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Effective Teaching Strategies for Chinese International Students at a Canadian University

2022· book-chapter· en· W4229378976 on OpenAlexaffabout
Xiangying Huo, Elaine Khoo

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2022
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsTransformative learningPsychologyReading (process)PedagogyAgency (philosophy)PerceptionMathematics educationMedical educationSociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Challenges that Chinese international students with low academic English proficiency encounter in academic reading and writing were exacerbated by remote learning during the COVID-19 pandemic. Qualitative and quantitative data were collected and analyzed from an online reading-writing support program at a Canadian university to examine Chinese students' challenges, perceptions of learning, as well as the impact of culturally responsive pedagogy (CRP) used in the program. The study indicates the benefits of CRP in improving language proficiency and critical thinking, facilitating student experience and satisfaction, constructing identities, promoting learner agency, and enhancing transformative inclusivity. The study also provides insights into effective online teaching pedagogies to help instructors better support low-proficiency international students in coping with academic challenges. In addition, the study suggests teaching practices for empowering international students studying remotely from various global locations, with potential applicability to different teaching contexts.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.711
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.257
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2022
Admission routes2
Has abstractyes

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