Development of An Instructional Model Based on the Professional Learning Community Process and Work-Based Learning to Enhance Classroom Research Competency
Bibliographic record
Abstract
The objectives of this research are to develop and study the effectiveness of the developed instructional model based on the professional learning community process and work-based learning in enhancing classroom research competencies. The sample groups used in the research were 32 third-year students of the Bachelor of Education Program in Teaching Chinese Language and English Language Teaching, selected by purposive sampling, from students who were preparing for teaching in educational schools. The data collection tools used in the research include; 1) a pre-test and a post-test, which are the same test with 25 multiple-choice questions (4 choices each) and complete worksheets 1-9. Statistics used in data analysis included mean, standard deviation, and t-test for dependent., the results indicate that the instructional model based on the professional learning community process and work-based learning to enhance classroom research competency called the PCW Model, consists of 4 important elements: 1) principles of the model, 2) objectives of the model, 3) steps of the model, and 4) measurement and evaluation of the model. Regarding the study on the effectiveness of the model; this indicates that after using the instructional model based on the professional learning community process and work-based learning to enhance classroom research competency, the students’ classroom research competency was significantly higher than before at the 0.01 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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".