Competencies of New Teachers in Learning Management in Teacher Production for Local Development
Bibliographic record
Abstract
The goals of this study were twofold: 1) to enhance the learning management competence of novice teachers in the teacher production for local development; and 2) to investigate the opinions of novice teachers toward the learning management competence based on the teacher production for local development. The key informants consisted of 84 teachers from 12 different academic disciplines working in the network for Thailand's lower northeastern region. A questionnaire for interviews and a test of one's competence in learning management were used as study tools. Mean and standard deviation were the two types of statistics used in the examination of quantitative data. According to the findings, the total level of competence in learning management was already rather high after the first round, and it reached its highest possible level after the second round. New instructors increased their skills in learning management and were able to use their newly acquired knowledge when constructing learning activities depending on the learning goals, topics, and ages of their students. In addition to this, students were able to employ teaching and learning management abilities appropriate for the 21st century, which included educational quality enhancement.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".