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Record W4381427553 · doi:10.5430/wjel.v13n6p1

Using Lexical Cohesion Cloze Exercises to Improve EFL Learners’ Reading Comprehension

2023· article· en· W4381427553 on OpenAlexvenueno aff
Sujunya Wilawan

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCohesion (chemistry)Reading comprehensionSignificant differencePsychologyMathematics educationReading (process)Computer scienceLinguisticsMathematicsStatistics

Abstract

fetched live from OpenAlex

The present research examined the effects of lexical cohesion cloze reading exercises (in which deletions involved various types of lexical cohesive devices) on improving EFL students’ reading comprehension. Sixty-four Thai university students were randomly assigned to control and experimental groups. Both groups received 12 sessions of skill-based instruction, but the experimental group had additional practice in recognizing lexical relations through cloze passages. The mean pretest and posttest scores were compared using paired samples t-test. Post-treatment questionnaires were used to investigate students’ attitudes towards the assigned extra reading exercises. Students in the experimental group achieved significantly higher mean posttest scores than the control group (p < 0.05). There were no significant difference in attitude mean scores between the control group and the experimental group (p > 0.05). The experimental group, however, were more positive towards the use of the extra reading exercises and found the technique to be useful in fostering their English reading comprehension. These findings indicated that lexical cohesion cloze reading is effective in improving the reading performance of Thai EFL students and should be used to equip EFL students with the knowledge they need for better reading comprehension.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.036
GPT teacher head0.348
Teacher spread0.312 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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