Development of Strategies to Promote Sustainable Professional Competences for University Lecturers in the Digital Era, Sichuan Province
Bibliographic record
Abstract
This research aimed to study the current situation of sustainable professional competencies for university lecturers in the digital era in Sichuan Province, provide guidelines for improving these competencies, and evaluate the adaptability and feasibility of the guidelines. The research sample comprised 375 university lecturers in Sichuan Province, selected by systematic and random sampling, 10 expert interviewees, and 7 high-level administrators who evaluated the guidelines. Research instruments included questionnaires, structured interviews, and evaluation forms. Data analysis used percentage, mean standard deviation, and content analysis. The results showed that the overall level of sustainable professional competencies for university lecturers in the digital era is relatively high but unbalanced across different aspects. The implementation level of subject knowledge competencies is the highest, while sustainable learning competencies are the lowest. The guidelines for improvement are divided into four elements with 31 measures: 7 for subject knowledge, 5 for teaching ability, 8 for digital skills, and 6 for sustainable learning. The adaptability and feasibility of the guidelines were evaluated at the highest level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".