Teaching Emotional Regulation and Awareness Through a VR-Based Rhythm Game
Bibliographic record
Abstract
Programs designed to promote social-emotional learning (SEL) in high-school students are increasingly being used in schools to facilitate academic and life success. Among a wide range of skills, these programs teach young students how to identify and manage their emotions (CASEL, 2015). Virtual reality holds increasing potential to support SEL programs given the great interest of adolescents in this technology (Yamada-Rice et al., 2017) and the opportunity it provides to practice these skills in a safe but novel way (Slovák & Fitzpatrick, 2015). Additionally, SEL and emotion regulation schemes based on music listening and rhythmic performance are particularly compelling programming for teens, due to the popularity of music among adolescents, the inherent emotional expressivity of music, as well as our natural ability to parse those emotions (Dingle et al., 2016; Faulkner, 2016). This paper explores the use of VR and music in emotional regulation education and proposes the development of a VR rhythm game that complements self-awareness and self-management learning in schools. The game seeks to help students develop skills such as relaxation techniques, emotional expression, emotional literacy through rhythm gaming and performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".