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Record W4392741646 · doi:10.23977/aetp.2024.080203

Study on the Effectiveness of Infusing Ideological Education into Extracurricular Activities in Foreign Language Teaching

2024· article· en· W4392741646 on OpenAlexvenueno aff
Tang Guifen

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPedagogyMathematics educationForeign languagePsychologySociologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This article aims to examine the effectiveness of infusing ideological education into extracurricular activities in foreign language teaching. The activity "International Communication Short Video Competition: Telling Stories about Chinese People" was taken as an illustrative example to interpret students' language performance from the perspective of discourse analysis. High words frequency processed by AntConc3.4.2 reflected the ideological elements in the video scripts, including patriotism, dedication, hard work, unity and friendship. A questionnaire was also administered to 539 students to find out their attitude to these activities. The results showed that students benefited a lot from participating in a wealth of extracurricular activities in the aspects of knowledge acquisition, philosophy of life, and professional skills. Since various extracurricular activities play an important role in shaping core values and morality standard, the article provides teachers with insight about how to integrate ideological education into practical tasks in curriculum-based ideological education.

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)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.371
Teacher spread0.361 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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