Confirmatory Factor Analysis of the Computational Thinking Learning Competency Measurement Model of Students in the Bachelor of Education Program in Computer at Rajabhat University
Bibliographic record
Abstract
The purpose of this research was to analyze the confirmatory components of the computational thinking learning competency measurement model. The sample group consisted of 240 students in the Bachelor of Education Program in Computer and Digital Technology for Education in Years 2-4 of five of the Rattanakosin Rajabhat Universities. Data collection involved the use of a 5-level (Level 5 indicates that the student’s awareness is at the highest level while Level 1 indicates that the student’s awareness is at the lowest level) checklist questionnaire after which confirmatory factor analysis (CFA) was performed. The results of the research found that the model created by the researcher has 3 components: knowledge, skills, and attributes which are latent variables, and which are consistent with the empirical data with Chi-Square values. The results are statistically significant at the .01 level (Chi-Square=47.680, p=0.112, df=37, Relative Chi-Square Ratio=1.288, GFI=0.970, AGFI=0.930, CFI=1.000, SRMR=0.035, RMSEA=0.035), which meets the specified criteria.
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 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 | 0.000 |
| Open science | 0.001 | 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".