University Students’ Perceptions of a 30-Minute Break During Class: A Realistic Practice for Wellness?
Bibliographic record
Abstract
This SoTL study aimed to discover how teacher education students engaged with a 30-minute unstructured break during a weekly three-hour lecture. Cognitive fatigue and resulting stress accumulation have negative effects on wellness. Education students can accumulate significant stress when studying and preparing. This, in turn, affects their career outlook and may affect teacher retention. Pausing a cognitively demanding task and taking a break can reverse the strain reaction and support sustainable, long-term wellness. However, taking an effective break is often difficult because it can be perceived as a waste of time and a loss of productivity. Research shows the opposite effect. If we educate higher education students about the benefits of taking effective breaks and then model this practice in class, can we promote an accessible and realistic stress management strategy? Can student teachers potentially take this strategy with them into their teaching careers and classrooms? Using pre- and post-surveys as well as “Weekly Break Logs” during class (N = 70), followed by a post-course focus group (n = 4), the study found that 100% of participants post-course valued the break. They spent it socializing or going for short walks and did not spend it on their devices. The majority felt refreshed and motivated to return to learning following the break. Focus group findings revealed the value of taking breaks, and participants were motivated to continue this practice in their professional lives.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".