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Record W7116650886 · doi:10.5539/hes.v16n1p97

Study the Quality Assurance and Evaluation Systems of Higher Education from the Perspectives of Representatives from Three Nations: Hungary, Iran, and Vietnam

2025· article· W7116650886 on OpenAlexvenueno aff
Nguyen Duc Hanh, Nguyen Thi Ngoc Linh

Bibliographic record

VenueHigher Education Studies · 2025
Typearticle
Language
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
FundersĐại học Quốc gia Hà Nội
KeywordsQuality assuranceAccreditationHigher educationContext (archaeology)Quality (philosophy)Work (physics)Quality policyDeveloping country

Abstract

fetched live from OpenAlex

In the context of the rapid changes in higher education, the maintenance of high quality and standards in education has been a significant concern for higher education institutions and governments. Therefore, demand for quality assurance and evaluation has increased. The national assurance system has been established in many countries and is planned for application in others. This paper examines the quality and assurance activities in higher education across three developing countries: Hungary, Iran, and Vietnam. This work reveals that the quality of higher education is not only a national interest but also an international issue, shaped by political, social, historical, cultural, academic, and commercial developments associated with globalization. All countries aim to enhance the quality of higher education, promote industrial accountability, and foster transparency. It is better than meeting learner needs and the labor market. However, the quality assurance systems and accreditation processes of each country differ according to the stage of development.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designmedium
grokno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
opusno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.107
GPT teacher head0.469
Teacher spread0.362 · 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

Labeled directly by 3 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designOther design · Observational · Qualitative
Domainnot available
GenreEmpirical · Review

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
Published2025
Admission routes1
Has abstractyes

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