MétaCan
Menu
Back to cohort
Record W1995313795 · doi:10.5539/ijel.v1n1p162

A Match or a Mismatch between Student and Teacher Learning Style Preferences

2011· article· en· W1995313795 on OpenAlexvenueno aff
Ghada Hasan Sabeh, Rima Bahous, Nahla Nola Bacha, Mona Nabhani

Bibliographic record

VenueInternational Journal of English Linguistics · 2011
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsnot available
Fundersnot available
KeywordsKinesthetic learningLearning stylesPreferencePsychologyStyle (visual arts)Auditory learningMathematics educationAffect (linguistics)Cognitive styleVisual learningPedagogyCommunicationMathematicsCognition

Abstract

fetched live from OpenAlex

The purpose of this study is to identify the learning styles of the students enrolled in an American affiliated Lebanese university who are currently registered in intensive English courses and to investigate whether there is a match between students’ learning styles and teachers’ teaching styles. The participants in this study were 103 students and five ESL teachers. A modified version of the PLSPQ has been used as an assessment instrument to determine the learning styles of the students. The results showed that Lebanese students have a preference for multiple learning styles, auditory, kinesthetic, tactile and visual and that age, gender, discipline and time spent studying English are variables that affect the learning styles of the students. The findings showed that there was no match between the teaching and learning styles of the teachers and students. Implications are made for the classroom.

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)0.006
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.354
Teacher spread0.297 · 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

Citations20
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicLearning Styles and Cognitive DifferencesFrench-language works237,207