MétaCan
Menu
Back to cohort
Record W3009470638 · doi:10.5430/ijhe.v8n8p16

The Practice of the Training on Stress Tolerance Increase of Ukrainian Students in the Sphere of Legal Education

2019· article· en· W3009470638 on OpenAlexvenueno aff
Т. А. Латковська, Mikhail Sidor, Tetiana Goloyadova, Andrey Kalimbet

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianTest (biology)CurriculumStress (linguistics)PsychologyWilcoxon signed-rank testValue (mathematics)Stress testMathematics educationPedagogyMedical educationMedicineComputer science

Abstract

fetched live from OpenAlex

The new national curriculum implementation in the sphere of higher education in Ukraine raises the need for using tests as the means of students’ knowledge assessment. The authors have identified the connection both between the level of stress tolerance and between the level of test passing by means of using the correlated analysis, the Kolmogorov-Smirnov test and the T-Wilcoxon test. The level of stress tolerance has a serious impact upon the students’ test result. The students show a higher efficiency level while passing the test after visiting the training on stress tolerance increase. It has been identified that the opportunity value as to how to focus the students’ attention on the test is an important harbinger of the thesis that the education of Ukrainian students should be followed by the assessment of their psychological state. The authors have identified that the students’ psychological architype has an impact upon the education efficiency giving both similar and best results according to the results from other cultures.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.377
Teacher spread0.356 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicEducational Methods and Teacher DevelopmentFrench-language works237,207