Assessing Digital Competence Frameworks in K-12 Educators: A Mixed-Methods Study of 66 Schools from China

Assessing Digital Competence Frameworks in K-12 Educators: A Mixed-Methods Study of 66 Schools from China | 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 Assessing Digital Competence Frameworks in K-12 Educators: A Mixed-Methods Study of 66 Schools from China Jianli Fan, Haibin Wang, Xiulin Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6717329/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital competence (DC) is essential in a digital era. This study explains the core concepts of DC, and develop a model of DC for primary and secondary school teachers in China. Based on Competence Theory, a model is developed and iteratively revised using the Delphi Method, based on questionnaire responses from 20 education experts. For 1229 teachers from 66 primary and secondary schools in two cities, we report teacher DC to not be high overall, to differ regionally, and for significant differences to exist between teachers of different ages and professional titles, but not between genders or job performance. To resolve differences in levels of teacher DC, series of recommendations are made to improve management and to guide policy such as establishing a digital environment on campus to improve teacher evaluation and incentives, and multi-level and multi-dimensional training to improve teacher professional skills and encourage multi-party cooperation and sharing, and promotion of coordinated development of digitalization across regions. Biological sciences/Psychology Biological sciences/Psychology/Human behaviour Digital competence frameworks Primary and middle school teachers Mixed-Methods Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction At a time of digital transformation of education and rapid development of artificial intelligence, the development of Digital Competence (DC) has moved from its origins in information technology to a critical phase shaped by generative AI and meta-universe technologies (Galindo-Domínguez et al., 2024). Tracing its conceptual genealogy from media literacy to the current OECD Digital Competency Framework (2023), DC now encompasses AI-driven pedagogical competencies as mandated by UNESCO's Global Monitoring Report on Education 2023 (Antoninis et al., 2023; Aufderheide et al., 1993). In China, the 14th Five-Year Plan for Digitalization of Education clearly lists the development of teachers' DC as a national strategic goal. In addition, the European Commission has successively launched action plans on digital education, making it one of the research areas that have attracted much attention in the field of education (MAJCHER et al., 2024; Xiao, 2023). The European Union issued the Proposal for a Council Recommendation on Key Competences for Lifelong Learning, which defined DC as a requirement to ‘confidently and critically use information and communication technology for employment, study, self-development and social participation (Hozjan, 2009). Different digital competencies were then exhibited for different social objects, such as teachers’ DC. In order to circumvent the potential for marginalization within the contemporary teaching milieu, it is imperative that educators possess the requisite DC to ensure efficacious pedagogical practices (Zhang et al., 2024). Specifically, contemporary pedagogical needs go beyond the basic requirements and urgently require newer iterations (Tynjälä & Gijbels, 2012). However, the rapid integration of ChatGPT-level technologies poses a dual challenge (Adeshola & Adepoju, 2024). In terms of competence, many teachers report insufficient training in the immediate engineering of educational AI tools, while institutionally, most of schools lack the infrastructure for real-time AI competency diagnostics (Chen et al., 2025). In addition, the technology-driven education reform has caused the ambiguity of roles, which leads teachers to develop metacognitive skills to supervise the personalized learning driven by artificial intelligence, while maintaining humanistic teaching methods (Porayska-Pomsta, 2016). It is not difficult to see that the application of digital technologies (such as 5G, Internet of Things, VR/AR/MR) in modern education has produced massive multi-source heterogeneous data, which cannot be effectively processed by traditional technologies (Sharma & Singh, 2024). However, the application of artificial intelligence (AI) pattern recognition, data mining, deep learning and other technologies can improve teachers' ability to use teaching data (Kim, 2024a; Yang, 2024). As workers of primary education, K-12 educators (mainly involving primary and secondary school teachers) bear the brunt of the challenges from these new developments (Chen et al., 2025). For example, in China, K-12 educators require to master new technology and apply it to teaching to improve students' learning effectiveness, which has put great pressure on most teachers, and managers are not clear about how to judge whether teachers have better ability (Kim, 2024b). Based on this background, we set out from the study of "what kind of DC teachers are" and "what kind of DC level teachers should have" to build a teacher DC model, evaluate the DC of primary and secondary school teachers, and discuss relevant solutions for the problems found, hoping to provide some practical experience and measurement tools for future research. 2 Literature Review 2.1 Concepts of Teacher Digital Competence Norhagen et al. (2024) maintained that teachers’ DC was a measure of their ability to judge and skillfully use digital technology (DT) in education and teaching, and their understanding of the influence of DT on learning strategies and students’ digital development. This definition focused on teachers’ application of DT in teaching, and both emphasized teachers’ “hard technology” ability (their ability to judge, choose, and skillfully use DT in teaching) and “soft technology” (to understand the influence of DT on teaching strategies or teaching methods). Maderick et al. (2015) held that teachers’ DC referred to the skills, abilities, and knowledge required to successfully use computers and related software in teaching and educational practice, from the perspective of learners performing specific tasks or cultivating related digital skills and abilities. This definition replicated the definition of students’ DC, and did not obviously highlight the difference between teacher and student digital competence—it basically reflected the ability to use DT, but not a teachers’ competence with the use of “soft technology.” Spiteri and Chang Rundgren (2017) defined teachers’ DC as how “teachers understand and use DT to manage information, cooperate and communicate with others, create new content, and evaluate and solve problems in an ethical and responsible way.” This definition focused on teacher abilities to evaluate and solve problems, but it did not clearly indicate the responsibility of performing teaching activities. Tsankov and Damyanov (2017) believed that digital competence represented the ability of teachers to obtain continuous professional qualifications in future education, and regarded DC to be closely related to the professional skills of teachers, and that it should be the core of teachers’ professional development. 2.2 Structural model of teachers’ digital competence Previous research divided teacher DC into the ability to use tools at the operational level, the lateral ability at the functional level, and the meta-ability at the strategic level (Rizza, 2023). With the development of technology and professional requirements, this concept has been constantly revised. For example, Krumsvik (2011) proposed a model of English teachers’ DC that focused on four aspects: basic digital skill, didactic ICT competence, learning strategies, and digital education. Instefjord and Munthe (2016) integrated the Technical Pedagogical Content Knowledge framework with that of the teachers’ DC proposed by Krumsvik (2011), and introduced technical proficiency (technology proficiency, pedagogical compatibility, and social awareness) as components of teachers’ DC. Afterwards,the European Union, Spain, and Norway released a “Digital Competence Framework for Teachers” in 2017 (Lisborg et al., 2021). Elements of DC fall into dimensions of: knowledge, skills and attitude; education and teaching activities; and the role of teachers in education and teaching. These dimensions can be evaluated and measured by teachers’ explicit behavior. For example, the European Framework for the Digital Competence of Educators divided elements of teachers’ DC into professional engagement, digital resources, teaching and learning, assessment, empowering learners, and facilitating learners’ DC (Suzer & Koc, 2024). While different frameworks of teachers’ DC have been proposed, they have in common that teachers’ DC is not merely the ability of a teacher to apply DT, but that it includes teaching and learning strategies, meta-ability, and broader awareness ability (e.g., understanding and ability in school culture and social services). Systematic research on the DC of teachers in China has not been performed, and minimal research has been performed regionally on the definition, components, and models of teachers’ DC. Although no specific research has been carried out in China, some researches have put forward a framework model of teachers' digital ability and a self-test tool based on these frameworks (Jiang & Yu, 2024; Tang et al., 2022; Yang et al., 2023). 3 Data and Methods 3.1 Construction of Teachers’ Digital Competence Model 3.1.1 Basis of model construction We construct a DC model of teachers based mainly on Competence Theory. A competence model usually consists of knowledge, skills, motivation, traits, self-image, and attitude or values (Mulder, 2017). Typical models include the iceberg model and onion model. In the iceberg model, knowledge and skills are competence characteristics that can be observed, measured, are easy to obtain, and are valued by various fields (Singh, 2024). In contrast, motivation, traits, self-image, attitudes, and values are inherent characteristics of individuals that are more difficult to find, measure, and change, and they are easily overlooked. Core elements of the onion model are motivation and characteristics, self-image and attitude/values, and knowledge and skills, and show changes of constituent elements from difficult development to easy cultivation. Based on the competence model, and concept connotation of DC analyses, we build a framework of teachers’ DC that comprises five elements: digital consciousness and motivation, digital knowledge and skills, high-order digital thinking ability, digital teaching/learning application ability, and related personality traits. Digital consciousness and motivation is the precondition of teachers’ DC, which corresponds to motivation, self-image, attitude and values in the competence model. Digital knowledge and skills is the foundation, which corresponds to knowledge and skills in the competence model. Related personality traits is the guarantee, which corresponds to the traits in the competence model. Higher-order digital thinking ability is the key to teachers’ DC, including critical and innovative thinking, which is formed based on digital knowledge and guides digital practice. Digital practice (solving teaching problems) is the ultimate goal, thus establishing the core position of “digital teaching/learning application ability.” 