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Record W4312167728 · doi:10.5430/jct.v11n9p98

Internal Quality Assurance of the Education Program at Higher Educational Institutions

2022· article· en· W4312167728 on OpenAlexvenueno aff
Halyna Yuzkiv, Valentyna Slipchuk, Nina Batechko, Mykola Mykhailichenko, Kateryna Yanchytska, Khrapatyi Serhii

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
Fundersnot available
KeywordsEducational programQuality (philosophy)CurriculumQuality assuranceProcess (computing)Higher educationEducational evaluationOrder (exchange)Strengths and weaknessesEducational researchEngineering ethicsMedical educationEngineering managementComputer sciencePedagogyPolitical scienceSociologyBusinessPsychologyEngineeringMedicineMarketing

Abstract

fetched live from OpenAlex

Ongoing efforts on internal quality assurance of the educational program at higher educational institutions should be based on comprehensiveness and constant innovative activity. The development of modern and effective ways and measures in order to ensure the quality of educational programs is relevant both for modern higher educational institutions in practice and in the theoretical and methodological plane. The purpose of the research lies in establishing the principles of ensuring the quality of the educational program, which should be applied to achieve high quality teaching following the educational program of higher educational institutions in Ukraine; determining the shortcomings and prospects for the development of educational programs (curricula) and their assessment by educators. In the course of the research, an interpretive qualitative study has been used; along with this, the experiment as the main method, the methods of description, questionnaire and observation have been also used in the academic paper. The research hypothesis lies in the fact that ensuring the quality of the educational program is based on a balanced complex of innovative theoretical principles and their practical implementation. The result of the research is the establishment of the fundamentals for ensuring the quality of educational programs of higher educational institutions, taking into account their innovative nature and the evaluation of the strengths and weaknesses of the curriculum by the participants of the educational process. In the prospect, the implementation of research programs for the further development and improvement of the quality and demand of educational programs of the HEI at the level of the world market of educational services is expected.

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)0.056
metaresearch head score (Gemma)0.083
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 score0.056
Threshold uncertainty score0.297

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.083
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0080.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.041
GPT teacher head0.377
Teacher spread0.336 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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