Effectiveness of Literature Circles in Developing English Language Reading Ability: A Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".