Use of Blended Learning for Effective Implementation of English-Medium Instruction in a Non-English Higher Education Context
Bibliographic record
Abstract
Although researchers agree with the strengths of an English-medium instruction (EMI) in addressing internationalization of a non-English higher education (HE) context, its implementation in classrooms has been widely criticized, mostly because of ineffective delivery of course content and a lack of evidence of English improvement. Grounded upon a critical review of the current state of internationalization of Korean HE and the subsequent examination of supplementary interview data from 15 college students who have taken EMI courses, this study proposes a model which integrates critical factors of EMI into one framework. This model aims at guiding the EMI policy from initiation to implementation. A major feature of this model is blended learning as a strategy to address the shortcomings of current EMI in this context and to facilitate the allocation of diverse online materials to scaffold EMI instruction. The benefits of the approach are presented from the perspectives both of policy-makers and of classroom participants.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".