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Record W3084045148 · doi:10.3390/su12187359

An Ecological Perspective on University Students’ Sustainable Language Learning during the Transition from High School to University in China

2020· article· en· W3084045148 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsYorkville University
Fundersnot available
KeywordsCurriculumThematic analysisMathematics educationPerspective (graphical)ChinaPromotion (chess)Sustainable developmentPedagogySociologyPsychologyEcologyGeographyQualitative researchPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

Transitioning from high school to university presents a significant challenge for many students on multiple fronts, including language learning. This mixed-method study draws on an ecological perspective to investigate students’ English learning experiences during the transition from high school to university in China, focusing on teaching content, teaching approach, assessment and feedback, and self-regulated learning. Data is collected from six universities at three different academic levels in China, and analyzed using both statistical and thematic analysis. The research finds that there are differences between high school and university English language education in the above-mentioned four areas, and students’ ecopotentials are of critical importance for their adaptation to university English learning. These findings suggest the necessity of the continuity of teaching content, the promotion of individualized curricula, and the cultivation of self-regulated learning capacities to support students’ sustainable English learning during the transition from high school to university.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.122
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.322
Teacher spread0.313 · 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