MétaCan
Menu
Back to cohort
Record W4246957039 · doi:10.1177/016146811511700107

Cautions about Inferences from International Assessments: The Case of PISA 2009

2015· article· en· W4246957039 on OpenAlexaffabout
Kadriye Ercikan, Wolff‐Michael Roth, Mustafa Asıl

Bibliographic record

VenueTeachers College Record The Voice of Scholarship in Education · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Graduation (instrument)Higher educationRanking (information retrieval)JurisdictionInternational comparisonsReading (process)LiteracyScale (ratio)International educationPolitical scienceEconomic growthPublic relationsGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

Background/Context Two key uses of international assessments of achievement have been (a) comparing country performances for identifying the countries with the best education systems and (b) generating insights about effective policy and practice strategies that are associated with higher learning outcomes. Do country rankings really reflect the quality of education in different countries? What are the fallacies of simply looking to higher performing countries to identify strategies for improving learning in our own countries? Purpose In this article we caution against (a) using country rankings as indicators of better education and (b) using correlates of higher performance in high ranking countries as a way of identifying strategies for improving education in our home countries. We elaborate on these cautions by discussing methodological limitations and by comparing five countries that scored very differently on the reading literacy scale of the 2009 PISA assessment. Population We use PISA 2009 reading assessment for five countries/jurisdictions as examples to elaborate on the problems with interpretation of international assessments: Canada, Shanghai-China, Germany, Turkey, and the US, i.e., countries from three continents that span the spectrum of high, average, and low ranking countries and jurisdictions. Research Design Using the five jurisdiction data in an exemplary fashion, our analyses focus on the interpretation of country rankings and correlates of reading performance within countries. We first examine the profiles of these jurisdictions with respect to high school graduation rates, school climate, student attitudes and disciplinary climate and how these variables are related to reading performance rankings. We then examine the extent to which two predictors of reading performance, reading enjoyment and out of school enrichment activities, may be responsible for higher performance levels. Conclusions This article highlights the importance of establishing comparability of test scores and data across jurisdictions as the first step in making international comparisons based on international assessments such as PISA. When it comes to interpreting jurisdiction rankings in international assessments, researchers need to be aware that there is a variegated and complex picture of the relations between reading achievement ranking and rankings on a number of factors that one might think to be related individually or in combination to quality of education. This makes it highly questionable to use reading score rankings as a criterion for adopting educational policies and practices of other jurisdictions. Furthermore, reading scores vary greatly for different student sub-populations within a jurisdiction – e.g., gender, language, and cultural groups – that are all part of the same education system in a given jurisdiction. Identifying effective strategies for improving education using correlates of achievement in high performing countries should be also done with caution. Our analyses present evidence that two factors, reading enjoyment and out of school enrichment activities, cannot be considered solely responsible for higher performance levels. The analyses suggests that the PISA 2009 results are variegated with regards to attitudes towards reading and out-of-school learning experience, rather than exhibiting clear differences that might explain the different performances among the five jurisdictions.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.201
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.162
GPT teacher head0.462
Teacher spread0.300 · 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.

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

Citations14
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueTeachers College Record The Voice of Scholarship in EducationSame topicEducational Assessment and ImprovementFrench-language works237,207