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Record W4386862464 · doi:10.52209/1609-1825_2023_2_258

Challenges of Online Learning in New Strategic Trends in the Educational Space

2023· article· en· W4386862464 on OpenAlexaff
Ludmila KUKALO, G.B. Kholodova, Bakhtyar TANAGUZOV, Gulmira SHAYAKHMETOVA

Bibliographic record

VenueTrudy Universiteta · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsArcelorMittal (Canada)
Fundersnot available
KeywordsAxiologyContext (archaeology)Space (punctuation)Process (computing)Synergetics (Haken)Modernization theoryKnowledge managementStrategic planningMathematics educationSociologyManagement scienceComputer sciencePsychologyPolitical scienceEngineeringBusinessPhysicsMarketing

Abstract

fetched live from OpenAlex

An innovative approach to the study of strategic dynamics is presented, pedagogical conditions are studied that optimize the educational process, and shows the heterogeneity of didactic and educational strategies. In the context of the purpose and objectives of the study, the issues with the implementation of innovative activities in line with new strategic trends are considered. The acmeological-synergetic approach is used as an internal source of the axiological core formation, taking into account psychological and didactic barriers. Characteristic strategies are singled out from the standpoint of the teaching staff and students. In connection with the strategic trends in the online education modernization in the study of mathematics and physics, psychological and pedagogical components are highlighted in the context of acmeology, synergetics, axiology

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0110.010
Open science0.0010.005
Research integrity0.0020.002
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 head0.114
GPT teacher head0.324
Teacher spread0.209 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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