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Record W3081645297 · doi:10.5430/wje.v10n4p139

The Relationship between High School Students' Internet Addiction, Social Media Disorder, and Smartphone Addiction

2020· article· en· W3081645297 on OpenAlexvenueno aff
Mehmet Ramazanoğlu

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionPsychologyThe InternetSocial mediaDescriptive statisticsAddictive behaviorSmartphone addictionApplied psychologyClinical psychologyMedical educationPsychiatryWorld Wide WebMedicineComputer science

Abstract

fetched live from OpenAlex

The purpose of this study is to examine the relationships between high school students' internet addiction, social media usage disorder, and smartphone addiction. The descriptive relational scanning model, one of the quantitative research methods, was used to determine this relationship. The research was carried out with 215 students who continue their education in the field of information technologies of 4 state vocational high schools in the province of Siirt in the 2018-2019 academic year. In this research, internet addiction, social media usage disorder, and smartphone addiction scales were used as data collection tools. The research data were analysed with the SPSS statistical calculation program. As a result of the research, it was observed that high school students' internet addiction, social media usage disorder, and smartphone addiction are moderate and similar in terms of gender and class levels. In the study, a positive relationship was found between high school students' internet addiction, social media usage disorder, and smartphone addiction. It was also found that all three problems were the predictors of each other. The findings were discussed with the literature along with some recommendations.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.065
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.335
Teacher spread0.305 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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