Development and validation of the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education

Development and validation of the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and validation of the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education Yingyi Xu, Chang Sun, Weifong Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8924262/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 22 You are reading this latest preprint version Abstract Evaluating the digital literacy of English as a Foreign Language (EFL) teachers plays a crucial role in supporting effective, secure, and inclusive digital teaching practices in Chinese higher education. Although digital technologies have become increasingly embedded in EFL instruction, validated measurement instruments that are sensitive to pedagogical contexts remain limited, particularly those tailored to EFL teachers. In response to this need, this study constructs and validates the Digital Literacy Scale for EFL Teachers (DLS-EFL) within the context of Chinese universities, drawing on an integrative theoretical framework that combines the teacher digital literacy framework proposed by the Ministry of Education of China (2022) with the Technological Pedagogical Content Knowledge (TPACK) model. Adopting a quantitative approach, the scale development process encompassed item construction, pilot administration, and psychometric validation using exploratory and confirmatory factor analyses. During the pilot phase, responses from 246 EFL teachers were analyzed through item analysis and EFA, leading to a 15-item instrument demonstrating strong internal consistency (Cronbach’s α = 0.945). The refined scale was then distributed to a larger sample of 388 participants, of whom 361 valid cases were retained following data screening procedures. Confirmatory factor analysis supported a three-dimensional structure and provided evidence of satisfactory reliability and construct validity. Overall, the validated DLS-EFL serves as a robust tool for measuring EFL teachers’ digital literacy and has practical value for guiding teacher professional development, institutional assessment, and policy initiatives aimed at strengthening digital competence in Chinese higher education. Social science/Education Humanities/Language and linguistics Social science/Language and linguistics Digital literacy English as a foreign language (EFL) Technological pedagogical content knowledge (TPACK) Scale development and validation Figures Figure 1 Figure 2 1 Introduction The rapid advancement of digital technologies has fundamentally transformed higher education, reshaping instructional design, assessment practices, and academic communication worldwide. Within this transformation, teachers are increasingly expected to integrate digital tools into pedagogical practice in ways that enhance learning effectiveness rather than merely supporting technical operations (Aslan et al., 2025 ; Falloon, 2020 ). These expectations are particularly salient in English as a Foreign Language (EFL) education, where digital technologies intersect with communicative pedagogy, multimodal meaning-making, and learner autonomy (Lu et al., 2025 ; Zhang, 2023 ). In China, educational digitalization has been elevated to a national strategic priority. The Ministry of Education has identified teachers’ digital literacy as a key driver of educational modernization and high-quality development in higher education (Lei & Jiang, 2025 ). This policy orientation underscores the importance of teachers’ capacity to design, implement, and evaluate technology-enhanced instruction in alignment with pedagogical goals and ethical standards. Universities are therefore increasingly required to strengthen teachers’ professional readiness for digital teaching, particularly in foreign language education, where instructional effectiveness relies heavily on interaction, feedback, and intercultural communication (Feng & Sumettikoon, 2024 ; Lei & Jiang, 2025 ). Despite increasing policy emphasis and institutional investment, the assessment of teachers’ digital literacy remains underdeveloped, especially in discipline-specific contexts. Many existing instruments have been designed for general teaching populations or adapted from Western educational settings, which limits their sensitivity to the pedagogical characteristics and contextual demands of EFL instruction in Chinese higher education. The absence of a contextually grounded and empirically validated measurement tool constrains systematic evaluation and hinders the development of targeted professional support for EFL teachers. To address this gap, the present study aims to develop and validate a Digital Literacy Scale for EFL Teachers (DLS-EFL) tailored to the Chinese higher education context. Drawing on the Teachers’ Digital Literacy Framework issued by the Ministry of Education of China (2022) and informed by pedagogically oriented theoretical perspectives, the study conceptualizes digital literacy as a multidimensional construct embedded in instructional practice. Using a quantitative research design, the study employs exploratory and confirmatory factor analyses to examine the reliability and validity of the proposed scale. By providing a context-sensitive measurement instrument, this research contributes to the literature on teacher digital literacy and offers practical implications for teacher education, institutional evaluation, and policy implementation. 2 Literature review 2.1 Digital literacy: Definitions and existing assessment frameworks Digital literacy has become a central construct in educational research, particularly in studies examining teachers’ professional competencies in technology-integrated learning environments. Although the terms digital literacy and digital competence are frequently used interchangeably, scholars have emphasized important conceptual distinctions between them. Digital literacy generally refers to the broad capacity to access, evaluate, create, and communicate information through digital technologies, whereas digital competence places greater emphasis on the effective and purposeful application of these abilities in professional or pedagogical contexts (Spante et al., 2018 ; Falloon, 2020 ). The conceptualization of digital literacy has evolved considerably over time. Early definitions focused primarily on basic technical proficiency, while more recent perspectives conceptualize digital literacy as a multidimensional construct integrating technical, cognitive, pedagogical, and ethical dimensions (Chu et al., 2023 ; Yang, 2024 ). Contemporary research further highlights teachers’ need to critically engage with digital technologies, interpret and utilize educational data, design innovative learning experiences, and promote responsible digital practices. In response to the growing datafication and automation of education, emerging competencies such as data literacy and AI literacy have also been incorporated into discussions of teacher digital literacy (Lin et al., 2023 ). In the Chinese context, these developments align with national policy priorities, as digital literacy has been positioned as a strategic foundation for innovation-oriented and high-quality education (Lei & Jiang, 2025 ; Wu et al., 2025 ). Building on these conceptual developments, a variety of digital literacy and digital competence assessment frameworks have been proposed. Many widely used instruments are grounded in international standards such as the European DigCompEdu framework (Redecker, 2017 ) and the ISTE Standards for Educators (ISTE, 2017), which commonly emphasize domains including professional engagement, digital resources, teaching and learning, assessment, and learner empowerment (Chifla-Villón et al., 2025 ; Dang et al., 2024 ; Ličen & Prosen, 2024 ). More recently, researchers have extended assessment efforts to include AI-related competencies, reflecting the changing technological landscape of education (Ng et al., 2023 ; Tenberga & Daniela, 2024 ). Nevertheless, prior studies have identified several limitations in existing assessment tools. Many instruments prioritize general digital skills while paying limited attention to disciplinary specificity, particularly in fields such as language education that involve academic literacy, discourse practices, and intercultural communication (Zhang & Hyland, 2025 ). In addition, most studies rely predominantly on self-report questionnaires, with relatively limited use of performance-based assessments, and concerns remain regarding construct validity and cross-cultural applicability (Nguyen & Habók, 2024 ; Avinç & Doğan, 2024 ). These limitations highlight the need for assessment frameworks that are both pedagogically grounded and contextually responsive. 2.2 Digital literacy in EFL teaching Digital literacy assumes distinctive characteristics in EFL teaching, where educators are required to integrate language pedagogy with digital technologies to support communicative competence, intercultural awareness, and learner autonomy. Beyond basic tool operation, EFL teachers are expected to employ digital resources for multimodal instruction, online interaction, collaborative learning, and individualized feedback (Feng & Sumettikoon, 2024 ; Lu et al., 2025 ). However, empirical research consistently reports challenges related to uneven levels of confidence, limited institutional support, and insufficient access to professional development, particularly within humanities-oriented disciplines such as foreign language education (Lei & Jiang, 2025 ). In higher education, the COVID-19 pandemic accelerated the adoption of digital teaching practices and exposed disparities in teachers’ preparedness for technology-integrated instruction (Romero-Hall & Jaramillo Cherrez, 2023 ). In China, EFL teachers have often reported lower levels of technology integration compared to their counterparts in STEM fields, a difference attributed to variations in infrastructure, training opportunities, and teaching workload (Feng & Sumettikoon, 2024 ; Zhang, 2023 ). At the policy level, initiatives such as the Education Digitalization Strategy Action (Lei & Jiang, 2025 ) emphasize the role of EFL teachers as designers of digital pedagogy, facilitators of hybrid learning environments, and users of data-informed assessment practices (Jiang & Yu, 2024 ). Given the pedagogical complexity of EFL instruction, scholars increasingly argue that digital literacy in this field cannot be adequately captured by generic skill-based frameworks alone. Instead, it requires an integrative perspective that accounts for the close interaction between digital technologies, pedagogical strategies, and disciplinary content (Akayoğlu et al., 2020 ; Su, 2023 ). In EFL classrooms, digital tools are not merely supportive resources but actively shape language input, learner interaction, and feedback processes, particularly through multimodal and communicative practices (Wang, 2022 ). This pedagogical specificity highlights the need for assessment approaches that are sensitive to how digital literacy is enacted within language teaching, thereby providing a rationale for adopting theoretically grounded and discipline-responsive frameworks in subsequent analysis. 2.3 Theoretical framework and research purpose Building on the pedagogical challenges identified in EFL digital instruction, recent research on teachers’ digital literacy has expanded considerably, with existing frameworks predominantly emphasizing cognitive and technical competencies related to technology use (Laupichler et al., 2023 ). While these perspectives have contributed to a clearer understanding of teachers’ digital knowledge and skills, they often insufficiently address the pedagogical enactment of digital literacy within discipline-specific instructional contexts. In particular, English as a Foreign Language (EFL) teaching involves distinctive pedagogical processes—such as language interaction, multimodal input, and formative feedback—that require more nuanced conceptualizations of how digital literacy is enacted in practice. To address this limitation, the present study adopts an integrative theoretical approach that combines the teacher digital literacy framework proposed by the Ministry of Education of China (2022) with the Technological Pedagogical Content Knowledge (TPACK) model (Mishra, 2019 ). The Ministry’s framework provides a policy-oriented and contextually grounded structure that reflects national priorities for teachers’ digital development, while TPACK offers a well-established theoretical lens for understanding the dynamic interplay between technology, pedagogy, and subject matter. By synthesizing these two perspectives, the study moves beyond a skills-based view of digital literacy and foregrounds its pedagogical and disciplinary dimensions within EFL instruction. The resulting integrated model (see Fig. 1 ) serves as the conceptual foundation for the Digital Literacy Scale for EFL Teachers (DLS-EFL). Drawing primarily on the Ministry’s framework, the scale is organized around five interrelated dimensions: Digital Awareness (DA), Digital Knowledge and Skills (DKS), Digital Teaching Application (DTA), Digital Responsibility (DR), and Professional Development (PD). These dimensions collectively capture EFL teachers’ awareness of the educational value of digital technologies, their technical competence, their ability to meaningfully integrate digital tools into language pedagogy, their ethical and responsible use of technology, and their engagement in continuous digital professional growth. Importantly, each dimension is operationalized with explicit consideration of the pedagogical demands and instructional practices characteristic of EFL teaching in higher education. To clarify the theoretical grounding of the DLS-EFL, Table 1 presents a comparison between the core dimensions of the TPACK framework and the Chinese Ministry of Education’s digital literacy framework, alongside their adaptations in the present study. While TPACK emphasizes the transformative integration of technology with pedagogy and content knowledge, the Ministry’s framework provides a structured and policy-aligned categorization of teachers’ digital competencies. The DLS-EFL builds on these foundations by aligning national policy expectations with discipline-specific pedagogical practices, thereby addressing the need for contextualized measurement in EFL education. Table 1 Theoretical basis and adaptations of the DLS-EFL Framework Core dimensions Present study’s adaptation TPACK (Mishra, 2019 ) Technological, pedagogical, content knowledge Integrates to emphasize EFL-specific pedagogical transformation Chinese Ministry of Education (2022) Framework Digital awareness, knowledge/skills, application, responsibility, development Serves as core structure, adapted for EFL disciplinary needs Despite growing scholarly attention to teachers’ digital literacy, several unresolved issues persist in the literature. First, conceptual distinctions between digital literacy and related constructs such as digital competence remain blurred, leading to inconsistencies in measurement and empirical findings. Second, much of the existing research has concentrated on pre-service teachers or generalized teaching populations, with comparatively limited focus on in-service EFL teachers working in higher education contexts. Third, the pedagogical dimension of digital literacy—particularly its role in reshaping instructional practices and supporting language learning outcomes—has often been under-theorized. Finally, there is a notable shortage of localized, discipline-sensitive, and psychometrically validated instruments specifically designed for the Chinese EFL context. In response to these gaps, the present study aims to develop and validate a context-sensitive Digital Literacy Scale for EFL Teachers (DLS-EFL) tailored to Chinese higher education. Specifically, the study seeks to (a) examine and confirm the underlying factor structure of the DLS-EFL through exploratory and confirmatory factor analyses, and (b) evaluate the scale’s reliability and construct validity as a measurement instrument for assessing EFL teachers’ digital literacy. By providing a theoretically grounded and empirically validated tool, this study contributes to more systematic assessment practices and offers practical implications for teacher professional development, institutional evaluation, and policy-informed decision-making in the ongoing digital transformation of EFL education. 