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Record W2769788082 · doi:10.5539/elt.v10n12p202

Improving English Reading Comprehension Ability through Survey, Questions, Read, Record, Recite, Review Strategy (SQ4R)

2017· article· en· W2769788082 on OpenAlexvenueno aff
Nurul Lailatul Khusniyah, Yumna Rasyid, Ninuk Lustyantie

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionPsychologyCredibilityTest (biology)Mathematics educationReading (process)Sample (material)ComprehensionComputer scienceLinguistics

Abstract

fetched live from OpenAlex

The aim of this study is to examine the effect of the survey, questions, read, record, recite, review (SQ4R) strategy of the reading comprehension ability students of 2nd semester. The research study was used action research method. The sampling was taken by 34 students. The validity of data used credibility, transferability, dependability, and conformability. Reading comprehension ability achievement questions prepared by the researcher. It was used as the data collecting tool. These questions were implemented to both groups before the experiment. Independent sample t-test was used for data analysis. The result of the study that t-test result is t-stat (11.3)> t-table (1.997). It means that there is a significant difference was found in English reading comprehension ability in favour of the students who studied reading comprehension before and after the use of the SQ4R strategy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.043
GPT teacher head0.360
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations26
Published2017
Admission routes1
Has abstractyes

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