Change of the National English Curricula in Korea and Considerations for the Next Curriculum
Bibliographic record
Abstract
This study investigates the national English curriculum, social and academic culture, roles and positions of (English) teachers and students, and their changes in Korean history. Based on this exploration, the author discusses considerations to advance the current Korean English curriculum and where the next curriculum is to be headed in the era of the fourth industrial revolution (4IR). Given that the 4IR welcomes people who have high qualities in complex problem-solving, critical thinking, creativity, management, collaboration, decision-making and negotiation, significant changes in teacher and student roles and teaching practice are needed. The Korean pedagogical background of teacher-led practice, text- or grammar-based learning, test-preparation lessons and pursuit of competition in English education should not be obstructions for these changes. Thus, the author suggests the application of AI programmes and problem-based learning for the realisation of more learner-centred, democratic, and constructive learning. This study will provide educators in East Asian countries as well as in Korea with several rationales to deliberate for their next curriculum design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".