3 Method 3.1 Research Design A quantitative research approach was employed to investigate the digital literacy of EFL teachers working in higher education institutions across China. The study was guided by the national framework for teachers’ digital literacy issued by the Ministry of Education of China (2022) and was further informed by the Technological Pedagogical Content Knowledge (TPACK) model (Mishra, 2019 ), enabling a comprehensive examination of both pedagogical and technological aspects of EFL instruction. Drawing on this integrated conceptual framework, the Digital Literacy Scale for EFL Teachers (DLS-EFL) was developed to measure digital literacy within the Chinese higher education context. Data were collected through an online survey administered via the Wenjuanxing platform ( https://www.wjx.cn ). To enhance geographical coverage, participants were recruited from three universities located in eastern, central, and western regions of China. The questionnaire was distributed to EFL instructors at these institutions. Before completing the survey, participants received detailed information regarding the study’s objectives and procedures. Informed consent was obtained electronically, and respondents were assured of their right to withdraw at any stage without consequences. All data were collected exclusively for research purposes and handled with strict confidentiality. The pilot study was conducted over a period of approximately one week. 3.2 Initial instrument development Guided by the proposed conceptual framework, an initial set of 35 items was generated to assess EFL teachers’ digital literacy across five domains: Digital Awareness (DA), Digital Knowledge and Skills (DKS), Digital Teaching Application (DTA), Digital Responsibility (DR), and Professional Development (PD). These domains were aligned with the teacher digital literacy framework issued by the Ministry of Education of China (2022). To establish content validity, the item pool underwent an expert review process involving two specialists in educational technology and two experienced EFL instructors. Based on their feedback, items exhibiting ambiguous wording or overlapping conceptual meanings were revised, combined, or removed. This refinement process resulted in a preliminary version of the Digital Literacy Scale for EFL Teachers (DLS-EFL) consisting of 28 items, with each dimension represented by four to eight items. All items were rated on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). 3.3 Pilot Study A pilot investigation was undertaken to preliminarily examine the suitability and reliability of the proposed scale prior to large-scale administration. The survey was completed by 246 EFL teachers drawn from three universities in China, representing institutions located in eastern, central, and western regions of the country. Data were collected using an online questionnaire distributed through the Wenjuanxing platform. Ethical approval for the study was obtained from the authors’ affiliated institution. The research protocol was reviewed and approved by the institutional ethics committee prior to the commencement of data collection, and all procedures were performed in accordance with the ethical standards of the institutional research committee. Participation was voluntary and anonymous. Informed consent was obtained from all participants, and anonymity and confidentiality of the data were strictly ensured throughout the research process. During the pilot phase, item analysis and exploratory factor analysis were conducted to evaluate item performance and the underlying factor structure. Only after the scale demonstrated acceptable levels of reliability and validity, and the dimensional structure was deemed appropriate, was the subsequent large-scale survey implemented. As presented in Table 2 , the pilot sample comprised 246 participants with diverse educational qualifications, academic ranks, and lengths of teaching experience, including 78 male teachers (31.7%) and 168 female teachers (68.3%). Participants’ ages ranged from under 30 years to above 51 years. All pilot data were analyzed using SPSS version 23. Table 2 Demographic information of participants in the pilot phase ( n = 246) Profile Categories Frequency Percentage(%) Gender Female 168 68.3 Male 78 31.7 Age Under 30 160 65.1 31–40 63 25.6 41–50 20 8.1 51 and over 3 1.2 Educational Background Doctoral degree 41 16.7 Master’s degree 111 45.1 Bachelor’s degree 94 38.2 Professional Title Professor 5 2 Associate professor 22 8.9 Lecturer 94 38.2 Assistant lecturer 125 50.8 Teaching Experience Under 5 years 184 74.8 6–10 years 48 19.5 11–20 years 8 3.3 Over 20 years 6 2.4 3.3.1 Item analysis Item-level analyses were carried out on the pilot data to evaluate the overall performance and quality of the scale items. Multiple statistical criteria were examined, including descriptive indices (means and standard deviations), distributional characteristics (skewness and kurtosis), item discrimination (corrected item-total correlations), and internal consistency as reflected by Cronbach’s alpha values when individual items were removed. In particular, the mean (M) and standard deviation (SD) for each item were computed to assess response central tendency and variability. Items with adequate variability (typically SD > 0.5-1.0 in Likert-type scales) were considered suitable. Normality of item distributions was assessed through skewness and kurtosis values. Values within acceptable ranges (generally skewness between − 2 and + 2, and kurtosis between − 7 and + 7, though more conservative thresholds of approximately − 1 to + 1 for skewness and close to 0 for kurtosis were targeted) were used to confirm the data’s suitability for subsequent parametric statistical analyses. Item discrimination and contribution to scale reliability were examined using corrected item-total correlation coefficients. Items with values greater than the recommended threshold of 0.30 were deemed to exhibit good discrimination and internal consistency. Additionally, Cronbach’s alpha values were computed for the full scale and after the deletion of each individual item. Items that did not substantially decrease (or that increased) the overall alpha when removed were flagged for potential revision or elimination, with the goal of retaining items that consistently supported the scale’s reliability. All items meeting these predefined criteria (adequate variability, acceptable normality, corrected item-total correlations > 0.30, and no substantial improvement in Cronbach’s alpha upon deletion) were retained for the main study analyses. This process ensured that only high-quality items proceeded to the full-scale administration. 3.3.2 Exploratory factor analysis After the preliminary item evaluation, an exploratory factor analysis (EFA) was conducted together with reliability testing to investigate the dimensional composition of the scale and to assess its measurement consistency. The purpose of the EFA was to uncover the underlying latent constructs reflected by the observed variables. Prior to factor extraction, the adequacy of the dataset was examined using the Kaiser-Meyer-Olkin (KMO) index and Bartlett’s test of sphericity. A KMO statistic above 0.60 indicates sufficient sampling adequacy, while a significant Bartlett’s test ( p < 0.05) suggests that inter-item correlations are appropriate for factor analysis (Shrestha, 2021 ). Factor extraction was performed using principal axis factoring (PAF), followed by oblique Promax rotation to enhance factor interpretability by reducing cross-loadings and clarifying item-factor relationships. Based on the resulting factor solution, items were allocated to their respective factors. To further evaluate the reliability of each dimension, Cronbach’s α coefficients were calculated. Values exceeding 0.70 were regarded as acceptable, indicating satisfactory internal consistency among items within each factor (Shrestha, 2021 ). Overall, these procedures provided empirical support for both the construct validity and reliability of the scale. 3.4 Formal test 3.4.1 Participants The study involved 388 in-service English teachers working in higher education institutions across China. Given the extensive geographical coverage and the heterogeneity of institutional settings nationwide, it was not feasible to obtain a fully randomized and nationally representative sample. Consequently, non-probability sampling strategies were adopted. Specifically, participants were initially recruited through purposive sampling based on predefined criteria, and additional respondents were subsequently identified through snowball sampling facilitated by professional networks. Purposive sampling was initially applied to recruit participants who satisfied the inclusion criteria, namely in-service English teachers working in higher education institutions. Subsequently, these respondents were asked to refer additional colleagues who met the same criteria, thereby extending the sample through a referral-based snowball sampling procedure. This recruitment strategy enabled the inclusion of teachers from a wide range of institutional contexts and geographic regions. Ultimately, valid responses were obtained from participants located in 30 out of China’s 34 provincial-level administrative regions. 3.4.2 Confirmatory factor analysis Following the exploratory factor analysis, the revised instrument containing 15 items was re-administered, yielding a total of 388 responses. After screening the dataset and excluding outliers, 361 valid cases were retained for confirmatory factor analysis (CFA), which was conducted using AMOS version 24. The CFA was performed to further evaluate the measurement quality of the scale, with particular attention to convergent validity and composite reliability. Convergent validity was examined by calculating the average variance extracted (AVE), with values exceeding 0.50 regarded as indicative of adequate convergence among items within the same construct. In addition, composite reliability (CR) was computed to assess the overall reliability of each latent variable. Consistent with commonly accepted criteria, CR values greater than 0.70 were interpreted as evidence of satisfactory reliability. Discriminant validity was assessed by comparing the square roots of the AVE values with the inter-construct correlation coefficients. When the square root of the AVE for a given construct was larger than its correlations with other constructs, discriminant validity was considered acceptable, reflecting sound structural validity of the measurement model (Fornell & Larcker, 1981 ). 4 Results 4.1 Results of item analysis An item-level evaluation was performed to assess the performance of all scale items. As shown in Table 3 , descriptive statistics indicated that item mean(M) scores ranged from 3.48 to 4.18, while standard deviation (SD) values varied between 0.75 and 0.97. These results demonstrate an adequate spread of responses, suggesting that the items were effective in capturing meaningful variability among participants. With regard to distributional properties, skewness values ranged from − 1.14 to − 0.42 and kurtosis values from − 0.09 to 1.72. All indices fell within commonly accepted limits, indicating no serious violations of the normality assumption and supporting the appropriateness of subsequent parametric analyses. Item-total correlation analysis further revealed that the corrected item-total correlation coefficients ranged from 0.426 to 0.694, exceeding the recommended minimum value of .30. This finding indicates satisfactory item discrimination and internal consistency. In addition, the Cronbach’s alpha values calculated after the deletion of individual items remained highly stable ( α = 0.942–0.945), suggesting that each item made a consistent contribution to the overall reliability of the scale. Accordingly, all items were retained for further analyses. Table 3 Results of the item analysis ( n = 246) Factor Items Mean(SD) S,K Corrected Item-total correlation (CITC) Cronbach’s α (if deleted) DA DA1 3.48(0.963) -0.895, 0.488 0.619 0.943 DA2 3.65(0.968) -0.92 0.575 0.646 0.943 DA3 3.91(0.946) -1.141 1.473 0.594 0.943 DA4 3.85(0.917) -1.064 1.354 0.694 0.942 DKS DKS1 3.61(0.877) -0.943 1.276 0.639 0.943 DKS2 3.7(0.872) -0.856 0.666 0.605 0.943 DKS3 3.77(0.865) -0.763 0.77 0.627 0.943 DKS4 3.74(0.876) -0.709 0.742 0.623 0.943 DKS5 3.74(0.947) -0.582 0.105 0.632 0.943 DKS6 4(0.813) -0.872 1.383 0.615 0.943 DKS7 3.87(0.79) -0.422 0.143 0.586 0.943 DKS8 3.74(0.878) -0.424 -0.094 0.426 0.945 DTA DTA1 3.81(0.851) -0.954 1.603 0.588 0.943 DTA2 3.93(0.789) -0.823 1.49 0.577 0.944 DTA3 3.93(0.753) -0.759 1.435 0.613 0.943 DTA4 3.84(0.846) -0.661 0.637 0.533 0.944 DTA5 3.84(0.879) -0.765 0.903 0.56 0.944 DTA6 3.9(0.799) -0.589 0.897 0.629 0.943 DR DR1 3.99(0.84) -0.643 0.39 0.514 0.944 DR2 4.12(0.842) -1.054 1.717 0.649 0.943 DR3 4.14(0.862) -1.089 1.579 0.637 0.943 DR4 4.09(0.932) -0.919 0.595 0.643 0.943 DR5 4.18(0.876) -1.017 0.812 0.682 0.942 DR6 4.08(0.861) -1.079 1.627 0.537 0.944 PD PD1 3.85(0.939) -0.998 1.192 0.635 0.943 PD2 3.96(0.837) -0.892 1.206 0.555 0.944 PD3 3.87(0.891) -0.71 0.573 0.56 0.944 PD4 3.91(0.913) -0.894 1.047 0.593 0.943 Note: DA digital awareness; DKS digital knowledge and skills; DTA digital teaching application; DR digital responsibility; PD professional development; S skewness; K kurtosis 4.2 Results of EFA Before performing the exploratory factor analysis (EFA), preliminary tests were conducted to assess the adequacy of the data for factor extraction. The Kaiser-Meyer-Olkin (KMO) index reached a value of 0.933, reflecting excellent sampling adequacy. In addition, Bartlett’s test of sphericity was statistically significant, χ²(378) = 3547.92, p < 0.001, indicating that the inter-item correlation matrix was suitable for factor analysis. The EFA was carried out using principal axis factoring (PAF) combined with oblique Promax rotation. Decisions regarding item retention were based on established criteria: items were required to load at 0.50 or above on a single factor, while items exhibiting notable cross-loadings—defined as factor loadings greater than 0.40 on two or more factors—were excluded (Howard, 2016 ). Based on these criteria, 13 items were removed during the EFA process. Several items (DKS4, DKS6, and DKS7) failed to load meaningfully on any factor, while others (DKS5, DR2, DTA1, DTA5, DTA6, and PD1) were eliminated due to factor loadings below the acceptable threshold. In addition, items exhibiting substantial cross-loadings—including DKS8 and DTA2–DTA4—were excluded from further analysis. Further examination of the factor structure revealed that items originally assigned to the DA construct (DA1–DA4) and the DKS construct (DKS1–DKS3) loaded strongly on a single common factor, indicating a lack of empirical distinction between the two constructs. Given both the empirical evidence and theoretical coherence, these two constructs were combined into a single factor, labeled DATS. Further inspection of the factor solution revealed that items originally designed to measure Digital Awareness (DA1–DA4) and Digital Knowledge and Skills (DKS1–DKS3) loaded strongly on a single factor, indicating insufficient empirical distinction between the two constructs. Given both the statistical evidence and theoretical consistency, these items were combined into a unified factor labeled Digital Awareness and Technical Skills (DATS) . Moreover, all items associated with the Digital Teaching Application (DTA) construct failed to meet the retention criteria and were therefore removed, resulting in the exclusion of this construct from the final scale. Following iterative item refinement and construct optimization, the finalized DLS-EFL comprised 15 items forming a well-defined and interpretable factor structure. Reliability testing was subsequently performed for each identified factor. As reported in Table 4 , Cronbach’s α values ranged from 0.791 to 0.886, all exceeding the commonly accepted benchmark of 0.70, thereby reflecting satisfactory to high levels of internal consistency (Shrestha, 2021 ). In addition, the overall Cronbach’s α coefficient for the full scale was 0.920, indicating excellent reliability. Overall, the findings from the exploratory factor analysis offer strong empirical support for both the structural soundness and measurement robustness of the DLS-EFL, confirming its suitability as a dependable instrument for evaluating digital literacy among EFL teachers. Table 4 EFA results of the DLS-EFL ( n = 246) Factor Items Factor loading Factor-Level Cronbach’sα Total Cronbach’sα Digital Awareness and Technical Skills (DATS) 1 I can articulate the core value of digital technology in English teaching (e.g., expanding resources, enabling personalized learning, and facilitating intercultural communication). 0.756 0.886 0.920 2 I can identify the teaching opportunities brought by digital technologies (e.g., innovation in teaching models) as well as the challenges they pose (e.g., the digital divide). 