Data Use in Ontario School Board Improvement Plans
Bibliographic record
Abstract
Board improvement plans (BIP) are documents which include goals and actions to improve student achievement and well-being within a school board. School boards in Ontario are encouraged to use data to make informed educational decisions about their students, educators, and schools to improve education. However, how data are used in board improvement planning is unknown. The purpose of this research is to explore the role of data in board improvement planning to gain an understanding of how data are used at the board level of the Ontario school system. To understand the role of data at the board level, the main research question is: What is the role of data in board improvement planning? Data were collected from three sources for this exploratory case study: three principal interviews, document analysis of relevant Ministry documents, and document analysis of 18 BIPs. Interviews were inductively coded and used to deductively analyze Ministry documents. BIPs were analyzed following the iterative process of skimming, reading, and interpretation using elements of content and thematic analysis. The three themes discovered during interview analysis and used to explore Ministry documents were: functional development of BIPs, relational aspect of BIPs, and data path in BIPs. From document analysis, four broad types of data were found: board-wide student achievement data, board-wide student well-being data, school level data, and classroom level student data. With respect to the uses of data, data were used to identify, plan, and monitor. Lastly, data were found to be interpreted by examining trends (deconstructing and comparing) and through triangulation (multiple sources and in teams). In conclusion, the results provide evidence that multiple types of data are being used for board improvement planning, for various different uses, with different ways of interpreting the data.
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 | 0.050 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".