The Influence of Faculty Members’ Educational Attainment on the Performance in the Licensure Examination for Teachers (LET) among State Universities and Colleges in the Philippines
Bibliographic record
Abstract
This quantitative study employed descriptive-correlational research design to analyze the influence of faculty members’ educational attainment on the performance in the licensure examination for teachers (LET) among 112 state universities and colleges (SUCs) in the Philippines. Results showed that almost half of the faculty members are bachelor’s degree holders, about two-fifths of them have master’s degree, and more than one-tenth are doctoral degree holders. The SUCs had an overall passing percentage higher as well as majority of the SUCs performed higher than the national passing rate. There is a significant inverse relationship between the educational attainment of faculty with bachelor’s degree and LET performance, in which higher proportion of faculty members with bachelor’s degree tends to result to a lower passing percentage. In contrast, the educational attainment of faculty with doctoral degree has significant direct relationship to LET performance, in which higher proportion of doctoral degree holders likely results to higher passing rate in the LET. However, the educational attainment of teaching staff with master’s degree does not significantly correlate with LET performance, hence it does not significantly influence LET performance. When the three categories of educational attainment are taken as independent variables, only doctoral degree significantly influences LET performance. Implications of the findings on faculty hiring and training are also discussed to continuously improve LET performance.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".