0.697 3 I actively experiment with new digital teaching methods and maintain an open attitude toward innovative teaching practices. 0.807 4 When encountering difficulties in digital teaching, I can persist in seeking solutions. 0.862 5 I can determine the applicable scenarios for different digital tools (e.g., AI essay grading, VR situational teaching). 0.693 6 I can recommend matching tools (e.g., online debate platforms) based on teaching objectives (e.g., cultivating critical thinking). 0.629 7 I can explain the pedagogical functions and applicable objectives of multimodal resources (text/image/audio/video). 0.510 Digital Responsibility (DR) 19 I understand and abide by laws and regulations regarding data security and internet usage. 0.624 0.862 21 I can maintain a positive and healthy communication environment in online teaching. 0.772 22 I can protect personal information and professional privacy (e.g., permission settings). 0.870 23 I can comply with student privacy protection requirements (e.g., encrypted data storage). 0.760 24 I can identify and prevent cybersecurity risks (e.g., cyberbullying/fraud). 0.784 Professional Development (PD) 26 I can use digital technology and data tools to conduct teaching research and analyze teaching effects. 0.669 0.791 27 I can design and implement innovative digital teaching practices (e.g., AI-assisted learning, VR immersive classrooms) to promote improvements in learning methods. 0.764 28 I can engage in professional exchange, collaborative lesson planning, resource sharing, and teaching reflection with colleagues through online collaborative communities (e.g., QQ, WeChat, shared cloud drives). 0.547 4.3 Results of CFA Based on the results of the exploratory factor analysis, poorly performing items were eliminated, and the refined 15-item instrument was administered again, resulting in 388 responses. Following data screening and the removal of outliers, 361 valid cases were retained for confirmatory factor analysis (CFA), as illustrated in Fig. 2 . Model fit statistics for the CFA are presented in Table 5 . The chi-square to degrees of freedom ratio (χ²/df) was 1.998, which falls within the recommended range of 1 to 3. Furthermore, the comparative fit index (CFI), Tucker–Lewis index (TLI), incremental fit index (IFI), goodness-of-fit index (GFI), and normed fit index (NFI) reached values of 0.968, 0.961, 0.968, 0.94, and 0.937, respectively, all surpassing the commonly accepted threshold of 0.90. In addition, both the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR) were below 0.08. Taken together, these results indicate that the CFA model achieved an overall satisfactory fit to the data (Hair et al., 2019 ). Table 5 Model fit indices for the measurement model ( n = 361) Fit Index Recommended Threshold Obtained Value x 2 /df < 3 good; <5 acceptable 1.998 RMSEA < 0.05 good; <0.08 acceptable 0.053 SRMR < 0.05 good; 0.9 good; >0.8 acceptable 0.968 TLI > 0.9 good; >0.8 acceptable 0.961 IFI > 0.9 good; >0.8 acceptable 0.968 GFI > 0.9 good; >0.8 acceptable 0.94 NFI > 0.9 good; >0.8 acceptable 0.937 Table 6 Convergent validity and composite reliability of the CFA model ( n = 361) Standardized factor loadings S.E. P CR AVE DATS1 <--- DATS 0.738 0.886 0.5268 DATS2 <--- DATS 0.761 0.071 *** DATS3 <--- DATS 0.72 0.071 *** DATS4 <--- DATS 0.776 0.068 *** DATS5 <--- DATS 0.724 0.069 *** DATS6 <--- DATS 0.699 0.066 *** DATS7 <--- DATS 0.656 0.066 *** DR1 <--- DR 0.623 0.8636 0.5616 DR2 <--- DR 0.737 0.102 *** DR3 <--- DR 0.811 0.113 *** DR4 <--- DR 0.853 0.107 *** DR5 <--- DR 0.701 0.1 *** PD1 <--- PD 0.726 0.794 0.5626 PD2 <--- PD 0.782 0.092 *** PD3 <--- PD 0.741 0.092 *** Note: ***indicates p < 0.001 As reported in Table 6 , all standardized factor loadings in the CFA exceeded 0.60. The average variance extracted (AVE) and composite reliability (CR) values for each construct were as follows: DATS (AVE = 0.5268, CR = 0.886), DR (AVE = 0.5616, CR = 0.8636), and PD (AVE = 0.5626, CR = 0.794). Since the AVE values for all dimensions were above 0.50 and the CR values exceeded 0.70, the first-order CFA model of the Digital Literacy Scale for EFL Teachers (DLS-EFL) exhibited adequate convergent validity and composite reliability (Fornell & Larcker, 1981 ). Table 7 Discriminant validity of the CFA model ( n = 361) PD PD DR DATS 0.75 DR 0.697** 0.749 DATS 0.683** 0.716** 0.726 Note: *indicates p < 0.05, **indicates p < 0.01 Discriminant validity was further examined using the Fornell–Larcker criterion, which requires the square root of the AVE for each latent construct to be greater than its correlations with other constructs. As shown in Table 7 , this condition was satisfied for all dimensions. Specifically, for PD, the square root of AVE (0.75) exceeded the highest observed inter-factor correlation (0.697). Likewise, the square root of AVE for DR (0.749) was greater than its maximum correlation with other factors (0.716), and the same pattern was observed for DATS, where the square root of AVE (0.726) surpassed the highest inter-factor correlation (0.716). These findings provide empirical evidence supporting satisfactory discriminant validity across all dimensions of the model. 5 Discussion 5.1 Discussion of the Key Findings The present study sought to develop and empirically validate a measurement instrument for assessing digital literacy among EFL teachers in higher education. A salient finding emerging from the exploratory factor analysis (EFA) was the consolidation of the originally proposed dimensions of Digital Awareness (DA) and Digital Knowledge and Skills (DKS) into a single latent construct, which was subsequently labeled Digital Awareness and Technical Skills (DATS). Although DA and DKS were theoretically specified as separate dimensions at the initial stage of scale development, the EFA results demonstrated that items associated with both constructs (DA1–DA4; DKS1–DKS3) exhibited strong loadings on the same factor. This pattern suggests that, at an empirical level, respondents did not clearly differentiate between awareness-related and skill-based aspects of digital literacy. From a theoretical standpoint, the emergence of a combined factor can be interpreted as reflecting the inherently interconnected nature of digital awareness and technical competence in teachers’ professional practice. Existing digital literacy frameworks have frequently conceptualized these two aspects as mutually reinforcing rather than analytically independent. For example, the European Commission’s DigComp framework (Redecker, 2017 ) implicitly treats awareness of digital technologies—such as recognizing their pedagogical potential, limitations, and associated risks—as grounded in teachers’ ability to operate and apply digital tools effectively. In this view, digital awareness is not a purely abstract or attitudinal construct but develops through practical engagement with technology. A similar perspective is evident in Mishra’s ( 2019 ) refinement of the Technological Pedagogical Content Knowledge (TPACK) framework, which positions teachers’ understanding of technology as embedded within their technological knowledge and skills. Rather than conceptualizing awareness as separate from technical competence, TPACK emphasizes that teachers’ perceptions, judgments, and pedagogical decisions regarding technology are shaped through hands-on experience and skill acquisition. Accordingly, the empirical merging of DA and DKS in the present study can be seen as theoretically coherent, reflecting the integrated way in which EFL teachers in higher education perceive and enact foundational aspects of digital literacy. From a developmental perspective, teachers’ digital awareness often emerges through hands-on engagement with digital tools, while technical skills are shaped by reflective understanding of how and why technologies function in instructional contexts (Instefjord & Munthe, 2017 ; Gudmundsdottir & Hatlevik, 2018 ). Empirical studies have also reported substantial overlap between teachers’ digital awareness and digital skills, particularly in contexts where digital competence is framed as a holistic capability rather than a set of isolated subskills (Hämäläinen et al., 2021 ; Tzafilkou et al., 2023 ). Therefore, the merging of DA and DKS into the DATS dimension is both empirically justified and theoretically coherent, reflecting the integrated nature of teachers’ cognitive understanding and technical proficiency in digital environments. Another important finding was the complete removal of the Digital Teaching Application (DTA) dimension, as all items associated with this construct failed to meet the established criteria for factor retention. This result suggests that digital teaching application may not function as an independent dimension of digital literacy among EFL teachers in the present sample. One plausible explanation is the conceptual and practical overlap between digital teaching application and professional development. Prior research suggests that teachers’ pedagogical use of digital technologies is more appropriately understood as an expression of sustained professional learning rather than as an isolated or self-contained competence. Studies by Reisoğlu ( 2021 ) and Foreman-Brown et al. ( 2023 ) emphasize that instructional innovation—particularly the integration of digital tools into teaching practices—emerges through continuous professional development processes, including reflective inquiry, collaborative engagement, and iterative pedagogical refinement. From this perspective, digital teaching application represents an evolving practice shaped by ongoing learning experiences, rather than a fixed dimension that can be readily separated from teachers’ broader professional growth. Empirical investigations into teachers’ digital competence further support this view by demonstrating that effective technology integration in instruction is strongly linked to professional development activities such as experimentation with digital tools, reflective evaluation of teaching practices, peer collaboration, and instructional redesign (e.g., Garzón Artacho et al., 2020 ; Reisoğlu, 2021 ; Tondeur et al., 2023 ). Consistent with these findings, the exploratory factor analysis (EFA) in the present study revealed that items intended to capture Digital Teaching Application (DTA) did not form a clearly distinct factor. Instead, these items exhibited conceptual and empirical overlap with the Professional Development (PD) dimension, resulting in weak factor loadings and cross-construct ambiguity. Comparable patterns have been reported in previous scale development research, where practice-oriented dimensions failed to emerge as independent constructs due to their strong reliance on broader professional learning contexts (Aydin et al., 2024 müş & Kukul, 2023; Tzafilkou et al., 2023 ). Taken together, these results suggest that EFL teachers’ digital literacy is most coherently conceptualized as an integrated construct. Within this structure, digital awareness and technical skills function as closely aligned foundational components, while pedagogical application of digital technologies is embedded within ongoing professional development processes. The refined factor structure identified in this study not only aligns with the empirical evidence but also resonates with contemporary theoretical perspectives on teacher digital competence. By elucidating the relationships among these dimensions, the present study advances a more parsimonious and theoretically informed measurement model for assessing digital literacy among EFL teachers in higher education. 5.2 Comparison with existing Digital Literacy Scales The digital literacy scale developed in the present study adopts a fundamentally different orientation from widely used general digital competence frameworks, highlighting the necessity of a discipline-sensitive instrument tailored to EFL teaching in Chinese higher education. Whereas most established frameworks conceptualize digital literacy as a transferable set of competences applicable across subject areas, the current scale treats digital literacy as a situated professional practice shaped by the pedagogical demands of language teaching. Internationally recognized frameworks, such as DigCompEdu (Redecker, 2017 ) and the ISTE Standards for Educators (ISTE, 2017), characterize educators’ digital competence through broad functional domains or professional roles. These models emphasize areas such as pedagogical integration, digital resource management, learner empowerment, and ethical engagement across diverse educational contexts. Similarly, a number of recent instruments draw on general digital competence, data literacy, or algorithmic literacy models (e.g., González-Mujico, 2024 müş & Kukul, 2023 ; Mattar et al., 2022 ; Nguyen & Habók, 2024 ), most often relying on self-reported indicators of teachers’ technical, cognitive, and ethical capabilities. Within the Chinese context, the Teachers’ Digital Literacy Framework issued by the Ministry of Education (2022) provides a policy-driven structure comprising five overarching domains: digital awareness, technological knowledge and skills, instructional application, social responsibility, and professional development. Despite their conceptual breadth and policy relevance, these frameworks largely remain discipline-neutral. As a result, they offer limited explanatory power for subject-specific practices in language education, where digital literacy is closely tied to communicative interaction, multimodal language input, cultural mediation, and critical engagement with AI-generated linguistic content. Moreover, many existing instruments depend heavily on self-report measures and may insufficiently capture the contextual and cultural conditions that influence technology use in policy-oriented, non-Western educational settings such as China (Lin et al., 2023 ; Li, 2025 ). By contrast, the scale proposed in this study is explicitly anchored in the instructional realities of EFL teachers working in Chinese higher education. Rather than focusing solely on generic technical competence, it foregrounds domain-relevant applications of digital literacy in language teaching and learning. These include the use of digital tools to support communicative competence development, the integration of data and AI literacy for individualized language feedback, the design of technology-enhanced tasks that promote intercultural understanding, and the ethical management of digital language practices, such as addressing plagiarism in AI-assisted translation or recognizing cultural bias in online language resources. This emphasis aligns with emerging perspectives that incorporate data and AI literacy into teacher competence models (Lin et al., 2023 ; Yang, 2024 ), while remaining consistent with national policy orientations (Jiang, 2025 ). A more detailed comparison further illustrates the distinctive positioning of the present scale. While DigCompEdu and ISTE offer comprehensive yet subject-agnostic structures, and the Chinese national framework prioritizes policy alignment over disciplinary specificity, the current instrument embeds pedagogical constructs that are particularly salient to EFL instruction. These include critical engagement with digital language corpora, AI-supported error analysis, and responsible participation in multilingual digital environments. In addition, the scale differentiates self-efficacy, attitudes, and contextual adoption conditions as explicit analytical dimensions, rather than subsuming them within broader professional categories. This design enables a more nuanced examination of motivational factors and sustained technology use in language teaching contexts. From a psychometric perspective, the scale was developed and validated through a multi-stage process involving exploratory and confirmatory factor analyses, as well as tests of convergent and discriminant validity, using a geographically and institutionally diverse sample of EFL teachers in Chinese higher education. This rigorous validation strategy enhances construct robustness and generalizability when compared with instruments derived from single-sample studies or minimally validated self-report measures. Overall, the present scale represents a complementary yet distinct contribution to the assessment of teacher digital literacy. By integrating international competence frameworks with national policy expectations and addressing long-standing disciplinary gaps in language education, it provides a contextually grounded and empirically validated tool for evaluating and supporting digital literacy development among EFL teachers. 