Developing a Model to Enhance Junior High School Teacher 21st Century Learning Management Competencies
Bibliographic record
Abstract
This study pursues a comprehensive tripartite agenda: firstly, to investigate the essential requisites for formulating a model geared towards augmenting teachers’ 21st century learning management competencies; secondly, to design a tailored model aligned with these competencies; and thirdly, to scrutinize the tangible impact of model implementation. Employing a multi-phased approach involving meticulous need analysis, model development, and real-world implementation, this study navigated a dynamic educational landscape. Phase 1 involved 353 participants, collectively shaping the foundation for subsequent phases. Phase 2 harnessed the expertise of five seasoned educators and scholars to collaboratively refine and assess the model. Phase 3 expanded the scope to encompass 24 junior high school teachers and 36 educational stakeholders, further validating the model’s utility. The outcomes highlight a model encompassing curriculum development, learner-centered active learning strategies, media and technology integration, authentic learning assessment, and conducting research for learner development. This comprehensive model was fashioned as a dynamic workshop, integrating face-to-face training, coaching training, and online modules, thereby catering to diverse learning preferences. Remarkably, the model’s implementation elicited substantial benefits: significant enhancements were observed in teachers’ 21st century learning management skills, while students’ 21st century skills also experienced discernible progress. Notably, the model’s overall quality, rigorously evaluated through the CIPPIEST model, reaffirms its excellence and potential for sustainable educational advancement.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".