The effects of sitting, dynamic sitting, and standing desks on classroom performance of university students
Bibliographic record
Abstract
There are many health risks associated with prolonged sedentary time, but breaking up periods of sitting can reduce these risks (Healy, 2008). University students experience excessive sedentary time during class and while studying. Standing desks are an option to reduce sedentary time. It has been suggested that standing desks might hinder learning and productivity. Commissaris et al (2014) evaluated the effectiveness of a series of office tasks while individuals used various dynamic workstations. The results showed that the workstation used did not negatively affect performance in most tasks. However, tasks university students have to perform in their daily lives vary from office workers. The purpose of this study was to determine the effect of sitting, dynamic sitting, and standing desks on classroom performance of university students. Based on a randomization sequence, 30 participants (N = 15, females, M age = 21.1) performed three 3-minute classroom simulations using a classic, dynamic sitting, and standing desk. Each simulation included a typing and memory task. Participants were asked to type the paragraph displayed as fast and as accurate as possible while paying attention to a video. Following the video, participants answered 3 multiple-choice questions to assess memory. Results showed no significant differences in typing speed and accuracy or memory (all p values > .05, partial ?2 effect size range .019-.045) between sitting, dynamic sitting, and standing desks. These findings need to be replicated over a longer period of time that simulates a university classroom-learning environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".