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Record W2090076291 · doi:10.5539/elt.v2n3p194

An Experimental Study of the Effects of Listening on Speaking for College Students

2009· article· en· W2090076291 on OpenAlexvenueno aff
Zhang Yan

Bibliographic record

VenueEnglish Language Teaching · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningPsychologyClass (philosophy)Informational listeningCollege EnglishMathematics educationForeign languagePedagogyLinguisticsListening comprehensionCommunicationComputer science

Abstract

fetched live from OpenAlex

As China enters WTO, more college graduates with higher oral English proficiency are required. However, we learned that even students in some distinguished universities are lack of this ability. Based on her teaching experiences and the theory proposed by Krashen and some other well-know foreign languages teaching researchers, the author of this thesis formulated two hypotheses: 1) Students’ listening ability and their oral English production ability are correlated. 2) Teachers who bring listening and audio-visual materials into oral English class are likely to have better teaching results.Krashen’s Comprehensive Input Hypothesis is the theoretical foundation of the author’s research. The author studies the nature of listening and speaking, by doing so she points out the effects of listening on improving students’ oral English from two broad aspects.This thesis aims at making a quantitative analysis on the effects of listening on speaking for college students. With the help of SPSS 11.5 software, a quantitative computerized analysis on this research hypothesis is made. Moreover, a quantitative analysis on correlation between listening and speaking is also made.The result shows that listening and speaking ability are closely related, and listening does have positive effects on improving college students’ oral English.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.448

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 communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.299
Teacher spread0.288 · 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 designQualitative
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

Citations12
Published2009
Admission routes1
Has abstractyes

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