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Record W6889633344 · doi:10.25946/21454098

Examination of low scoring nine year old respondents in the IEA reading literacy study from English speaking countries

2022· dissertation· en· W6889633344 on OpenAlexaboutno aff

Bibliographic record

VenueCentral Queensland University · 2022
Typedissertation
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)LiteracySample (material)Rasch modelSet (abstract data type)Data collectionMultivariate analysisMandarin Chinese

Abstract

fetched live from OpenAlex

The reading literacy study, conducted in 1990/91 by the International Association for the Evaluation of Educational Achievement (IEA), measured the performance of 9 year old students from 27 countries across the world. Until now, no specific analyses of the low scoring students has been undertaken. The aim of this secondary analysis of lEA reading literacy data was to examine the following question: What factors operate to influence the identification of low scores in reading literacy within and between identifiable cultural categories? Low scoring students were included when their scores fell below 100 rasch points (approximately 2.5 years) below their respective country mean. English speaking countries included in the analysis, all of which have historical ties to England, were Canada, New Zealand, Trinidad and Tobago, and the United States. Low scoring sample sizes exceeded 12% of their respective total sample. Typical differences featured when the background qualities of students (i.e. sex, language background, wealth) in the low scoring and respective country samples were compared. To examine the reading factors influencing low scores, the models of reading proposed by the lEA were tested across and within low scoring country and international data sets. Through conducting principal components analyses (PCA), it was found that the text and skills based models proposed by the lEA were not supported. New models of reading for each data set were devised and saved for further multivariate analyses. The factors of the newly theorized reading literacy constructs are concerning with poor fitting data, though similar patterns are found across the data sets. These results indicate that the variables in the reading test examined other skills, knowledge and experiences. Procedures of MANCOVA or MANOVA were applied to each data set to facilitate identification of significant personal background factors (independent variables) on the saved component scores (dependent variables). The reading behaviour constructs (Reading in Class, Voluntary Reading, Home Literacy Interaction) devised by the lEA were included as covariates following respecification using PCA where appropriate. A socio-economic construct was devised for each country using PCA and was included as another covariate. Canada was the only country to have no significant covariates, and so, a straight MANOVA was applied. Socioeconomic status predicted student performance in all countries except Canada. Home Literacy Interaction predicted performance on one component in the United States and Internationally. Low scoring boys obtained higher scores than the girls on items with a mathematical component, and girls tended to obtain higher scores when information was presented in a narrative or literal form. Where significant differences feature, native English speaking students consistently out perform non-native speakers. Questions are raised about traditional cognitive views of reading comprehension and standardized testing. Evidence accumulated throughout the thesis lends credence for explanations of reading literacy favouring sociocultural viewpoints.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.269
Teacher spread0.258 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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