Components and Indicators of Learning Management Competency for Promoting Creative Thinking Among Art Department Teachers in Schools Under the Provincial Administrative Organization
Bibliographic record
Abstract
This research aimed to: 1) study the components and indicators of learning management competency for promoting creative thinking skills among art department teachers, and 2) examine the model fit of these components and indicators with empirical data. The sample consisted of 130 art department teachers from schools under the Provincial Administrative Organization, determined using a 10:1 parameter ratio and selected through multi-stage random sampling. The research instrument was a questionnaire for developing components and indicators of learning management competency, with an index of item-objective congruence (IOC) ranging from 0.80 to 1.00, discrimination values using Pearson Product Moment Correlation between 0.37 and 0.82, and a reliability coefficient (Cronbach’s alpha) of 0.97. Data were analyzed using confirmatory factor analysis (CFA). The findings revealed that: 1) Through document synthesis and related research, three components of learning management competency were identified: curriculum, learning management, and assessment and evaluation, comprising 13 indicators. 2) The model fit indices showed strong alignment with empirical data: χ² = 43.485, df = 46, χ²/df = 0.945, p = 0.578, GFI = 0.951, CFI = 1.000, NFI = 0.957, RMR = 0.028, and RMSEA = 0.000. These results validate the instrument’s effectiveness for assessing learning management competency in promoting creative thinking skills among art department teachers.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".