MétaCan
Menu
Back to cohort
Record W2567357141 · doi:10.5539/elt.v10n1p112

Changes in the Motivation of Chinese ESL Learners: A Qualitative Investigation

2016· article· en· W2567357141 on OpenAlexvenueno aff
Qi Li

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersJiangsu University
KeywordsPsychologyResidenceMathematics educationPeriod (music)Qualitative researchPedagogy

Abstract

fetched live from OpenAlex

This article reports on a case study that investigated changes in the motivation of Chinese ESL learners over a three month period of residence in an English-speaking environment. The participants consisted of 11 Chinese ESL learners, who were tertiary students and taking general or academic English courses in New Zealand. They took part in the present research shortly after their arrival in New Zealand. Data were collected over a period of three months using qualitative data collection instruments such as learner diaries and individual interviews. The results revealed some dynamic changes in the motivation of Chinese ESL learners over a three month period of residence in an English-speaking country. According to the similarities and differences in their motivation, they were categorized into five learner types. In general, the learners belonging to the first three types were able to maintain or increase their overall motivation. The learners in the last two types were not able to maintain their overall motivation: Their motivation decreased over the three months.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0080.005
Scholarly communication0.0020.002
Open science0.0010.003
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicEFL/ESL Teaching and LearningFrench-language works237,207