Mindful Pieces: Promoting Self-Regulation in Students with Learning Differences
Bibliographic record
Abstract
In the field of developmental psychology, mindfulness, or the state of present awareness, has shown promising results in enhancing self-regulation abilities in children in classroom settings. Such findings may suggest particular benefits to children with learning differences, who commonly struggle with the ability to control thoughts, emotions, and behaviours due to neurologically- based challenges. Unfortunately, many mindfulness interventions, which include activities such as yoga and martial arts, require specialized training on behalf of the instructors. Furthermore, formal mindfulness practices (such as meditation) do not suit the limited attentional capacities of young children.\nA similar intervention that requires introspection, art therapy has presented art-making as a familiar, non-verbal, engaging, and enjoyable action that “demands presence in body, mind, feelings and, many would say, soul” (Learmonth & Huckvale, 2008, p.11). Borrowing concepts from art therapy, this thesis project explores art-making as a way for children with learning differences to practice mindfulness in classroom settings. Specifically, this project introduces a tool that implements mindful art-making as a transitional activity in grades 1-3 classrooms. The research takes place at Kenneth Gordon Maplewood School (KGMS), an alternative elementary school in North Vancouver for students with learning differences. Through iterative processes of prototyping, user testing, and feedback, this research devises a mindfulness tool that accommodates both for the gifts and challenges of children with learning differences, and the skillsets of their teachers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".