Second Order Confirmatory Factor Analysis of Pre-Service Teachers’ Competence Based on TPACK Concept
Bibliographic record
Abstract
The purposes of this research were to: 1) study the components and indicators of the competence of pre-service teachers based on the TPACK concept, and 2) study the results of confirmatory factor analysis (CFA) on the competence of pre-service teachers based on the TPACK concept. This research was divided into four phases and data were collected from a sample group of 890 fifth-year trainee teachers. Research instruments comprise: 1) interview form for 5 experts, 2) questionnaire for 6 university supervisors and 6 associate teachers and 3) CFA questionnaire for collecting data from 890 preservice teachers. The result of this research found that the competence of pre-service teachers at Northeastern Rajabhat University, Thailand, based on the TPACK concept, comprises 7 components and 57 indicators as follow: 1) content knowledge (CK) including 7 indicators; 2) pedagogical knowledge (PK) including 8 indicators; 3) technological knowledge (TK) including 8 indicators; 4) pedagogical content knowledge (PCK) including 10 indicators; 5) technological content knowledge (TCK) including 6 indicators; 6) technological pedagogical knowledge (TPK) including 9 indicators; and 7) technological pedagogical content knowledge (TPACK) including 9 indicators and the results of confirmatory factor analysis (CFA) on the competence of pre-service teachers based on the TPACK concept found that the model aligns with the empirical data at a good level (c2 = 1,328.900, df = 1251, p = 0.062, CFI = 0.999, TLI = 0.998, RMSEA = 0.008, SRMR = 0.023, and c2/df = 1.06), which are statistically significant at the .01 level.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".