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Record W4384210686 · doi:10.23977/aetp.2023.070607

Research on Cultivating Students' Speculative Ability in English Writing Classes from an Ecological Perspective

2023· article· en· W4384210686 on OpenAlexvenueno aff
Yan Yang

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmPerspective (graphical)CreativityClass (philosophy)Mathematics educationProcess (computing)PsychologyCritical thinkingCreative thinkingEnglish languagePedagogyComputer scienceArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

This paper discusses the cultivation of students' thinking ability in English writing class from the ecological perspective. According to the existing research and practical experience, the author believes that English writing class can cultivate students' thinking ability by creating ecological scenes, diversified teaching methods and encouraging students to reflect. In terms of specific implementation, teachers need to incorporate the ecological perspective into the teaching design and organically combine different teaching materials, activities and tools to form a comprehensive framework covering multiple fields and factors. At the same time, teachers should pay attention to guiding students to keep active thinking in the learning process, promote students to use English flexibly in various situations, and fully mobilize their enthusiasm and creativity. Through these efforts, English writing class can really become an effective way to cultivate students' thinking ability and language ability.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.488
Teacher spread0.440 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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