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Record W3037411847 · doi:10.5539/elt.v13n7p130

Using Interactive Picture-Book Read-Alouds with Middle School EFL Students

2020· article· en· W3037411847 on OpenAlexvenueno aff
Chia‐Ho Sun

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyReading (process)Reading aloudThink aloud protocolMathematics educationShared readingIntervention (counseling)PedagogyLiteracyLinguisticsComputer science

Abstract

fetched live from OpenAlex

The current study investigated the effects of interactive picture-book read-alouds on middle school EFL students’ word inference ability and attitudes toward reading in English. To this end, two classes of seventh-grade students from a public middle school in Taiwan participated and were randomly divided into two groups: an interactive picture-book read-aloud (IPBRA) group and a control group. The intervention took place in a 40-minute morning study hall per week over a period of 10 weeks. The two groups were tested using Reading Attitude Survey (RAS) and Word Inference Assessment (WIA) two weeks before and then two weeks after the intervention was implemented to assess improvement. Results indicated that interactive picture-book read-alouds had a positive impact on EFL middle schoolers’ reading attitudes and use of text and prior knowledge to infer the meanings of unfamiliar words. This study suggests that the motivating learning atmosphere of interactive picture-book read-alouds provided positive classroom environment and enabled students to have fun while interacting freely with one another, which, in turn, facilitated reading attitude changes, leading to better learning and better performance on word inference in subsequent reading activities.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.161
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.319
Teacher spread0.291 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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