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Record W4407667251 · doi:10.5539/elt.v18n3p31

The Survey of PAD Teaching Mode on College English Reading Teaching Performance

2025· article· en· W4407667251 on OpenAlexvenueno aff
Jianfeng Zhang

Bibliographic record

VenueEnglish Language Teaching · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyReading (process)Mathematics educationCollege EnglishMode (computer interface)Teaching methodPedagogySurvey researchLinguisticsApplied psychologyComputer science

Abstract

fetched live from OpenAlex

The presentation-assimilation-discussion (PAD) teaching mode combines the advantages of “lecture classroom” and “discussion classroom,” adopts the separation of presentation and discussion, and focuses on the formative evaluation system, which can improve students’ independent inquiry ability and thus obtain effective output. Based on an empirical study of the PAD teaching mode and a statistical analysis of teaching performance on college English reading, PAD can increase teaching performance significantly. Students are satisfied with the whole teaching process. Although traditional teaching methods can increase performance as well, a long cycle of instruction is needed. Moreover, according to a questionnaire analysis, students have negative attitudes toward traditional teaching. Therefore, the PAD teaching mode should become the dominant mode of college English reading teaching in China.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.275
Teacher spread0.267 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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