An Extended Systematic Review of Canadian Policy Documents on e-Learning: What We’re Doing and Not Doing
Bibliographic record
Abstract
This systematic review builds upon the work of Authors (2006) and McGreal and Anderson (2007). It seeks to provide a synthesis and discussion of publicly available government policy documents with regard to e-learning in Canada. There is general consensus, both in public opinion and in the research literature, that the educational practices associated with rapidly advancing computer information technologies are gaining popularity and are expected to be increasingly effective in enhancing learning. The purpose of this review is to uncover and describe areas of commonality and inconsistency in e-learning policy documents dated from 2000 to 2010, and to determine where discussions about e-learning are lacking. In total, 138 policy documents from Canadian provinces and territories and several federal agencies were retrieved and analyzed using prescriptive and emergent coding approaches. The review confirmed that Canadian policy makers view technology as offering potential benefits to learners, but also revealed a troubling lack of specific details, consistency and coordination in facilitating the development of e-learning to fulfill these optimistic expectations.
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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 | 0.122 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.044 | 0.059 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".