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Record W4387585618 · doi:10.23977/aetp.2023.071219

Research on Innovative Teaching of Chinese Characters in International Education Based on Traditional Chinese Culture

2023· article· en· W4387585618 on OpenAlexvenueno aff
Yafei Li

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPronunciationChinaChinese as a foreign languageChinese languageChinese cultureMathematics educationValue (mathematics)International languageMeaning (existential)Class (philosophy)PedagogyLanguage educationTeaching methodSociologyPsychologyLinguisticsComputer sciencePolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

The teaching of Chinese as a foreign language is booming both domestically and internationally, but many problems have gradually emerged in terms of teaching staff and talent development. Therefore, this article conducts innovative research on the teaching of Chinese characters in international education based on traditional Chinese culture. By making kites, international students can understand traditional Chinese culture in games, gain a deep understanding of Chinese culture, and better understand the subtleties of Chinese language. Kites have a history of over 2000 years in China, and teachers can explain the meaning of kites to international students during lectures. Teachers can play some recordings in class, with two words forming a phrase. Let these students listen first, then read two words to strengthen their hearing, practice their oral pronunciation, and enhance their memory. Traditional education in our country makes language teaching not flexible enough. Therefore, in international Chinese language teaching, teachers should analyze the content that international students are interested in, so that teaching can achieve value.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.478
Teacher spread0.423 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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