3.1.2 Construction of model We take these five factors as first-level elements of the teacher’s DC model, and then use natural coding, word frequency statistics, and other methods to determine second-level elements of the model in the description of teachers’ DC from existing literature. Because DC was first proposed in 2006, we examine literature from 2006–2022. With the title of “digital competence + teachers”, 96 periodical papers and dissertations were retrieved from China, and 273 articles with open access were retrieved from the Web of Science database platform with the title of “digital competence/competence+teacher/educator”. After screening, irrelevant articles were excluded. 165 documents related to teachers’ DC were retained. Through in-depth analysis of these 165 articles, we identify 26 secondary index elements. To improve model reliability and the effectiveness of guiding teachers’ practice, we revised the model twice using the Delphi method. 3.1.3 Modification of model Round 1 model revision. Primary and secondary elements of the preliminary model were compiled into an Expert Consultation Questionnaire on the Elements of Teachers’ Digital Competence, which described and explained the questionnaire’s secondary elements. The questionnaire was then emailed to 32 experts of teacher and information education, and related fields to solicit their opinions. Of 20 respondents, 19 were professors and doctoral tutors, and one was an associate professor and master tutor (see Table 1). Table 1. Respondent organisations. Universities Number of experts Nanjing Normal University 4 South China Normal University 2 Jiangsu Normal University 2 Capital Normal University 2 Wuhan University 2 Anhui Normal University 1 Beijing Institute of Education 1 Guangxi Normal University 1 Hunan Normal University 1 East China Normal University 1 Shaanxi Normal University 1 Xizang Minzu University 1 Zhejiang International Studies University 1 Based on these responses, we revised our preliminary model, and changed digital consciousness and motivation as a level 1 element into digital consciousness and concept, deleted three and added two secondary elements, and modified descriptions of 10 secondary elements. Round two of expert consultation involved emailing the revised model revision (with 5 class 1 elements and 25 class 2 elements) in a semi-open questionnaire to the original 20 respondents. All agreed that the revised model better reflected required levels of digital literacy for a digital era; minor modifications were made to the wording of select elements. Structural elements of the revised model are shown in Figure 1. 3.2 Research design and measurement 3.2.1 Questionnaire design and reliability and validity testing Based on revised model, we compiled a Questionnaire on Digital Competence of Primary and Secondary School Teachers, and divided it into two parts: (1) basic information such as teacher gender, age, professional title, job performance (the work achievements and performance of individuals, teams or organizations, to achieve established goals and tasks within a specific period of time) and work place. (2) a self-evaluation of teacher DC. According to 25 secondary index elements, 50 questions were designed and scored on 5-point Likert scales (very inconsistent = 1, relatively inconsistent = 2, uncertain = 3, relatively consistent = 4, and very consistent = 5). We selected 4 primary schools, 3 junior high schools, and 2 senior high schools in Huangshan by random stratified sampling, and had teachers answer online questionnaires. Of 254 collected questionnaires, 215 (84.6%) were valid (questionnaires that took less than 180 seconds to answer, and those questionnaires with the same answers were eliminated). We used SPSS26.0 statistical software to test the reliability and validity of the questionnaire. Cronbach’s α coefficient (0.977) reveals the questionnaire to have good stability and high reliability, although the Kaiser–Meyer–Olkin (KMO) value (0.757) reveals the internal differentiation of the 50 questions to be average and the validity to be not high. Compared with the correlation matrix, highly correlated topics, and those with small loading value were deleted. Through tests and adjustments, the questionnaire was optimized to improve its explanatory power, and the number of questions in the teacher self-assessment of DC was reduced to 44. For the revised questionnaire, Cronbach’s α was improved (0.984), as were coefficients for individual dimensions: digital consciousness and concept (0.915), digital knowledge and skills (0.949), higher-order digital thinking ability (0.934), digital teaching/learning application ability (0.983), and related personality traits (0.946). The KMO value (0.935) is close to 1, with P = 0.000, which is very significant. These data demonstrate that the revised questionnaire has good reliability and validity. Questionnaire dimensions and items are detailed in Table 2. Table 2. Survey dimensions and items of digital competence for primary and secondary school teachers. Dimension Number of questions Description Digital consciousness and concept 8 Be aware of the influence of DT on personal and social development, recognize and support the use of DT, have technical rationality and awareness of digital teaching exploration, and have the concept of using DT to improve my professional development and promote students’ independent development. Digital knowledge and skills 10 Understand knowledge of digital concepts, theory, attribute characteristics, technology, tools, and functions. Have knowledge of man–machine collaborative teaching modes, master basic AI technology and skills of data collection, cleaning, interpretation and management, and have data decision-making ability. High-order digital thinking ability 6 Deal with division of tasks between man and machine with cooperative thinking, be good at questioning and critically evaluating machine suggestions in digital teaching activities, analyze teaching problems with creative thinking and put forward innovative ideas, and be good at finding and solving problems from educational teaching data. Digital teaching/learning application ability 14 Can promote professional growth through digital teaching and research activities, effectively integrate and manage interdisciplinary digital teaching resources, guide students’ personalized learning by adopting appropriate digital teaching modes, evaluate courses and teaching using digital evaluation tools and propose improvement plans, effectively communicate and educate people digitally, and promote students’ digital ability. Related personality traits 6 Curiosity and passion for emerging technologies, be convinced that you are qualified for digital teaching. Be optimistic when you feel frustrated. Have a sense of responsibility, actively participate in the digital construction of campus, adhere to digital ethics, and have strong motivation and persistence in digital teaching achievements. 3.2.2 Object of investigation We selected teachers from 66 primary and secondary schools in Nanjing and Huangshan cities as survey objects, performed an online survey, and sent the Questionnaire on Digital Competence of Primary and Secondary School Teachers to principals of these schools for them to forward it on to teachers for online answering. The entire investigation lasted 46 days, and 1395 questionnaires were received. After eliminating invalid questionnaires, 1229 (88.1%) remained (from 529 primary school, 381 junior high school, and 319 senior high school teachers). 3.3 Data analysis SPSS26.0 statistical software was used to analyze levels of teacher DC, and relationships between teacher gender, age, professional title, work place, job performance, and DC score via one-way Analysis of Variance (ANOVA). 4 Results 4.1 Overall levels of digital competence The mean (M) DC score of 1229 teachers was 3.56 (standard deviation (SD) 0.66), of which 687 (55.9%) teachers scored above M (N1), and 542 (44.1%) below M (N2) (see Table 3). Although the number of teachers with scores exceeding the M value was slightly higher than the number of teachers with scores below it, M scores of all elements were < 4, indicating that overall teacher DC was not high (see Table 2). Comparatively speaking, teachers had a certain digital consciousness and concept, and basic digital-related personality traits, but they lacked digital knowledge and skills, and their digital thinking ability and digital application ability were relatively low. Table 3. Overall levels of teacher digital competency. Element M SD N1 (%) N2 (%) Digital consciousness and concept 3.86 0.60 781 (63.5%) 448 (36.5%) Digital knowledge and skills 3.37 0.81 675 (54.9%) 554 (45.1%) High-order digital thinking ability 3.47 0.77 722 (58.7%) 507 (41.3%) Digital teaching/learning application ability 3.49 0.77 722 (58.7%) 507 (41.3%) Related personality traits 3.71 0.68 789 (64.2%) 440 (35.8%) 4.2 Effect of gender on digital competence Of the 1229 teachers, 795 (64.7%) were female and 434 (35.3%) were male. There was no significant difference in DC between genders (male M = 3.60, SD = 0.66; female M = 3.53, SD = 0.65; F(1, 1227) = 3.106, P = 0.078). However, the difference between male and female teachers in digital knowledge and skills (P = 0.008) was extremely significant, and the difference in advanced digital thinking ability (P = 0.043) was significant, indicating that male teachers had greater digital knowledge, and digital skills and thinking than female teachers (see Table 4). There were no significant differences between male and female teachers in digital consciousness and concepts and related personality traits, or digital teaching/learning application ability. Table 4. Teacher digital competence scores by gender. Element Male Female F P M SD M SD Digital consciousness and concept 3.88 0.58 3.85 0.61 0.639 0.424 Digital knowledge and skills 3.45 0.79 3.33 0.81 6.970 0.008 High-order digital thinking ability 3.53 0.79 3.44 0.76 4.104 0.043 Digital teaching/learning application ability 3.53 0.78 3.47 0.76 1.585 0.208 Related personality traits 3.73 0.69 3.70 0.68 0.459 0.498 4.3 Digital competence by age Of teachers, 58 were aged ≤ 25 (M = 3.74, SD = 0.58), 396 aged 26–35 (M = 3.66, SD = 0.62), 344 aged 36–45 (M = 3.45, SD = 0.68), 345 aged 46–55 (M = 3.53, SD = 0.69), and 86 > 55 (M = 3.51, SD = 0.53), F(4, 1224) = 6.099, P = 0.000. Digital competence of teachers in different age categories differed; digital competence trended downward with increased age. Significant differences in digital consciousness and concept (P < 0.05) occurred between ages, and very significant differences existed for the other four elements (P < 0.01) (see Table 5). With increased age, scores in each element of teachers’ DC decreased, with teachers aged 36–45 having the lowest score (see Figure 2). Table 5. Digital competence scores for teachers in different age categories. Element ≤ 25 26–35 36–45 46–55 > 55 F P M SD M SD M SD M SD M SD Digital consciousness and concept 4.02 0.54 3.91 0.63 3.81 0.63 3.83 0.58 3.83 0.43 2.896 0.021 Digital knowledge and skills 3.54 0.77 3.47 0.80 3.23 0.83 3.37 0.81 3.38 0.70 4.839 0.001 High-order digital thinking ability 3.72 0.65 3.57 0.71 3.34 0.82 3.45 0.80 3.46 0.66 5.926 0.000 Digital