Effectiveness of Read-aloud Instruction on Motivation and Learning Strategy among Japanese College EFL Students
Bibliographic record
Abstract
Poor English performance among Japanese college EFL students has often been explained by grammar-translation and lecture-memorization instruction. This study investigated the effectiveness of a recently designed teaching method, namely, “The read-aloud instruction package,” which consists of four major activities: Slash/chunked reading practice (S/CRP), repeated read-aloud practice (RRAP), cloze test, and simultaneous read-aloud and write-out practice (SRAWOP). The study also examined how EFL college students’ motivation to learn English and their choice of EFL learning strategies changed after intensive three-month’ instruction using these methods. Thirty-two participants with an elementary level of English proficiency completed a pretest and posttest using the TOEIC Bridge®, Strategy Inventory for Language Learning (SILL) and a motivation survey. Results indicated that the participants’ score on the posttests on the TOEIC Bridge® improved significantly, but no change was found in their motivation. As to EFL learning strategy, the study found that the participants used more mental processes but less learning with others strategy after the instruction. The article discusses some possible explanations of the effectiveness of the read-aloud instruction package from cognitive and neuro-linguistic perspectives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".