Age- and environment-related differences in motivational predictors of academic achievement among adolescents in online and offline learning
DOI:
https://doi.org/10.33910/1992-6464-2026-219-155-165Keywords:
academic achievement, learning motivation, age differences, educational environment, selfregulation, intrinsic motivation, GRIT, online learning, offline learningAbstract
Introduction. The digital transformation of schooling has highlighted the question of which motivational factors sustain adolescents’ academic achievement across different learning formats. From a developmental psychology perspective, it is important to identify how predictors of achievement change with age and the learning environment (online vs offline) to better understand the mechanisms of adaptation and success in adolescent development.
Materials and Methods. The study involved 1,490 school students aged 10–18, enrolled in either an online school (Foxford Home School) or a traditional format (15 schools in the Moscow Region). The dependent variable was the GPA based on official school records. Predictors encompassed six blocks of learning motivation according to T. O. Gordeeva’s integrative structural–process model: motivational–semantic, goal-oriented, regulatory, cognitive–motivational, behavioral, and failure-response. Hierarchical regression analysis with stepwise block inclusion was applied, with models constructed separately for age subgroups and learning formats (p < 0.05).
Results. In the offline format, academic achievement was primarily supported by intrinsic motives, namely epistemic motivation and the striving for self-development (β up to 0.35; R. up to 19 %). In the online format, the leading roles were played by perseverance of interests (GRIT), achievement motivation, and overall self-regulation (combined R. ≈ 7–9 %). Age-related changes followed a wave-like pattern: the contribution of intrinsic motives increased by the ages of 14–15, whereas by 16–18 regulatory and attributional factors became more prominent, with intrinsic motives maintaining their influence. A single negative predictor was identified: the Achievement Domain indicator of optimistic attributional style among high school students studying offline (β = –0.23).
Conclusions. Differences in the composition and magnitude of statistically significant predictors are observed between educational settings (online vs offline), while differences in their relative importance are found across age groups. In practice, this suggests prioritizing the development of intrinsic motivation in traditional (offline) learning and strengthening self-regulation and perseverance of interests in online environments. These findings expand the developmental-psychological understanding of learning motivation in the context of digitalization and provide guidance for targeted adolescent support.
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