Application of Learner-Centered Approach in College English Instruction in China: A Case Study
Bibliographic record
Abstract
With a development history of more than half a century, the learner-centered approach has become a new teaching paradigm worldwide. The approach can produce effective and significant learning focusing on learners’ development, learning, and learning outcomes. This study aimed to determine how a learner-centered approach can increase students’ engagement, improve their English learning strategies, and enhance their academic performance in a specific EFL setting. Bloom’s taxonomy, Zhao’s “Neo Tri-Centers,” Krashen’s L2 acquisition theory, and primarily Biggs’ constructive alignment functioned as valuable guidelines when designing the instructional activities. Constructive alignment holds that intended learning outcomes, teaching and learning activities, and assessment tasks should be aligned organically to achieve effective learning. Data collected through the instructor’s observations, questionnaires, assessments of the students’ performances, and colleague’s feedback show that the learner-centered approach has remarkably motivated the students, improved their learning strategies, and enhanced their academic performance. The results and implications of this study may be of reference importance for future language teaching in a foreign language or second language setting.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".