teaching/learning application ability 3.67 0.66 3.61 0.70 3.38 0.80 3.45 0.82 3.40 0.70 5.906 0.000 Related personality traits 3.85 0.54 3.82 0.64 3.62 0.73 3.66 0.71 3.63 0.59 5.273 0.000 4.4 Digital competence by professional title (professional technical level: 1, 2, 3, and advanced) Significant differences existed in DC scores between teachers with different professional titles (F(3, 1225) = 3.194, P = 0.023). Significant differences in levels of DC existed among 144 level 3 teachers (M = 3.70, SD = 0.61), 344 level 2 teachers (M = 3.57, SD = 0.67), 519 level 1 teachers (M = 3.53, SD = 0.65), and 222 senior teachers (M = 3.50, SD = 0.66), and DC gradually decreased with increased professional title. There were no significant differences in digital consciousness and concept (P = 0.553) and digital knowledge and skills (P = 0.057) between teachers with different titles, but there were significant differences in high-order digital thinking ability (P = 0.029), digital teaching/learning application ability (P = 0.008), and related personality traits (P = 0.016). Teachers with high professional titles had relatively low digital thinking ability and digital application ability (Table 6). Table 6. Teacher digital competence scores by professional teacher title. Element Level 3 Level 2 Level 1 Senior F P M SD M SD M SD M SD Digital consciousness and concept 3.92 0.65 3.85 0.62 3.85 0.59 3.86 0.57 0.699 0.553 Digital knowledge and skills 3.53 0.80 3.36 0.84 3.37 0.78 3.30 0.81 2.518 0.057 High-order digital thinking ability 3.65 0.67 3.48 0.79 3.44 0.78 3.43 0.75 3.020 0.029 Digital teaching/learning application ability 3.67 0.70 3.52 0.76 3.46 0.77 3.41 0.80 3.936 0.008 Related personality traits 3.84 0.59 3.75 0.68 3.66 0.70 3.68 0.71 3.468 0.016 Scores for each element of DC were similar between professional titles; scores for digital consciousness and concept were highest, followed by related personality traits, and scores for digital knowledge and skills were lowest, consistent with the overall level trend of teacher DC (see Figure 3). Scores for each element for level 3 teachers were significantly higher than those for teachers at other levels (with no overlap), while scores for level 1, 2, and senior teachers overlapped, indicating that no obvious differences existed in DC levels between them. 4.5 Geographic differences in teacher digital competence Of 1229 valid respondents, 605 teachers were from Huangshan (49.2%), and 624 from Nanjing (50.8%). Significant differences in digital competence (F = 6.711, P = 0.010) existed between teachers in Huangshan (M = 3.52, SD = 0.63) and Nanjing (M = 3.61, SD = 0.68). There were no significant differences in digital consciousness and concept (P = 0.061 > 0.05) and digital knowledge and skills (P = 0.189 > 0.05) between teachers in these two cities, but there were significant differences in high-order digital thinking ability (P = 0.021 < 0.05) and related personality traits (P = 0.42 < 0.05), especially in digital teaching/learning application ability (P = 0.001) (see Table 7). Table 7. Teacher digital competence scores by city. Element Huangshan Nanjing F P M SD M SD Digital consciousness and concept 3.83 0.57 3.90 0.64 3.523 0.061 Digital knowledge and skills 3.34 0.79 3.41 0.84 1.725 0.189 High-order digital thinking ability 3.43 0.76 3.53 0.77 5.334 0.021 Digital teaching/learning application ability 3.43 0.76 3.58 0.76 11.264 0.001 Related personality traits 3.67 0.68 3.76 0.69 4.163 0.042 According to the division of urban and rural areas where the school is located (identified in the questionnaire), there are 358 (29.1%) township teachers, 296 (24.1%) county teachers, and 575 (46.8%) urban teachers. Based on ANOVA results (F(2,1226) = 0.245, P = 0.783 > 0.05) we infer that there was no significant difference in the level of DC among township (M = 3.55, SD = 0.69), county (M = 3.56, SD = 0.67), and urban (M = 3.58, SD = 0.59) teachers. 4.6 Differences in teacher digital competence in job performance There was no significant difference between levels of teacher DC and job performance: 50 teachers had a job performance < 95% of their work units (M = 3.41, SD = 0.86), for 96 teachers it was 75% (M = 3.61, SD = 0.63), and for 60 it was > 95% (M = 3.61, SD = 0.76) (F(4, 1224) = 1.603, P = 0.171). Teacher scores increased with increased performance level in each element (see Figure 4). However, there were very significant differences between teachers with different performance levels in digital consciousness and concept (P = 0.003), but differences between other elements were not obvious, indicating that the overall level of teachers’ DC was not high (see Table 8). Table 8. Teacher digital competence score by job performance. Element < 95% 75% > 95% F P M SD M SD M SD M SD M SD Digital consciousness and concept 3.69 0.79 3.72 0.65 3.85 0.59 3.94 0.56 3.94 0.71 4.035 0.003 Digital knowledge and skills 3.22 1.00 3.29 0.80 3.37 0.80 3.42 0.78 3.40 0.91 1.054 0.378 High-order digital thinking ability 3.43 0.94 3.41 0.77 3.45 0.76 3.52 0.74 3.56 0.82 0.898 0.464 Digital teaching/learning application ability 3.34 0.99 3.45 0.81 3.48 0.75 3.53 0.74 3.54 0.87 0.865 0.485 Related personality traits 3.49 0.91 3.69 0.71 3.70 0.66 3.76 0.65 3.77 0.81 1.953 0.100 5 Discussion 5.1 Teachers’ digital competence levels are generally low Mean teacher scores in DC for each element are < 4, indicating generally low levels of competence. In contrast, teachers’ digital consciousness and concepts and related personality traits scored slightly higher, indicating that teachers were aware of changes in their roles and teaching methods and modes in a digital era, recognized DT, were willing to explore digital teaching, were more active, optimistic and confident in digital application, and had a sense of responsibility and morality. However, because teachers lacked digital knowledge, and they had low data-processing skills and poor AI technology, they lacked digital thinking and their ability to apply digital teaching and learning was not high. Digital competence is essential for teachers in a digital era. We report male and female teachers to have comparable digital consciousness and concept (comparable digital knowledge, skills, and thinking), and comparable digital application ability, inconsistent with findings reported by Liang et al. (2016), indicating that female teachers more actively applied and practiced numbers under the influence of digital consciousness and the digital environment. We also report that the higher a teacher’s job performance, the higher their scores of digital consciousness and concept, indicating that digital consciousness and concept positively affected teacher digital application ability and job performance. 5.2 Teachers’ digital competence tends to decrease with age Significant differences existed in teachers’ DC with age, and this trended downwards (but was lowest among teachers aged 36–45), consistent with findings reported by Ma et al. (2018). Teachers < 35 years age grew up in a network era surrounded by digital technology (e.g., computers, networks, mobile phones), and they are adept at using digital information technology to communicate and interact, are sensitive to new technologies, and can quickly familiarize themselves with and master emerging digital technologies. Older teachers grew up before Internet Era, and they are used to more conventional teaching environments and modes. Teaching with DT generally involves a difficult learning process, and they are generally afraid of the software and applications and find it difficult to adapt to new digital teaching environments. This result is consistent with Zhang (2015), who reported young teachers to be better than middle-aged teachers at developing their teaching skills supported by smart technology. 5.3 The higher a teacher’s professional title, the lower their digital competence Teacher DC gradually decreased with promotion in title, with significant differences in high-order digital thinking ability, digital teaching/learning application ability, and related personality traits. Level 3 teachers scored significantly higher in DC elements than level 2, 1, and senior teachers, because most of them were young and enthusiastic and embraced new DT quickly. Senior teachers are generally older (professional title correlates significantly, positively with age; Pearson Correlation Coefficient 0.659, P = 0.000), so it is more difficult for them to adapt to rapid changes in the DT environment, and the change and development of its application in education. Although senior teachers have greater levels of educational and teaching ability, they are not proficient in application of DT. Because senior teachers are school leaders, and their education and teaching level represents the level of the entire school, they should actively learn and apply DT and make every effort to improve the quality of education and teaching in the school. 5.4 Regional differences in teacher digital competence Education informatization is greatly influenced by levels of economic and social development. To avoid formation of a “digital divide,” the Chinese government and education administrative departments at all levels have paid attention to the balanced development of education informatization. As advocated in national policies such as “data governance” and “precise intellectual support,” rural schools receive more support from software and hardware configuration to technical skills training, to minimize differences in DC among teachers in towns, counties, and urban areas. This viewpoint is consistent with He et al. (2022). However, a gap remains between Huangshan (in a relatively underdeveloped central region) and Nanjing (in the developed eastern region) in digital development. The speed at which schools update infrastructure and equipment affects teachers’ acceptance of new technologies, and ultimately their application of emerging digital technologies. This leads to differences in the DC of teachers between cities, with this level being greater in the eastern than central region—a finding largely consistent with that of Liu et al. (2018), who demonstrated that the application effect of information technology teaching in primary and secondary schools in more-developed eastern regions was significantly better than that in central and western regions. Although efforts to eliminate the digital divide have achieved some results among schools and teachers in urban and rural areas, differences between central and eastern regions remain. The goal of regional balanced development of educational digitalization has yet to be realized. 6 Conclusions Effective measures are needed to improve the DC of primary and secondary school teachers to promote high-quality development of education in China. To achieve this requires the following: 1) Attention must be given to top-level design and policy guidance, to guide primary and secondary school teachers to ideologically accept digital technology. The digital teaching needs of front-line primary and secondary school teachers should be investigated to facilitate improvement in teacher DC. 2) A campus digital environment should be developed to improve teacher evaluation and incentive mechanisms. Teams of teachers of all ages could be established to engage interaction between staff with differing levels of digital proficiency, especially middle-aged teachers who may be experiencing some bottleneck, according to disciplines, grades, and periods of each school. Through efficient team-member communication and cooperation, the potential of each teacher can be tapped and the overall DC of the team can be improved. 