Standing and dynamic sitting in the university classroom: Perceptions of students and faculty
Bibliographic record
Abstract
There are health risks associated with prolonged periods of sitting. Although alternative workstations have health benefits and do not appear to impact classroom performance, questions still remain on their acceptability. This study investigated university students and faculty perceptions of using alternative workstations (standing, sit-stand, and dynamic sitting) in the classroom. University students (N=1005) and faculty (N=218) completed a mixed-method online survey assessing their perceptions of alternative workstations in the classroom. From the quantitative data, a large portion of students believed standing, sit-stand, and to a lesser extent dynamic sitting options should be available for students in the classroom. A majority of the students also stated that they would use these options if they were available in the classroom. Students and faculty preferred alternative desks to be located in the back of small classes. Faculty members were less supportive of alternative workstations in the classroom. Qualitative themes that emerged from the data included overall perception (supportive, undecided, and opposed), personal factors (classroom performance and health/ injury) that acted as facilitators and barriers, and environmental factors (depends on the day, depends on the location/ availability, depends on the social norm, depends on the class type/ time/ task, and depends on the length/ number of classes). These factors must be considered when designing interventions to reduce sedentary behaviour or when implementing alternative workstations. At this time we recommend providing standing, sit-stand, and to a lesser extent dynamic sitting options in university classrooms to allow students to receive potential health benefits as they learn.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".