Theme-Based, Sheltered, or Adjunct? Evaluating CBI Models for Improving English Reading Skills in Chinese EFL Classrooms
Bibliographic record
Abstract
Content-Based Instruction (CBI) has been widely promoted as an effective approach for integrating language development with subject matter learning in English as a Foreign Language contexts. However, limited empirical evidence exists comparing the effects of different CBI models on students’ reading proficiency, particularly within Chinese tertiary education. Existing studies often focus on general outcomes or perceptions, leaving a gap in understanding how specific instructional models influence distinct reading sub-skills. This study aimed to address this gap by comparing the effectiveness of three CBI models (theme-based, adjunct, and sheltered) on university students’ English reading proficiency, focusing on three sub-skills: understanding explicit information, understanding implicit information, and using linguistic features to understand texts. Employing a mixed-methods design, the study involved 105 Chinese university students across three intact classes, with quantitative data collected from the English reading pretest and posttest and qualitative data collected from classroom observations and semi-structured interviews. Quantitative results indicated that while all groups improved significantly from pretest to posttest, the theme-based model led to significantly higher gains in total reading scores and in the ability to use linguistic cues. Qualitative findings suggested that the instructional design, input organization, and opportunities for textual scaffolding varied across models and contributed to the outcomes. These findings offer practical implications for EFL curriculum planners and educators seeking to align reading instruction with model-specific strengths for more effective literacy development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".