Personalized gamification versus one-size-fits-all gamification in fully online learning: Effects on student motivational, behavioral and cognitive outcomes

Abstract

The “one-size-fits-all” approach commonly used in gamified learning is unlikely to optimize the effects of gamification due to individual differences. Personalized gamification based on player types has been considered a promising alternative. However, the jury is still out on whether personalized gamification improves student learning. Previous experiments examining its effectiveness have reported mixed results. This study employed a mixed-method research design to examine the effects of personalized gamification on student learning in terms of motivational (intrinsic motivation), behavioral (task completion rate), and cognitive (cognitive engagement and learning performance) outcomes, comparing it to a one-size-fits-all gamification approach over eight weeks. The results showed that students in the personalized gamification group (n = 36) significantly outperformed those in the one-size-fits-all group (n = 29) in motivational, behavioral, and cognitive outcomes. The follow-up individual interviews provide insights into the reasons underlying the reported effects. Educational relevance and implications statement: The present study found that personalized gamification, tailored to individual differences in player types, has a significantly larger positive influence on student motivational, behavioral, and cognitive outcomes compared to the one-size-fits-all gamification approach widely used in education. Students' responses in the follow-up interview revealed that personalized gamification enhances their positive emotions and reduces negative emotions, which are antecedents of an improved motivational state. The improved motivational state exerts a positive influence on behaviors, which, in turn, promote cognitive learning outcomes. This study provides empirical evidence for the effectiveness of personalized gamification, sheds light on the underlying mechanisms of its effectiveness and offers guidance for its implementation in online learning.