Teachers’ Enactment of Digital Transformation in Technical and Vocational Education and Training in China: An Activity Theory Perspective
Bibliographic record
Abstract
This qualitative study examines how technical and vocational education and training (TVET) teachers in China conceptualize and implement digital transformation in their educational practices. Using cultural-historical activity theory (CHAT) as an analytical framework, the research investigates digital transformation as a socially and culturally mediated process, analyzing the dynamic interactions between subjects, objects, tools, rules, communities, and division of labor. Through content analysis of systematically documented narratives from 70 teachers at a Chinese TVET institution, the study explores the nature and processes of digital transformation in course delivery and practical training contexts. The findings reveal significant shifts in teachers’ pedagogical understanding, institutional relationships, and professional roles, highlighting the complex interplay between individual agency, institutional structures, and technological affordances. The analysis identifies critical tensions between personalization and standardization, as well as between institutional autonomy and industry alignment, revealing contradictions in how digital transformation is conceptualized and implemented. This research extends activity theory by demonstrating how digital transformation generates novel forms of mediation and contradiction within educational settings. The study concludes with practical recommendations for TVET institutions undertaking digital transformation initiatives and emphasizes the importance of supporting teachers’ professional development in increasingly digitalized educational environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".