Data-Based Decision Making by Teachers in K-12 Schools: A Scoping Review
Bibliographic record
Abstract
Despite the widespread adoption of data-based decision making (DBDM) policies in schools around the world, there is limited understanding of how teachers use DBDM in K-12 classrooms and the impact of DBDM training on teacher practices and student outcomes. This scoping review aims to provide an overview of the existing literature on the uses of DBDM by teachers globally and identify gaps in the field. The findings (a) highlight a geographical and temporal clustering, with a notable emphasis on studies conducted in the United States and the Netherlands and published in 2016–2017 and 2020–2022; (b) identify a gap in the literature, particularly in the context of online and secondary schools, where the predominant focus has been on elementary and in-person settings; and (c) suggest that although DBDM interventions have been found helpful in altering teacher practices and student outcomes, there is still a need for more sustainable support to enhance DBDM implementation. The study concludes with recommendations for future DBDM research, building on implications from previous interventions.
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 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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".