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Record W133508838 · doi:10.5281/zenodo.8003940

Assessing Adult Literacy: The Aim, Use and Benefits of Standardized Screening Tools

2009· article· en· W133508838 on OpenAlexaboutno aff
Lode Vermeersch, Joke Drijkoningen, Matthias Vienne, Anneloes Vandenbroucke

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracyStandardized testPsychologyComputer scienceMedicineMedical educationPedagogyMathematics education

Abstract

fetched live from OpenAlex

Large-scale surveys, such as the International Adult Literacy Survey (IALS) (OECD & Canada, 2000), provide interesting data on literacy and numeracy skills on a cross-country level. They attempt to answer policy-related questions like: how many adults have low first language literacy or are at risk of becoming low-literate and what are their characteristics? In these studies, groups of adults are commonly described as having either high or low literacy skills. But since reading or writing ability itself is a continuum, the question arises: what is the cut-off point? In other words: where does the "problem" of low literacy begin and when is educational or some other kind of intervention in a specific context necessary or desirable? When answering these questions and promoting adult literacy development, most educational sectors will make use of micro-level analysis to complement the macro-level data. In that case, tools that describe the learning needs and interests of individuals are necessary. The research we report on in this article examines the (practical) possibilities, difficulties and policy measures which underlie the use of such standardized literacy screening devices or basic skills audits among adults having Dutch as their mother tongue. Built upon a qualitative analysis of existing screening instruments in Belgium (Flemish Community) and the Netherlands, this study explores how screening procedures are adopted today in different sectors and in which way these procedures are able to identify the particularities of individual adults' literacy skills. By conducting in-depth interviews with experts (policy makers, academic experts, educational practitioners, low-literates) on the topic of (low) literacy, the advantages and disadvantages of the implementation of a single and uniform standardized screening tool for different educational sectors were explored. The results of this study (D'hertefelt et al., 2007) show that not all social domains are equally open to educational assessment using a standardized literacy test in an objective and accurate way. Moreover, the results show that literacy screening may lead to several negative effects. It is argued that in some contexts, those negative effects might overshadow the positive ones. Furthermore, none of the existing tools in Belgium and the Netherlands is able to screen all aspects of literacy in one short and practical way. From this we conclude that although there is a powerful internal logic in the use of one single screening instrument for assessment, the practical benefits of such a device can be questioned and so can the ethical ones. The use of several instruments aligned with the needs of specific target groups is therefore strongly recommended. The context of the screening procedure and the literacy context (such as a health care and workplace) should be incorporated in the instrument. Other results will be presented in this paper, such as the importance of oral feedback on the candidate results, the training of the assessors, the integration of the screening in normal educational procedures and the link between the assessment and the methods of training.

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)0.075
metaresearch head score (Gemma)0.109
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 score0.075
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.109
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.002
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
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 head0.114
GPT teacher head0.358
Teacher spread0.244 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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