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 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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".