Compensatory Reading among ESL Learners: A Reading Strategy Heuristic
Bibliographic record
Abstract
This paper aims to gain an insight to the relationship of two different concepts about reading comprehension, namely, the linear model of comprehension and the interactive compensatory theory. Drawing on both the above concepts, a heuristic was constructed about three different reading strategies determined by the specific ways the literal, reorganisation and inferential skills of comprehension interact. The concepts of apt and smart reading strategies were introduced, which refer to two possible ways of compensatory reading. Applying the reading strategy heuristic to the secondary analysis of a large reading assessment data from Malaysian secondary school 3567 ESL learners, compensatory reading was found to be used by a significant group of those students who struggle with poor reading skills. Furthermore, one of the compensatory strategies, namely, smart reading, was found to be positively correlated with the learners' motivation to read and their belief about own reading competence, the proportion of positive answers among smart readers being 3.5% and 3.9% higher than among the mainstream readers, respectively. The findings suggest that the language talent of apt and smart readers (18.2% of the current sample to be discovered and cultivated in the L2 classroom, especially among low-achiever learners who would most benefit from the recognition of compensation as a legitimate skill of reading comprehension.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".