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Record W1519268564

A Glimpse of New Mode for English Listening & Speaking Teaching for College Art Students

2014· article· en· W1519268564 on OpenAlexvenueno aff
Lu Ji

Bibliographic record

VenueCross-cultural communication · 2014
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningCollege EnglishMode (computer interface)Mathematics educationPedagogyPsychologySociologyComputer scienceCommunication
DOInot available

Abstract

fetched live from OpenAlex

With the increasing integration of economies and cultures in recent years, different countries have enhanced exchanges and cooperation. English, as language extensively used for international exchanges, is playing a prominent role with each passing day. All colleges attach much importance to English learning of their students, especially for art students. If they hope to be able to go to the world stage in their future development, art students have to master English skillfully. So art colleges are required adopt new English listening and speaking teaching mode during teaching. They should try hard to train the students’ abilities in English listening and speaking so as to lay a good foundation for their future art career. This article mainly analyzes the current situation of English listening and speaking teaching for college art students and the reasons for employing the new mode for English listening and speaking teaching. In addition, it mentions the differences between the new model and the traditional teaching. The article also offers the methods in order that both teachers and students are appropriate to the new mode.

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 score0.709
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.412
Teacher spread0.369 · 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
Published2014
Admission routes1
Has abstractyes

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