MétaCan
Menu
Back to cohort
Record W3136652779 · doi:10.5430/wjel.v11n1p34

A Study on the Problems of Junior English Teaching Effectiveness and Corresponding Strategies: Taking English Grammar Teaching as an Example

2021· article· en· W3136652779 on OpenAlexvenueno aff
Yang Tianfu

Bibliographic record

VenueWorld Journal of English Language · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPerspective (graphical)GrammarTeaching englishComputer scienceEnglish grammarTeaching methodProcess (computing)College EnglishTeaching and learning centerFocus (optics)PsychologyLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

Currently, English teaching focus has been gradually transferred from teacher-centeredness to learner-centeredness. However, some problems still exist in the process of English teaching so that English teachers are encouraged to seek strategies to solve them, which is expected to enhance the English classroom teaching effectiveness. It has been the vital goal for English classroom teaching in junior high school because through the unremitting improvement of it, English teachers will boost their teaching capabilities, optimize teaching methods and attain further professional development. Thus, this research mainly focuses on the problems and strategies of effectiveness of English teaching in junior high school from the perspective of English grammar teaching to attain further foreign teachers’ professional development.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.025
GPT teacher head0.316
Teacher spread0.291 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of English LanguageSame topicEducational Technology and AssessmentFrench-language works237,207