2026-10-12T08:13:03Z https://data.mendeley.com/oai

oai:data.mendeley.com/h5fgc6bf2k.1 2026-09-06T09:13:33Z

Rong, Leilei Big Data Technology in English Education and Teachers’ Professional Practices: An Explanatory Sequential Mixed-Methods Study Mendeley Data The increasing use of big data technologies in education has created new opportunities for English teachers to use learning information to support instructional practices. However, empirical evidence remains limited regarding how data-driven technologies are associated with teachers’ pedagogical competencies, instructional decision-making, classroom instructional performance, and reflective teaching practices. This study examines the role of big data technology in English education in relation to professional dimensions of in-service English teachers. An explanatory sequential mixed-methods design was employed, and data were collected from 287 in-service English teachers in China. The quantitative data were analyzed using Descriptive Statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM). Qualitative data were subsequently analyzed using thematic analysis. The quantitative findings demonstrated significant positive relationships between big data technology in English education and pedagogical competencies (β = 0.622, p < 0.001), decision-making skills (β = 0.568, p < 0.001), classroom instructional performance (β = 0.546, p < 0.001), and reflective teaching practices (β = 0.551, p < 0.001). Qualitative findings showed that teachers used learning analytics to identify student needs, modify instruction, support evidence-based decisions, evaluate classroom effectiveness, and adapt subsequent teaching. However, difficulties in data interpretation, time constraints, technical infrastructure, monitoring pressure, and student-data privacy were identified as implementation challenges. The study contributes empirical evidence linking big data technology with multiple dimensions of English teachers’ professional practice. Practical recommendations include strengthening teachers’ data literacy, providing institutional technical support, allocating time for data interpretation, and establishing clear data-governance and privacy procedures. Education Dataset doi:10.17632/h5fgc6bf2k.1 oai:data.mendeley.com/h5fgc6bf2k.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/h5fgc6bf2k 2026-09-06T09:13:33Z