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Record W3097885237 · doi:10.5430/ijhe.v9n8p84

Pedagogical Competence of the High School Teacher

2020· article· en· W3097885237 on OpenAlexvenueno aff
Anastasia V. Fakhrutdinova, M Ziganshina, Veronika Alexandrovna Mendelson, Lyubov Chumarova

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
FundersKazan Federal University
KeywordsCompetence (human resources)PedagogyMathematics educationPsychologyProfessional developmentCompetence-based managementSocial psychologyManagement

Abstract

fetched live from OpenAlex

The need conditions the relevance of the article for the formation of higher education teachers’ pedagogical competence as the optimal system and purposeful work in this direction has not yet been formed. The goal of the article lies in the analysis of the study of the theoretical aspects of higher education teachers’ pedagogical competence and identifying the main types of professional competence. The leading approach to the study of this problem for the authors was their understanding of the concept of "competence", the distinction between the concepts of “qualification” and “competence”, as well as an understanding of the essence of higher school teacher’s pedagogical competence. The article presents a scheme of competencies of a higher school teacher, a distinction between key, subject and professional competencies. Taking into account the results of this study, it is possible to identify several scientific problems and promising areas that require further consideration: in particular, the development of new training programs for teachers of higher education institutions.

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)0.009
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.079
GPT teacher head0.367
Teacher spread0.288 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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