The Origins of the Term Distance Education and the Roots of Digital Teaching and Learning
Bibliographic record
Abstract
By no means is the digitalization of learning and teaching a new phenomenon (cf. Inglis, et al., 1999). Since the 1960s and 70s, open and distance teaching universities have spearheaded new and emerging technologies to bridge the distance between students and teachers. Since the turn of the millennium, online learning has spread worldwide, particularly in countries with a long tradition of distance education (e.g., Canada, Australia, India, or South Africa, see Qayyum & Zawacki-Richter, 2018; Zawacki-Richter & Qayyum, 2018). Online study programs have also been increasingly established at campus-based universities. In 1999, Alan Tait observed that the boundaries between distance teaching and conventional campus-based universities were blurring: "The secret garden of open and distance learning has become public, and many institutions are moving from single conventional mode activity to dual mode activity" (Tait, 1999, p. 141).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".