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Record W3202661066 · doi:10.3968/12241

A study of English Writing Test in Senior High School Entrance Examination on English Writing Teaching

2021· article· en· W3202661066 on OpenAlexvenueno aff
Wenrong Fan

Bibliographic record

VenueStudies in literature and language · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationCompetence (human resources)Test (biology)Quality (philosophy)Reading (process)EnlightenmentCollege EnglishPsychologyPedagogyProcess (computing)Teaching methodComputer scienceLinguistics

Abstract

fetched live from OpenAlex

Writing plays a more and more important role in the English test of senior high school entrance examination, and teachers pay more and more attention to the teaching of writing. This paper attempts to start with the backwash effect of English writing test on English writing teaching, and further think about its enlightenment to English writing teaching, so as to give full play to the positive backwash effect of English writing test and minimize the negative backwash effect. Theoretical Significance: It provides a feasible teaching model and implementable design process for the current English writing teaching that pursues high scores. The integration of reading and writing based on the development of thinking quality is an important way to realize the cultivation goals mentioned in the English subject key competence, and it is a key direction to implement and deepen the concept of instrumental and humanistic courses. Through this teaching mode, students will be given a learning atmosphere of overall learning and positive thinking, and at the same time, the educational goal of developing quality education will continue to advance. Practical significance: It provides a new connection point for junior high school English teachers to teach in the reading-to-write method, and also provides a certain reference for the teaching design involved in each link part of teaching process. Through the research methods such as questionnaires, interviews and tests, we can compare the students’ classroom feedback and performance before and after, analyze and demonstrate their effectiveness in improving students’ writing ability and writing attitude. Then hope to provide a practical paths and teaching references for the future teaching.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.339
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.320
Teacher spread0.306 · 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.

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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