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Record W3009630743 · doi:10.5539/jel.v9n2p191

Comparison of the Effects of Sports Education and Direct Teaching Models on the Attitude and Cognitive Domain Level of Undergraduate Students

2020· article· en· W3009630743 on OpenAlexvenueno aff
Ender Eyüboğlu, Oğuzhan Dalkıran

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTurkishCognitionCompetence (human resources)Mathematics educationPhysical educationLikert scalePerceptionSocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to compare the effects of the sports education model and the direct teaching model used in badminton courses on the attitudes of undergraduate students towards the course and its permanence on cognitive domain skills. The study group consisted of a total of 45 undergraduate students, 24 of whom were experimental groups and 21 were control groups. In the study, for collection of data, the “Intrinsic Motivation Inventory” which developed by Ryan (2000), adapted to Turkish by Çalışkur after being tested for validity and reliability (Çalışkur & Demirhan, 2013) and the “Badminton Cognitive Domain Information Form” which was prepared by the course instructor were used. Descriptive statistical analysis was used for data analysis of attitudes of groups after application, but, bacause of the lack of a normal distribution, the “Mann Whitney U” test was used for the significance of the difference between the cognitive domain and the permanence of learning, As a result; significant differences were determined between the students’ interest/enjoyment aspect and the permanence of cognitive learning, whereas significant differences were not detected in the aspects of perceived competence, value/benefit, effort/importance, job perception/perceived choice and pressure/tension.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.194
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.153
GPT teacher head0.511
Teacher spread0.358 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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