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Record W3025331309 · doi:10.5539/jel.v9n3p99

Examining the Lifelong Learning Tendencies of Sport Sciences Faculty Students

2020· article· en· W3025331309 on OpenAlexvenueno aff
Kazım Kaya

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningPsychologySignificant differenceSports scienceMathematics educationMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

The aim of this research study is to investigate the lifelong learning tendency levels of sport sciences faculty students concerning some variables. The research group was consisted of 662 students who were attending the Faculty of Sport Sciences, Erciyes University, during the 2018-2019 academic year and were randomly sampled. The research was conducted with the survey model. The data of the research were collected using the Lifelong Learning Tendency Scale. SPSS program was used in the analysis of the data. According to the results of the research, it was determined that the lifelong learning tendency levels of sport sciences faculty students are at the medium level. Moreover, the lifelong learning tendency levels of the sports sciences faculty students were compared concerning certain variables such as gender, sports branch, age, department, grade level, and GPA, and it was determined that there was statistically no significant difference.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.189
GPT teacher head0.370
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2020
Admission routes1
Has abstractyes

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