3) Multi-level and multi-dimensional training would improve teacher professional skills. In the digital age, full application of technologies (e.g., educational big data, AI) should be used to empower teacher training. Through analysis of teacher data, personalized training programs could be formulated to fully tap the potential of senior teachers, and improve the pertinence and effect of training. China-wide, the development of teacher DC could be achieved in a flexible and on-demand way, with the effects of this training regularly evaluated. Additionally, online and cloud training could be performed with the help of DT, so that teachers could complete training only if they have digital literacy and application ability, thereby improving their DC. 4) Encouragement of multi-party cooperation and sharing to promote the coordinated development of digitalization across regions. The application of digital technologies such as 5G, AI, and the Internet of Things makes it possible to develop remotely, and to migrate and outsource virtually. Our education is more open, inclusive, and shared, which will promote development of regional and cross-regional education. At a regional level, developed regions could be encouraged to take advantage of DT in the construction of educational resources, while underdeveloped regions take advantage of DT in the sharing of high-quality educational resource to jointly promote the balanced development of regional education. Additionally, local education sectors should unite, exchange each other’s resources, perform more academic exchange activities, and encourage developed regions to perform remote on-the-spot guidance regarding application of DT, impart experience to underdeveloped regions, and collaborate for common development and improvement. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research was funded by 2023 Anhui Education Science Research Fund in China, grant number “JKZ23015”. Acknowledgments The authors would like to express their gratitude to all the teachers who participated in the experiment. Data Availability Statement The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Institutional Review Board Statement The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Huangshan University (protocol code 20250016 and 2025/04/27). All participants signed a written informed consent before the study. References Adeshola, I., & Adepoju, A. P. (2024). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments , 32 (10), 6159-6172. Antoninis, M., Alcott, B., Al Hadheri, S., April, D., Fouad Barakat, B., Barrios Rivera, M., Baskakova, Y., Barry, M., Bekkouche, Y., & Caro Vasquez, D. (2023). Global Education Monitoring Report 2023: Technology in education: A tool on whose terms? Aufderheide, P., Firestone, C. M., Communications, A. I. P. o., & Society. (1993). Media Literacy: A Report of the National Leadership Conference on Media Literacy, the Aspen Institute Wye Center, Queenstown Maryland, December 7-9, 1992 . Communications and Society Program, the Aspen Institute. https://books.google.com.sg/books?id=gKTLAAAACAAJ Chen, H. R., Song, W. R., Xie, J., Wang, H. D., Zheng, F. F., & Wen, Y. (2025). The Impact of Chinese Teachers' Career Calling on Job Burnout: A Dual Path Model of Career Adaptability and Work Engagement. International Journal of Mental Health Promotion , 27 (3), 379-400. https://doi.org/10.32604/ijmhp.2025.060370 Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research , 126 . https://doi.org/10.1016/j.ijer.2024.102381 Hozjan, D. (2009). Key competences for the development of lifelong learning in the European Union. European journal of vocational training , 46 (1), 196-207. Instefjord, E., & Munthe, E. (2016). Preparing pre-service teachers to integrate technology: an analysis of the emphasis on digital competence in teacher education curricula. European Journal of Teacher Education , 39 (1), 77-93. Jiang, L., & Yu, N. (2024). Developing and validating a Teachers' Digital Competence Model and Self-Assessment Instrument for secondary school teachers in China. Education and Information Technologies , 29 (7), 8817-8842. Kim, J. (2024a). Leading teachers' perspective on teacher-AI collaboration in education. Education and Information Technologies , 29 (7), 8693-8724. Kim, J. (2024b). Types of teacher-AI collaboration in K-12 classroom instruction: Chinese teachers’ perspective. Education and Information Technologies , 29 (13), 17433-17465. Krumsvik, R. J. (2011). Digital competence in the Norwegian teacher education and schools. Högre utbildning , 1 (1), 39-51. Lisborg, S., Händel, V. D., Schrøder, V., & Rehder, M. M. (2021). Digital competences in Nordic teacher education: An expanding agenda. Nordic Journal of Comparative and International Education (NJCIE) , 5 (4), 53-69. Maderick, J. A., Zhang, S., Hartley, K., & Marchand, G. (2015). Preservice Teachers and Self-Assessing Digital Competence. Journal of Educational Computing Research , 54 (3). MAJCHER, K., BECK, T. H. L., BEDRE DEFOLIE, Ö., BOSOER, L., BOTTA, M., BROGI, E., CALZOLARI, G., CANTERO GAMITO, M., CARLINI, R., & DA COSTA LEITE BORGES, D. (2024). Charting the digital and technological future of Europe: what priorities for the European Commission in 2024-2029? (9294666352). Mulder, M. (2017). Competence theory and research: A synthesis. Competence-based vocational and professional education: Bridging the worlds of work and education , 1071-1106. Norhagen, S. L., Krumsvik, R. J., & Røkenes, F. M. (2024). Developing professional digital competence in Norwegian teacher education: a scoping review. Frontiers in Education, Porayska-Pomsta, K. (2016). AI as a methodology for supporting educational praxis and teacher metacognition. International Journal of Artificial Intelligence in Education , 26 , 679-700. Rizza, C. (2023). Digital Competences. In F. Maggino (Ed.), Encyclopedia of Quality of Life and Well-Being Research (pp. 1786-1790). Springer International Publishing. https://doi.org/10.1007/978-3-031-17299-1_731 Sharma, R., & Singh, A. (2024). Use of Digital Technology in Improving Quality Education: A Global Perspectives and Trends. Implementing Sustainable Development Goals in the Service Sector , 14-26. Singh, A. P. (2024). Rethinking Education: Embracing the Iceberg Model for Well-Being. Education for well-being , 177. Spiteri, M., & Chang Rundgren, S. N. (2017). Maltese primary teachers' digital competence: implications for continuing professional development. European Journal of Teacher Education , 1-14. Suzer, E., & Koc, M. (2024). Teachers’ digital competency level according to various variables: A study based on the European DigCompEdu framework in a large Turkish city. Education and Information Technologies , 1-27. Tang, L., Gu, J., & Xu, J. (2022). Constructing a digital competence evaluation framework for in-service teachers’ online teaching. Sustainability , 14 (9), 5268. Tsankov, N., & Damyanov, I. (2017). Education Majors’ Preferences on the Functionalities of E-Learning Platforms in the Context of Blended Learning. International Journal of Emerging Technologies in Learning (iJET) , 12 (05). https://doi.org/10.3991/ijet.v12i05.6971 Tynjälä, P., & Gijbels, D. (2012). Changing world: Changing pedagogy. In Transitions and transformations in learning and education (pp. 205-222). Springer. Xiao, J. (2023). Digital transformation in top Chinese universities: An analysis of their 14th five-year development plans (2021-2025). Asian Journal of Distance Education , 18 (2), 186-201. Yang, A. (2024). Challenges and opportunities for foreign language teachers in the era of artificial intelligence. International Journal of Education and Humanities , 4 (1), 39-50. Yang, L., García-Holgado, A., & Martínez-Abad, F. (2023). Digital competence of K-12 pre-service and in-service teachers in China: A systematic literature review. Asia Pacific Education Review , 24 (4), 679-693. Zhang, K., Fang, H. G., Kong, X. M., Hong, X., & Assoc Computing, M. (2024, Oct 12-14). Research on Component Model of Digital Competence and the Key Pathways to Enhancement for Primary and Secondary School Teachers. [2024 the international conference on artificial intelligence and teacher education, icaite 2024]. 2024 International Conference on Artificial Intelligence and Teacher Education, Beijing, PEOPLES R CHINA. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6717329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471472625,"identity":"8d09cb7f-b2af-4276-94f1-df2427aeeaa4","order_by":0,"name":"Jianli Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBAC9gYGBoMEBgYZNgYeILdCQk6ekBaeAxAtPBAtZyyMDRuI0AKmwYixrSKR4QAhLexnDxQ8qLnDw8fAe/gz7zyJBMYG5oePbuDTwpOXYJBw7BnQYXxp0rzbJPLYGdiMjXPwaLFnyDEwSGA7DPKLGTNQSzFjAw+bND4tPPxvgFr+gbUYf+adI5HYcICQFgmgLYltYC0G0rwNRGkB2pLYB3GY5JxjEsaGzQT8wsOfY2b449thOfkGHuMPb2rq5OTZmx8+xqcFCNgMwJT8AyifGb9ysJIHBJWMglEwCkbByAYA3n8+sicdxL4AAAAASUVORK5CYII=","orcid":"","institution":"Huangshan University","correspondingAuthor":true,"prefix":"","firstName":"Jianli","middleName":"","lastName":"Fan","suffix":""},{"id":471472626,"identity":"0afa2bf4-f826-44ff-aa77-4d80b6670e34","order_by":1,"name":"Haibin Wang","email":"","orcid":"","institution":"Huangshan University","correspondingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Wang","suffix":""},{"id":471472627,"identity":"4b937f14-3423-4e8c-b8b2-0cb349b8459f","order_by":2,"name":"Xiulin Gu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Xiulin","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2025-05-21 14:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6717329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6717329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84787634,"identity":"6fd76cdc-14b2-4427-96c9-58bf81dc9ec5","added_by":"auto","created_at":"2025-06-17 10:46:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99501,"visible":true,"origin":"","legend":"\u003cp\u003eTeacher digital competence model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6717329/v1/de75382e9840b7d63c442765.png"},{"id":84787635,"identity":"cc205ed3-bc2f-43d6-8f86-1d86959390ec","added_by":"auto","created_at":"2025-06-17 10:46:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150065,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in teacher digital competence with age.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6717329/v1/cbf4a44b17606046727debd6.png"},{"id":84787637,"identity":"e4cf29e1-0ca1-4118-8613-cf3aa0b59996","added_by":"auto","created_at":"2025-06-17 10:46:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59123,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in teacher digital competence by professional title.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6717329/v1/512a2fa3283cbf5e1bde849d.png"},{"id":84788542,"identity":"09c3ff2d-9275-45df-b6bc-343b1d7720e0","added_by":"auto","created_at":"2025-06-17 10:54:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169272,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in teacher digital competence score by performance level.