Exploring factors influencing pre-service teacher's digital teaching competence and the mediating effects of data literacy: empirical evidence from China

The study addresses how pre-service teachers develop digital teaching competence and which factors most strongly influence it in an era of increasing educational technology and data use. Prior work shows that although pre-service teachers value digital competence, they often lack the skills to implement technology effectively. Influences on digital teaching competence span external (e.g., school support) and personal factors (e.g., attitudes, competencies, ethics, data literacy). With data becoming central to instructional decision-making, the authors focus on four personal factors—technology attitudes, technology operations, technology ethics, and data literacy—and examine whether data literacy mediates effects on digital teaching competence. The research question asks which of these factors directly affect digital teaching competence and to what extent data literacy mediates their relationships. The study is important for informing teacher education programs to better integrate ethics and data literacy into technology preparation for future teachers.

The literature review defines and links four focal constructs to digital teaching competence and to each other, developing ten hypotheses tested via a research model. - Technology attitudes: Teachers’ cognitive, emotional, and behavioral orientations toward using digital technologies. Positive attitudes are associated with better ICT skills, operations, ethical practices, and classroom technology integration, though prior findings on direct links to digital teaching competence are mixed. - Technology operations: Proficiency with ICT hardware and software for teaching. Prior work suggests stronger operations can support data literacy and may enhance digital teaching competence, though operations alone are insufficient without pedagogical knowledge and other supports. - Technology ethics: Adherence to ethical and legal standards when using technology in education (e.g., privacy, security, appropriate content). Ethics may influence how educators handle data and broader competencies and is increasingly salient with AI tools in education. Few studies have examined its relationship with data literacy and digital teaching competence. - Data literacy: The ability to collect, analyze, evaluate, and apply data to inform instructional decisions. It is posited to directly enhance digital teaching competence and may be shaped by attitudes, operations, and ethics. Research model and hypotheses (H1–H10): The model posits direct effects of technology attitudes (TA), technology operations (TO), technology ethics (TE), and data literacy (DL) on digital teaching competence (DTC); TA effects on TO and TE; TA, TO, and TE effects on DL; and a mediating role for DL between TA/TO/TE and DTC.

Design: Cross-sectional survey with exploratory and confirmatory analyses and PLS-SEM to test measurement and structural models and mediation. Participants and sampling: 244 Chinese pre-service teachers from a Normal University in Hangzhou, Zhejiang Province, China. Data were collected online (Wenjuanxing) between 2021 and 2022. Two independent samples were used: 130 (2021) for EFA and 114 (2022) for CFA/SEM. Demographics: 18.0% male, 82.0% female; 47.1% sophomores, 52.9% juniors; 52.9% had taken a computing fundamentals or related course. Instrument: Two sections: demographics and five constructs—Technology Attitudes (3 items), Technology Operations (3 items), Technology Ethics (3 items), Data Literacy (5 items), Digital Teaching Competence (5 items). Items adapted from established sources; 5-point Likert scale (1=strongly disagree to 5=strongly agree). Total 19 items after EFA refinement. Procedure and analysis: SPSS 26 used for EFA and descriptive statistics; SmartPLS 3 for CFA/measurement model assessment, PLS-SEM structural model evaluation, bootstrapping for significance testing, and mediation analyses. EFA supported a five-factor solution (fixed extraction, varimax rotation), eliminating items with low (<0.3) or cross-loadings. KMO and Bartlett’s tests indicated suitability for factor analysis (reported KMO values up to 0.929; KMO shown as 0.880 in table; Bartlett’s p<0.001). The five factors explained 78.29% of variance. Measurement model: Adequate convergent validity and reliability: AVE≥0.695 (DL 0.815; TA 0.782; TE 0.695; TO 0.729; DTC 0.735); Composite reliability 0.872–0.957; Cronbach’s alpha 0.784–0.943. Discriminant validity supported by Fornell–Larcker criterion, cross-loadings, and HTMT values. Structural model: Bootstrapped path coefficients tested hypothesized relationships; predictive accuracy assessed via R² (DL 0.556; TE 0.255; TO 0.125; DTC 0.739) and predictive relevance via Q² (all >0; DL 0.434; TE 0.164; TO 0.081; DTC 0.522). Mediation examined using indirect effects and VAF, classifying as full or partial mediation based on thresholds.

