Cloud Computing and K-12 School IT Infrastructure in Western Canada: From Challenges to Opportunities
Bibliographic record
Abstract
This paper is based on the findings of an exhaustive study of all 75 large K-12 districts in Canada's three western-most provinces: British Columbia, Alberta, and Saskatchewan. This study encompassed over 1.1 million students and a geographical area of 2,258,483 square kilometers. Facilitating teaching and learning activities for so many students across such a large territory, with diverse provincial regulations, is an impressive feat achieved by the information technology leaders of the K-12 school districts. Multiple case study analysis, followed by correlation analysis, were used to explore the nature of IT infrastructure and cloud computing use in Western Canada. A data transformation model mixed methods triangulation design methodology was used. This paper discusses the strategies used in Western Canada to deliver educational technology resources through to students, teachers, parents, and district staff. The findings of this study are that cloud computing is the primary IT infrastructure in Western Canadian K-12 education. All school districts in the three provinces studied use cloud computing for some aspects of their infrastructure. In instances where cloud computing infrastructure is not used, school-level LAN and server infrastructure is used. In addition to being an alternative to cloud computing, the rare instances of school-level server use are either to supplement or complement a district’s centralized cloud computing infrastructure, with cloud computing infrastructure existing in parallel.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".