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Record W4323314849 · doi:10.5430/wjel.v13n2p455

A Study on English Learning Motivation of Sports Major Students in Vocational Colleges of Henan Province

2023· article· en· W4323314849 on OpenAlexvenueno aff
Xiaohan Li

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmVocational educationCompetence (human resources)Mathematics educationMotivation to learnAutonomous learningPsychologyEnglish languagePedagogy

Abstract

fetched live from OpenAlex

After becoming the universal language of the world, English has effectively promoted the development of global integration and become an essential tool for international communication, and sports international exchange activities have increased greatly. Therefore, for PE Students in independent Colleges, to master English well is not only a requirement of times, but also a necessity to improve their professional competence.This study believes that the cultivation of their professional English learning is highly necessary, exploring their interest and study their English learning motivation is very significant. English teachers are supposed to stimulating PE students’ interest in English learning, exploring their learning enthusiasm based on students’ characteristics and help students to promote learning initiative and achieve the purpose of improving students’ English interest and performance.

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)0.001
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.300
Teacher spread0.288 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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