A Study of Components on Effective Academic Administration with Participation of Parents and Community in Small-Sized Schools under the Office of the Basic Education Commission
Bibliographic record
Abstract
The research aimed to examine the components of effective academic administration involving collaborative participation from parents and the local community in small-sized schools under the authority of the Basic Education Commission. The study employed a mixed-method research approach, incorporating both quantitative and qualitative methodologies, divided into two phases. Phase 1: Examine the components of academic administration by synthesizing documents and related studies. Phase 2: Conduct interviews and verify the effectiveness of academic administration. The information providers consisted of 7 highly qualified individuals in the field of educational administration. The selection method was purposive sampling. The tools utilized included interview and assessment forms. Statistical analysis involved mean, standard deviation, and content analysis. The research findings indicated that the effective collaborative components of academic administration involving parents and communities in small-sized schools consisted of three main components: 1) The workload and scope of academic administration in small-sized schools, comprising 10 aspects, 2) Effective academic administration, consisting of 4 aspects, and 3) Collaborative management processes, involving 4 steps. The assessment results demonstrated a high level of suitability for the effective collaborative components of academic administration involving parents and communities in small-sized schools, both at the individual component level and overall. The component with the highest level was the development of an internal quality assurance system within the educational institution, including measurement, assessment, and comparison of learning outcomes. Conversely, the component with the lowest level was the development of learning resources.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".