Exploring Educational Exergames in Wellbeing Education: A Study Finnish Primary School
Bibliographic record
Abstract
Integrating exergames into well-being education has garnered substantial attention for promoting physical wellness. This study focused on assessing student preferences in exergames, particularly examining variables such as game selection, difficulty level, and collaborative mode. Furthermore, we scrutinized play duration across these variables to gain an in-depth understanding. Our exploration was guided by self-determination theory, centering on autonomy, competence, and relatedness concepts. Conducted within a primary school in Jyväskylä, Finland, our study utilized iWall, an Interactive Gaming Wall, to capture data on these variables, amassing 1707 data points from student frequency log data. Analysis of game frequency delineates game preferences emphasizing physical endurance, rhythmic coordination, and multiplayer engagement. Correlation analyses illuminate the dynamic interaction between game types and difficulty levels, elucidating the connection between gaming challenges and student choices. Moreover, examining play duration underscores how game genres significantly impact gameplay duration, emphasizing the need for diversified cognitive and physical challenges in exergame design. These findings are anticipated to offer insights into future exergame development for well-being education, drawing from a detailed understanding of student preferences established in this study.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".