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6717329/v1/ef6090dcf996c7849c8572b6.png"},{"id":86871248,"identity":"43826e95-dbe8-4008-b5db-8c14b8a2e4dc","added_by":"auto","created_at":"2025-07-16 14:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1682038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6717329/v1/2c23e214-579b-4ddf-8228-764954a4708a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Digital Competence Frameworks in K-12 Educators: A Mixed-Methods Study of 66 Schools from China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAt a time of digital transformation of education and rapid development of artificial intelligence, the development of Digital Competence (DC) has moved from its origins in information technology to a critical phase shaped by generative AI and meta-universe technologies (Galindo-Dom\u0026iacute;nguez et al., 2024). Tracing its conceptual genealogy from media literacy to the current OECD Digital Competency Framework (2023), DC now encompasses AI-driven pedagogical competencies as mandated by UNESCO\u0026apos;s Global Monitoring Report on Education 2023 (Antoninis et al., 2023; Aufderheide et al., 1993). In China, the 14th Five-Year Plan for Digitalization of Education clearly lists the development of teachers\u0026apos; DC as a national strategic goal. In addition, the European Commission has successively launched action plans on digital education, making it one of the research areas that have attracted much attention in the field of education (MAJCHER et al., 2024; Xiao, 2023).\u003c/p\u003e\n\u003cp\u003eThe European Union issued the Proposal for a Council Recommendation on Key Competences for Lifelong Learning, which defined DC as a requirement to \u0026lsquo;confidently and critically use information and communication technology for employment, study, self-development and social participation (Hozjan, 2009). Different digital competencies were then exhibited for different social objects, such as teachers\u0026rsquo; DC. In order to circumvent the potential for marginalization within the contemporary teaching milieu, it is imperative that educators possess the requisite DC to ensure efficacious pedagogical practices (Zhang et al., 2024). Specifically, contemporary pedagogical needs go beyond the basic requirements and urgently require newer iterations (Tynj\u0026auml;l\u0026auml; \u0026amp; Gijbels, 2012). However, the rapid integration of ChatGPT-level technologies poses a dual challenge (Adeshola \u0026amp; Adepoju, 2024). In terms of competence, many teachers report insufficient training in the immediate engineering of educational AI tools, while institutionally, most of schools lack the infrastructure for real-time AI competency diagnostics (Chen et al., 2025). In addition, the technology-driven education reform has caused the ambiguity of roles, which leads teachers to develop metacognitive skills to supervise the personalized learning driven by artificial intelligence, while maintaining humanistic teaching methods (Porayska-Pomsta, 2016).\u003c/p\u003e\n\u003cp\u003eIt is not difficult to see that the application of digital technologies (such as 5G, Internet of Things, VR/AR/MR) in modern education has produced massive multi-source heterogeneous data, which cannot be effectively processed by traditional technologies (Sharma \u0026amp; Singh, 2024). However, the application of artificial intelligence (AI) pattern recognition, data mining, deep learning and other technologies can improve teachers\u0026apos; ability to use teaching data (Kim, 2024a; Yang, 2024). As workers of primary education, K-12 educators (mainly involving primary and secondary school teachers) bear the brunt of the challenges from these new developments (Chen et al., 2025). For example, in China, K-12 educators require to master new technology and apply it to teaching to improve students\u0026apos; learning effectiveness, which has put great pressure on most teachers, and managers are not clear about how to judge whether teachers have better ability (Kim, 2024b). Based on this background, we set out from the study of \u0026quot;what kind of DC teachers are\u0026quot; and \u0026quot;what kind of DC level teachers should have\u0026quot; to build a teacher DC model, evaluate the DC of primary and secondary school teachers, and discuss relevant solutions for the problems found, hoping to provide some practical experience and measurement tools for future research.\u003c/p\u003e"},{"header":"2\tLiterature Review","content":"\u003ch2\u003e2.1 Concepts of Teacher Digital Competence\u003c/h2\u003e\n\u003cp\u003eNorhagen et al. (2024) maintained that teachers\u0026rsquo; DC was a measure of their ability to judge and skillfully use digital technology (DT) in education and teaching, and their understanding of the influence of DT on learning strategies and students\u0026rsquo; digital development. This definition focused on teachers\u0026rsquo; application of DT in teaching, and both emphasized teachers\u0026rsquo; \u0026ldquo;hard technology\u0026rdquo; ability (their ability to judge, choose, and skillfully use DT in teaching) and \u0026ldquo;soft technology\u0026rdquo; (to understand the influence of DT on teaching strategies or teaching methods). Maderick et al. (2015) held that teachers\u0026rsquo; DC referred to the skills, abilities, and knowledge required to successfully use computers and related software in teaching and educational practice, from the perspective of learners performing specific tasks or cultivating related digital skills and abilities. This definition replicated the definition of students\u0026rsquo; DC, and did not obviously highlight the difference between teacher and student digital competence\u0026mdash;it basically reflected the ability to use DT, but not a teachers\u0026rsquo; competence with the use of \u0026ldquo;soft technology.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eSpiteri and Chang Rundgren (2017) defined teachers\u0026rsquo; DC as how \u0026ldquo;teachers understand and use DT to manage information, cooperate and communicate with others, create new content, and evaluate and solve problems in an ethical and responsible way.\u0026rdquo; This definition focused on teacher abilities to evaluate and solve problems, but it did not clearly indicate the responsibility of performing teaching activities. Tsankov and Damyanov (2017) believed that digital competence represented the ability of teachers to obtain continuous professional qualifications in future education, and regarded DC to be closely related to the professional skills of teachers, and that it should be the core of teachers\u0026rsquo; professional development.\u003c/p\u003e\n\u003ch2\u003e2.2 Structural model of teachers\u0026rsquo; digital competence\u003c/h2\u003e\n\u003cp\u003ePrevious research divided teacher DC into the ability to use tools at the operational level, the lateral ability at the functional level, and the meta-ability at the strategic level (Rizza, 2023). With the development of technology and professional requirements, this concept has been constantly revised. For example, Krumsvik (2011) proposed a model of English teachers\u0026rsquo; DC that focused on four aspects: basic digital skill, didactic ICT competence, learning strategies, and digital education. Instefjord and Munthe (2016) integrated the Technical Pedagogical Content Knowledge framework with that of the teachers\u0026rsquo; DC proposed by Krumsvik (2011), and introduced technical proficiency (technology proficiency, pedagogical compatibility, and social awareness) as components of teachers\u0026rsquo; DC. Afterwards,the European Union, Spain, and Norway released a \u0026ldquo;Digital Competence Framework for Teachers\u0026rdquo; in 2017 (Lisborg et al., 2021). Elements of DC fall into dimensions of: knowledge, skills and attitude; education and teaching activities; and the role of teachers in education and teaching. These dimensions can be evaluated and measured by teachers\u0026rsquo; explicit behavior. For example, the European Framework for the Digital Competence of Educators divided elements of teachers\u0026rsquo; DC into professional engagement, digital resources, teaching and learning, assessment, empowering learners, and facilitating learners\u0026rsquo; DC (Suzer \u0026amp; Koc, 2024).\u003c/p\u003e\n\u003cp\u003eWhile different frameworks of teachers\u0026rsquo; DC have been proposed, they have in common that teachers\u0026rsquo; DC is not merely the ability of a teacher to apply DT, but that it includes teaching and learning strategies, meta-ability, and broader awareness ability (e.g., understanding and ability in school culture and social services). Systematic research on the DC of teachers in China has not been performed, and minimal research has been performed regionally on the definition, components, and models of teachers\u0026rsquo; DC. Although no specific research has been carried out in China, some researches have put forward a framework model of teachers\u0026apos; digital ability and a self-test tool based on these frameworks (Jiang \u0026amp; Yu, 2024; Tang et al., 2022; Yang et al., 2023).\u003c/p\u003e"},{"header":"3\tData and Methods","content":"\u003ch2\u003e3.1 Construction of Teachers\u0026rsquo; Digital Competence Model\u003c/h2\u003e\n\u003ch3\u003e3.1.1 Basis of model construction\u003c/h3\u003e\n\u003cp\u003eWe construct a DC model of teachers based mainly on Competence Theory. A competence model usually consists of knowledge, skills, motivation, traits, self-image, and attitude or values (Mulder, 2017). Typical models include the iceberg model and onion model. In the iceberg model, knowledge and skills are competence characteristics that can be observed, measured, are easy to obtain, and are valued by various fields (Singh, 2024). In contrast, motivation, traits, self-image, attitudes, and values are inherent characteristics of individuals that are more difficult to find, measure, and change, and they are easily overlooked. Core elements of the onion model are motivation and characteristics, self-image and attitude/values, and knowledge and skills, and show changes of constituent elements from difficult development to easy cultivation.\u003c/p\u003e\n\u003cp\u003eBased on the competence model, and concept connotation of DC analyses, we build a framework of teachers\u0026rsquo; DC that comprises five elements: digital consciousness and motivation, digital knowledge and skills, high-order digital thinking ability, digital teaching/learning application ability, and related personality traits. Digital consciousness and motivation is the precondition of teachers\u0026rsquo; DC, which corresponds to motivation, self-image, attitude and values in the competence model. Digital knowledge and skills is the foundation, which corresponds to knowledge and skills in the competence model. Related personality traits is the guarantee, which corresponds to the traits in the competence model. Higher-order digital thinking ability is the key to teachers\u0026rsquo; DC, including critical and innovative thinking, which is formed based on digital knowledge and guides digital practice. Digital practice (solving teaching problems) is the ultimate goal, thus establishing the core position of \u0026ldquo;digital teaching/learning application ability.\u0026rdquo;\u003c/p\u003e\n\u003ch3\u003e3.1.2 Construction of model\u003c/h3\u003e\n\u003cp\u003eWe take these five factors as first-level elements of the teacher\u0026rsquo;s DC model, and then use natural coding, word frequency statistics, and other methods to determine second-level elements of the model in the description of teachers\u0026rsquo; DC from existing literature.