Model Development of Academic Administration Effectiveness in the Digital Era for Extra Large-Size Primary Schools Under the Office of Basic Education Commission
Bibliographic record
Abstract
This research aimed to develop an effective academic administration model for extra large-size primary schools under the Office of the Basic Education Commission (OBEC) in the digital era. The study employed a mixed-methods approach and was conducted in three phases. Phase 1: Investigating the current state, desired state, and the need for effective academic administration in the digital era. The sample consisted of 377 school administrators and teachers from extra large-size primary schools, determined using Krejcie and Morgan’s table and multistage sampling. Data were collected using a 5-point Likert scale questionnaire, with an index of congruence (IOC) of 1.00 and a reliability coefficient of 0.93. Phase 2: Development of the model and its user manual for effective academic administration in the digital era. Data for this phase were gathered through interviews with three experts selected based on specified criteria and a focus group discussion with 10 experts. Phase 3: Five experts who met the requirements evaluated the model and user manual. Statistical tools used for data analysis included percentage, mean, standard deviation, priority needs index (PNI), and content analysis. Research Findings: 1) The current state of effective academic administration in extra large-size primary schools in the digital era was rated high overall. However, the desired state received the highest rating. The priority needs for development, in order, are as follows: internal supervision, research, measurement and evaluation, media development, innovation and educational technology, quality assurance and monitoring, learning process development, participation in academic administration, and curriculum development, respectively. 2) The academic administration model for extra large-size primary schools in the digital era comprised: 1) Principles, 2) Objectives, 3) Systems and mechanisms, and 4) Components and operational methods, driven by a PDCA quality cycle with seven components and 105 operational methods: (1) Curriculum Development 20 operational methods, (2) Participation in academic administration 14 operational methods (3) Development of media, innovation, and technology for education 13 operational methods (4) Learning process development 16 operational methods (5) Internal supervision 16 operational methods, (6) Quality assurance and monitoring 12 operational methods), (7) Research, measurement, and evaluation 14 operational methods, 5) Conditions for success, and 6) effectiveness evaluation. The user manual for implementing the model included an introduction, objectives, model details, guidelines for implementation, and effectiveness evaluation. 3) The evaluation result of the model and user manual revealed the highest levels of appropriateness, feasibility, and usefulness.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".