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Record W4381546577 · doi:10.5539/elt.v16n7p47

Effectiveness of Literature Circles in Developing English Language Reading Ability: A Systematic Review

2023· review· en· W4381546577 on OpenAlexvenueno aff
Lei Ma, Lilliati Ismail, Norzihani Saharuddin

Bibliographic record

VenueEnglish Language Teaching · 2023
Typereview
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)PsychologyReading comprehensionContext (archaeology)Systematic reviewCritical thinkingMathematics educationPedagogyLinguistics

Abstract

fetched live from OpenAlex

English reading ability is essential to overall language proficiency because our learning depends heavily on written materials. As a highly evolved form of collaborative learning originating in the L1 context, literature circles can provide a perfect scaffolding for reading, discussion and sharing. Thus, this reading strategy has also been widely used in L2 learning. This systematic review examines the effectiveness of literature circles in improving English language reading ability. An analysis is conducted in reference to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology. Based on the search keywords, a total of 19 articles related to the benefits of literature circles for English reading comprehension ability are identified from Scopus, Google Scholar, Dimensions and Education Resources Information Center databases. The findings from the synthesis show a trend among literature circles to integrate more modern technology and to adopt more diversified reading materials. The identified themes related to reading ability consist of six areas: self-regulation, reading skills, positive attitude, cultural awareness, critical-thinking skills and reading engagement. It is hoped this systematic review will inspire language instructors to implement literature circles to improve training students’ English reading ability.

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)0.014
metaresearch head score (Gemma)0.068
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.027
GPT teacher head0.390
Teacher spread0.363 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2023
Admission routes1
Has abstractyes

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