Investigating the relationship between educational inequity and teacher participation in professional development: A cross-national and quasi-experimental approach using TIMSS
Bibliographic record
Abstract
Abstract The relationship between improving teaching through professional development (PD) and promoting educational equality remains under-researched. This study addresses this gap using a cross-national and quasi-experimental approach. We examine disparities in teachers’ participation in subject-specific PD across student socioeconomic groups and estimate the effects of PD participation on student achievement. Our analysis draws on data from 16 education systems participating in the Trends in International Mathematics and Science Study (TIMSS) between 2003 and 2019, focusing on mathematics and science. To account for unobserved student characteristics and school quality, we employ a within-student, between-subjects design. In most cases, we find that teacher PD participation has statistically insignificant effects on student achievement, regardless of student socioeconomic status or teacher qualification levels. In some instances, PD is associated with small negative effects for students from higher SES backgrounds. Exceptions include Australia, Ontario (Canada), South Korea, and Singapore, where specific forms of PD (content and pedagogy) are linked to modest improvements in achievement among low-SES students. However, these benefits are not consistent across PD measures. Overall, the findings suggest that in most countries, teacher PD is not currently being leveraged to reduce socioeconomic achievement gaps, and that PD forms interact with the student socioeconomic profile in heterogenous and unexpected ways.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".