5.3 Limitations and future research Notwithstanding the contributions of the present study, several methodological and conceptual constraints warrant consideration, while simultaneously suggesting avenues for further investigation. Although data were collected from 361 EFL teachers across 31 provinces in China, the sample size remains limited in relation to the heterogeneity of institutional missions, regional educational conditions, and professional trajectories characterizing Chinese higher education. Subsequent research could enhance the stability and external validity of the Digital Literacy Scale for EFL Teachers by conducting validation studies with larger samples and more differentiated institutional coverage, including research-intensive universities, teaching-focused institutions, and vocational or application-oriented colleges. In addition, the study relied predominantly on self-administered questionnaires, which capture teachers’ perceived levels of digital literacy but may not fully represent how digital competencies are enacted in authentic instructional settings. To address this limitation, future inquiries may adopt mixed-method or multi-source research designs that combine survey data with classroom observations, instructional artifacts, digital activity logs, or performance-based assessments of technology-enhanced teaching. Such triangulation would allow for a more nuanced understanding of the relationship between perceived competence and actual pedagogical practice. Furthermore, the scope of the present research was intentionally confined to scale construction and psychometric evaluation, without examining how teachers’ digital literacy operates within instructional processes or influences educational outcomes. Building on the validated scale, future studies could explore the functional role of its dimensions in mediating relationships between technology use and EFL teaching outcomes, such as students’ language proficiency development, learning engagement, and intercultural communicative competence. Comparative analyses across institutional types and career stages may yield deeper insights into how digital literacy supports effective teaching under varying professional conditions. Finally, longitudinal research approaches are needed to capture the developmental trajectories of EFL teachers’ digital literacy over time. Tracking changes across multiple points would make it possible to identify key influencing factors—including professional learning opportunities, institutional support mechanisms, and policy environments—that shape the evolution of digital competence. Such evidence would contribute to a more dynamic understanding of digital literacy as a professional capability and inform the design of sustained, targeted interventions aimed at enhancing the quality of EFL instruction in digitally mediated learning contexts. 6 Conclusion This present research developed and validated the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education using a quantitative approach. Scale construction involved item generation, pilot testing (n = 246) with item analysis and exploratory factor analysis (EFA) to produce a 15-item instrument (Cronbach’s α = 0.945), followed by confirmatory factor analysis (CFA) on 361 valid responses from 388 participants, confirming a robust three-factor structure with strong reliability and construct validity. The findings establish EFL teachers’ digital literacy as a multidimensional construct comprising three core dimensions: Digital Awareness and Technical Skills (DATS), Digital Responsibility (DR), and Professional Development (PD). DATS encompasses awareness of digital technologies in language education and proficiency in applying them to EFL-specific pedagogical goals, such as designing communicative tasks, providing multimodal input, delivering personalized feedback, and fostering authentic interaction. DR emphasizes ethical and socially responsible use, including data privacy, protection of student information, cultural sensitivity in digital content, and mitigation of biases in AI-generated language resources. PD focuses on ongoing professional growth through continuous learning, reflection, and adaptation of digital tools to enhance teaching practice. Together, these dimensions address the distinctive demands of EFL instruction—such as promoting authentic language interaction, leveraging AI and data-driven tools for language analysis, and cultivating intercultural competence—while maintaining strong alignment with the Ministry of Education’s (2022) Teachers’ Digital Literacy framework in China. Theoretically, the DLS-EFL refines existing frameworks (e.g., DigCompEdu, ISTE Standards) by incorporating disciplinary specificity for EFL teaching in the Chinese higher education context. It highlights the interplay of technical awareness and skills (DATS), ethical and responsible practices (DR), and sustained professional growth (PD)—elements often underexplored or aggregated in general instruments—while adapting them to EFL-unique pedagogical needs and national policy priorities. Practically, the scale serves as a diagnostic tool for targeted professional development. Low scores in DATS can guide workshops on selecting and integrating digital tools for EFL tasks (e.g., multimodal communicative activities or AI-supported feedback); deficiencies in DR can prompt training on ethical AI applications, data privacy, and bias mitigation in language resources; and gaps in PD can signal needs for reflective practices or ongoing learning modules. Teacher educators can design scaffolded activities to build competence and confidence, progressing from basic tool proficiency to innovative, EFL-specific lesson design. Curriculum developers and policymakers can embed the scale into training programs to support evidence-based digital pedagogy aligned with national strategies. Researchers can employ it to investigate links between teachers’ digital literacy dimensions and student outcomes (e.g., language proficiency, engagement, intercultural awareness) or mediating factors such as institutional support. Overall, the DLS-EFL provides a reliable, contextually grounded instrument for assessing and enhancing EFL teachers’ digital literacy in Chinese higher education. It bridges national policy imperatives with disciplinary practice, promotes evidence-based professional development, and contributes to higher-quality, inclusive, and innovative language education in the digital era. Declarations Ethical approval Ethical approval for this study was granted by the Institutional Review Board (IRB) of Tianfu College of Southwestern University of Finance and Economics (Approval number: TFSWUFE/IRB_2025_128; Date of approval: September 11, 2025). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and its later amendments, as well as all relevant institutional and national guidelines for research involving human participants. The approval covered the full scope of the research protocol titled Digital Literacy for EFL Teachers, including participant recruitment, online questionnaire data collection, and the use of de-identified data for analysis and research dissemination. Informed consent Informed consent was obtained by the research team from all participants prior to participation. As the study was conducted through an anonymous online questionnaire, participants provided electronic consent by clicking the “Agree and Continue” button before starting the survey between October 1, 2025, and December 31, 2025. The consent covered voluntary participation, the anonymous collection of responses, and the use of de-identified data for academic analysis and publication. Participants were informed of their right to withdraw at any time without penalty. The study involved no more than minimal risk. Author Contribution YYX and WFC designed the research. CS collected data. YYX analysed data. YYX wrote the first draft of the article, all authors interpreted the results, revised the manuscript, and read and approved the final manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8924262","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":610021951,"identity":"7334e190-be8f-478c-b779-7bc48a3cd659","order_by":0,"name":"Yingyi Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3PMUvDQBTA8RcOEocXbr0Qab/CK4EiOPhVIgVdUigInYoGAjeVzvVjiOAsPDg35w6CnTo5KFlEMngpFFwuZBR6f7g7ON6P4wB8vn/as10oIdjac9BeUD+SaNFOZv3IPjJ9CW2KEc9++DR7r0QdaxpAVD0pWLw5SbIuiO9XjGMjwtSSDNDMFZidk0hlSbxsiTRiqpvLUhVjFZTsJOGBZFqIeqrprhx+dJP9K/jNSKGA1JIcFHaTZLmbcVxeozKTMG1eaaTx6uYsN25CL5PHGpvzC1mx+FrPaSgjfth8LtwE4IQg0NWf37Vb3gEAoi1Ac9s54vP5fEfeL5XZTwr0L3apAAAAAElFTkSuQmCC","orcid":"","institution":"Tianfu College of Southwestern University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Yingyi","middleName":"","lastName":"Xu","suffix":""},{"id":610021952,"identity":"2f21eaa7-48d6-4c2b-9d0c-6a3b91fb5b0e","order_by":1,"name":"Chang Sun","email":"","orcid":"","institution":"Tianfu College of Southwestern University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Sun","suffix":""},{"id":610021953,"identity":"0ec588c6-5537-4a5b-b196-34c26b5ea090","order_by":2,"name":"Weifong Cheng","email":"","orcid":"","institution":"Tunku Abdul Rahman University of Management and Technology","correspondingAuthor":false,"prefix":"","firstName":"Weifong","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2026-02-20 09:29:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8924262/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8924262/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105297040,"identity":"dacdcb6f-2db1-4c15-95c2-6f6393969327","added_by":"auto","created_at":"2026-03-24 13:15:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136674,"visible":true,"origin":"","legend":"\u003cp\u003eProposed DLS-EFL framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8924262/v1/9c9767f71ed5bccb5444d409.png"},{"id":105297039,"identity":"ddb0c1d5-99b7-4ec9-ad88-370c4c311bc1","added_by":"auto","created_at":"2026-03-24 13:15:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151429,"visible":true,"origin":"","legend":"\u003cp\u003eCFA model with standardized estimates (\u003cem\u003en\u003c/em\u003e=361)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8924262/v1/1fd803ca61637409883195c9.png"},{"id":105564810,"identity":"4011c846-0462-4e92-9fdf-99defb9abcc1","added_by":"auto","created_at":"2026-03-27 12:50:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1515942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8924262/v1/b82e3879-bbe9-48de-97f5-d846d83a729b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe rapid advancement of digital technologies has fundamentally transformed higher education, reshaping instructional design, assessment practices, and academic communication worldwide. Within this transformation, teachers are increasingly expected to integrate digital tools into pedagogical practice in ways that enhance learning effectiveness rather than merely supporting technical operations (Aslan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Falloon, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These expectations are particularly salient in English as a Foreign Language (EFL) education, where digital technologies intersect with communicative pedagogy, multimodal meaning-making, and learner autonomy (Lu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn China, educational digitalization has been elevated to a national strategic priority. The Ministry of Education has identified teachers\u0026rsquo; digital literacy as a key driver of educational modernization and high-quality development in higher education (Lei \u0026amp; Jiang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This policy orientation underscores the importance of teachers\u0026rsquo; capacity to design, implement, and evaluate technology-enhanced instruction in alignment with pedagogical goals and ethical standards. Universities are therefore increasingly required to strengthen teachers\u0026rsquo; professional readiness for digital teaching, particularly in foreign language education, where instructional effectiveness relies heavily on interaction, feedback, and intercultural communication (Feng \u0026amp; Sumettikoon, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lei \u0026amp; Jiang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite increasing policy emphasis and institutional investment, the assessment of teachers\u0026rsquo; digital literacy remains underdeveloped, especially in discipline-specific contexts. Many existing instruments have been designed for general teaching populations or adapted from Western educational settings, which limits their sensitivity to the pedagogical characteristics and contextual demands of EFL instruction in Chinese higher education. The absence of a contextually grounded and empirically validated measurement tool constrains systematic evaluation and hinders the development of targeted professional support for EFL teachers.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study aims to develop and validate a Digital Literacy Scale for EFL Teachers (DLS-EFL) tailored to the Chinese higher education context. Drawing on the Teachers\u0026rsquo; Digital Literacy Framework issued by the Ministry of Education of China (2022) and informed by pedagogically oriented theoretical perspectives, the study conceptualizes digital literacy as a multidimensional construct embedded in instructional practice. Using a quantitative research design, the study employs exploratory and confirmatory factor analyses to examine the reliability and validity of the proposed scale. By providing a context-sensitive measurement instrument, this research contributes to the literature on teacher digital literacy and offers practical implications for teacher education, institutional evaluation, and policy implementation.\u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital literacy: Definitions and existing assessment frameworks\u003c/h2\u003e \u003cp\u003eDigital literacy has become a central construct in educational research, particularly in studies examining teachers\u0026rsquo; professional competencies in technology-integrated learning environments. Although the terms \u003cem\u003edigital literacy\u003c/em\u003e and \u003cem\u003edigital competence\u003c/em\u003e are frequently used interchangeably, scholars have emphasized important conceptual distinctions between them. Digital literacy generally refers to the broad capacity to access, evaluate, create, and communicate information through digital technologies, whereas digital competence places greater emphasis on the effective and purposeful application of these abilities in professional or pedagogical contexts (Spante et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Falloon, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe conceptualization of digital literacy has evolved considerably over time. Early definitions focused primarily on basic technical proficiency, while more recent perspectives conceptualize digital literacy as a multidimensional construct integrating technical, cognitive, pedagogical, and ethical dimensions (Chu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Contemporary research further highlights teachers\u0026rsquo; need to critically engage with digital technologies, interpret and utilize educational data, design innovative learning experiences, and promote responsible digital practices. In response to the growing datafication and automation of education, emerging competencies such as data literacy and AI literacy have also been incorporated into discussions of teacher digital literacy (Lin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the Chinese context, these developments align with national policy priorities, as digital literacy has been positioned as a strategic foundation for innovation-oriented and high-quality education (Lei \u0026amp; Jiang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on these conceptual developments, a variety of digital literacy and digital competence assessment frameworks have been proposed. Many widely used instruments are grounded in international standards such as the European DigCompEdu framework (Redecker, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the ISTE Standards for Educators (ISTE, 2017), which commonly emphasize domains including professional engagement, digital resources, teaching and learning, assessment, and learner empowerment (Chifla-Vill\u0026oacute;n et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ličen \u0026amp; Prosen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More recently, researchers have extended assessment efforts to include AI-related competencies, reflecting the changing technological landscape of education (Ng et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tenberga \u0026amp; Daniela, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, prior studies have identified several limitations in existing assessment tools. Many instruments prioritize general digital skills while paying limited attention to disciplinary specificity, particularly in fields such as language education that involve academic literacy, discourse practices, and intercultural communication (Zhang \u0026amp; Hyland, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In addition, most studies rely predominantly on self-report questionnaires, with relatively limited use of performance-based assessments, and concerns remain regarding construct validity and cross-cultural applicability (Nguyen \u0026amp; Hab\u0026oacute;k, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Avin\u0026ccedil; \u0026amp; Doğan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These limitations highlight the need for assessment frameworks that are both pedagogically grounded and contextually responsive.