Academic reading strategies used by Chinese EFL learners : five case studies
Bibliographic record
Abstract
The number of people learning English as a second or foreign language has increased dramatically over the last two decades. Many of these second language learners are university students who must attain very sophisticated academic skills. To a great extent, their academic success hinges on their ability to read a second language. This multiplecase study investigated first language (LI) and second language (L2) reading strategies in academic settings. The study drew on Bernhardt's (2000) socio-cognitive model of second language reading. Five Chinese students in a graduate program in Teaching English as a Foreign Language (TEFL) volunteered to participate in the study. A combination of data collection techniques was employed including think-alouds, interviews, learning logs, classroom observations, course materials, and the participants' reading samples. The results showed that there were similarities and differences between LI and L2 reading strategies. Although evidence was found supporting the view of cognitive universals and socio-cultural constraints, individual differences at the cognitive level and similarities across cultures were also identified. The findings of this study indicate that the comparison between LI and L2 academic reading should take into consideration the similarities and differences at both cognitive and cultural levels. Implications are discussed in relation to the construction of an L2 transfer model as well as the delivery of L2 reading 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".