The Development and Validation of an Instrument to Measure the Learning of Context University Skills and Workplace-recognized Skills Test among Students WIE in Rural College Thailand
Bibliographic record
Abstract
A questionnaire for measuring Context University Skills and Workplace-Recognized Skills (LCUS-WRS) requires a validated and reliable instrument. The purpose of this study was to develop and validate a questionnaire to measure learning of context university skills and workplace-recognized skills, in order to carry out an advanced psychometric properties analysis of Work-Integrated Education (WIE) students and the skills of LCUS-WRS, including internal and external reliability. Methods: We conducted multi-stage random sampling, with a sample consisting of 728 students WIE from 20 rural colleges in Thailand. Exploratory Factor Analysis (EFA) was performed on a subset of the sample (n = 279), and Confirmatory Factor Analysis (CFA) was conducted on another subset (n = 449), both in different subgroups. Additionally, the researcher analyzed the internal consistency and temporal reliability of the scale in the instrument. Results: Using exploratory factor analysis with 39 items 6 factors and accounted for 55.881% of variance was identified of the variance for the CFA model. The model achieved a goodness-of-fit of chi-square (c2 ) = 582.907, df = 308, p = .000, c2 /df = 1.90, GFI = .979, AGFI = .985, RMSEA = .030, SRMR = .030 Conclusion: LCUS-WRS scale has solid construct of validity and excellent internal consistency which to analyze psychometric properties It has optimum temporal reliability. Therefore, the indicated that the dimensions of measuring of LCUS-WRS guaranteed its use in higher education institutions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".