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Record W2982703098 · doi:10.5430/ijhe.v8n7p79

Teacher's Readiness to Work under the Conditions of Educational Space Digitalization

2019· article· en· W2982703098 on OpenAlexvenueno aff
Rina Samatovna Kamahina, T.V. Yakovenko, Evgenia Vladimirovna Daibova

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
FundersKazan Federal University
KeywordsDigital transformationProcess (computing)The InternetComputer scienceSpace (punctuation)Resource (disambiguation)Knowledge managementEducational resourcesTransformation (genetics)MultimediaWorld Wide WebPedagogySociology

Abstract

fetched live from OpenAlex

The information environment of the Internet, turns into the powerful educational tool. This article explores the issues of digital transformation of an educational space. These are the results of the monitoring study on the problems of the teacher's readiness to effectively use digital tools for the organization of the educational process: electronic forms of textbooks, educational applications, online services and educational platforms. The advantages and disadvantages of digital transformation are further explored. The binary learning effect of digital educational tools is acknowledged, as the Educational nature is not only the content of the resource, but also the process of working with it, which contributes to the development of not only the student, but also the professional skills of the teacher. The rapid development of digital technologies and methods of network integration, allow for the implementation of advanced learning technologies that take into account the processes of human self-organization and social communication in the conditions of a digital transformation.

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)0.007
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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 integrity0.0010.001
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 head0.019
GPT teacher head0.336
Teacher spread0.317 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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