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Record W4286809449 · doi:10.1080/0969594x.2022.2103516

The Education and Assessment System in Lithuania

2022· article· en· W4286809449 on OpenAlexaff
Irena Raudienė, Lina Kaminskienè, Liying Cheng

Bibliographic record

VenueAssessment in Education Principles Policy and Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsQueen's University
Fundersnot available
KeywordsDeclarationAccountabilityPolitical scienceCurriculumBologna declarationPoliticsTest (biology)Public administrationIndependence (probability theory)Function (biology)European unionEducational assessmentPedagogyPublic relationsHigher educationSociologyBusinessLaw

Abstract

fetched live from OpenAlex

This article presents a historical and contemporary account of Lithuania’s national public education assessment system and its transformation since the country’s declaration of independence from the Soviet Union in 1990. We explore how the external examination system has developed in relation to ongoing curriculum reforms over the last 30 years, and how external examinations and standardised testing have taken priority over classroom assessment throughout this period. What becomes clear is that certain political decisions, guided by increased accountability demands, have significantly impacted classroom assessment practices, school cultures, and the mindsets of stakeholders about the role and function of assessment in Lithuania. Finally, we deploy our national and international expertise to recommend some changes to the current education system to make assessment an effective tool to improve student learning.

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)0.003
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: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0040.006
Scholarly communication0.0080.002
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.472
Teacher spread0.428 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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