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Record W2344905920 · doi:10.4018/ijdet.2016040101

Exergaming for Physical Activity in Online Physical Education

2016· article· en· W2344905920 on OpenAlexaff
Brian J. Kooiman, Dwayne P. Sheehan, Michael Wesolek, Eliseo Berní Reategui

Bibliographic record

VenueInternational Journal of Distance Education Technologies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMount Royal University
Fundersnot available
KeywordsThe InternetDeskPhysical activityPhysical educationMultimediaSittingMathematics educationPsychologyComputer scienceWorld Wide WebPhysical therapyMedicine

Abstract

fetched live from OpenAlex

For many the thought of students taking an online course conjures up images of students sitting at a computer desk. Students taking online physical education (OLPE) at home may lack opportunities for competitive or cooperative physical activity that are available to students in a traditional setting. Active video games (exergames) can be played over the internet between students. Exergames allow for a new and possibly effective genre of physical activity that offers OLPE students the opportunity to interact in relevant, engaging, and entertaining physical activity with other students. Secondary student (N=124) heart rates were recorded before exergaming, after playing a non-player character, and after playing another student remotely over the internet. The results show that exergaming between students over the internet can raise student heart rates to moderate levels of physical intensity commensurate with guidelines for Physical Intensity for secondary students. Exergames show promise for physical activity in an OLPE course when played against a non-player character and a remote partner.

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.

How this classification was reachedexpand

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.020
GPT teacher head0.397
Teacher spread0.378 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2016
Admission routes1
Has abstractyes

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