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Record W1994064368 · doi:10.1109/icmew.2012.91

Exerlearn Bike: An Exergaming System for Children's Educational and Physical Well-Being

2012· article· en· W1994064368 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsModular designModalitiesHuman–computer interactionPhysical activityInterface (matter)Computer scienceMultimediaPsychologyPhysical educationMathematics educationPhysical medicine and rehabilitationMedicineSociology

Abstract

fetched live from OpenAlex

Recently, games that incorporate exertion interfaces have emerged and are gaining attention from both academic researchers and commercial companies. Exergaming refers to video games that promote physical activity through playing. Exergames are believed to be a good method of promoting physical activity in children. Such games encourage children to engage in physical activity while enjoying their gaming experience. Nonetheless, we wanted to investigate whether combining exercising and learning modalities could be more beneficial for children's well-being. In this paper, we present our exergaming system called the ExerLearn Bike System, which combines both physical and educational aspects. The ExerLearn Bike System not only engages children in exercising through playing, but also provides them with learning experiences at the same time. We adopted a modular design approach that makes it possible to use any stationary bicycle as an input interface by attaching a number of devices on the bike.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.278
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations15
Published2012
Admission routes1
Has abstractyes

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