ESL Elementary Teachers’ Use of Children’s Picture Books to Initiate Explicit Instruction of Reading Comprehension Strategies
Bibliographic record
Abstract
Reading comprehension instruction has been recognized as a key factor in developing any reading and literacy program. Therefore, many attempts were devoted to improve explicit comprehension strategy instruction at different school levels and fields including EFL and ESL. Despite these efforts, explicit comprehension instruction is still drought and far from satisfactory. Additionally, a great deal of teachers and educators are still struggling to find the appropriate ways to explicitly and effectively teach comprehension strategies. Consequently, the purpose of the present study was to explore the general perceptions and experiences of elementary English language teachers in using children’s picture books to initiate explicit comprehension strategy instruction. In order to obtain naturalistic and in-depth understanding of participating teachers’ perceptions, structured classroom observations were carried out for four weeks. Findings indicated that in about 718 minutes of instruction of both classrooms, 603 minutes were allotted for explicit comprehension strategy instruction. Despite some flaws in time management, the use of only one resource to collect data, and the focus on only comprehension strategy instruction, this study could contribute to the body of research of comprehension strategy instruction in language learning classrooms.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".