Supporting educators to implement behavior-specific praise: a research synthesis
Bibliographic record
Abstract
We conducted a synthesis of recent systematic reviews with a specific focus on the professional learning aspects of behavior-specific praise (BSP). We examined training procedures and support types aimed at facilitating effective BSP implementation, addressing the following research questions: What procedures were used in training the intervention agent to deliver BSP? To what degree were training integrity and social validity of training procedures assessed? What feedback did intervention agents receive about their implementation of BSP (e.g., emailed graph of BSP rate; self-monitored rate of BSP)? We examined implications of these findings for informing future efforts to support educators implementing BSP, given effective implementation is a critical skill for reinforcing prosocial behaviors, fostering positive school cultures, and mitigating issues associated with exclusionary practices. Results indicated many studies included training for educators on how to implement praise and provide feedback effectively, yet few reported training integrity. We found social validity was primarily assessed in studies through surveys and interviews. Few studies included checks for understanding for educators with permanent products measuring knowledge to identify areas for support. Most studies utilized in-person and verbal BSPs, with some using notes. We discuss limitations and future directions, suggesting results from this review may be useful for informing future professional learning efforts to assist teachers in implementing BSP, a low-intensity strategy for supporting students and educators to promote a positive culture within educational settings.
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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 | 0.036 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".