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Record W1770276977

A Study on Strategies-Based Reading Instruction at College Level

2012· article· en· W1770276977 on OpenAlexvenueno aff
Yao Fu

Bibliographic record

VenueStudies in literature and language · 2012
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Reading comprehensionMathematics educationReciprocal teachingTest (biology)Computer sciencePsychologyPerceptionExtensive readingForeign languagePedagogyLinguistics
DOInot available

Abstract

fetched live from OpenAlex

This thesis intends to change the traditional English foreign language reading teaching pattern which focuses mainly on structural analysis, a representative in many Chinese universities. It studies the practical value of strategies in reading interaction and tries to explore a more effective way of teaching reading to students at college level. The aim of the study is to find out (a) if a strategies-based teaching approach to teaching reading will improve reading comprehension. (b) if the reading strategies used interact with the level of English proficiency of the students. The study investigates, with a questionnaire at the beginning of the experiment, the perceptions of college students in China in reading English. The experiment was carried out during a course, which was conducted under a 16-week period semester. The statistical analysis of a survey test on the effect of a strategic reading instruction indicates that strategies-based reading instruction aids in reading comprehension. The questionnaire for the experiment group after the survey test proves an improvement in students’ reading attitude and proficiency. The result is of some help in choosing teaching materials and methods for the improvement and efficiency of teaching reading. Key words: Reading strategies; Reading comprehension; Language proficiency; Teaching Approach

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.059
GPT teacher head0.396
Teacher spread0.337 · 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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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