\u003c/p\u003e\n\u003cp\u003eBecause DC was first proposed in 2006, we examine literature from 2006\u0026ndash;2022. With the title of\u0026nbsp;\u0026ldquo;digital competence + teachers\u0026rdquo;, 96 periodical papers and dissertations were retrieved from China, and 273 articles with open access were retrieved from the Web of Science database platform with the title of \u0026ldquo;digital competence/competence+teacher/educator\u0026rdquo;. After screening, irrelevant articles were excluded. 165 documents related to teachers\u0026rsquo; DC were retained. Through in-depth analysis of these 165 articles, we identify 26 secondary index elements. To improve model reliability and the effectiveness of guiding teachers\u0026rsquo; practice, we revised the model twice using the Delphi method.\u003c/p\u003e\n\u003ch3\u003e3.1.3 Modification of model\u003c/h3\u003e\n\u003cp\u003eRound 1 model revision. Primary and secondary elements of the preliminary model were compiled into an Expert Consultation Questionnaire on the Elements of Teachers\u0026rsquo; Digital Competence, which described and explained the questionnaire\u0026rsquo;s secondary elements. The questionnaire was then emailed to 32 experts of teacher and information education, and related fields to solicit their opinions. Of 20 respondents, 19 were professors and doctoral tutors, and one was an associate professor and master tutor (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Respondent organisations.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"521\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of experts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eNanjing Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eSouth China Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eJiangsu Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eCapital Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eWuhan University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eAnhui Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eBeijing Institute of Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eGuangxi Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eHunan Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eEast China Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eShaanxi Normal University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eXizang Minzu University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67.1785%;\"\u003e\n \u003cp\u003eZhejiang International Studies University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.8215%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on these responses, we revised our preliminary model, and changed digital consciousness and motivation as a level 1 element into digital consciousness and concept, deleted three and added two secondary elements, and modified descriptions of 10 secondary elements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRound two of expert consultation involved emailing the revised model revision (with 5 class 1 elements and 25 class 2 elements) in a semi-open questionnaire to the original 20 respondents. All agreed that the revised model better reflected required levels of digital literacy for a digital era; minor modifications were made to the wording of select elements. Structural elements of the revised model are shown in Figure 1.\u003c/p\u003e\n\u003ch2\u003e3.2 Research design and measurement\u003c/h2\u003e\n\u003ch3\u003e3.2.1 Questionnaire design and reliability and validity testing\u003c/h3\u003e\n\u003cp\u003eBased on revised model, we compiled a Questionnaire on Digital Competence of Primary and Secondary School Teachers, and divided it into two parts: (1) basic information such as teacher gender, age, professional title, job performance (the work achievements and performance of individuals, teams or organizations, to achieve established goals and tasks within a specific period of time) and work place. (2) a self-evaluation of teacher DC. According to 25 secondary index elements, 50 questions were designed and scored on 5-point Likert scales (very inconsistent = 1, relatively inconsistent = 2, uncertain = 3, relatively consistent = 4, and very consistent = 5).\u003c/p\u003e\n\u003cp\u003eWe selected 4 primary schools, 3 junior high schools, and 2 senior high schools in Huangshan by random stratified sampling, and had teachers answer online questionnaires. Of 254 collected questionnaires, 215 (84.6%) were valid (questionnaires that took less than 180 seconds to answer, and those questionnaires with the same answers were eliminated).\u003c/p\u003e\n\u003cp\u003eWe used SPSS26.0 statistical software to test the reliability and validity of the questionnaire. Cronbach\u0026rsquo;s \u0026alpha; coefficient (0.977) reveals the questionnaire to have good stability and high reliability, although the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) value (0.757) reveals the internal differentiation of the 50 questions to be average and the validity to be not high. Compared with the correlation matrix, highly correlated topics, and those with small loading value were deleted.\u003c/p\u003e\n\u003cp\u003eThrough tests and adjustments, the questionnaire was optimized to improve its explanatory power, and the number of questions in the teacher self-assessment of DC was reduced to 44. For the revised questionnaire, Cronbach\u0026rsquo;s \u0026alpha; was improved (0.984), as were coefficients for individual dimensions: digital consciousness and concept (0.915), digital knowledge and skills (0.949), higher-order digital thinking ability (0.934), digital teaching/learning application ability (0.983), and related personality traits (0.946). The KMO value (0.935) is close to 1, with P = 0.000, which is very significant. These data demonstrate that the revised questionnaire has good reliability and validity. Questionnaire dimensions and items are detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2.\u003c/strong\u003e Survey dimensions and items of digital competence for primary and secondary school teachers.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6195%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of questions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71.8929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4876%;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6195%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71.8929%;\"\u003e\n \u003cp\u003eBe aware of the influence of DT on personal and social development, recognize and support the use of DT, have technical rationality and awareness of digital teaching exploration, and have the concept of using DT to improve my professional development and promote students\u0026rsquo; independent development.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4876%;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6195%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71.8929%;\"\u003e\n \u003cp\u003eUnderstand knowledge of digital concepts, theory, attribute characteristics, technology, tools, and functions. Have knowledge of man\u0026ndash;machine collaborative teaching modes, master basic AI technology and skills of data collection, cleaning, interpretation and management, and have data decision-making ability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4876%;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6195%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71.8929%;\"\u003e\n \u003cp\u003eDeal with division of tasks between man and machine with cooperative thinking, be good at questioning and critically evaluating machine suggestions in digital teaching activities, analyze teaching problems with creative thinking and put forward innovative ideas, and be good at finding and solving problems from educational teaching data.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4876%;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6195%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71.8929%;\"\u003e\n \u003cp\u003eCan promote professional growth through digital teaching and research activities, effectively integrate and manage interdisciplinary digital teaching resources, guide students\u0026rsquo; personalized learning by adopting appropriate digital teaching modes, evaluate courses and teaching using digital evaluation tools and propose improvement plans, effectively communicate and educate people digitally, and promote students\u0026rsquo; digital ability.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.4876%;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.6195%;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71.8929%;\"\u003e\n \u003cp\u003eCuriosity and passion for emerging technologies, be convinced that you are qualified for digital teaching. Be optimistic when you feel frustrated. Have a sense of responsibility, actively participate in the digital construction of campus, adhere to digital ethics, and have strong motivation and persistence in digital teaching achievements.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e3.2.2 Object of investigation\u003c/h3\u003e\n\u003cp\u003eWe selected teachers from 66 primary and secondary schools in Nanjing and Huangshan cities as survey objects, performed an online survey, and sent the Questionnaire on Digital Competence of Primary and Secondary School Teachers to principals of these schools for them to forward it on to teachers for online answering. The entire investigation lasted 46 days, and 1395 questionnaires were received. After eliminating invalid questionnaires, 1229 (88.1%) remained (from 529 primary school, 381 junior high school, and 319 senior high school teachers).\u003c/p\u003e\n\u003ch2\u003e3.3 Data analysis\u003c/h2\u003e\n\u003cp\u003eSPSS26.0 statistical software was used to analyze levels of teacher DC, and relationships between teacher gender, age, professional title, work place, job performance, and DC score via one-way Analysis of Variance (ANOVA).\u003c/p\u003e"},{"header":"4\tResults","content":"\u003ch2\u003e4.1 Overall levels of digital competence\u003c/h2\u003e\n\u003cp\u003eThe mean (M) DC score of 1229 teachers was 3.56 (standard deviation (SD) 0.66), of which 687 (55.9%) teachers scored above M (N1), and 542 (44.1%) below M (N2) (see Table 3).\u003c/p\u003e\n\u003cp\u003eAlthough the number of teachers with scores exceeding the M value was slightly higher than the number of teachers with scores below it, M scores of all elements were \u0026lt; 4, indicating that overall teacher DC was not high (see Table 2). Comparatively speaking, teachers had a certain digital consciousness and concept, and basic digital-related personality traits, but they lacked digital knowledge and skills, and their digital thinking ability and digital application ability were relatively low.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3.\u003c/strong\u003e Overall levels of teacher digital competency.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.96947%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2214%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN1 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8397%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8397%;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1298%;\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.96947%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2214%;\"\u003e\n \u003cp\u003e781 (63.