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Record W4399010889 · doi:10.5430/jct.v13n2p374

A Survey of Issues of Activity-Based Teaching in The Context of Teaching Chinese as a Foreign Language in Beijing

2024· article· en· W4399010889 on OpenAlexvenueno aff
Lifei Liu, Mohd Ridhuan Mohd Jamil, Nadzimah Idris, Yulu Jin, Mohd Muslim Bin Md Zalli, Nurulrabihah Binti Mat Noh, AbdulTalib bin Mohamed Hashim

Bibliographic record

VenueJournal of Curriculum and Teaching · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsBeijingContext (archaeology)Chinese as a foreign languageForeign language teachingForeign languageLanguage educationChinese languageMathematics educationPsychologyPedagogyComputer scienceChinaLinguisticsPolitical scienceHistoryPhilosophy

Abstract

fetched live from OpenAlex

This study aims to explore the issues associated with activity-based teaching in the context of teaching Chinese as a foreign language (TCFL) in Beijing. This study adopts a descriptive quantitative survey method. To identify the issues encountered by teachers in activity-based teaching, we administered a questionnaire to collect data. The survey includes three aspects: activity design, activity implementation, and activity evaluation. The sample for this study comprises 234 TCFL teachers in Beijing. The results indicate that TCFL teachers are confronted with issues in the areas of activity design, implementation, and evaluation when conducting activity-based teaching.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.316
Teacher spread0.295 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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