A Principled Approach to Facilitating Distance Education: The Internet, Higher Education and Higher Levels of Learning
Bibliographic record
Abstract
In an earlier study I investigated the essential principles that facilitate higher levels of learning in Internet-based distance learning university courses. In this study I explored how these teaching and learning principles could be applied in Internet- based distance learning environments. I used an open-ended questionnaire to determine how (or if) the teaching and learning principles identified in the earlier study could be applied. The outcomes of this study provide many suggestions for Internet-based distance activities that can support the facilitation of higher levels of learning. Dans une recherche antérieure, j’ai examiné les principes essentiels qui facilitent des niveaux d’apprentissage élevés dans des cours universitaires à distance utilisant surtout l’Internet. Dans la présente recherche, j’ai exploré comment ces principes d’enseignement et d’apprentissage pouvaient être appliqués dans des environnements d’apprentissage à distance utilisant surtout l’Internet. J’ai utilisé un questionnaire ouvert pour déterminer comment (ou si) les principes d’enseignement et d’apprentissage identifiés dans la recherche antérieure pouvaient être appliqués. Les résultats de cette recherche fournissent plusieurs suggestions pour des activités à distance utilisant l’Internet qui peuvent faciliter des niveaux d’apprentissage élevés.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| 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".