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Record W2028750477 · doi:10.5539/elt.v7n2p90

ESL Elementary Teachers’ Use of Children’s Picture Books to Initiate Explicit Instruction of Reading Comprehension Strategies

2014· article· en· W2028750477 on OpenAlexvenueno aff
Al Tiyb Al Khaiyali

Bibliographic record

VenueEnglish Language Teaching · 2014
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsComprehensionReading comprehensionMathematics educationPsychologyReciprocal teachingPerceptionTeaching methodLiteracyReading (process)PedagogyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Reading comprehension instruction has been recognized as a key factor in developing any reading and literacy program. Therefore, many attempts were devoted to improve explicit comprehension strategy instruction at different school levels and fields including EFL and ESL. Despite these efforts, explicit comprehension instruction is still drought and far from satisfactory. Additionally, a great deal of teachers and educators are still struggling to find the appropriate ways to explicitly and effectively teach comprehension strategies. Consequently, the purpose of the present study was to explore the general perceptions and experiences of elementary English language teachers in using children’s picture books to initiate explicit comprehension strategy instruction. In order to obtain naturalistic and in-depth understanding of participating teachers’ perceptions, structured classroom observations were carried out for four weeks. Findings indicated that in about 718 minutes of instruction of both classrooms, 603 minutes were allotted for explicit comprehension strategy instruction. Despite some flaws in time management, the use of only one resource to collect data, and the focus on only comprehension strategy instruction, this study could contribute to the body of research of comprehension strategy instruction in language learning classrooms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.016
GPT teacher head0.286
Teacher spread0.270 · 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.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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