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Digital literacy in EFL teaching\u003c/h2\u003e \u003cp\u003eDigital literacy assumes distinctive characteristics in EFL teaching, where educators are required to integrate language pedagogy with digital technologies to support communicative competence, intercultural awareness, and learner autonomy. Beyond basic tool operation, EFL teachers are expected to employ digital resources for multimodal instruction, online interaction, collaborative learning, and individualized feedback (Feng \u0026amp; Sumettikoon, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, empirical research consistently reports challenges related to uneven levels of confidence, limited institutional support, and insufficient access to professional development, particularly within humanities-oriented disciplines such as foreign language education (Lei \u0026amp; Jiang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn higher education, the COVID-19 pandemic accelerated the adoption of digital teaching practices and exposed disparities in teachers\u0026rsquo; preparedness for technology-integrated instruction (Romero-Hall \u0026amp; Jaramillo Cherrez, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In China, EFL teachers have often reported lower levels of technology integration compared to their counterparts in STEM fields, a difference attributed to variations in infrastructure, training opportunities, and teaching workload (Feng \u0026amp; Sumettikoon, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the policy level, initiatives such as the \u003cem\u003eEducation Digitalization Strategy Action\u003c/em\u003e (Lei \u0026amp; Jiang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) emphasize the role of EFL teachers as designers of digital pedagogy, facilitators of hybrid learning environments, and users of data-informed assessment practices (Jiang \u0026amp; Yu, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the pedagogical complexity of EFL instruction, scholars increasingly argue that digital literacy in this field cannot be adequately captured by generic skill-based frameworks alone. Instead, it requires an integrative perspective that accounts for the close interaction between digital technologies, pedagogical strategies, and disciplinary content (Akayoğlu et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Su, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In EFL classrooms, digital tools are not merely supportive resources but actively shape language input, learner interaction, and feedback processes, particularly through multimodal and communicative practices (Wang, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This pedagogical specificity highlights the need for assessment approaches that are sensitive to how digital literacy is enacted within language teaching, thereby providing a rationale for adopting theoretically grounded and discipline-responsive frameworks in subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Theoretical framework and research purpose\u003c/h2\u003e \u003cp\u003eBuilding on the pedagogical challenges identified in EFL digital instruction, recent research on teachers\u0026rsquo; digital literacy has expanded considerably, with existing frameworks predominantly emphasizing cognitive and technical competencies related to technology use (Laupichler et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While these perspectives have contributed to a clearer understanding of teachers\u0026rsquo; digital knowledge and skills, they often insufficiently address the pedagogical enactment of digital literacy within discipline-specific instructional contexts. In particular, English as a Foreign Language (EFL) teaching involves distinctive pedagogical processes\u0026mdash;such as language interaction, multimodal input, and formative feedback\u0026mdash;that require more nuanced conceptualizations of how digital literacy is enacted in practice.\u003c/p\u003e \u003cp\u003eTo address this limitation, the present study adopts an integrative theoretical approach that combines the teacher digital literacy framework proposed by the Ministry of Education of China (2022) with the Technological Pedagogical Content Knowledge (TPACK) model (Mishra, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Ministry\u0026rsquo;s framework provides a policy-oriented and contextually grounded structure that reflects national priorities for teachers\u0026rsquo; digital development, while TPACK offers a well-established theoretical lens for understanding the dynamic interplay between technology, pedagogy, and subject matter. By synthesizing these two perspectives, the study moves beyond a skills-based view of digital literacy and foregrounds its pedagogical and disciplinary dimensions within EFL instruction.\u003c/p\u003e \u003cp\u003eThe resulting integrated model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) serves as the conceptual foundation for the Digital Literacy Scale for EFL Teachers (DLS-EFL). Drawing primarily on the Ministry\u0026rsquo;s framework, the scale is organized around five interrelated dimensions: Digital Awareness (DA), Digital Knowledge and Skills (DKS), Digital Teaching Application (DTA), Digital Responsibility (DR), and Professional Development (PD). These dimensions collectively capture EFL teachers\u0026rsquo; awareness of the educational value of digital technologies, their technical competence, their ability to meaningfully integrate digital tools into language pedagogy, their ethical and responsible use of technology, and their engagement in continuous digital professional growth. Importantly, each dimension is operationalized with explicit consideration of the pedagogical demands and instructional practices characteristic of EFL teaching in higher education.\u003c/p\u003e \u003cp\u003eTo clarify the theoretical grounding of the DLS-EFL, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comparison between the core dimensions of the TPACK framework and the Chinese Ministry of Education\u0026rsquo;s digital literacy framework, alongside their adaptations in the present study. While TPACK emphasizes the transformative integration of technology with pedagogy and content knowledge, the Ministry\u0026rsquo;s framework provides a structured and policy-aligned categorization of teachers\u0026rsquo; digital competencies. The DLS-EFL builds on these foundations by aligning national policy expectations with discipline-specific pedagogical practices, thereby addressing the need for contextualized measurement in EFL education.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTheoretical basis and adaptations of the DLS-EFL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore dimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent study\u0026rsquo;s adaptation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPACK (Mishra, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnological, pedagogical, content knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntegrates to emphasize EFL-specific pedagogical transformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese Ministry of Education (2022) Framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital awareness, knowledge/skills, application, responsibility, development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eServes as core structure, adapted for EFL disciplinary needs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite growing scholarly attention to teachers\u0026rsquo; digital literacy, several unresolved issues persist in the literature. First, conceptual distinctions between digital literacy and related constructs such as digital competence remain blurred, leading to inconsistencies in measurement and empirical findings. Second, much of the existing research has concentrated on pre-service teachers or generalized teaching populations, with comparatively limited focus on in-service EFL teachers working in higher education contexts. Third, the pedagogical dimension of digital literacy\u0026mdash;particularly its role in reshaping instructional practices and supporting language learning outcomes\u0026mdash;has often been under-theorized. Finally, there is a notable shortage of localized, discipline-sensitive, and psychometrically validated instruments specifically designed for the Chinese EFL context.\u003c/p\u003e \u003cp\u003eIn response to these gaps, the present study aims to develop and validate a context-sensitive Digital Literacy Scale for EFL Teachers (DLS-EFL) tailored to Chinese higher education. Specifically, the study seeks to (a) examine and confirm the underlying factor structure of the DLS-EFL through exploratory and confirmatory factor analyses, and (b) evaluate the scale\u0026rsquo;s reliability and construct validity as a measurement instrument for assessing EFL teachers\u0026rsquo; digital literacy. By providing a theoretically grounded and empirically validated tool, this study contributes to more systematic assessment practices and offers practical implications for teacher professional development, institutional evaluation, and policy-informed decision-making in the ongoing digital transformation of EFL education.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eA quantitative research approach was employed to investigate the digital literacy of EFL teachers working in higher education institutions across China. The study was guided by the national framework for teachers\u0026rsquo; digital literacy issued by the Ministry of Education of China (2022) and was further informed by the Technological Pedagogical Content Knowledge (TPACK) model (Mishra, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), enabling a comprehensive examination of both pedagogical and technological aspects of EFL instruction. Drawing on this integrated conceptual framework, the Digital Literacy Scale for EFL Teachers (DLS-EFL) was developed to measure digital literacy within the Chinese higher education context. Data were collected through an online survey administered via the \u003cem\u003eWenjuanxing\u003c/em\u003e platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo enhance geographical coverage, participants were recruited from three universities located in eastern, central, and western regions of China. The questionnaire was distributed to EFL instructors at these institutions. Before completing the survey, participants received detailed information regarding the study\u0026rsquo;s objectives and procedures. Informed consent was obtained electronically, and respondents were assured of their right to withdraw at any stage without consequences. All data were collected exclusively for research purposes and handled with strict confidentiality. The pilot study was conducted over a period of approximately one week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Initial instrument development\u003c/h2\u003e \u003cp\u003eGuided by the proposed conceptual framework, an initial set of 35 items was generated to assess EFL teachers\u0026rsquo; digital literacy across five domains: Digital Awareness (DA), Digital Knowledge and Skills (DKS), Digital Teaching Application (DTA), Digital Responsibility (DR), and Professional Development (PD). These domains were aligned with the teacher digital literacy framework issued by the Ministry of Education of China (2022).\u003c/p\u003e \u003cp\u003eTo establish content validity, the item pool underwent an expert review process involving two specialists in educational technology and two experienced EFL instructors. Based on their feedback, items exhibiting ambiguous wording or overlapping conceptual meanings were revised, combined, or removed. This refinement process resulted in a preliminary version of the Digital Literacy Scale for EFL Teachers (DLS-EFL) consisting of 28 items, with each dimension represented by four to eight items. All items were rated on a five-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pilot Study\u003c/h2\u003e \u003cp\u003eA pilot investigation was undertaken to preliminarily examine the suitability and reliability of the proposed scale prior to large-scale administration. The survey was completed by 246 EFL teachers drawn from three universities in China, representing institutions located in eastern, central, and western regions of the country. Data were collected using an online questionnaire distributed through the \u003cem\u003eWenjuanxing\u003c/em\u003e platform. Ethical approval for the study was obtained from the authors\u0026rsquo; affiliated institution. The research protocol was reviewed and approved by the institutional ethics committee prior to the commencement of data collection, and all procedures were performed in accordance with the ethical standards of the institutional research committee. Participation was voluntary and anonymous. Informed consent was obtained from all participants, and anonymity and confidentiality of the data were strictly ensured throughout the research process.\u003c/p\u003e \u003cp\u003eDuring the pilot phase, item analysis and exploratory factor analysis were conducted to evaluate item performance and the underlying factor structure. Only after the scale demonstrated acceptable levels of reliability and validity, and the dimensional structure was deemed appropriate, was the subsequent large-scale survey implemented.\u003c/p\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the pilot sample comprised 246 participants with diverse educational qualifications, academic ranks, and lengths of teaching experience, including 78 male teachers (31.7%) and 168 female teachers (68.3%). Participants\u0026rsquo; ages ranged from under 30 years to above 51 years. All pilot data were analyzed using SPSS version 23.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic information of participants in the pilot phase (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctoral degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eProfessional Title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate professor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLecturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssistant lecturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTeaching Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnder 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver 20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Item analysis\u003c/h2\u003e \u003cp\u003eItem-level analyses were carried out on the pilot data to evaluate the overall performance and quality of the scale items. Multiple statistical criteria were examined, including descriptive indices (means and standard deviations), distributional characteristics (skewness and kurtosis), item discrimination (corrected item-total correlations), and internal consistency as reflected by Cronbach\u0026rsquo;s alpha values when individual items were removed.\u003c/p\u003e \u003cp\u003eIn particular, the mean (M) and standard deviation (SD) for each item were computed to assess response central tendency and variability. Items with adequate variability (typically SD\u0026thinsp;\u0026gt;\u0026thinsp;0.5-1.0 in Likert-type scales) were considered suitable.\u003c/p\u003e \u003cp\u003eNormality of item distributions was assessed through skewness and kurtosis values. Values within acceptable ranges (generally skewness between \u0026minus;\u0026thinsp;2 and +\u0026thinsp;2, and kurtosis between \u0026minus;\u0026thinsp;7 and +\u0026thinsp;7, though more conservative thresholds of approximately\u0026thinsp;\u0026minus;\u0026thinsp;1 to +\u0026thinsp;1 for skewness and close to 0 for kurtosis were targeted) were used to confirm the data\u0026rsquo;s suitability for subsequent parametric statistical analyses.