Give me a break! Can strategic recess scheduling increase on-task behaviour for first graders? Ontario Action Researcher, 9 (2). Head
Bibliographic record
Abstract
The purpose of this study was to determine whether strategically scheduled recess breaks throughout the school day will increase student on-task behaviours during the time when they work independently. As an intervention for this action research study, recess breaks were given more often but for less time; recess breaks were scheduled before and after academic lessons throughout the whole day. Methodology included observations with checklist and field notes. Results of the study suggest that recess breaks scheduled directly before or after academic lessons positively affect student on-task behaviours. Research Focus As a first grade teacher in a rural western New York school, I usually schedule recess breaks after all academic material has been covered for the day. While there is no written rule governing recess in the school district, recess is traditionally offered once after all academic content is covered, and again at the end of the day. Some teachers even treat recess as optional and only give it as a reward if students have had good behaviour throughout the day. Because I also struggle with students ’ off-task behaviour, I was curious to see if they needed more recess breaks to help them focus during academic
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".