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Application of Schema Theory in Teaching College English Reading

2010· article· en· W1705548635 on OpenAlexvenueno aff
Yuhui Liu

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Computational Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSchema (genetic algorithms)Reading comprehensionComprehensionHumanitiesComputer sciencePhilosophyLinguisticsReading (process)Information retrieval

Abstract

fetched live from OpenAlex

The paper first introduces three models of reading comprehension theory: Bottom-up Model, Top-down Model and Interactive Model, and then discusses in detail the schema theory based on interactive model. Three types of schema theory ——language schema, content schema and form schema are introduced and their different functions in teaching college English reading are discussed with sufficient teaching practice. Key words: reading comprehension; schema theory; reading ability Resume: Tout d’abord, l'article presente trois premiers modeles de la theorie de comprehension en lecture: modele bas-haut, modele haut-bas et modele interactif, et ensuite il discute en detail la theorie des schemas basee sur le modele interactif.Trois types de theorie des schemas - schema de la langue, schema du contenu et schema de la forme sont introduites et leurs fonctions differentes dans l'enseignement de la lecture en anglais dans les colleges sont discutees avec une pratique pedagogique suffisante. Mots-Cles: comprehension en lecture, theorie des schemas, capacite de lecture

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)0.006
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.275
Teacher spread0.269 · 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

Citations39
Published2010
Admission routes1
Has abstractyes

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