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Record W2394956843 · doi:10.5539/elt.v9n6p213

Ambiguity Tolerance and Perceptual Learning Styles of Chinese EFL Learners

2016· article· en· W2394956843 on OpenAlexvenueno aff
Haishan Li, Qingshun He

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsnot available
Fundersnot available
KeywordsKinesthetic learningPsychologyAmbiguityLearning stylesPerceptual learningAuditory learningPerceptionAmbiguity toleranceStyle (visual arts)Visual learningCognitive psychologyCognitive styleLinguisticsMathematics educationCognition

Abstract

fetched live from OpenAlex

Ambiguity tolerance and perceptual learning styles are the two influential elements showing individual differences in EFL learning. This research is intended to explore the relationship between Chinese EFL learners’ ambiguity tolerance and their preferred perceptual learning styles. The findings include (1) the learners are sensitive to English ambiguities and are more reliable on the tactile and kinesthetic learning styles than on visual and auditory styles, (2) most Chinese EFL learners have more than one learning style preferences, (3) significant gender difference exists in ambiguity tolerance, but not in perceptual learning style preferences, and (4) the four perceptual learning styles are significantly correlated with ambiguity tolerance and the auditory learning style would exert more influence on ambiguity tolerance than the visual, tactile and kinesthetic learning styles.

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.009
GPT teacher head0.292
Teacher spread0.283 · 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

Citations18
Published2016
Admission routes1
Has abstractyes

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