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Record W4312185271 · doi:10.5430/jct.v11n9p1

A New Model of Knowledge Acquisition: Distance Education

2022· article· en· W4312185271 on OpenAlexvenueno aff
I.V. Borak, Yaroslav Shcherbakov, Tetiana Kolomiiets, Serhii Khrapatyi, Svitlana Malazoniia

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationPaceProcess (computing)Computer scienceThe InternetKnowledge managementAsynchronous learningQuality (philosophy)Asynchronous communicationMathematics educationSynchronous learningWorld Wide WebTeaching methodCooperative learningPsychologyTelecommunications

Abstract

fetched live from OpenAlex

Taking into consideration the growing pace of globalization and the development of the technological sphere, it is significant to pay particular attention and use the ability of modern information technologies in order to significantly improve the quality and efficiency of education. Currently, the concept of distance education is gradually developing and spreading at all levels of the educational system. It involves the application of various technological achievements, such as: systems of synchronous, asynchronous, mixed acquisition of knowledge, the use of Internet platforms, multimedia solutions and the latest systems for organizing the educational process. Distance learning is the most flexible and affordable type of organization of classes. In addition, this type of learning meets the requirements of the new educational paradigm of the information society, namely: the support of open, person-oriented and continuous training of a person throughout his life; implementation of mass education of all strata of the population, regardless of their place of residence; diversifying and improving the process of acquiring knowledge by meeting the diverse needs of students from different social and demographic groups. The purpose of the article is to clarify the features of the use of distance education tools as a modern learning paradigm, their advantages and disadvantages, to study individual practical aspects of the use of distance education and to highlight the most promising directions for the development of this type of organization of the educational process. Methodology. In the course of writing the academic paper, an analytical method of working with materials was used on applying distance form of learning, as well as a questionnaire survey for practical clarification of certain aspects of distance learning from the viewpoint of its participants. Results. The viewpoint of scientists regarding the features of distance form of learning, its components, advantages and disadvantages was studied, as well as the standpoint of the participants of the educational process regarding the features of using distance education was clarified.

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)0.006
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 score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.006
Scholarly communication0.0060.012
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0140.003

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 head0.007
GPT teacher head0.223
Teacher spread0.215 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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