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Record W4384432518 · doi:10.54012/jcell.v3i1.178

Effect of Guided Reading Strategy to the Reading Comprehension of Grade IX Students at SMP Negeri 12 Pematang Siantar on a Narrative Text

2023· article· en· W4384432518 on OpenAlexfundno aff
Rachel Yolanda Sitepu, Sanggam Siahaan, Bertaria Sohnata Hutauruk

Bibliographic record

VenueJournal Corner of Education Linguistics and Literature · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
FundersUniversitas Negeri YogyakartaMcMaster University
KeywordsReading comprehensionMathematics educationClass (philosophy)Null hypothesisTest (biology)Reading (process)NarrativePopulationPsychologyExperimental researchAlternative hypothesisSample (material)Computer scienceMathematicsLinguisticsStatisticsArtificial intelligenceMedicinePhysics

Abstract

fetched live from OpenAlex

The research utilized quantitative research with a quasi-experimental design. A total of 287 students from class IX at SMP Negeri 12 Pematang Siantar was chosen as the population. The sample was split into two classes: the experimental class (IX 1), which had 28 students and used guided reading, and the control class (IX 5), which had 28 students and utilized lectures. The research instruments were pre-test and post-test of multiple choice of narrative text. To process statistical data, the researcher utilized SPSS version 26. The Kolmogorov-Smirnov and Shapiro-Wilk showed results of 0.200 (experimental) and 0.140 (control). The experimental group's post-test mean score exceeded the control group’s post-test by 70 points, leading to the acceptance of the alternative hypothesis and rejection of the null hypothesis. This demonstrates that the guided reading strategy significantly enhances students' narrative text 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)0.004
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.384
Teacher spread0.352 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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