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Record W4399975684 · doi:10.20343/teachlearninqu.12.17

University Students’ Perceptions of a 30-Minute Break During Class: A Realistic Practice for Wellness?

2024· article· en· W4399975684 on OpenAlexaff
Shannon Kell

Bibliographic record

VenueTeaching & Learning Inquiry The ISSOTL Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsMount Royal University
Fundersnot available
KeywordsClass (philosophy)PerceptionPsychologyMathematics educationMedical educationPedagogyMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.385
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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