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Record W2939024862

프로젝트 기반의 한국어 교육 -북트레일러 제작 수업 및 학습자 반응 연구

2018· article· ko· W2939024862 on OpenAlexaboutno aff
최유정, 류나영

Bibliographic record

Venue외국어로서의 한국어교육 · 2018
Typearticle
Languageko
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceVocabularyClass (philosophy)CurriculumContext (archaeology)GrammarThe InternetMultimediaReading (process)Mathematics educationPedagogyWorld Wide WebPsychologyLinguisticsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The technological advances and ubiquitous use of Internet-access devices in recent years are changing the trajectory of language learning. Combining technology and project-based learning as a teaching method can help students gain knowledge and develop skills that help them solve problems and become responsible and autonomous in their learning. The development and sharing of project-based activities for classroom use is now becoming an important issue. In response to the need, this paper introduces the phases of planning and implementing a book trailer activity as an instruction model as part of the curriculum for use in an advanced Korean language class. A book trailer is a video that introduces a book and used in the classroom can help students exercise language, interaction, and complex thinking skills. Through the project learners highlight the story using their imaginations in a persuasive manner. The paper also discusses the results of implementation of the activity with learner responses on the benefits of the project. The results of the learners'' responses suggest that after this activity they better understood the content of the books, felt more confident in reading Korean literature, and were able to understand new vocabulary and grammar in the context. (University of Toronto)

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.019

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 head0.041
GPT teacher head0.342
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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