Using Interactive Picture-Book Read-Alouds with Middle School EFL Students
Bibliographic record
Abstract
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".