Remote supervision of teacher trainee internships: Using digital technology to increase social presence
Bibliographic record
Abstract
When digital technologies are used to supervise teacher trainees, internship supervisors adjust their practices to enhance their presence within their cohort in order to reduce the isolation felt by those who choose to do their internship locally, when home is in a remote location from their campus or university. In this article, we will share findings about the concept of social presence through a description of practices according to three indicators from the online community of inquiry theoretical model: emotional expression, open communication and group cohesion. From a qualitative methodology, our results attest to the humanistic nature of the remote supervision. During their online interactions with trainees, the internship supervisors interviewed share their feedback about videos and graded work tactfully, bearing in mind the distance that separates them. Despite how difficult it is to show empathy in mediated communication, they try by many means, including video and immediacy, to comfort trainees who may feel alone. They offer them frequent practical support and check in with them at the beginning and throughout the internship. Their support is bolstered by the authenticity of the situations observed in video footage, above and beyond the institutional systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".