Relationship between the Kinarm Standard Test of upper body coordination and school success in secondary students
Bibliographic record
Abstract
Background: Almost 18% of Ontario students received special education support through an IEP in the 2023-2024 school year. These students may have been identified as needing support with diagnosis such as learning disability or Autism or with a non-identified IEP. In this paper we will refer to these students as having a learning differences. The connection between learning difference and difficulties with motor control is well documented but not well understood, partly due to underdiagnosis and the lack of standardized assessment criteria. The Kinarm, a robotic assessment tool, is a quick and objective measure of sensory, motor, and cognitive functions. This research explored the use of the Kinarm as an assessment tool for coordination in high school students and the relationship between Kinarm standard tests (KSTs) and school success measures such as naming speed tasks, word reading, teacher generated grades, and standardized testing.Methods: Thirty-nine high school students completed 7 of the KSTs. Students also completed rapid digit naming, word reading, and working memory task. Academic and testing scores were collected. Visual exploratory analysis was conducted along with Pearson correlations (1-tailed).Results: There were moderate correlations between a variety of co-ordination tasks and cognitive/school scores. The correlations remained moderate after controlling for speed and continued to be evident after an adjustment for false discovery rate.Discussion: Despite a small sample size and limited number of neurodiverse participants this research confirms a relationship between co-ordination and measures of school success. Students with Individual Education Plans appeared more often as outliers in movement measures. This study confirms the feasibility of using a robotic assessment with high school students. Future studies can explore the use of Kinarm with a larger sample and larger number of exceptional learners.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".