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8397%;\"\u003e\n \u003cp\u003e448 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8397%;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1298%;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.96947%;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2214%;\"\u003e\n \u003cp\u003e675 (54.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8397%;\"\u003e\n \u003cp\u003e554 (45.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8397%;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1298%;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.96947%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2214%;\"\u003e\n \u003cp\u003e722 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8397%;\"\u003e\n \u003cp\u003e507 (41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8397%;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1298%;\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.96947%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2214%;\"\u003e\n \u003cp\u003e722 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8397%;\"\u003e\n \u003cp\u003e507 (41.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.8397%;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.1298%;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.96947%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2214%;\"\u003e\n \u003cp\u003e789 (64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.8397%;\"\u003e\n \u003cp\u003e440 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e4.2 Effect of gender on digital competence\u003c/h2\u003e\n\u003cp\u003eOf the 1229 teachers, 795 (64.7%) were female and 434 (35.3%) were male. There was no significant difference in DC between genders (male M = 3.60, SD = 0.66; female M = 3.53, SD = 0.65; F(1, 1227) = 3.106, P = 0.078). However, the difference between male and female teachers in digital knowledge and skills (P = 0.008) was extremely significant, and the difference in advanced digital thinking ability (P = 0.043) was significant, indicating that male teachers had greater digital knowledge, and digital skills and thinking than female teachers (see Table 4). There were no significant differences between male and female teachers in digital consciousness and concepts and related personality traits, or digital teaching/learning application ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4.\u003c/strong\u003e Teacher digital competence scores by gender.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e6.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e4.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e1.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 223px;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e4.3 Digital competence by age\u003c/h2\u003e\n\u003cp\u003eOf teachers, 58 were aged\u0026nbsp;\u0026le;\u0026nbsp;25 (M = 3.74, SD = 0.58), 396 aged 26\u0026ndash;35 (M = 3.66, SD = 0.62), 344 aged 36\u0026ndash;45 (M = 3.45, SD = 0.68), 345 aged 46\u0026ndash;55 (M = 3.53, SD = 0.69), and 86 \u0026gt; 55 (M = 3.51, SD = 0.53), F(4, 1224) = 6.099, P = 0.000. Digital competence of teachers in different age categories differed; digital competence trended downward with increased age. Significant differences in digital consciousness and concept (P \u0026lt; 0.05) occurred between ages, and very significant differences existed for the other four elements (P \u0026lt; 0.01) (see Table 5). With increased age, scores in each element of teachers\u0026rsquo; DC decreased, with teachers aged 36\u0026ndash;45 having the lowest score (see Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5.\u003c/strong\u003e Digital competence scores for teachers in different age categories.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le; 25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u0026ndash;35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36\u0026ndash;45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e46\u0026ndash;55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 55\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e2.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e4.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e5.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e5.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e5.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003e4.4 Digital competence by professional title (professional technical level: 1, 2, 3, and advanced)\u003c/h2\u003e\n\u003cp\u003eSignificant differences existed in DC scores between teachers with different professional titles (F(3, 1225) = 3.194, P = 0.023). Significant differences in levels of DC existed among 144 level 3 teachers (M = 3.70, SD = 0.61), 344 level 2 teachers (M = 3.57, SD = 0.67), 519 level 1 teachers (M = 3.53, SD = 0.65), and 222 senior teachers (M = 3.50, SD = 0.66), and DC gradually decreased with increased professional title.\u003c/p\u003e\n\u003cp\u003eThere were no significant differences in digital consciousness and concept (P = 0.553) and digital knowledge and skills (P = 0.057) between teachers with different titles, but there were significant differences in high-order digital thinking ability (P = 0.029), digital teaching/learning application ability (P = 0.008), and related personality traits (P = 0.016). Teachers with high professional titles had relatively low digital thinking ability and digital application ability (Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.\u003c/strong\u003e Teacher digital competence scores by professional teacher title.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSenior\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e2.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 187px;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 43px;\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e3.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eScores for each element of DC were similar between professional titles; scores for digital consciousness and concept were highest, followed by related personality traits, and scores for digital knowledge and skills were lowest, consistent with the overall level trend of teacher DC (see Figure 3). Scores for each element for level 3 teachers were significantly higher than those for teachers at other levels (with no overlap), while scores for level 1, 2, and senior teachers overlapped, indicating that no obvious differences existed in DC levels between them.\u003c/p\u003e\n\u003ch2\u003e4.5 Geographic differences in teacher digital competence\u003c/h2\u003e\n\u003cp\u003eOf 1229 valid respondents, 605 teachers were from Huangshan (49.2%), and 624 from Nanjing (50.8%). Significant differences in digital competence (F = 6.711, P = 0.010) existed between teachers in Huangshan (M = 3.52, SD = 0.63) and Nanjing (M = 3.61, SD = 0.68).\u003c/p\u003e\n\u003cp\u003eThere were no significant differences in digital consciousness and concept (P = 0.061 \u0026gt; 0.05) and digital knowledge and skills (P = 0.189 \u0026gt; 0.05) between teachers in these two cities, but there were significant differences in high-order digital thinking ability (P = 0.021 \u0026lt; 0.05) and related personality traits (P = 0.42 \u0026lt; 0.05), especially in digital teaching/learning application ability (P = 0.001) (see Table 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7.\u003c/strong\u003e Teacher digital competence scores by city.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 233px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHuangshan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNanjing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e3.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e5.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e11.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e4.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to the division of urban and rural areas where the school is located (identified in the questionnaire), there are 358 (29.1%) township teachers, 296 (24.1%) county teachers, and 575 (46.8%) urban teachers. Based on ANOVA results (F(2,1226) = 0.245, P = 0.783 \u0026gt; 0.05) we infer that there was no significant difference in the level of DC among township (M = 3.55, SD = 0.69), county (M = 3.56, SD = 0.67), and urban (M = 3.58, SD = 0.59) teachers.\u003c/p\u003e\n\u003ch2\u003e4.6 Differences in teacher digital competence in job performance\u003c/h2\u003e\n\u003cp\u003eThere was no significant difference between levels of teacher DC and job performance: 50 teachers had a job performance \u0026lt; 95% of their work units (M = 3.41, SD = 0.86), for 96 teachers it was \u0026lt; 75% (M = 3.49, SD = 0.67), for 688 it was in the middle 50% (M = 3.55, SD = 0.64), for 335 teachers it was \u0026gt; 75% (M = 3.61, SD = 0.63), and for 60 it was \u0026gt; 95% (M = 3.61, SD = 0.76) (F(4, 1224) = 1.603, P = 0.171).\u003c/p\u003e\n\u003cp\u003eTeacher scores increased with increased performance level in each element (see Figure 4). However, there were very significant differences between teachers with different performance levels in digital consciousness and concept (P = 0.003), but differences between other elements were not obvious, indicating that the overall level of teachers\u0026rsquo; DC was not high (see Table 8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e8.\u003c/strong\u003e Teacher digital competence score by job performance.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"682\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle 50%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 75%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eDigital consciousness and concept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e4.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eDigital knowledge and skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eHigh-order digital thinking ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eDigital teaching/learning application ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003eRelated personality traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 42px;\"\u003e\n \u003cp\u003e3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e1.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"5\tDiscussion","content":"\u003ch2\u003e5.1 Teachers\u0026rsquo; digital competence levels are generally low\u003c/h2\u003e\n\u003cp\u003eMean teacher scores in DC for each element are \u0026lt; 4, indicating generally low levels of competence. In contrast, teachers\u0026rsquo; digital consciousness and concepts and related personality traits scored slightly higher, indicating that teachers were aware of changes in their roles and teaching methods and modes in a digital era, recognized DT, were willing to explore digital teaching, were more active, optimistic and confident in digital application, and had a sense of responsibility and morality. However, because teachers lacked digital knowledge, and they had low data-processing skills and poor AI technology, they lacked digital thinking and their ability to apply digital teaching and learning was not high.