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Record W4380520751 · doi:10.6000/1929-4409.2020.09.274

Adaptation of Students Depending on the Type of Temperament to Educational Activities in Higher School in the Conditions of Online Learning

2022· article· en· W4380520751 on OpenAlexvenueno aff
Julia V. Kashina, Iuliya V. Gluzman, Nina A. Oparina, Galina Ivanovna Gribkova, О. Б. Ершова, Sofiya Shavkatovna Umerkaeva

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsTemperamentAdaptation (eye)Context (archaeology)PsychologyDevelopmental psychologyMathematics educationPersonalitySocial psychologyBiology

Abstract

fetched live from OpenAlex

The article examines the influence of the type of temperament on the adaptation of students to educational activities at the university in the context of online learning. The article's main aim is to study the regulatory-adaptive status of students depending on the classical and mixed types of temperament. To do so, we analyze and investigate a number of sources on this issue. For the successful adaptation of students to educational activities in the context of online learning, higher school teachers should determine what properties and characteristics of the nervous system their students have. The authors conclude that the regulatory-adaptive abilities of students to the educational process depend on the type of temperament. They are the highest among phlegmatic/sanguine students and the lowest among melancholic students.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.172
GPT teacher head0.450
Teacher spread0.278 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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