The Quality Management of Teaching Among Instructors in Vocational Institutions in Guilin, Guangxi, People’s Republic of China
Bibliographic record
Abstract
The purpose of this research was to examine the current state, desired state, and essential needs regarding teaching quality management among instructors in vocational institutions. The research sample consisted of 335 instructors from educational institutions in Guilin, Guangxi, People’s Republic of China. The sample size was determined using Krejcie and Morgan’s table, and participants were selected through proportional stratified random sampling. The data collection instrument was a questionnaire, validated with Item-Objective Congruence (IOC) values ranging from 0.67 to 1.00. The discrimination indices for current conditions ranged from 0.386 to 0.838, while those for desired conditions ranged from 0.495 to 0.872. Reliability coefficients for current and desired conditions were 0.974 and 0.975, respectively. Data analysis employed frequency, percentage, mean, and standard deviation. Additionally, a needs assessment was conducted using the Priority Needs Index (PNI Modified). The findings revealed that (1) the current state of teaching quality management among instructors in vocational institutions in Guilin was moderate, whereas the desired state was at the highest level, and (2) the priority ranking of essential needs for teaching quality management, from highest to lowest, was instructor team development, teaching resource management, and teaching quality control.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".