Tele-proximity: Tele-community of Inquiry Model. Facial Cues for Social, Cognitive, and Teacher Presence in Distance Education
Bibliographic record
Abstract
Distance education is expanding in all continents, and the use of video has dominated internet. Synchronous Video Communication (SVC) has not been an option thoroughly investigated and practitioners, who use and design synchronous learning scenarios, are in urgent need of guidance. Distant learners face many barriers, and as a result, they drop out more frequently than on-campus students. Educators seem to be equally affected by the “transactional distance” and the new digital literacies needed for facilitating online learning. This study explores the educators’ perspective on how SVC could offer alternative educational forms and possibilities for distance learning. Findings had indicated that the use of visual communication and human to human contact (prosopogonosia: seeing faces) could have a strong impact on learning and teaching, therefore, a theory called Tele-proximity was formulated. Tele-proximity is defined as online embodiment that explains how instructors and students are connected in synchronous networked environment via tele-operations. SVC creates a sense of place or a stage where online identities perform and highlights recent research on audio-visual signals in communication and team work (Pentland, 2012, 2008). The theory can be seen as an extension of the Community of Inquiry Model (Garrison, Anderson & Archer, 2000) and a theoretical framework according to which learning objectives could be designed. Transactional distance could be minimized and may be implemented to facilitate more synchronous, visual, and humane options in distance education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".