The Impact of Dynamic Furniture on Classroom Performance: A Pilot Study
Bibliographic record
Abstract
The purpose of this study was to understand how dynamic classroom furniture may impact classroom performance such as attention, work neatness, and work completion in a second grade general education classroom of 19 students. All students in the classroom were included in this study to understand the implications of environmental modifications on the learning process in general education settings. A descriptive method provided information about the interaction of dynamic furniture on identified learning components. Three different dynamic furniture options were provided: Zuma chairs®, Disc‘O’Sits® (inflated seat cushions), and standing desks with the Original FootFidget®. The class was randomly divided into four groups of up to five students. The groups were rotated through the furniture, allowing one week per group with each type of furniture. The Sensory Processing Measure (Parham & Ecker, 2007) was used to screen the sensory processing of students and a daily self-report rubric provided data on attention behaviors and perception of the dynamic furniture options. Data were graphed and visually analyzed for differences in responses to types of furniture. Responses on the rubrics indicate that the different types of furniture impacted different components of learning in a variety of ways. The data from this study indicates that no one type of furniture provides the same effect for all elementary students, but rather that personal characteristics may dictate the best match for focus, work completion, and neatness.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".