Moving online physical education from oxymoron to efficacy
Bibliographic record
Abstract
The use of the Internet within education has created an urgent need for research into online learning models, delivery methods and curriculum content modifications especially in online physical education (OLPE). Most secondary OLPE courses focus on the cognitive pieces of the curriculum, and to a lesser degree, fitness for life due to a lack of research. The omission of the physical, social and emotional components of the OLPE curriculum has created a rift in the understanding, growth and development of students who take these courses. For this reason, Kinesiology professionals need to take a lead in the development of OLPE curricula through efficacious research. Exergaming has shown positive results for each of these four components when played proximally; however, exergames have not been widely studied in remote Internet settings. An exploration of the present need for more research into the ramifications of OLPE and the role Kinesiology professionals can take in guiding this process along with the potential of exergames in OLPE to fill this curricular void is presented.
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 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 | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".