\u003c/p\u003e \u003cp\u003eItem discrimination and contribution to scale reliability were examined using corrected item-total correlation coefficients. Items with values greater than the recommended threshold of 0.30 were deemed to exhibit good discrimination and internal consistency.\u003c/p\u003e \u003cp\u003eAdditionally, Cronbach\u0026rsquo;s alpha values were computed for the full scale and after the deletion of each individual item. Items that did not substantially decrease (or that increased) the overall alpha when removed were flagged for potential revision or elimination, with the goal of retaining items that consistently supported the scale\u0026rsquo;s reliability.\u003c/p\u003e \u003cp\u003eAll items meeting these predefined criteria (adequate variability, acceptable normality, corrected item-total correlations\u0026thinsp;\u0026gt;\u0026thinsp;0.30, and no substantial improvement in Cronbach\u0026rsquo;s alpha upon deletion) were retained for the main study analyses. This process ensured that only high-quality items proceeded to the full-scale administration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Exploratory factor analysis\u003c/h2\u003e \u003cp\u003eAfter the preliminary item evaluation, an exploratory factor analysis (EFA) was conducted together with reliability testing to investigate the dimensional composition of the scale and to assess its measurement consistency. The purpose of the EFA was to uncover the underlying latent constructs reflected by the observed variables. Prior to factor extraction, the adequacy of the dataset was examined using the Kaiser-Meyer-Olkin (KMO) index and Bartlett\u0026rsquo;s test of sphericity. A KMO statistic above 0.60 indicates sufficient sampling adequacy, while a significant Bartlett\u0026rsquo;s test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggests that inter-item correlations are appropriate for factor analysis (Shrestha, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFactor extraction was performed using principal axis factoring (PAF), followed by oblique Promax rotation to enhance factor interpretability by reducing cross-loadings and clarifying item-factor relationships. Based on the resulting factor solution, items were allocated to their respective factors. To further evaluate the reliability of each dimension, Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e coefficients were calculated. Values exceeding 0.70 were regarded as acceptable, indicating satisfactory internal consistency among items within each factor (Shrestha, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, these procedures provided empirical support for both the construct validity and reliability of the scale.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Formal test\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Participants\u003c/h2\u003e \u003cp\u003eThe study involved 388 in-service English teachers working in higher education institutions across China. Given the extensive geographical coverage and the heterogeneity of institutional settings nationwide, it was not feasible to obtain a fully randomized and nationally representative sample. Consequently, non-probability sampling strategies were adopted. Specifically, participants were initially recruited through purposive sampling based on predefined criteria, and additional respondents were subsequently identified through snowball sampling facilitated by professional networks.\u003c/p\u003e \u003cp\u003ePurposive sampling was initially applied to recruit participants who satisfied the inclusion criteria, namely in-service English teachers working in higher education institutions. Subsequently, these respondents were asked to refer additional colleagues who met the same criteria, thereby extending the sample through a referral-based snowball sampling procedure. This recruitment strategy enabled the inclusion of teachers from a wide range of institutional contexts and geographic regions. Ultimately, valid responses were obtained from participants located in 30 out of China\u0026rsquo;s 34 provincial-level administrative regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Confirmatory factor analysis\u003c/h2\u003e \u003cp\u003eFollowing the exploratory factor analysis, the revised instrument containing 15 items was re-administered, yielding a total of 388 responses. After screening the dataset and excluding outliers, 361 valid cases were retained for confirmatory factor analysis (CFA), which was conducted using AMOS version 24. The CFA was performed to further evaluate the measurement quality of the scale, with particular attention to convergent validity and composite reliability.\u003c/p\u003e \u003cp\u003eConvergent validity was examined by calculating the average variance extracted (AVE), with values exceeding 0.50 regarded as indicative of adequate convergence among items within the same construct. In addition, composite reliability (CR) was computed to assess the overall reliability of each latent variable. Consistent with commonly accepted criteria, CR values greater than 0.70 were interpreted as evidence of satisfactory reliability. Discriminant validity was assessed by comparing the square roots of the AVE values with the inter-construct correlation coefficients. When the square root of the AVE for a given construct was larger than its correlations with other constructs, discriminant validity was considered acceptable, reflecting sound structural validity of the measurement model (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Results of item analysis\u003c/h2\u003e \u003cp\u003eAn item-level evaluation was performed to assess the performance of all scale items. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, descriptive statistics indicated that item mean(M) scores ranged from 3.48 to 4.18, while standard deviation (SD) values varied between 0.75 and 0.97. These results demonstrate an adequate spread of responses, suggesting that the items were effective in capturing meaningful variability among participants.\u003c/p\u003e \u003cp\u003eWith regard to distributional properties, skewness values ranged from \u0026minus;\u0026thinsp;1.14 to \u0026minus;\u0026thinsp;0.42 and kurtosis values from \u0026minus;\u0026thinsp;0.09 to 1.72. All indices fell within commonly accepted limits, indicating no serious violations of the normality assumption and supporting the appropriateness of subsequent parametric analyses.\u003c/p\u003e \u003cp\u003eItem-total correlation analysis further revealed that the corrected item-total correlation coefficients ranged from 0.426 to 0.694, exceeding the recommended minimum value of .30. This finding indicates satisfactory item discrimination and internal consistency.\u003c/p\u003e \u003cp\u003eIn addition, the Cronbach\u0026rsquo;s alpha values calculated after the deletion of individual items remained highly stable (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.942\u0026ndash;0.945), suggesting that each item made a consistent contribution to the overall reliability of the scale. Accordingly, all items were retained for further analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the item analysis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS,K\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCorrected\u003c/p\u003e \u003cp\u003eItem-total correlation\u003c/p\u003e \u003cp\u003e(CITC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach\u0026rsquo;s\u003c/p\u003e \u003cp\u003eα (if deleted)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.48(0.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.895, 0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.65(0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.91(0.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.141\u003c/p\u003e \u003cp\u003e1.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85(0.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.064\u003c/p\u003e \u003cp\u003e1.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eDKS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.61(0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.943\u003c/p\u003e \u003cp\u003e1.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.7(0.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.856\u003c/p\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.77(0.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.763\u003c/p\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74(0.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.709\u003c/p\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74(0.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.582\u003c/p\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.872\u003c/p\u003e \u003cp\u003e1.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.87(0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.422\u003c/p\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDKS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.74(0.878)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.424\u003c/p\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.81(0.851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.954\u003c/p\u003e \u003cp\u003e1.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.93(0.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.823\u003c/p\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.93(0.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.759\u003c/p\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.84(0.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.661\u003c/p\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.84(0.879)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.765\u003c/p\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9(0.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.589\u003c/p\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.99(0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.643\u003c/p\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.12(0.842)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.054\u003c/p\u003e \u003cp\u003e1.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.14(0.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.089\u003c/p\u003e \u003cp\u003e1.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.09(0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.919\u003c/p\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18(0.876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.017\u003c/p\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.08(0.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.079\u003c/p\u003e \u003cp\u003e1.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85(0.939)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.998\u003c/p\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.96(0.837)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.892\u003c/p\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.87(0.891)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.91(0.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.894\u003c/p\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cem\u003eDA\u003c/em\u003e digital awareness; \u003cem\u003eDKS\u003c/em\u003e digital knowledge and skills; \u003cem\u003eDTA\u003c/em\u003e digital teaching application; \u003cem\u003eDR\u003c/em\u003e digital responsibility; \u003cem\u003ePD\u003c/em\u003e professional development; \u003cem\u003eS\u003c/em\u003e skewness; \u003cem\u003eK\u003c/em\u003e kurtosis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results of EFA\u003c/h2\u003e \u003cp\u003eBefore performing the exploratory factor analysis (EFA), preliminary tests were conducted to assess the adequacy of the data for factor extraction. The Kaiser-Meyer-Olkin (KMO) index reached a value of 0.933, reflecting excellent sampling adequacy. In addition, Bartlett\u0026rsquo;s test of sphericity was statistically significant, χ\u0026sup2;(378)\u0026thinsp;=\u0026thinsp;3547.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating that the inter-item correlation matrix was suitable for factor analysis.\u003c/p\u003e \u003cp\u003eThe EFA was carried out using principal axis factoring (PAF) combined with oblique Promax rotation. Decisions regarding item retention were based on established criteria: items were required to load at 0.50 or above on a single factor, while items exhibiting notable cross-loadings\u0026mdash;defined as factor loadings greater than 0.40 on two or more factors\u0026mdash;were excluded (Howard, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on these criteria, 13 items were removed during the EFA process. Several items (DKS4, DKS6, and DKS7) failed to load meaningfully on any factor, while others (DKS5, DR2, DTA1, DTA5, DTA6, and PD1) were eliminated due to factor loadings below the acceptable threshold. In addition, items exhibiting substantial cross-loadings\u0026mdash;including DKS8 and DTA2\u0026ndash;DTA4\u0026mdash;were excluded from further analysis.\u003c/p\u003e \u003cp\u003eFurther examination of the factor structure revealed that items originally assigned to the DA construct (DA1\u0026ndash;DA4) and the DKS construct (DKS1\u0026ndash;DKS3) loaded strongly on a single common factor, indicating a lack of empirical distinction between the two constructs. Given both the empirical evidence and theoretical coherence, these two constructs were combined into a single factor, labeled DATS.\u003c/p\u003e \u003cp\u003eFurther inspection of the factor solution revealed that items originally designed to measure Digital Awareness (DA1\u0026ndash;DA4) and Digital Knowledge and Skills (DKS1\u0026ndash;DKS3) loaded strongly on a single factor, indicating insufficient empirical distinction between the two constructs. Given both the statistical evidence and theoretical consistency, these items were combined into a unified factor labeled \u003cb\u003eDigital Awareness and Technical Skills (DATS)\u003c/b\u003e. Moreover, all items associated with the \u003cb\u003eDigital Teaching Application (DTA)\u003c/b\u003e construct failed to meet the retention criteria and were therefore removed, resulting in the exclusion of this construct from the final scale.\u003c/p\u003e \u003cp\u003eFollowing iterative item refinement and construct optimization, the finalized DLS-EFL comprised 15 items forming a well-defined and interpretable factor structure. Reliability testing was subsequently performed for each identified factor. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Cronbach\u0026rsquo;s α values ranged from 0.791 to 0.886, all exceeding the commonly accepted benchmark of 0.70, thereby reflecting satisfactory to high levels of internal consistency (Shrestha, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the overall Cronbach\u0026rsquo;s α coefficient for the full scale was 0.920, indicating excellent reliability.\u003c/p\u003e \u003cp\u003eOverall, the findings from the exploratory factor analysis offer strong empirical support for both the structural soundness and measurement robustness of the DLS-EFL, confirming its suitability as a dependable instrument for evaluating digital literacy among EFL teachers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEFA results of the DLS-EFL (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor-Level Cronbach\u0026rsquo;sα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Cronbach\u0026rsquo;sα\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eDigital Awareness and Technical Skills\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(DATS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 I can articulate the core value of digital technology in English teaching (e.g., expanding resources, enabling personalized learning, and facilitating intercultural communication).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 I can identify the teaching opportunities brought by digital technologies (e.g., innovation in teaching models) as well as the challenges they pose (e.g., the digital divide).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 I actively experiment with new digital teaching methods and maintain an open attitude toward innovative teaching practices.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 When encountering difficulties in digital teaching, I can persist in seeking solutions.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 I can determine the applicable scenarios for different digital tools (e.g., AI essay grading, VR situational teaching).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 I can recommend matching tools (e.g., online debate platforms) based on teaching objectives (e.g., cultivating critical thinking).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 I can explain the pedagogical functions and applicable objectives of multimodal resources (text/image/audio/video).