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Record W2914821466 · doi:10.5296/jsel.v7i1.14342

An Investigation of Verb-Forms Errors in the Spoken English of English-Majors and Non-English Majors at Emirates Canadian University College/ U.A.E.

2019· article· en· W2914821466 on OpenAlexaboutno aff
Mohammed Hamid Al-Ta’ani

Bibliographic record

VenueJournal for the Study of English Linguistics · 2019
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsRemedial educationVerbCurriculumPsychologyMathematics educationLinguisticsPedagogy

Abstract

fetched live from OpenAlex

This study aimed at identifying the strategies used by University students in learning English as a second language and their weaknesses in the grammatical and the lexical use of the English verb-forms. A total number of (8) university students were interviewed personally. Interviews were taped and each student’s speech was transcribed in order to be analyzed. The grammatical and the lexical errors were categorized and put to further analysis and investigation which explained the reasons and strategies behind their occurrences. The findings indicated that: - the learners’ errors were developmental and they benefited from instructions, most of the frequent errors were due to interference of the first language and the majority of errors were interlanguages errors, simplification and overgeneralization proved to be the most two widely used strategies in learning a second language and the learners’ motivation to communicate may exceed their motivation to produce grammatically correct sentences. A major conclusion of this study is the need of the English-major students for a remedial course in which they may have the opportunity to practice the basic structures of the English Language. Finally, based on the results of this study some pedagogical implications for English teachers and university instructors, curriculum designers and policy makers were highlighted.

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)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal for the Study of English LinguisticsSame topicSecond Language Acquisition and LearningFrench-language works237,207