Model Development of Academic Administration Effectiveness in the Digital Era for Extra Large-Size Primary Schools Under the Office of Basic Education Commission
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Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle