Evolving definitions in digital learning: a national framework for categorizing commonly used terms
Bibliographic record
Abstract
"This report builds upon several years of research conducted by the Canadian Digital Learning Research Association (CDLRA). Our team first put forth a proposed set of definitions related to digital learning in the 2018 National Report (available at www.cdlra-acrfl.ca/publications). Since that time, we have continued to investigate how institutions are defining terms such as online learning, distance learning, remote learning, and hybrid learning through qualitative interviews with senior administrators and consultations with provincial and national organizations and working groups. The 2021 National Survey of Online and Digital Learning asked institutions to provide their institutional definitions (if they had one) for these terms and their responses have informed this report."--(CDLRA)
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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 | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.039 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".