Educational Technology Decision-Making: Technology Acquisition for 746,000 Ontario Students.
Bibliographic record
Abstract
The author explores the technology procurement process in Ontario’s publicly funded school districts to determine if it is aligned with relevant research, is grounded in best practices, and enhances student learning. Using a qualitative approach, 10 senior leaders (i.e., chief information officers, superintendents, etc.) were interviewed to reveal the most important factors driving technology acquisition, governance procedures, and assessment measures utilized by school districts in their implementation of educational technology. The data were transcribed and submitted to “ computer-assisted NCT analysis ” (Friese, 2014). The findings show that senior leaders are making acquisitions that are not aligned with current scholarship, that districts struggle to use data-driven decision-making to support the governance of educational technology spending, and that districts do not have effective assessment measures in place to determine the efficacy of a purchased technology. The study is meant to serve as an informative resource for senior leaders and to present research- based approaches to technology procurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".