\u003c/p\u003e\n\u003cp\u003eDigital competence is essential for teachers in a digital era. We report male and female teachers to have comparable digital consciousness and concept (comparable digital knowledge, skills, and thinking), and comparable digital application ability, inconsistent with findings reported by Liang et al. (2016), indicating that female teachers more actively applied and practiced numbers under the influence of digital consciousness and the digital environment. We also report that the higher a teacher\u0026rsquo;s job performance, the higher their scores of digital consciousness and concept, indicating that digital consciousness and concept positively affected teacher digital application ability and job performance.\u003c/p\u003e\n\u003ch2\u003e5.2 Teachers\u0026rsquo; digital competence tends to decrease with age\u003c/h2\u003e\n\u003cp\u003eSignificant differences existed in teachers\u0026rsquo; DC with age, and this trended downwards (but was lowest among teachers aged 36\u0026ndash;45), consistent with findings reported by Ma et al. (2018). Teachers \u0026lt; 35 years age grew up in a network era surrounded by digital technology (e.g., computers, networks, mobile phones), and they are adept at using digital information technology to communicate and interact, are sensitive to new technologies, and can quickly familiarize themselves with and master emerging digital technologies. Older teachers grew up before Internet Era, and they are used to more conventional teaching environments and modes. Teaching with DT generally involves a difficult learning process, and they are generally afraid of the software and applications and find it difficult to adapt to new digital teaching environments. This result is consistent with Zhang (2015), who reported young teachers to be better than middle-aged teachers at developing their teaching skills supported by smart technology.\u003c/p\u003e\n\u003ch2\u003e5.3 The higher a teacher\u0026rsquo;s professional title, the lower their digital competence\u003c/h2\u003e\n\u003cp\u003eTeacher DC gradually decreased with promotion in title, with significant differences in high-order digital thinking ability, digital teaching/learning application ability, and related personality traits. Level 3 teachers scored significantly higher in DC elements than level 2, 1, and senior teachers, because most of them were young and enthusiastic and embraced new DT quickly. Senior teachers are generally older (professional title correlates significantly, positively with age; Pearson Correlation Coefficient 0.659, P = 0.000), so it is more difficult for them to adapt to rapid changes in the DT environment, and the change and development of its application in education. Although senior teachers have greater levels of educational and teaching ability, they are not proficient in application of DT. Because senior teachers are school leaders, and their education and teaching level represents the level of the entire school, they should actively learn and apply DT and make every effort to improve the quality of education and teaching in the school.\u003c/p\u003e\n\u003ch2\u003e5.4 Regional differences in teacher digital competence\u003c/h2\u003e\n\u003cp\u003eEducation informatization is greatly influenced by levels of economic and social development. To avoid formation of a \u0026ldquo;digital divide,\u0026rdquo; the Chinese government and education administrative departments at all levels have paid attention to the balanced development of education informatization. As advocated in national policies such as \u0026ldquo;data governance\u0026rdquo; and \u0026ldquo;precise intellectual support,\u0026rdquo; rural schools receive more support from software and hardware configuration to technical skills training, to minimize differences in DC among teachers in towns, counties, and urban areas. This viewpoint is consistent with He et al. (2022). However, a gap remains between Huangshan (in a relatively underdeveloped central region) and Nanjing (in the developed eastern region) in digital development. The speed at which schools update infrastructure and equipment affects teachers\u0026rsquo; acceptance of new technologies, and ultimately their application of emerging digital technologies. This leads to differences in the DC of teachers between cities, with this level being greater in the eastern than central region\u0026mdash;a finding largely consistent with that of Liu et al. (2018), who demonstrated that the application effect of information technology teaching in primary and secondary schools in more-developed eastern regions was significantly better than that in central and western regions. Although efforts to eliminate the digital divide have achieved some results among schools and teachers in urban and rural areas, differences between central and eastern regions remain. The goal of regional balanced development of educational digitalization has yet to be realized.\u003c/p\u003e"},{"header":"6\tConclusions","content":"\u003cp\u003eEffective measures are needed to improve the DC of primary and secondary school teachers to promote high-quality development of education in China. To achieve this requires the following:\u003c/p\u003e\n\u003cp\u003e1) Attention must be given to top-level design and policy guidance, to guide primary and secondary school teachers to ideologically accept digital technology. The digital teaching needs of front-line primary and secondary school teachers should be investigated to facilitate improvement in teacher DC.\u003c/p\u003e\n\u003cp\u003e2) A campus digital environment should be developed to improve teacher evaluation and incentive mechanisms. Teams of teachers of all ages could be established to engage interaction between staff with differing levels of digital proficiency, especially middle-aged teachers who may be experiencing some bottleneck, according to disciplines, grades, and periods of each school. Through efficient team-member communication and cooperation, the potential of each teacher can be tapped and the overall DC of the team can be improved.\u003c/p\u003e\n\u003cp\u003e3) Multi-level and multi-dimensional training would improve teacher professional skills. In the digital age, full application of technologies (e.g., educational big data, AI) should be used to empower teacher training. Through analysis of teacher data, personalized training programs could be formulated to fully tap the potential of senior teachers, and improve the pertinence and effect of training. China-wide, the development of teacher DC could be achieved in a flexible and on-demand way, with the effects of this training regularly evaluated. Additionally, online and cloud training could be performed with the help of DT, so that teachers could complete training only if they have digital literacy and application ability, thereby improving their DC.\u003c/p\u003e\n\u003cp\u003e4) Encouragement of multi-party cooperation and sharing to promote the coordinated development of digitalization across regions. The application of digital technologies such as 5G, AI, and the Internet of Things makes it possible to develop remotely, and to migrate and outsource virtually. Our education is more open, inclusive, and shared, which will promote development of regional and cross-regional education. At a regional level, developed regions could be encouraged to take advantage of DT in the construction of educational resources, while underdeveloped regions take advantage of DT in the sharing of high-quality educational resource to jointly promote the balanced development of regional education. Additionally, local education sectors should unite, exchange each other\u0026rsquo;s resources, perform more academic exchange activities, and encourage developed regions to perform remote on-the-spot guidance regarding application of DT, impart experience to underdeveloped regions, and collaborate for common development and improvement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by 2023 Anhui Education Science Research Fund in China, grant number \u0026ldquo;JKZ23015\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to express their gratitude to all the teachers who participated in the experiment.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Huangshan University (protocol code 20250016 and 2025/04/27). All participants signed a written informed consent before the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeshola, I., \u0026amp; Adepoju, A. P. (2024). The opportunities and challenges of ChatGPT in education. \u003cem\u003eInteractive Learning Environments\u003c/em\u003e,\u003cem\u003e 32\u003c/em\u003e(10), 6159-6172. \u003c/li\u003e\n\u003cli\u003eAntoninis, M., Alcott, B., Al Hadheri, S., April, D., Fouad Barakat, B., Barrios Rivera, M., Baskakova, Y., Barry, M., Bekkouche, Y., \u0026amp; Caro Vasquez, D. (2023). Global Education Monitoring Report 2023: Technology in education: A tool on whose terms? \u003c/li\u003e\n\u003cli\u003eAufderheide, P., Firestone, C. M., Communications, A. I. P. o., \u0026amp; Society. (1993). \u003cem\u003eMedia Literacy: A Report of the National Leadership Conference on Media Literacy, the Aspen Institute Wye Center, Queenstown Maryland, December 7-9, 1992\u003c/em\u003e. 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[2024 the international conference on artificial intelligence and teacher education, icaite 2024]. 2024 International Conference on Artificial Intelligence and Teacher Education, Beijing, PEOPLES R CHINA.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital competence frameworks, Primary and middle school teachers, Mixed-Methods","lastPublishedDoi":"10.21203/rs.3.rs-6717329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6717329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Digital competence (DC) is essential in a digital era. This study explains the core concepts of DC, and develop a model of DC for primary and secondary school teachers in China. Based on Competence Theory, a model is developed and iteratively revised using the Delphi Method, based on questionnaire responses from 20 education experts. For 1229 teachers from 66 primary and secondary schools in two cities, we report teacher DC to not be high overall, to differ regionally, and for significant differences to exist between teachers of different ages and professional titles, but not between genders or job performance. To resolve differences in levels of teacher DC, series of recommendations are made to improve management and to guide policy such as establishing a digital environment on campus to improve teacher evaluation and incentives, and multi-level and multi-dimensional training to improve teacher professional skills and encourage multi-party cooperation and sharing, and promotion of coordinated development of digitalization across regions.","manuscriptTitle":"Assessing Digital Competence Frameworks in K-12 Educators: A Mixed-Methods Study of 66 Schools from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 10:46:19","doi":"10.21203/rs.3.rs-6717329/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af52dc21-0187-49dc-8f8c-604a5a349c52","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50067371,"name":"Biological sciences/Psychology"},{"id":50067372,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-07-16T14:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 10:46:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6717329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6717329","identity":"rs-6717329","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}