Harnessing Text Structure Strategy for Reading Expository and Medical Texts among EFL College Students
Bibliographic record
Abstract
This classroom-based study was conducted in Kuwait to investigate the impact of text structure strategy (TSS) instruction on the ways in which 54 English as a foreign language (EFL) college students approached expository and medical texts. Data collection involved two surveys, fieldnotes, class observations, and group interviews. A system of codes and categories was developed from the recurrent patterns and commonalities in the interview data and classroom observations. Two surveys were distributed in 2 intervals, 8 weeks apart, which focused on identifying text structure strategies such as introducing the concept of text structures, asking guided questions, identifying signaling words, and using graphic organizers, as well as the extent to which the participants applied text structure strategies to approach expository medical texts. Data analysis involved using the Microsoft Excel program to generate two tables and descriptive statistics including the means, standard deviations and percentages of the results of the two surveys. Findings indicated that the participants benefited from TSS instruction in strategies that involved group discussions rather than strategies that relied on individual class work. Moreover, a large percentage of the participants applied most of what they learned in analyzing expository texts into reading medical texts. Implications were drawn for EFL teachers to conduct action research studies on text structure strategy for EFL learners and to apply TSS instruction in class in group and pair work which are suitable for EFL leaners. Finally, EFL researchers were invited to conduct classroom-based studies of TSS instruction.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".