MétaCan
Menu
Back to cohort
Record W6892662611 · doi:10.5281/zenodo.10953099

REVOLUTIONIZING PHYSICAL EDUCATION IN COLLEGES: AN ADVANCED STRATEGY UTILIZING INFORMATION TECHNOLOGY

2024· article· en· W6892662611 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysical educationCurriculumEnthusiasmInformation technologyProcess (computing)Quality (philosophy)

Abstract

fetched live from OpenAlex

Multilevel teaching, a dynamic pedagogical approach distinct from traditional methods, redefines and enhances the educational process by incorporating diverse content, organizational structures, and teaching techniques, all aligned with curriculum requirements. Traditional physical education programs at universities often follow fixed curricula and procedural norms, fostering an incremental and uninspiring learning environment. The rigidity of this approach dampens students' enthusiasm for physical activities, compromising the overall teaching quality. To address these challenges, the integration of information technology into university physical education classes emerges as a promising solution. This article explores the development of multilevel teaching models for university-level physical education, enhanced by information technology. By capitalizing on the benefits of information technology, this research not only revitalizes physical education instruction but also fuels students' engagement and holistic well-being, ultimately enhancing the quality of physical education in higher education.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.008

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 head0.076
GPT teacher head0.420
Teacher spread0.344 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicPhysical Education and PedagogyFrench-language works237,207