Evaluating Digital Competence in Graduate-Level Chinese International Education Programs: A Dual Perspective on Teacher Needs and Training Models
Bibliographic record
Abstract
This paper employs a mixed research methodology, integrating a questionnaire survey and sample interviews, to assess the current state of digital competence and identify the factors influencing it among international Chinese pre-service teachers. Building upon the authoritative model framework, this study developed a questionnaire on the digital competence of international Chinese pre-service teachers and collected data from 100 master candidates of international Chinese language education from leading domestic universities in China. Upon analysis of the data, it was determined that the digital competence of international Chinese pre-service teachers is currently satisfactory overall, although there is a notable discrepancy in development. In particular, competences related to digital teaching, research, and pedagogical innovation are less developed. The training experience of international Chinese pre-service teachers has a significant correlation with their digital competence. However, the experience of international Chinese teaching does not have a significant effect on their digital competence, nor does it have a significant effect on their digital teaching competence or digital pedagogical innovation competence. This study identifies the reasons for this and proposes to refine the training system for digital competence, develop and optimize shared digital resource repositories, and enhance the capacity for innovation in pedagogical practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".