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Record W3130072700 · doi:10.5430/wje.v11n1p1

How Do Kastamonu University Education Faculty Fine Arts Education Department Music Education Department Students Use Their Smartphones?

2021· article· en· W3130072700 on OpenAlexvenueno aff
Mustafa Kabataş

Bibliographic record

VenueWorld Journal of Education · 2021
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
FundersKastamonu Üniversitesi
KeywordsPsychologyMusic educationMedical educationDescriptive researchThe artsWork (physics)PedagogySociologyMedicineEngineeringPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This work; Kastamonu University, Department of Fine Arts Education Faculty of Education was made to students in the Music Department in Turkey. The study is a descriptive field study and it was conducted with a questionnaire model. The study group of this study consists of all students of Kastamonu University Education Faculty Music Education Department. The aim of this study is to get an idea about the use of smart phones by music department students. In the study, a review was made of how music students use their smartphones. The questionnaire method was used to answer questions such as what kind of applications they use on smartphones and how much they benefit from the applications they use professionally. Research questions were asked which applications are the most popular for personal and school use, which applications are satisfied and which applications they are not satisfied with. The data obtained were presented in the form of a table and interpreted. It was concluded that the students used certain programs on their smartphones beneficial for their field education. The study is important because it contributes to researchers, field experts and similar studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.283
Teacher spread0.258 · 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 teacher head, not a consensus.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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