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<p>As mobile apps continue to reshape how languages are learned, educators and learners need clear evidence about what drives app choice and sustained use. Using a netnographic approach, we analyzed 20,000 English-language user reviews from five widely used language-learning applications (Babbel, Duolingo, Memrise, Mondly, and Quizlet). Reviews first filtered by an AI-assisted API and then thematically coded in ATLAS.ti. The analysis yielded 34 themes that cluster around practical selection drivers and day-to-day engagement. Users tended to choose and stay with apps that supported competence through clear progression and level scaffolding, immediate corrective feedback, pronunciation guidance, and meaningful practice; supported autonomy through flexible pacing, customizable paths, offline access, and transparent pricing; and supported relatedness through community challenges, peer interaction, and access to tutors. Additional selection factors included content breadth and quality, L1 support and translation options, spaced repetition, microlearning sessions, reminders, certification, accessibility, privacy controls, ad intrusiveness, technical stability, and responsive support. Demotivators included bugs and crashes, confusing navigation, limited speaking practice, aggressive monetization, and weak grammar explanations. Framed by Self-Determination Theory, the pattern is consistent: apps that nurture autonomy, competence, and relatedness receive higher ratings and stronger intentions to persist. We close with design implications for app teams and practical guidance for instructors who recommend apps.</p>

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Exploring learners' psychology and engagement in mobile language applications through self-determination theory

  • 期刊论文

  • 发表日期：

    2025年11月29日

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  • Saieed Moslemi Nezhad Arani,
  • Arzu Atasoy

摘要

As mobile apps continue to reshape how languages are learned, educators and learners need clear evidence about what drives app choice and sustained use. Using a netnographic approach, we analyzed 20,000 English-language user reviews from five widely used language-learning applications (Babbel, Duolingo, Memrise, Mondly, and Quizlet). Reviews first filtered by an AI-assisted API and then thematically coded in ATLAS.ti. The analysis yielded 34 themes that cluster around practical selection drivers and day-to-day engagement. Users tended to choose and stay with apps that supported competence through clear progression and level scaffolding, immediate corrective feedback, pronunciation guidance, and meaningful practice; supported autonomy through flexible pacing, customizable paths, offline access, and transparent pricing; and supported relatedness through community challenges, peer interaction, and access to tutors. Additional selection factors included content breadth and quality, L1 support and translation options, spaced repetition, microlearning sessions, reminders, certification, accessibility, privacy controls, ad intrusiveness, technical stability, and responsive support. Demotivators included bugs and crashes, confusing navigation, limited speaking practice, aggressive monetization, and weak grammar explanations. Framed by Self-Determination Theory, the pattern is consistent: apps that nurture autonomy, competence, and relatedness receive higher ratings and stronger intentions to persist. We close with design implications for app teams and practical guidance for instructors who recommend apps.