Research Synthesis of Meta-Analyses of Preservice Teacher Preparation Practices in Higher Education
Bibliographic record
Abstract
Findings from a meta-analysis of meta-analyses of 14 different types of preservice student and beginning teacher preparation practices are described. The research synthesis included 118 meta-analyses and 12 other research studies of preservice practices-preservice student and beginning teacher outcomes. The research reports included between 5000 and 6000 studies and an estimated 2.5 to 3 million study participants. The outcomes included two different teacher quality measures and two different preservice student and beginning teacher measures. Mean difference effect sizes, confidence intervals for the average effect sizes, and generalized patterns of results were used to identify very high impact, high impact, medium impact, low impact, and no impact preservice practices. Results showed that clinically rich field experiences (extended and limited student teaching), learning experiences that included multiple opportunities for deliberate practice, faculty and school-based coaching, clinical supervision and performance feedback, different types of experiences and opportunities to learn to teach, course-based experiential learning experiences, and cooperative learning opportunities stood out as especially important practices that were related to optimal preservice and beginning teacher outcomes. The patterns of results are consistent with a practice-based approach to teacher preparation where the focus of preservice and beginning teacher education is the learning experiences and opportunities to learn and use optimal effective teaching practices.
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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 | 0.064 | 0.202 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.059 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".