Understanding optimal problem-solving in a digital game: The interplay of learner attributes and learning behavior

Educational video games, with various motivational features and scaffolding support, have high potential for facilitating optimal learning and achievement. However, research on how students utilize game features, identify useful information, and explore solutions to in-game problem scenarios continues to be under-researched. This study aims to unpack the mechanisms underlying users’ in-game behaviors to identify emergent markers of optimal problem-solving performance in an educational video game. Survey data and computer logs were collected from 61 participants (36.4% middle and high-school students, Mage = 13; 63.6% university students, Mage = 21) to address the research inquiry of the present study. Results from the regression analysis not only showed an important link between individual characteristics (i.e., self-efficacy, prior knowledge) and success-striving in-game behavior, but also highlighted the role of self-regulated help-seeking behavior in determining students’ optimal problem-solving pathways. Our findings add new perspectives to existing research of what learning behaviors are crucial for promoting self-regulation in digital game-based learning. These findings provide useful insight on how to design scaffolding tools in an open-ended problem-solving space to encourage student engagement in effective help-seeking behaviors for optimal learning performance.