2026-10-13T02:53:34Z https://www.scidb.cn/oai

10.57760/sciencedb.27214 2026-02-24T15:53:30Z

2026-02-24 Teacher Digital Literacy Assessment SEM Dataset (N=368) doi:10.57760/sciencedb.27214 en Teacher Digital Literacy Assessment SEM Dataset (N=368) - Brief IntroductionThis dataset is a comprehensive collection of data designed for studying the digital literacy of in-service teachers, with a sample size of 368 valid responses. It adopts a structured questionnaire approach and uses a 3-point Likert scale (1-3 points) for standardized measurement, ensuring high data integrity with no missing values.1. Dataset CompositionThe dataset consists of 19 variables, categorized into two core modules:Demographic Variables (5 items): Include Gender (Female/Male), Age_Group (25-34/35-44/45-54/55+), Degree_Level (Bachelor/Master/Doctoral), Teaching_Experience (1-5 yrs/6-10 yrs/>10 yrs), and Institution_Type (Urban/Semi-Urban). These variables support group difference analysis across teacher backgrounds.Digital Literacy Assessment Variables (14 items): Measure teacher digital literacy from 7 key dimensions, with 2 items per dimension:TDL (Teacher Digital Literacy): Core literacy level evaluationIDQ (Information Discrimination Quality): Ability to identify and evaluate informationFPW (Functional Proficiency with Web Tools): Skill in using digital toolsMS (Multimedia Skills): Competence in creating and applying multimedia resourcesHTP (Higher-Order Thinking Processes): Critical thinking and problem-solving in digital contextsADR (Adaptation, Design, and Remediation): Innovation and adaptive application abilitiesPG (Professional Growth): Digital literacy-driven professional development2. Key FeaturesSample Representativeness: The sample covers teachers of different genders (59.2% Female, 40.8% Male), age groups, educational backgrounds, and teaching experience levels, ensuring broad applicability of research results.Analytical Versatility: Labeled as an SEM (Structural Equation Modeling) dataset, it supports complex analytical tasks such as dimension validation, correlation analysis, and influence mechanism exploration.Data Standardization: The uniform 3-point Likert scale and complete data structure reduce analytical bias and facilitate cross-study comparisons.3. Application ScenariosSuitable for educational technology research, teacher professional development studies, and digital education policy analysis, including but not limited to:Exploring factors influencing teacher digital literacyComparing digital literacy differences across teacher groupsValidating digital literacy assessment frameworksProviding empirical support for digital education reform policies Teacher Digital Literacy; Digital Tool Proficiency; Multimedia Skills; Higher-Order Thinking; Professional Growth; SEM (Structural Equation Modeling); Educational Technology jin song PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank