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Record W4391389701 · doi:10.1515/jccall-2023-0021

Examining the effects of two cognitive styles (field dependence vs. field independence) on learners’ mobile-assisted vocabulary acquisition

2024· article· en· W4391389701 on OpenAlexaff
Danial Mehdipour-Kolour, Mohamad Bilal Ali

Bibliographic record

VenueJournal of China Computer-Assisted Language Learning · 2024
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsConcordia University
Fundersnot available
KeywordsIndependence (probability theory)Field dependenceVocabularyCognitive stylePsychologyField (mathematics)CognitionCognitive psychologyComputer scienceMathematicsLinguisticsStatisticsNeurosciencePhysics

Abstract

fetched live from OpenAlex

Abstract Learners with different cognitive styles (here, field dependence vs. field independence ) may learn second language vocabulary differently in different vocabulary learning settings. Although cognitive style has been widely studied in second language research, little is known about how field dependence/independence affects learners’ vocabulary acquisition in a mobile-assisted learning setting. One approach to solve this problem is to investigate the possible effect(s) of field dependence/independence on learners’ short-term vocabulary recall in a mobile-assisted vocabulary acquisition setting (here, Memrise ). To investigate such effect(s), this study adopted a pretest-posttest design involving 147 intermediate-level learners of English as a second language. Using the Group Embedded Figures Test, participants were divided into two groups: field dependent and field independent learners. For 4 weeks, both groups practiced and reinforced a set of English vocabulary, selected from the Vocabulary Level Test, through Memrise flashcards. Our findings reveal a post-intervention improvement among both field-dependent and field-independent learners, but with field-independent learners slightly outperforming their counterparts in the short-term recall of the vocabulary. Implications and recommendations for future research are discussed.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.304
Teacher spread0.293 · 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.

Study designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of China Computer-Assisted Language LearningSame topicSecond Language Acquisition and LearningFrench-language works237,207