Reading Strategies Employed by University Business English Majors with Different Levels of Reading Proficiency
Bibliographic record
Abstract
The purpose of this study was to investigate the use of reading strategies by the university Business English majors in relation to their levels of reading proficiency. The participants were 926 university Business English majors from 6 universities in southwest China. The Strategy Questionnaire for Business English Reading (SQBER) and the Business English Reading Comprehension Test (BERCT) were used to collect the data. The results showed that the students with good reading proficiency reported significantly greater use of reading strategies than the students with either fair or poor reading proficiency at the overall and category levels. At the individual strategy level, 25 out of the 45 reading strategies across the inventory varied significantly in terms of the students’ levels of reading proficiency. Most of these strategies with significant differences showed positive correlation, with the higher reading proficiency students reporting employing reading strategies significantly more frequently than the lower reading proficiency students. In addition, the students with different proficiency levels had different tendencies in the use of individual reading strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".