Supporting Data-Driven Decision Making in a Canadian School District
Bibliographic record
Abstract
A main objective in educational settings is to build system and school capacity in data driven decisionmaking and evidence-informed practice in support of the development, monitoring, and measuring of initiatives targeted to support student achievement and well-being. In 2016-2017, the Ottawa-Carleton District School Board (OCDSB) implemented an innovative Data Support Model to provide support to senior staff, school administrators and teams of educators: (1) in the exploration/understanding of data reflecting student achievement/attitudes/ demographics, (2) in the design of school-specific initiatives, and (3) through the development of online and interactive data resource tools. This paper discusses the conceptual framework and collaborative approach of the model, as well as the use and impact of the model. The results indicate a strong positive relation between the Data Support Model and improved Data Literacy at both a systemand school-level. Implications and opportunities for model improvement and growth are discussed. There has been considerable commitment from participants and a continued desire to grow and enhance this model. With time, it is anticipated that student outcomes will also be positively impacted. This approach is unique to this school district and as such, can serve as a valuable model to other school districts.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".