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Record W1766077709 · doi:10.5539/elt.v8n8p79

Pedagogical Significance of Morphological Awareness in Korean and English

2015· article· en· W1766077709 on OpenAlexvenueno aff
Young Ok Jong, Chae Kwan Jung

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyNounTask (project management)Test (biology)Korean languageVerbLinguisticsComprehensionPerspective (graphical)Artificial intelligence

Abstract

fetched live from OpenAlex

This study investigated whether Korean children understand the internal structure of compound words in Korean and English and whether there is a relationship between their performance in tasks that measure their understanding of the morphological structure of compounds in Korean and English. This study also examined the effects of gender, grade, and verbal ability on the performance of the Korean and English tasks. 106 primary school children completed a Korean and English compound task, which consisted of 32 compound test items in Korean and in English respectively. Each compound task included 16 comprehension test items and 16 production test items. Half of them were real words and the other half were pseudo words. The structures of the compounds were noun-noun or noun-verb. Korean task scores made a significant contribution to predicting English test scores after controlling for gender, grade, and verbal ability. It is concluded that L1 task performance can be a significant factor in L2 task performance, supporting evidence that L1 morphological awareness is transferable to L2 morphological processes. From a pedagogical perspective, research findings will be useful for teachers when designing compound tasks to develop Korean children’s morphological awareness for language and literacy development in English.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.058
GPT teacher head0.359
Teacher spread0.301 · 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 designObservational
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

Citations2
Published2015
Admission routes1
Has abstractyes

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