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Record W3197944153 · doi:10.5539/elt.v14n9p39

An Online Teaching Design of Oral English against COVID-19: An “Ideological-and-Political-Theories-Education-in-All-Courses” Perspective

2021· article· en· W3197944153 on OpenAlexvenueno aff
Jiejing Pan

Bibliographic record

VenueEnglish Language Teaching · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPolitical philosophyCurriculumPerspective (graphical)PoliticsSociologyPedagogyTeaching methodPsychologyMathematics educationPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

Under the outbreak of the Coronavirus Disease 2019 (COVID-19) and the philosophy of “Ideological and Political Theories Education in all Courses (IPTEC)” by China’s Ministry of Education, college curriculum reform has become a pressing issue in both form and content. Oral English course is characterized with flexible organization and a wide selection of teaching materials, thus closely related to the shaping of college students’ values. An online teaching mode of oral English featuring “DingTalk + WeChat Group + FiF” is proposed after a mining of “ideological and political elements”, with the sophomore oral English course of School of Foreign Studies, Guangzhou University of Chinese Medicine as a case. The highlights of this mode are as follows. First, all links of the teaching design are permeated with ideological and political elements, which realizes the blending of explicit and implicit educations. Second, it supports teacher-to-student and student-to-student voice interactions in a multi-party manner at any time. Third, a complex is created where one online classroom is systematically nested in another among the various platforms. Fourth, group and single games enrich the organization of the classroom. Fifth, it provides private and convenient classroom and homework management.

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)0.003
metaresearch head score (Gemma)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.409
Teacher spread0.361 · 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.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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