Structural Model of Administrators' ICT Competency
Bibliographic record
Abstract
This study aimed to develop a structural equation model for ICT competency in administrators affecting school administration and propose implementation guidelines. The research involved two phases: model development and focus group discussions for guideline formulation. The sample included 310 educational administrators and department heads from secondary schools under the Songkhla and Satun Secondary Educational Service Area Office. A questionnaire was used as the primary research instrument. The findings showed the developed structural equation model had an excellent fit with empirical data (Chi-Square=188.4, df=166, p-value=.11, GFI=.95, NFI=.97, TLI=.99, CFI=.99, RMSEA=.02, RMR=.00). A key finding was the significant positive direct influence of the competency to appropriately use and manage ICT for education and work performance (influence coefficient = 0.66, p < .01). Overall, the ability to use, manage, promote, and support ICT legally, ethically, professionally, appropriately, and safely for administrative improvement collectively explained 72% of the variance in educational institution administration. Recommended guidelines for applying the model include organizing workshops, developing ICT strategic plans, creating learning networks, establishing Professional Learning Communities (PLCs) for administrators, and setting ICT competency indicators. These measures are expected to boost administrative efficiency and quality, aligning with current Ministry of Education policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".