Visions and realities of Internet use in schools: Canadian perspectives
Bibliographic record
Abstract
Abstract Teachers in many countries are being expected to use the Internet in their work. Research on the Canadian experience of Internet implementation provides insights that may be valuable for researchers and educators in other countries. A three‐year study, using both quantitative and qualitative approaches, examined both the visions for Internet use and the realities of everyday practice related to Internet use in Canadian schools. Participants in the study included ministry of education officials, teacher association officials, classroom teachers, and school administrators. Findings of the study suggest that all four participant groups were positive about the visions of the Internet as a tool with the potential to contribute to the enhancement of teaching and the development of information literate students. The realities of Internet use, however, were quite different from the visions. All four participant groups reported that the Internet was being used mostly to increase access to information. Its potential as an innovative learning tool for students and for teachers was largely unrealised. Few respondents reported using the collaboration, creation, and dissemination capabilities of the Internet. This outcome appeared to be the result of limited infrastructure support, difficulties in infusing Internet use into curriculum, and lack of appropriate teacher professional development.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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 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".