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Record W2142847746 · doi:10.5539/ells.v5n1p102

The Teaching Reform of Strategies and Skills in Perspective of English Reading: A Case Study of Chinese Mongolian Students

2015· article· en· W2142847746 on OpenAlexvenueno aff
Lili Zhao

Bibliographic record

VenueEnglish Language and Literature Studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)VocabularyCompetence (human resources)Perspective (graphical)Mathematics educationAffect (linguistics)Computer scienceContext (archaeology)Process (computing)PsychologyPedagogyLinguisticsArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

This thesis discusses some issues commonly observed in English reading. The purpose of the paper is to throw light on issues that arise from students’ reading obstacles and put forward some innovative and feasible teaching methodologies to improve the students’ reading abilities. A simple survey is conducted by the author, using six question items with each item representing an important issue about English reading in an attempt to explore the most common problems that Mongolian students may meet with during their reading process. These items/issues are as follows: (1) small vocabulary; (2) limited strategies and skills; (3) inadequate culture background knowledge; (4) unable to understand the context; (5) bad reading habits; and (6) lack of Language sense. The paper then discusses each issue one by one and makes some practical suggestions to help address these issues as a way to introduce some innovative teaching methods to give guidance to students. With due consideration of the analysis above, we may draw a conclusion that the teacher should take into account the comprehensive factors that affect English reading and strive for the inspiring teaching strategies to help improve students’ reading competence.

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)0.001
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 score0.034
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.012
GPT teacher head0.301
Teacher spread0.289 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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