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Teacher Online ELT Experiences in a Rural Primary School in China During the COVID-19 Pandemic

2022· book-chapter· en· W4206323538 on OpenAlexaff
Di Liang, Xingtan Cao

Bibliographic record

VenueAdvances in mobile and distance learning book series · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Online teachingThematic analysisNarrativePandemicPsychologyChinaPedagogyPerspective (graphical)Sociocultural perspectiveOnline learning2019-20 coronavirus outbreakSociocultural evolutionMedical educationMathematics educationSociologyQualitative researchMedicinePolitical scienceMultimediaComputer science

Abstract

fetched live from OpenAlex

This study adopts narrative inquiry to report five English teachers' online teaching experiences in a rural primary school in China during the COVID-19 pandemic. From a sociocultural theoretical perspective, this study shows how the participating teachers navigated the transition in instructional mode and developed familiarity with online teaching over time. Using thematic analysis, the findings of this study reveal that the teachers showed anxiety toward online teaching, that they formed a virtual community of practice, that they incorporated lived experiences into online teaching, and that they called for professional development centered on online teaching and spoke to the changing needs for English teaching in the post-COVID era. Implications are provided regarding how the teachers could transfer face-to-face ELT to an online setting and how they could be better supported professionally by the school and their colleagues.

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)0.001
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.299
Teacher spread0.289 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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