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Reading comprehension in university students: relevance of PASS theory of intelligence

2012· article· en· W2153845225 on OpenAlexaff
George K. Georgiou, J. P. Das

Bibliographic record

VenueJournal of Research in Reading · 2012
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluencyReading comprehensionPsychologyReading (process)ComprehensionCognitionCognitive psychologyRelevance (law)LinguisticsMathematics education

Abstract

fetched live from OpenAlex

We examined how Planning, Attention, Simultaneous and Successive (PASS) processes predict reading comprehension in a sample of university students (Study 1) and what PASS processes distinguish adults with and without reading difficulties (Study 2). In Study 1, 128 university students were tested on Das‐Naglieri Cognitive Assessment System, reading fluency and reading comprehension. The results of path analysis indicated that successive processing predicted reading comprehension only through the effects of text‐ and word‐reading fluency, whereas simultaneous processing predicted reading comprehension both directly and through the effects of text‐reading fluency. In Study 2, university students with (n = 20) and without (n = 23) reading difficulties were assessed on the same measures as in Study 1. The results of group comparisons indicated that the university students with reading difficulties were experiencing cognitive weaknesses primarily in successive processing. The implications of these findings for PASS theory and comprehension are discussed.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.117
GPT teacher head0.449
Teacher spread0.332 · 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

Citations25
Published2012
Admission routes1
Has abstractyes

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