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Record W4282914092 · doi:10.5430/wjel.v12n5p320

The Effect of Scaffolded Reading Experience and Collaborative Strategic Reading on Students’ Reading Comprehension Skills across Different Reading Proficiency Levels

2022· article· en· W4282914092 on OpenAlexvenueno aff
Ida Puji Lestari, Mirjam Anugerahwati

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionReading (process)IndonesianMathematics educationPsychologySignificant differenceComprehensionPedagogyComputer scienceLinguisticsMathematics

Abstract

fetched live from OpenAlex

This study investigated the effects of Scaffolded Reading Experience (SRE) and Collaborative Strategic Reading (CSR) on the students' performance in ELT reading comprehension classes. A quasi-experimental approach using factorial design was employed with 28 participants, who were Indonesian college students in two complete classes of the Cultural Studies Faculty of Universitas Brawijaya Malang. The results showed that there were no significant differences in the performance of those students taught with SRE and those taught with CSR. A possible cause for this lack of difference could be the greater influence of other factors, including the classroom environment, the teachers’ experience, and the students’ ability. This study suggests that English lecturers can apply SRE and CSR as two alternative strategies to teach reading comprehension. Future researchers should conduct studies involving larger numbers of participants to identify the factors affecting the learning of 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 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)0.001
metaresearch head score (Gemma)0.011
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.345
Teacher spread0.327 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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