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Record W2513017503 · doi:10.5539/ells.v6n3p32

The Use of Inflectional Morphemes by Kuwaiti EFL Learners

2016· article· en· W2513017503 on OpenAlexvenueno aff
Abdullah Alotaibi

Bibliographic record

VenueEnglish Language and Literature Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMorphemeTest (biology)ComprehensionLinguisticsPsychologySignificant differenceMathematics educationComputer scienceNatural language processingMathematics

Abstract

fetched live from OpenAlex

<p>This research paper aims to test the extent to which 100 Kuwaiti EFL learners are aware of the correct use of inflectional morphemes in English. It also explores the main causes of the errors that Kuwaiti EFL learners may make. Additionally, it checks whether the English proficiency level of the participants plays a role in their answers on the test. To this end, a multiple-choice test was used to measure the participants’ ability to use the correct inflectional morphemesin English. Following data analysis, the results reveal that Kuwaiti EFL learners are aware of the correct use of the inflectional morphemes in English to a certain degree (total mean=65.5%). Additionally, the t-test shows that the participants’ English proficiency level plays a central role in their comprehension of these morphemes. In particular, there is a statistically significant difference between the answers of the advanced learners (ALs) (73.5%) and intermediate learners (ILs) (57.5%). The number of correct answers provided by ALs is higher than that provided by ILs. Regarding the types of errors made by the participants, it has been argued that the most noticeable ones are due to first language (L1) negative transfer and the irregularity of some types of inflectional morphemes in English. Finally, the study concludes with some pedagogical implications and recommendations for further research.</p>

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.304
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 teacher head, not a consensus.

Study designNot applicable
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

Citations4
Published2016
Admission routes1
Has abstractyes

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