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Record W3037485830 · doi:10.5430/wje.v10n3p113

Students’ Opinions about the Distance Education to Art and Design Courses in the Pandemic Process

2020· article· en· W3037485830 on OpenAlexvenueno aff
Sehran Dilmaç

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationContext (archaeology)Focus groupSocializationPsychologyVisual arts educationQualitative researchMathematics educationHigher educationPerceptionPedagogyMedical educationSociologySocial scienceThe artsSocial psychologyGeographyPolitical science

Abstract

fetched live from OpenAlex

This research was carried out to determine student views on distance education through art and design courses. The study group of the research consists of 45 undergraduate students studying at different faculties of İzmir Katip Çelebi University in Turkey during the spring semester of the 2019-2020 academic year. The focus of the article is to determine the effect of distance education, which is carried out suddenly, on students' perceptions of art lesson. A semi-structured interview technique was performed in this study which covered qualitative data collection techniques. Findings regarding the positive and negative aspects of distance education were obtained from students in the context of their impact on art classes. The results obtained in the research are as follows: The students who take art lessons via distance education are required distance education, also they do not experience any technical difficulties due to the availability of technology, they lost of motivation, they do not meet their socialization needs, and especially some techniques that require practice in art lessons.

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)0.004
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.106
GPT teacher head0.382
Teacher spread0.276 · 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

Citations73
Published2020
Admission routes1
Has abstractyes

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