Teachers’ Enactment of Digital Transformation in Technical and Vocational Education and Training in China: An Activity Theory Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it