Changes in perceptions of teaching quality in secondary schools in Rwanda during the COVID-19 global pandemic and the subsequent closing and reopening of schools
Bibliographic record
Abstract
The stability of measures of teaching quality is essential for making generalizations of results stemming from these measures to other teaching situations. However, no research has examined the effects of unexpected situational factors on the stability of these measures. Therefore, the purpose of this two-phase quantitative research study was to examine the following aspects among secondary school teachers in Rwanda, using a score-validated, multiple-dimension measure: (a) perceptions of teaching quality (PTQ) prior to the onset of the COVID-19 context (Phase 1; descriptive and correlational design); and (b) the extent to which COVID-19 and the subsequent closing and reopening of secondary schools affected PTQ among STEM teachers in Rwanda, and the associations between these changes in PTQ and selected socio-demographic/locational variables (Phase 2; descriptive and correlational research design). Phase 1 findings revealed that two measures of cultural values (i.e., Attitudes Towards Cultural Values Scale, Inculcating Cultural Values Scale, respectively) generated the most positive attitudes, whereas the Satisfaction with Resources and Material Subscale yielded the least positive attitudes. Phase 2 findings revealed that for four of the nine PTQ scales/subscales, the COVID-19 context negatively affected PTQ. These findings provide compelling evidence of the importance of monitoring PTQ, especially during times of crises. Moreover, these findings have implications for Rwandan educational policymakers, Rwandan administrators, teacher training administrators, and, above all, the teachers themselves, as they all seek to maximize teaching quality in Rwandan secondary schools.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".