Exploring human-computer interaction in higher education: Associations between blended learning, mobile phone addiction, and problematic internet use.

This study explores the interplay between blended learning experiences, mobile phone addiction, and problematic internet use among students, while also investigating the relationships between these variables and a combination of sociodemographic and behavioral factors. Using inferential statistics a...

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Detalles Bibliográficos
Publicado en:Journal of Human Behavior in the Social Environment pp. 1 - 15
Autores principales: Kaya Keles, Mumine, Ozer, Omer
Formato: Journal Article
Publicado: Taylor & Francis Ltd Sep2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2026
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      pub: Taylor & Francis Ltd
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        10.1080/10911359.2026.2729449
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        atl: Exploring human-computer interaction in higher education: Associations between blended learning, mobile phone addiction, and problematic internet use.
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          Kaya Keles, Mumine
          Ozer, Omer
        affil: Adana Alparslan Turkes Science and Technology University
      sug:
      ab: This study explores the interplay between blended learning experiences, mobile phone addiction, and problematic internet use among students, while also investigating the relationships between these variables and a combination of sociodemographic and behavioral factors. Using inferential statistics and association rule mining, the research examines data from undergraduate students across 17 different programs at a state university in Turkey. The sample consisted of 763 full-time undergraduate students. The results indicate that students with higher mobile phone addiction scores tend to exhibit higher levels of problematic internet use. Additionally, being female, single, and enrolled in a non-engineering discipline are associated with heightened vulnerability to mobile phone addiction. In addition, being single and engaging in extended daily mobile phone use emerged as significant risk factors for problematic internet behaviors. Association rule mining largely corroborated these patterns, as the rules with the highest confidence values suggested that potentially problematic internet users share distinctive traits.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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