Meta-Analysis for Math Teachers’ Professional Development and Students’ Achievement
Bibliographic record
Abstract
Background: Recent research emphasizes the need to synthesize empirical studies on “K–12” math teachers’ professional development (PD) programs and their impact on student learning. Objective: This meta-analysis examines how teachers’ participation in PD programs affects students’ math achievement, analyzing the influence of program characteristics, such as duration, PD teaching approach, modality, grade level, type of math content, PD category, and study design. Design: Using online databases, 30 randomized or quasi-experimental studies from the U.S. and Canada (2003–2021) were selected, yielding 164 independent effect sizes, as some studies reported multiple interventions. Results: Only 1% of publications met the inclusion criteria. Most were excluded due to duplication, geographic location, lack of K–12 focus, missing data, or non-empirical content. PD was most effective when programs were under a year, focused on geometry, combined content and pedagogy, targeted grades 6–8, used online video, were reform-initiated, and employed randomized designs. Modality did not significantly impact outcomes. Conclusions: While extensive research exists on PD best practices, few studies empirically link program features to student achievement. This study offers evidence that well-designed math PD can significantly improve student outcomes, providing actionable insights for educators and policymakers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".