Components and Indicators of Digital Citizenship Among Teachers in Private Schools Under the Office of the Private Education Commission, Northeastern Region
Bibliographic record
Abstract
This research aims to: 1) study the components and indicators of digital citizenship for private school teachers, and 2) examine the goodness-of-fit of the component model and indicators of digital citizenship for private school teachers. The sample consisted of 260 general private school teachers under the Office of the Private Education Commission in the Northeastern Region. The researchers determined the sample size using a 20:1 ratio of parameters and employed a multi-stage random sampling technique. The research instrument was a questionnaire for designed to develop components and indicators for enhancing digital citizenship among private school teachers, with an Index of Item-Objective Congruence (IOC) values ranging from 0.80–1.00, discriminative power measured using Pearson Product-Moment Correlation Coefficients ranging from 0.252–0.857, and reliability using Cronbach’s alpha coefficient (α) of 0.97 for the entire instrument. Data were analyzed using Confirmatory Factor Analysis (CFA). Results: 1) The development of components and indicators for enhancing digital citizenship among private school teachers, based on synthesis of documents and related research, comprised 3 components: 1) Digital Literacy, 2) Digital Ethics, and 3) Self-Protection and Protection of Others, with a total of 10 indicators. 2) The examination of model fit for the components and indicators of digital citizenship enhancement among private school teachers showed consistency with empirical data: χ² = 43.093, df = 30, χ²/df = 1.436, p-value = 0.057, GFI = 0.960, CFI = 0.989, NFI = 0.964, RMR = 0.017, RMSEA = 0.047, indicating that this instrument can be used to assess digital citizenship among private school teachers.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".