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Record W3047050695 · doi:10.5430/ijhe.v9n7p12

The Implementation of the Principles of Lifelong Learning as the Basis of Quality Specialize Education

2020· article· en· W3047050695 on OpenAlexvenueno aff
Oleksandr Broiakovskyi, Vita V. Ilchuk, Nataliia M. Mas, O.S. Kapinus, Anastasiia V. Okaievych

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor Market and Education
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningProcess (computing)Quality (philosophy)PopulationTRACE (psycholinguistics)ScarcityPsychologyDuration (music)Mathematics educationSample (material)PedagogySociologyComputer scienceEpistemologyDemography

Abstract

fetched live from OpenAlex

The article reflects the relevant factors of introducing specialized education. The first group of factors relates to the specifics of adolescent development. The second group of factors relates to the development of a network of educational institutions in the education system of modern Ukraine. Small towns and rural areas suffer from scarcity of gymnasiums, lyceums, schools with in-depth study of subjects. At the same time, major million cities also feel the need to increase their numbers. The third group of factors is determined by pan-European educational development objectives. The main attributes of lifelong learning that determine the transformations of specialized education are: formal, substantive and meaningful attributes. Let’s briefly consider each of these attributes. A formal attribute implies a temporary duration of the educational process and can be extended to the period of adult life (“lifelong learning”). A substantative attribute orients each pupil to the gradual enrichment of their creative potential. A meaningful attribute reflects the integrity of the lifelong learning cycle.The study of the effectiveness of the implementation of principles of lifelong learning as a basis for specialized education was based on a number of features. First of all, we are talking about the selected sample. After all, to trace the reasonability of applying the principles of continuing education in the process of professional formation it is necessary to diagnose individuals who have passed all the steps of this process. At the same time, the selected population of respondents was divided by gender into two groups. This made it possible to establish the diversity of views of men and women on the problem of continuing education.The purpose of this study is to provide a comprehensive review of the scientific literature on the specifics of the implementation of the lifelong learning principles and to determine whether professionals feel the need for additional 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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.045
GPT teacher head0.345
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

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