21st Century Skills in Curriculums of Turkey, Alberta, Korea and Singapore
Bibliographic record
Abstract
It is aimed to compare the content of mother tongue curriculums at secondary school level in Alberta, Korea and Singapore. When we look at curriculum in general, it has been determined that the learning outcomes of 21st century skills in all of the programs are close to equitable distribution in grades. Among the 21st century skills, learning and innovation skills were found to be the skill most associated with learning outcomes for Korea Secondary School Mother Tongue Curriculum, Singapore Secondary School Mother Tongue Curriculum and Turkish Language Curriculum. It is understood that the skills based on learning to learn, problem solving, associating knowledge with previous learning, critical and creative thinking comes to the fore in the learning outcomes, and it is aimed to educate individuals in this direction. It is understood that the learning outcomes related to flexibility and adaptation skills under life and career skills are not included in all programs. In addition, the lack of learning outcomes in initiative and self-direction, productivity and accountability skills in Alberta Secondary School Mother Tongue Curriculum and Singapore Secondary School Mother Tongue Curriculum shows that life and career skills, which are among the 21st century skills, are neglected in these programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".