Revisiting Transactional Distance Theory in a Context of Web- Based High-School Distance Education
Bibliographic record
Abstract
The purpose of this paper is to report on a study that considered Transactional Distance Theory (TDT) in a current technology context of web-based distance education (DE) in a high school environment. Data collection relied on semi-structured interviews conducted with 13 e-teachers and seven other personnel within an organization responsible for high-school distance education in Newfoundland and Labrador, Canada. Findings are presented in three categories labelled as follows: rapport and community-building; curriculum and teacher-centered tools as barriers; and the role of real-time interaction and engagement. We relate these categories respectively to the TDT concepts of dialogue, structure, and learner autonomy. Resumé Le but de cet article est de présenter une étude en lien avec la théorie de la distance transactionnelle (TDT) dans un contexte technologique actuel d'éducation à distance en ligne au secondaire. La collecte des données s'est faite à partir d'entrevues semi-dirigées conduite auprès de treize formateurs en ligne et de sept autres membres du personnel d'une organisation responsable de l'éducation à distance au secondaire à Terre-Neuve et au Labrador, au Canada. Les résultats sont présentés dans trois catégories: relation et construction de la communauté, curriculum et outils centrés sur l'enseignant comme barrières, et rôle des interactions en temps réel et l'engagement. Nous associons respectivement ces catégories aux concepts TDT de dialogue, de structure et d'autonomie de l'apprenant.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".