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Record W2168221503 · doi:10.5539/ijel.v2n5p18

The Effect of Textual Cohesive Reference Instruction on the Reading Comprehension of Iranian EFL Students

2012· article· en· W2168221503 on OpenAlexvenueno aff
Nader Assadi Aidinlou, Noushin Khodamard, Jaber Azami

Bibliographic record

VenueInternational Journal of English Linguistics · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionSignificant differenceMathematics educationArabicTest (biology)PsychologyNull hypothesisClass (philosophy)Reading (process)ComprehensionComputer scienceLinguisticsMathematicsStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This study aimed to investigate the effect of textual cohesive reference instruction on the reading comprehension of Iranian EFL students. To do so, about 60 students at advanced level participated in this study. The researcher divided them into two groups: control group and experimental group (30 students in each class). The control group received placebo (without useful instruction about cohesive references), but experimental group received treatment (textual cohesive reference instruction). The researcher’s subjects were collected from Islamic Azad university of Rasht, Iran. They were studying English as a foreign language (B.A).The age average amongst them was 23 to 26. Also as the level of our instruction and by the help of test-retest analysis it was proved that these subjects can do that kind of test and instruction. In order to investigate the effect of textual cohesive reference on the reading comprehension of Iranian EFL students, the results of the pre and post tests were used to gain the mean and standard deviation. Finally with the help of our experimental study this research led to the conclusion that there was a significant difference between the mean scores of the two groups allowing the researchers to reject the null hypothesis with more than 95% confidence.

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)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.067
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)0.0000.000

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 head0.031
GPT teacher head0.367
Teacher spread0.336 · 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 designTheoretical or conceptual
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

Citations8
Published2012
Admission routes1
Has abstractyes

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