- Direct effects (PLS-SEM): - H1 TA → DL: β=0.353, p<0.05 (supported) - H2 TA → TO: β=0.353, p<0.01 (supported) - H3 TA → TE: β=0.505, p<0.001 (supported) - H4 TA → DTC: β=0.189, p<0.05 (supported) - H5 TO → DL: β=0.351, p<0.01 (supported) - H6 TO → DTC: β=0.065, p>0.05 (not supported) - H7 TE → DL: β=0.270, p<0.01 (supported) - H8 TE → DTC: β=0.177, p<0.01 (supported) - H9 DL → DTC: β=0.578, p<0.001 (supported) - Mediation (indirect effects, VAF): - TA → DL via TE: indirect=0.136, p<0.01; VAF=27.8% (complementary partial mediation) - TA → DL via TO: NS (no mediation) - TA → DTC via TE: indirect=0.090, p<0.05; VAF=32.3% (complementary partial mediation) - TA → DTC via TO: NS (no mediation) - TA → DTC via DL: indirect=0.204, p<0.05; VAF=51.9% (complementary partial mediation) - TO → DTC via DL: indirect=0.203, p<0.01; VAF=100% (full mediation) - TE → DTC via DL: indirect=0.156, p<0.001; VAF=46.8% (complementary partial mediation) - Predictive accuracy and relevance: - R²: DL=0.556 (moderate), TE=0.255 (weak), TO=0.125 (weak), DTC=0.739 (substantial) - Q²: DL=0.434, TE=0.164, TO=0.081, DTC=0.522 (all >0 indicating predictive relevance) Overall: Technology attitudes, technology ethics, and data literacy directly predict digital teaching competence; technology operations influence digital teaching competence only indirectly through data literacy.

The findings address the research question by demonstrating that pre-service teachers’ digital teaching competence is driven by technology attitudes, technology ethics, and data literacy. Technology operations, while important for working with data, do not directly translate into digital teaching competence, suggesting that operational skills must be coupled with data use and pedagogical application to impact teaching competence. Data literacy emerges as a central mechanism, partially mediating the effects of attitudes and ethics and fully mediating the effect of operations on competence, underscoring that the translation of technical skills and ethical orientations into effective teaching with technology occurs through data-informed instructional decision-making. The complementary mediating role of technology ethics between attitudes and both data literacy and digital teaching competence highlights the growing importance of ethical considerations (e.g., privacy, security, responsible use) in contemporary, AI-enhanced educational contexts. Practically, teacher preparation should: cultivate positive technology attitudes; integrate ethical literacy throughout technology courses; and build robust data literacy (from data collection to decision-making) so that pre-service teachers can responsibly and effectively leverage technology to enhance instruction.

This study advances understanding of how personal factors shape pre-service teachers’ digital teaching competence. Key contributions: (1) Technology attitudes, technology ethics, and data literacy have significant direct effects on digital teaching competence; (2) Data literacy mediates the effects of technology attitudes, technology operations, and technology ethics on digital teaching competence, fully mediating the effect of operations; (3) Technology ethics also partially mediates the relationships between technology attitudes and both data literacy and digital teaching competence. Implications for teacher education include embedding ethical literacy and comprehensive data literacy training alongside cultivating positive attitudes and foundational operational skills. Future research directions proposed include: cross-national comparisons to generalize findings; comparisons between pre-service and in-service teachers; and employing mixed-methods or configurational approaches (e.g., Qualitative Comparative Analysis) to identify combinations of factors influencing digital teaching competence.

- Single-institution sample from one regular university in China with imbalanced gender distribution, limiting generalizability. - Reliance solely on self-report questionnaire data; future studies should triangulate with interviews, observations, performance assessments, and more diverse samples.