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eDigital Responsibility\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(DR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 I understand and abide by laws and regulations regarding data security and internet usage.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 I can maintain a positive and healthy communication environment in online teaching.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 I can protect personal information and professional privacy (e.g., permission settings).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 I can comply with student privacy protection requirements (e.g., encrypted data storage).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 I can identify and prevent cybersecurity risks (e.g., cyberbullying/fraud).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eProfessional Development\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(PD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 I can use digital technology and data tools to conduct teaching research and analyze teaching effects.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 I can design and implement innovative digital teaching practices (e.g., AI-assisted learning, VR immersive classrooms) to promote improvements in learning methods.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 I can engage in professional exchange, collaborative lesson planning, resource sharing, and teaching reflection with colleagues through online collaborative communities (e.g., QQ, WeChat, shared cloud drives).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Results of CFA\u003c/h2\u003e \u003cp\u003eBased on the results of the exploratory factor analysis, poorly performing items were eliminated, and the refined 15-item instrument was administered again, resulting in 388 responses. Following data screening and the removal of outliers, 361 valid cases were retained for confirmatory factor analysis (CFA), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eModel fit statistics for the CFA are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The chi-square to degrees of freedom ratio (χ\u0026sup2;/df) was 1.998, which falls within the recommended range of 1 to 3. Furthermore, the comparative fit index (CFI), Tucker\u0026ndash;Lewis index (TLI), incremental fit index (IFI), goodness-of-fit index (GFI), and normed fit index (NFI) reached values of 0.968, 0.961, 0.968, 0.94, and 0.937, respectively, all surpassing the commonly accepted threshold of 0.90. In addition, both the root mean square error of approximation (RMSEA) and the standardized root mean square residual (SRMR) were below 0.08. Taken together, these results indicate that the CFA model achieved an overall satisfactory fit to the data (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel fit indices for the measurement model (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecommended Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObtained Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 good; \u0026lt;5 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05 good; \u0026lt;0.08 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05 good; \u0026lt;0.08 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9 good; \u0026gt;0.8 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9 good; \u0026gt;0.8 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9 good; \u0026gt;0.8 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9 good; \u0026gt;0.8 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9 good; \u0026gt;0.8 acceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConvergent validity and composite reliability of the CFA model (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized\u003c/p\u003e \u003cp\u003efactor loadings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.5268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDATS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.8636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.5616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.5626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: ***indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs reported in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, all standardized factor loadings in the CFA exceeded 0.60. The average variance extracted (AVE) and composite reliability (CR) values for each construct were as follows: DATS (AVE\u0026thinsp;=\u0026thinsp;0.5268, CR\u0026thinsp;=\u0026thinsp;0.886), DR (AVE\u0026thinsp;=\u0026thinsp;0.5616, CR\u0026thinsp;=\u0026thinsp;0.8636), and PD (AVE\u0026thinsp;=\u0026thinsp;0.5626, CR\u0026thinsp;=\u0026thinsp;0.794). Since the AVE values for all dimensions were above 0.50 and the CR values exceeded 0.70, the first-order CFA model of the Digital Literacy Scale for EFL Teachers (DLS-EFL) exhibited adequate convergent validity and composite reliability (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity of the CFA model (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;361)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePD\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDATS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.697**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDATS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.683**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was further examined using the Fornell\u0026ndash;Larcker criterion, which requires the square root of the AVE for each latent construct to be greater than its correlations with other constructs. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, this condition was satisfied for all dimensions. Specifically, for PD, the square root of AVE (0.75) exceeded the highest observed inter-factor correlation (0.697). Likewise, the square root of AVE for DR (0.749) was greater than its maximum correlation with other factors (0.716), and the same pattern was observed for DATS, where the square root of AVE (0.726) surpassed the highest inter-factor correlation (0.716). These findings provide empirical evidence supporting satisfactory discriminant validity across all dimensions of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Discussion of the Key Findings\u003c/h2\u003e \u003cp\u003eThe present study sought to develop and empirically validate a measurement instrument for assessing digital literacy among EFL teachers in higher education. A salient finding emerging from the exploratory factor analysis (EFA) was the consolidation of the originally proposed dimensions of Digital Awareness (DA) and Digital Knowledge and Skills (DKS) into a single latent construct, which was subsequently labeled Digital Awareness and Technical Skills (DATS). Although DA and DKS were theoretically specified as separate dimensions at the initial stage of scale development, the EFA results demonstrated that items associated with both constructs (DA1\u0026ndash;DA4; DKS1\u0026ndash;DKS3) exhibited strong loadings on the same factor. This pattern suggests that, at an empirical level, respondents did not clearly differentiate between awareness-related and skill-based aspects of digital literacy.\u003c/p\u003e \u003cp\u003eFrom a theoretical standpoint, the emergence of a combined factor can be interpreted as reflecting the inherently interconnected nature of digital awareness and technical competence in teachers\u0026rsquo; professional practice. Existing digital literacy frameworks have frequently conceptualized these two aspects as mutually reinforcing rather than analytically independent. For example, the European Commission\u0026rsquo;s DigComp framework (Redecker, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) implicitly treats awareness of digital technologies\u0026mdash;such as recognizing their pedagogical potential, limitations, and associated risks\u0026mdash;as grounded in teachers\u0026rsquo; ability to operate and apply digital tools effectively. In this view, digital awareness is not a purely abstract or attitudinal construct but develops through practical engagement with technology.\u003c/p\u003e \u003cp\u003eA similar perspective is evident in Mishra\u0026rsquo;s (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) refinement of the Technological Pedagogical Content Knowledge (TPACK) framework, which positions teachers\u0026rsquo; understanding of technology as embedded within their technological knowledge and skills. Rather than conceptualizing awareness as separate from technical competence, TPACK emphasizes that teachers\u0026rsquo; perceptions, judgments, and pedagogical decisions regarding technology are shaped through hands-on experience and skill acquisition. Accordingly, the empirical merging of DA and DKS in the present study can be seen as theoretically coherent, reflecting the integrated way in which EFL teachers in higher education perceive and enact foundational aspects of digital literacy.\u003c/p\u003e \u003cp\u003eFrom a developmental perspective, teachers\u0026rsquo; digital awareness often emerges through hands-on engagement with digital tools, while technical skills are shaped by reflective understanding of how and why technologies function in instructional contexts (Instefjord \u0026amp; Munthe, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gudmundsdottir \u0026amp; Hatlevik, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Empirical studies have also reported substantial overlap between teachers\u0026rsquo; digital awareness and digital skills, particularly in contexts where digital competence is framed as a holistic capability rather than a set of isolated subskills (H\u0026auml;m\u0026auml;l\u0026auml;inen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tzafilkou et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the merging of DA and DKS into the DATS dimension is both empirically justified and theoretically coherent, reflecting the integrated nature of teachers\u0026rsquo; cognitive understanding and technical proficiency in digital environments.\u003c/p\u003e \u003cp\u003eAnother important finding was the complete removal of the Digital Teaching Application (DTA) dimension, as all items associated with this construct failed to meet the established criteria for factor retention. This result suggests that digital teaching application may not function as an independent dimension of digital literacy among EFL teachers in the present sample. One plausible explanation is the conceptual and practical overlap between digital teaching application and professional development.\u003c/p\u003e \u003cp\u003ePrior research suggests that teachers\u0026rsquo; pedagogical use of digital technologies is more appropriately understood as an expression of sustained professional learning rather than as an isolated or self-contained competence. Studies by Reisoğlu (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Foreman-Brown et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasize that instructional innovation\u0026mdash;particularly the integration of digital tools into teaching practices\u0026mdash;emerges through continuous professional development processes, including reflective inquiry, collaborative engagement, and iterative pedagogical refinement. From this perspective, digital teaching application represents an evolving practice shaped by ongoing learning experiences, rather than a fixed dimension that can be readily separated from teachers\u0026rsquo; broader professional growth.\u003c/p\u003e \u003cp\u003eEmpirical investigations into teachers\u0026rsquo; digital competence further support this view by demonstrating that effective technology integration in instruction is strongly linked to professional development activities such as experimentation with digital tools, reflective evaluation of teaching practices, peer collaboration, and instructional redesign (e.g., Garz\u0026oacute;n Artacho et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reisoğlu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tondeur et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consistent with these findings, the exploratory factor analysis (EFA) in the present study revealed that items intended to capture Digital Teaching Application (DTA) did not form a clearly distinct factor. Instead, these items exhibited conceptual and empirical overlap with the Professional Development (PD) dimension, resulting in weak factor loadings and cross-construct ambiguity. Comparable patterns have been reported in previous scale development research, where practice-oriented dimensions failed to emerge as independent constructs due to their strong reliance on broader professional learning contexts (Aydin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003em\u0026uuml;ş \u0026amp; Kukul, 2023; Tzafilkou et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTaken together, these results suggest that EFL teachers\u0026rsquo; digital literacy is most coherently conceptualized as an integrated construct. Within this structure, digital awareness and technical skills function as closely aligned foundational components, while pedagogical application of digital technologies is embedded within ongoing professional development processes. The refined factor structure identified in this study not only aligns with the empirical evidence but also resonates with contemporary theoretical perspectives on teacher digital competence. By elucidating the relationships among these dimensions, the present study advances a more parsimonious and theoretically informed measurement model for assessing digital literacy among EFL teachers in higher education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Comparison with existing Digital Literacy Scales\u003c/h2\u003e \u003cp\u003eThe digital literacy scale developed in the present study adopts a fundamentally different orientation from widely used general digital competence frameworks, highlighting the necessity of a discipline-sensitive instrument tailored to EFL teaching in Chinese higher education. Whereas most established frameworks conceptualize digital literacy as a transferable set of competences applicable across subject areas, the current scale treats digital literacy as a situated professional practice shaped by the pedagogical demands of language teaching.\u003c/p\u003e \u003cp\u003eInternationally recognized frameworks, such as DigCompEdu (Redecker, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the ISTE Standards for Educators (ISTE, 2017), characterize educators\u0026rsquo; digital competence through broad functional domains or professional roles. These models emphasize areas such as pedagogical integration, digital resource management, learner empowerment, and ethical engagement across diverse educational contexts. Similarly, a number of recent instruments draw on general digital competence, data literacy, or algorithmic literacy models (e.g., Gonz\u0026aacute;lez-Mujico, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003em\u0026uuml;ş \u0026amp; Kukul, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mattar et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen \u0026amp; Hab\u0026oacute;k, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), most often relying on self-reported indicators of teachers\u0026rsquo; technical, cognitive, and ethical capabilities. Within the Chinese context, the Teachers\u0026rsquo; Digital Literacy Framework issued by the Ministry of Education (2022) provides a policy-driven structure comprising five overarching domains: digital awareness, technological knowledge and skills, instructional application, social responsibility, and professional development.