are in line with previous research that has addressed the assessment and development of digital competence in teaching from psychometric and formative perspectives. For example, Cabero-Almenara et al. (2020) validated competence frameworks through expert judgement, highlighting the importance of the analysis of internal consistency and relevance of indicators. Palacios-Rodríguez et al. (2025) conducted a comparative macroassessment between Spain and Portugal based on the DigCompEdu framework, underlining the need for more specific diagnostic approaches. At the conceptual level, studies such as Marcelo-Martínez et al. (2025) have highlighted the gap between the instrumental use of DSN and its pedagogical use in teacher education. This paper extends these contributions by integrating media literacy into the assessment of teaching competence, with a contextual specificity (LinkedIn) that has not been previously addressed.

The findings also reinforce the idea that media and digital literacy is essential in teacher education, in line with research that has assessed TMC and its pedagogical applications (Jiang & Yu, 2024; Simons et al., 2017). In particular, this study provides a differentiation between TMC in DSN and its specificity in LinkedIn, which has not been sufficiently addressed in previous studies.

However, there are discrepancies with previous studies that have addressed TMC and digital as a global construct without differentiating their specific applications in academic outreach (Ho et al., 2024; Kara et al., 2017; Luan et al., 2023; Vergili & Kara, 2024). Previous research (Palacios-Rodríguez et al., 2025) has analysed TMC in more general digital-oriented terms by highlighting media competence in general DSN and specific DSN in professional settings. This distinction brings a novel approach by highlighting that scientific communication requires specific strategies that are not fully covered in general models of teacher media literacy.

This study makes an original contribution to the field of teacher education and media literacy by proposing two distinct and empirically validated constructs for assessing teachers' media competence in science communication. Unlike previous models, the proposal presented here allows for a specific assessment for professional contexts such as LinkedIn, responding to the current demands of academic communication in digital environments. This specificity broadens the field of application of the TMC and provides a useful evaluative framework both for designing training programmes and for future research in the area.

In addition to its training applications, the validated model offers relevant implications for institutional policies in higher education. The inclusion of specific indicators on science outreach in DSN allows universities to design action plans more aligned with the principles of open science and knowledge transfer. This is particularly relevant in the context of the Sustainable Development Goals (SDGs), which promote inclusive and accessible science communication. Institutionalising the recognition of media competence as an assessable dimension in teaching careers can not only favour the visibility of research, but also a more socially connected academic culture. In this line, the proposal is articulated as a useful tool for diagnosing needs, defining training itineraries and establishing quality indicators in digital academic dissemination, aspects that are increasingly valued by quality assurance agencies and competitive calls for funding.

The results of the EFA show a factorial structure that, although it does not coincide exactly with the theoretical proposal of six dimensions, presents an organisation of the factors that is in line with previous findings in studies on media and digital competences. Several studies (García et al., 2022; Romero-Rodríguez et al., 2019) have found or assessed similar structures, where the dimensions of technology, interaction and production tend to be grouped together due to their interdependence in digital contexts. This pattern suggests that, in practice, media competences may not manifest themselves as strictly independent constructs, but as interrelated dimensions that respond to the dynamics of the digital environment. Future studies could apply a CFA to assess the stability of the model and compare it with factor structures reported in the literature.

The integration of TMC into teacher training and institutional strategies is key to strengthening science outreach in DSN. The results confirm the robustness of the content validation, supported by clear theoretical criteria and expert judgement, and provide theoretical value for future research and training programmes. LinkedIn stands out as an essential tool for the visibility of scientific production and knowledge transfer. At the institutional level, it is necessary to recognise dissemination in DSN as a legitimate academic activity and to align teacher training with the MRCDD 2.2 in order to promote media literacy.

From an applied perspective, the instruments developed offer a starting point for the diagnosis and orientation of training plans and institutional policies aimed at ethical and effective scientific dissemination. However, the factorial evidence obtained is preliminary and needs to be contrasted with large samples before a definitive model can be consolidated. Educational policies should promote the critical use of these platforms, providing resources and training in line with the principles of open science and the Sustainable Development Goals.

Limitations and lines of future work

The main limitations of the study include the small sample size (n = 30), which makes it necessary to consider exploratory factor analysis (EFA) as a preliminary approximation. Although it is a purposive sample of experts specialising in media literacy, educational technology and teacher training, the factorial validity of the instrument needs to be contrasted with large and heterogeneous samples.

As a future line, a CFA is proposed to test the proposed structure of the TMC, under a second-order hierarchical model with six dimensions grouped into a general factor. The CFA will be applied to a sample of more than 300 university teachers, using robust maximum likelihood (MLR) with AMOS and assessing the goodness of fit by means of χ²/df, CFI, TLI, RMSEA and SRMR. The empirical application of the instruments in real training contexts is also proposed in order to contrast their diagnostic usefulness, carry out cross-validation and develop test-retest reliability studies. We also plan to extend their application to international samples and compare results between different social platforms, which will allow us to reinforce their predictive validity and comparative usefulness.

Acknowledgments

Dieter Reynaldo Fuentes Cancell has been funded under the UVa 2024 predoctoral contracts call, co-funded by Banco Santander.

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