Implications of Online and Blended Learning for the Conceptual Development and Practice of Distance Education
Bibliographic record
Abstract
The purpose of this article is to examine the foundational principles and practices of distance education for the purpose of understanding recent developments in the areas of online and blended learning. It is argued that mainstream distance education has not embraced the full collaborative potential of online learning. Distance education continues to hold to the principles of autonomy and self-direction. On the other hand, online learning practices in traditional face-to-face higher education are increasingly being adapted to support collaborative and blended learning opportunities. The question is whether the conceptual development of distance education has reached an evolutionary dead-end. Resume Le but de cet article est d’examiner les principes et les pratiques fondamentaux de l’education a distance dans le contexte des recents developpements dans les domaines de l’apprentissage en ligne. On fait la demonstration que l’apprentissage en ligne a pris naissance en-dehors de l’education a distance ordinaire. Il en decoule donc que l’education a distance n’aurait pas entierement integre le potentiel de collaboration de l’apprentissage en ligne. L’etude conclut avec la question a savoir si les concepts et pratiques de l’education a distance peuvent etre reformules et enlignes de maniere a incorporer le potentiel et les possibilites qu’offre l’apprentissage en ligne.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".