A Study on the Elements and Indicators of Academic Management Models in the Digital Era to Improve the Academic Achievement of Secondary Schools Under the Office of the Basic Education Commission
Bibliographic record
Abstract
This study aimed to identify, develop, and validate components and indicators of academic management models in the digital era for improving academic achievement in secondary schools under Thailand’s Office of the Basic Education Commission. A mixed-method approach was employed, encompassing comprehensive literature review, qualitative content analysis, expert validation, and quantitative assessment. The research was conducted in three phases: 1) identification and development of components and indicators through literature review and content analysis; 2) validation of the model by nine experts in educational administration and academic leadership, using the Index of Item-Objective Congruence (IOC) and a 5-point rating scale; and 3) assessment of acceptance and feasibility by 434 secondary school administrators, along with evaluation of internal consistency using Cronbach’s alpha. Results revealed five key components with 16 indicators: curriculum, academic management process, learning activities, participation in education, and quality control of education. Expert validation indicated high suitability across all components (mean scores ranging from 4.56 to 4.70). The assessment by school administrators and teachers showed high acceptance and feasibility (overall mean 3.56). Internal consistency analysis demonstrated good to excellent reliability (Cronbach’s alpha 0.78–0.87 for individual components, 0.94 overall). This study provides a comprehensive, validated framework for academic management in the digital era, potentially contributing to improved educational outcomes in Thai secondary schools. Future research should focus on practical implementation and long-term impact assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".