MATHEMATICS TEACHERS' WORKLOADS AS A CORRELATION OF QUALITY ASSURANCE IN UPPER BASIC EDUCATION
Bibliographic record
Abstract
This study was designed to examine mathematics teachers’ workload vis-à-vis the students’ performance in Mathematics and as a correlation to quality assurance in upper basic education. As a descriptive study it consisted of four research questions and hypotheses at 5% level of significance. The study sample was comprised of twenty public secondary schools from which thirty-two mathematics teachers and one thousand and two hundred upper basic level 2 students were purposively selected for the study. Two instruments, a Mathematics Achievement Test (r = 0.78) and a “Questionnaire for Mathematics Teachers’ Workloads in Upper Basic Education Level 2” (r = 0.83) were used for the study. Data were analysed through simple percentages, Pearson moment correlation, t-test and one way ANOVA. Findings revealed that there was a significant relationship between mathematics teachers’ gender and students’ performance in Mathematics (t-cal>t-ratio, df = 1198; P<0.05) but there was no significant relationship between mathematics teachers’ qualification and students’ performance in Mathematics (F-cal 0.05). However, it was found that there was a significant relationship between mathematics teachers’ subject(s) taught and students’ performance in Mathematics (t-cal>t-ratio, df = 1198; P<0.05). Furthermore, study revealed that there was a significant relationship between mathematics teachers’ workload and students’ performance in Mathematics (F-cal>F-ratio, df = {7, 1191}; P<0.05). The implications of the findings were discussed and recommendation suggested towards ensuring better quality assurance for Mathematics in upper basic education.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".