MétaCan
Menu
Back to cohort
Record W2072999928 · doi:10.5539/elt.v7n6p168

The Effect of Speed Reading Strategies on Developing Reading Comprehension among the 2nd Secondary Students in English Language

2014· article· en· W2072999928 on OpenAlexvenueno aff
Mahmoud Sulaiman Hamad Bani Abdelrahman, Muwafaq Bsharah

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionReading (process)PsychologyMathematics educationTest (biology)ComprehensionEnglish languageSample (material)LinguisticsChemistry

Abstract

fetched live from OpenAlex

This study aimed to find the effect of speed reading strategies on developing reading comprehension among second secondary literary stream students in English language. The sample of the study consists of (42) students assigned into two groups who were chosen randomly from schools, a controlled group (21) students, and an experimental (21) students trained on speed reading strategies during the academic year 2013/2014. The study used a training material, pre and post reading comprehension tests were administrated (Rababa’h, 1991). T. test results revealed that there were significant differences at (? ? 0.05) among the students’ means in favor of the experimental group. In the light of the results, it is recommended that teachers should train students extensively on the use of speed reading strategies.

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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.309
Teacher spread0.300 · 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

Citations31
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicEducational Methods and Media UseFrench-language works237,207