Educational Technology Procurement at Canadian Colleges and Universities: An Environmental Scan
Bibliographic record
Abstract
There has been an increase in the use of education technology (EdTech) within post-secondary institutions, which has resulted in an unprecedented overflow of EdTech in the market. Institutions then make decisions on which EdTech to procure. This procurement process occurs on a continuum, where on one extreme, an institution takes a decentralized (bottom–up) approach where individuals within an institution independently decide on EdTech procurement, or a centralized (top–down) approach where the institution decides on criteria and standards that the EdTech must meet. This study administered a questionnaire and conducted structured interviews to explore how important standards are, and to identify the associated challenges with implementing centralized procurement. It was distributed to individuals involved in EdTech procurement at universities and colleges across Canada. The results showed that standards related to Privacy and Security, Accessibility, and Care of Data Practices play a larger role in EdTech procurement within most institutions. The use of standards is increasing as institutions become more centralized; however, they are not yet relied on in a structured way. This study suggests ways to move towards a procurement process that incorporates standards and addresses many of the identified challenges with procuring EdTech, thus, improving the efficiency and efficacy of EdTech procurement.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.044 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".