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Record W3184139257

역량기반 교육과정의 범교과적 접근 방식 탐색: 에스토니아, 핀란드, 호주, 캐나다를 중심으로

2021· article· ko· W3184139257 on OpenAlexaboutno aff
임유나

Bibliographic record

Venue미래교육연구 · 2021
Typearticle
Languageko
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumLinkage (software)PedagogyNational curriculumMathematics educationPsychologyMedical educationMedicine
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to analyze the cross-curricular approaches that appear in the competency-based curriculum and to derive implications for the future Korean national curriculum development. For this, literature research was conducted to analyze foreign national curriculum and related materials on Estonia, Finland, Australia, and BC Canada. As results, in terms of the status, it is reinforcing its status by revealing the cross-curricular competencies or themes in the curriculum structure or statement, and supporting implementation. In terms of elements and contents, cross-curricular competencies and themes are specified, and the linkage between competencies, cross-curricular themes, and subjects learning are improved. In the operation method, topic-oriented and integrated approaches, contextual learning in connection with life, and competency evaluation are emphasized. Based on main results, four implications were proposed in terms of the status, elements and contents, and the operation method of the cross-curricular approaches.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.028
GPT teacher head0.290
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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