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Record W2982650524 · doi:10.5430/ijhe.v8n7p17

Pedagogical Model of Preservice Teachers Social Adaptation to the Future Professional Activity

2019· article· en· W2982650524 on OpenAlexvenueno aff
Evgeniya V. Gutman, Rasilya R. Nurmieva

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
FundersKazan Federal University
KeywordsRestructuringAdaptation (eye)CurriculumProfessional developmentProcess (computing)PsychologyPedagogyMathematics educationComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The article is dedicated to the students' social adjustment to acknowledged informative exercises problems. The study uses a modeling method to build preservice teachers social adaptation pedagogical model. The model comprises a list of operations contributory to successful students social evolution to instructional activities. Students' successful conversion to professional activity is permissible under the status of the university, specially organized work, which is focused on the lecturer teaching student composition of natures. The article details the university organizational and pedagogical conditions, which will help the cerebral and social restructuring of students' experience and will form an adequate position in the teaching staff. The following means are proposed which increase the social evolution level: the development of curricula which contribute to the professional social adaptation; the educational practice programs improvement; development of a system of integrated psychological and pedagogical support in the learning process; the organization of the psychological and pedagogical 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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.091
GPT teacher head0.418
Teacher spread0.327 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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