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Record W2101864773 · doi:10.5539/ijel.v4n6p39

Evaluating the Performance of Iraqi EFL College Students in Using Frequency Adverbs

2014· article· en· W2101864773 on OpenAlexvenueno aff
Sabeeha Hamza Dehham

Bibliographic record

VenueInternational Journal of English Linguistics · 2014
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAdverbRemedial educationTest (biology)Mathematics educationGrammarPsychologyFace (sociological concept)English grammarFunction (biology)Linguistics

Abstract

fetched live from OpenAlex

Frequency adverb is used to express approximately how many times a customary or habitual action or condition is repeated. It is one of the essential constructions in English grammar and a problematic area for the Iraqi EFL university learners. The study aims at investigating the performance of Iraqi EFL university students in using frequency adverbs by form and function and finding out the area of difficulty in this regard and suggesting remedial work for the alleviation of these difficulties. It is hypothesized that Iraqi EFL learners encounter difficulties in using adverbs of frequency. The study begins with the theoretical aspect encompassing definitions, types, and functions of frequency adverbs in English. Then, the practical aspect represented in a diagnostic test applied to a random sample of (50) students taken from the fourth level in the Department of English ,College of Education for human Sciences, University of Babylon. The findings of the test show that Iraqi EFL university students face difficulty in using frequency adverbs which, in turn, verifies the hypothesis of the study. In the light of the results of the test, some conclusions are drawn and a number of suggestions and remedial work are presented so that the learners can overcome the difficulty they encounter in using adverbs of frequency in English.

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)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.027
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 integrity0.0000.000
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 head0.039
GPT teacher head0.409
Teacher spread0.369 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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