\u003c/p\u003e \u003cp\u003eDespite their conceptual breadth and policy relevance, these frameworks largely remain discipline-neutral. As a result, they offer limited explanatory power for subject-specific practices in language education, where digital literacy is closely tied to communicative interaction, multimodal language input, cultural mediation, and critical engagement with AI-generated linguistic content. Moreover, many existing instruments depend heavily on self-report measures and may insufficiently capture the contextual and cultural conditions that influence technology use in policy-oriented, non-Western educational settings such as China (Lin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, the scale proposed in this study is explicitly anchored in the instructional realities of EFL teachers working in Chinese higher education. Rather than focusing solely on generic technical competence, it foregrounds domain-relevant applications of digital literacy in language teaching and learning. These include the use of digital tools to support communicative competence development, the integration of data and AI literacy for individualized language feedback, the design of technology-enhanced tasks that promote intercultural understanding, and the ethical management of digital language practices, such as addressing plagiarism in AI-assisted translation or recognizing cultural bias in online language resources. This emphasis aligns with emerging perspectives that incorporate data and AI literacy into teacher competence models (Lin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while remaining consistent with national policy orientations (Jiang, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA more detailed comparison further illustrates the distinctive positioning of the present scale. While DigCompEdu and ISTE offer comprehensive yet subject-agnostic structures, and the Chinese national framework prioritizes policy alignment over disciplinary specificity, the current instrument embeds pedagogical constructs that are particularly salient to EFL instruction. These include critical engagement with digital language corpora, AI-supported error analysis, and responsible participation in multilingual digital environments. In addition, the scale differentiates self-efficacy, attitudes, and contextual adoption conditions as explicit analytical dimensions, rather than subsuming them within broader professional categories. This design enables a more nuanced examination of motivational factors and sustained technology use in language teaching contexts.\u003c/p\u003e \u003cp\u003eFrom a psychometric perspective, the scale was developed and validated through a multi-stage process involving exploratory and confirmatory factor analyses, as well as tests of convergent and discriminant validity, using a geographically and institutionally diverse sample of EFL teachers in Chinese higher education. This rigorous validation strategy enhances construct robustness and generalizability when compared with instruments derived from single-sample studies or minimally validated self-report measures.\u003c/p\u003e \u003cp\u003eOverall, the present scale represents a complementary yet distinct contribution to the assessment of teacher digital literacy. By integrating international competence frameworks with national policy expectations and addressing long-standing disciplinary gaps in language education, it provides a contextually grounded and empirically validated tool for evaluating and supporting digital literacy development among EFL teachers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Limitations and future research\u003c/h2\u003e \u003cp\u003eNotwithstanding the contributions of the present study, several methodological and conceptual constraints warrant consideration, while simultaneously suggesting avenues for further investigation. Although data were collected from 361 EFL teachers across 31 provinces in China, the sample size remains limited in relation to the heterogeneity of institutional missions, regional educational conditions, and professional trajectories characterizing Chinese higher education. Subsequent research could enhance the stability and external validity of the Digital Literacy Scale for EFL Teachers by conducting validation studies with larger samples and more differentiated institutional coverage, including research-intensive universities, teaching-focused institutions, and vocational or application-oriented colleges.\u003c/p\u003e \u003cp\u003eIn addition, the study relied predominantly on self-administered questionnaires, which capture teachers\u0026rsquo; perceived levels of digital literacy but may not fully represent how digital competencies are enacted in authentic instructional settings. To address this limitation, future inquiries may adopt mixed-method or multi-source research designs that combine survey data with classroom observations, instructional artifacts, digital activity logs, or performance-based assessments of technology-enhanced teaching. Such triangulation would allow for a more nuanced understanding of the relationship between perceived competence and actual pedagogical practice.\u003c/p\u003e \u003cp\u003eFurthermore, the scope of the present research was intentionally confined to scale construction and psychometric evaluation, without examining how teachers\u0026rsquo; digital literacy operates within instructional processes or influences educational outcomes. Building on the validated scale, future studies could explore the functional role of its dimensions in mediating relationships between technology use and EFL teaching outcomes, such as students\u0026rsquo; language proficiency development, learning engagement, and intercultural communicative competence. Comparative analyses across institutional types and career stages may yield deeper insights into how digital literacy supports effective teaching under varying professional conditions.\u003c/p\u003e \u003cp\u003eFinally, longitudinal research approaches are needed to capture the developmental trajectories of EFL teachers\u0026rsquo; digital literacy over time. Tracking changes across multiple points would make it possible to identify key influencing factors\u0026mdash;including professional learning opportunities, institutional support mechanisms, and policy environments\u0026mdash;that shape the evolution of digital competence. Such evidence would contribute to a more dynamic understanding of digital literacy as a professional capability and inform the design of sustained, targeted interventions aimed at enhancing the quality of EFL instruction in digitally mediated learning contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis present research developed and validated the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education using a quantitative approach. Scale construction involved item generation, pilot testing (n\u0026thinsp;=\u0026thinsp;246) with item analysis and exploratory factor analysis (EFA) to produce a 15-item instrument (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.945), followed by confirmatory factor analysis (CFA) on 361 valid responses from 388 participants, confirming a robust three-factor structure with strong reliability and construct validity.\u003c/p\u003e \u003cp\u003eThe findings establish EFL teachers\u0026rsquo; digital literacy as a multidimensional construct comprising three core dimensions: Digital Awareness and Technical Skills (DATS), Digital Responsibility (DR), and Professional Development (PD). DATS encompasses awareness of digital technologies in language education and proficiency in applying them to EFL-specific pedagogical goals, such as designing communicative tasks, providing multimodal input, delivering personalized feedback, and fostering authentic interaction. DR emphasizes ethical and socially responsible use, including data privacy, protection of student information, cultural sensitivity in digital content, and mitigation of biases in AI-generated language resources. PD focuses on ongoing professional growth through continuous learning, reflection, and adaptation of digital tools to enhance teaching practice. Together, these dimensions address the distinctive demands of EFL instruction\u0026mdash;such as promoting authentic language interaction, leveraging AI and data-driven tools for language analysis, and cultivating intercultural competence\u0026mdash;while maintaining strong alignment with the Ministry of Education\u0026rsquo;s (2022) Teachers\u0026rsquo; Digital Literacy framework in China.\u003c/p\u003e \u003cp\u003eTheoretically, the DLS-EFL refines existing frameworks (e.g., DigCompEdu, ISTE Standards) by incorporating disciplinary specificity for EFL teaching in the Chinese higher education context. It highlights the interplay of technical awareness and skills (DATS), ethical and responsible practices (DR), and sustained professional growth (PD)\u0026mdash;elements often underexplored or aggregated in general instruments\u0026mdash;while adapting them to EFL-unique pedagogical needs and national policy priorities.\u003c/p\u003e \u003cp\u003ePractically, the scale serves as a diagnostic tool for targeted professional development. Low scores in DATS can guide workshops on selecting and integrating digital tools for EFL tasks (e.g., multimodal communicative activities or AI-supported feedback); deficiencies in DR can prompt training on ethical AI applications, data privacy, and bias mitigation in language resources; and gaps in PD can signal needs for reflective practices or ongoing learning modules. Teacher educators can design scaffolded activities to build competence and confidence, progressing from basic tool proficiency to innovative, EFL-specific lesson design. Curriculum developers and policymakers can embed the scale into training programs to support evidence-based digital pedagogy aligned with national strategies. Researchers can employ it to investigate links between teachers\u0026rsquo; digital literacy dimensions and student outcomes (e.g., language proficiency, engagement, intercultural awareness) or mediating factors such as institutional support.\u003c/p\u003e \u003cp\u003eOverall, the DLS-EFL provides a reliable, contextually grounded instrument for assessing and enhancing EFL teachers\u0026rsquo; digital literacy in Chinese higher education. It bridges national policy imperatives with disciplinary practice, promotes evidence-based professional development, and contributes to higher-quality, inclusive, and innovative language education in the digital era.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003e Ethical approval for this study was granted by the Institutional Review Board (IRB) of Tianfu College of Southwestern University of Finance and Economics (Approval number: TFSWUFE/IRB_2025_128; Date of approval: September 11, 2025). The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and its later amendments, as well as all relevant institutional and national guidelines for research involving human participants. The approval covered the full scope of the research protocol titled Digital Literacy for EFL Teachers, including participant recruitment, online questionnaire data collection, and the use of de-identified data for analysis and research dissemination.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003e Informed consent was obtained by the research team from all participants prior to participation. As the study was conducted through an anonymous online questionnaire, participants provided electronic consent by clicking the \u0026ldquo;Agree and Continue\u0026rdquo; button before starting the survey between October 1, 2025, and December 31, 2025. The consent covered voluntary participation, the anonymous collection of responses, and the use of de-identified data for academic analysis and publication. Participants were informed of their right to withdraw at any time without penalty. The study involved no more than minimal risk.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYYX and WFC designed the research. CS collected data. YYX analysed data. YYX wrote the first draft of the article, all authors interpreted the results, revised the manuscript, and read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the 2025 Sichuan Provincial Education Digitalization Research Project (No. 2025LXKTPS385) \u0026ldquo;Study on the Current Status and Improvement Pathways of Digital Literacy among English Teachers in Private Higher Education Institutions in the Era of Artificial Intelligence\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supporting documents.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkayoğlu S, Satar HM, Dikilitaş K, \u0026Ccedil;irit NC, Korkmazgil S (2020) Digital literacy practices of Turkish pre-service EFL teachers. 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Comput Assist Lang Learn 38(5\u0026ndash;6):1060\u0026ndash;1085. https://doi.org/10.1080/09588221.2023.2256815\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digital literacy, English as a foreign language (EFL), Technological pedagogical content knowledge (TPACK), Scale development and validation","lastPublishedDoi":"10.21203/rs.3.rs-8924262/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8924262/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEvaluating the digital literacy of English as a Foreign Language (EFL) teachers plays a crucial role in supporting effective, secure, and inclusive digital teaching practices in Chinese higher education. Although digital technologies have become increasingly embedded in EFL instruction, validated measurement instruments that are sensitive to pedagogical contexts remain limited, particularly those tailored to EFL teachers. In response to this need, this study constructs and validates the Digital Literacy Scale for EFL Teachers (DLS-EFL) within the context of Chinese universities, drawing on an integrative theoretical framework that combines the teacher digital literacy framework proposed by the Ministry of Education of China (2022) with the Technological Pedagogical Content Knowledge (TPACK) model. Adopting a quantitative approach, the scale development process encompassed item construction, pilot administration, and psychometric validation using exploratory and confirmatory factor analyses. During the pilot phase, responses from 246 EFL teachers were analyzed through item analysis and EFA, leading to a 15-item instrument demonstrating strong internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.945). The refined scale was then distributed to a larger sample of 388 participants, of whom 361 valid cases were retained following data screening procedures. Confirmatory factor analysis supported a three-dimensional structure and provided evidence of satisfactory reliability and construct validity. Overall, the validated DLS-EFL serves as a robust tool for measuring EFL teachers\u0026rsquo; digital literacy and has practical value for guiding teacher professional development, institutional assessment, and policy initiatives aimed at strengthening digital competence in Chinese higher education.\u003c/p\u003e","manuscriptTitle":"Development and validation of the Digital Literacy Scale for EFL Teachers (DLS-EFL) in Chinese higher education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 13:15:48","doi":"10.21203/rs.3.rs-8924262/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T12:08:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T00:27:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T12:39:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T10:30:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T08:13:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T05:47:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T03:27:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T00:57:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T16:35:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278170144914974779596435477055601938950","date":"2026-03-23T23:11:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206559741246860321520927122492605847419","date":"2026-03-22T02:52:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205712413076518816518759949616999366683","date":"2026-03-20T03:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316221715553705352598808243755584049459","date":"2026-03-19T14:14:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214993553298227644187104245290011413670","date":"2026-03-19T08:23:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226179484368389616857663401241645337927","date":"2026-03-19T07:18:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57237385496057947496179448733583178399","date":"2026-03-19T07:05:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175278316017502176164251510284685156002","date":"2026-03-19T07:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T06:45:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T11:43:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T09:37:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-08T08:59:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-03-08T08:56:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8d3cc7b8-3fe7-4865-aa26-0cecc1210922","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64910551,"name":"Social science/Education"},{"id":64910552,"name":"Humanities/Language and linguistics"},{"id":64910553,"name":"Social science/Language and linguistics"}],"tags":[],"updatedAt":"2026-05-16T14:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 13:15:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8924262","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8924262","identity":"rs-8924262","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}