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Record W7027151388

The case study technologies as the means of competency building approach realization in higher education of Russia

2014· other· en· W7027151388 on OpenAlexaboutno aff

Bibliographic record

Venuezvestiya of the National Academy of Sciences of Belarus (National Academy of Sciences of Belarus) · 2014
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationProcess (computing)PersonalityRealization (probability)Field (mathematics)PhilologyInternationalizationEngineering education
DOInot available

Abstract

fetched live from OpenAlex

© Canadian Center of Science and Education. The problem of increasing education efficiency in Russian high school under the condition of well-organized educational process with using “Case-study” technology is considered in the article. The article is devoted to the methodology of case study, and especially its implementation in Kazan Federal University, the Institute of Philology and Intercultural Communication. The experiment was held in two groups (experimental and control) diagnosing communicative, social, occupational and educational cognitive motives and motives of self-actualization, avoiding failure and prestige. The study was conducted in such disciplines as “English Language”, “Training and education in the field of foreign languages”. To measure the number of observed qualities of student’s personality the diagnostic technique level of formation of students learning motivation was selected. The resulting figures revealed a positive trend from the use of case technology in the classroom with 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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.067
GPT teacher head0.356
Teacher spread0.289 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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