Experiences of Using a Telepresence Robot During the COVID-19 Pandemic
Bibliographic record
Abstract
Background School absenteeism due to mental disorders and physical disabilities is an international problem. When children are absent from primary school, they do not receive the fundamental educational foundation they are entitled to. This affects their further opportunities to receive higher education later in their life. Studies show that telepresence robots can include absent students in the teachings and social life at school. Objective The purpose of this project was therefore to investigate the opportunities and limitations of using an OriHime telepresence robot to teach absent primary school students during the COVID-19 pandemic. Methods This project was a case study from a primary school in Denmark. The study included primary school students (n=3), teachers (n=5), parents (n=2), a school principal, a pedagogue, a school absentee consultant, and a psychologist. The 14 participants were interviewed based on interview guides. In all, 20 hours of observation of OriHime have been made in the classroom conducting in the pilot test. Afterward, OriHime was tested by an absent primary school student for a 2-month period during the COVID-19 pandemic. Results The absent students found that OriHime was useful and a good alternative for them to be able to attend class. Teachers and pupils found that OriHime was useful in a class setting but not when conducting outdoor activities. The parents found that OriHime could include the absent students in the teachings and social life in class. Conclusions The absent students experienced that OriHime could function as an educational and social tool during COVID-19 and that it was possible to participate in the indoor teachings while being physically absent. The absent students, teachers, and parents found OriHime useful with opportunities but stated some limitations. Based on the results, a guideline for the implementation of OriHime in Danish primary schools was produced. Conflicts of Interest None declared.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".