Abstract

A significant issue in research on educational games lies in evaluating their educational impact. Although game analytics is often leveraged in the game industry, it can also provide insight into player actions, strategy development, and the learning process in educational games separate from external evaluation measures. This paper explores the potential of game analytics for learning by analyzing player strategies of an educational game that is designed to support algorithmic thinking. We analyze player strategies from nine cases in our data, combining quantitative and qualitative game analysis techniques: hierarchical player clustering, game progression visualizations, playtraces, and think-aloud data. Results suggest that this combination of data analysis techniques provides insights into level progression and learning strategies that may have been otherwise overlooked.

GhostLab Authors

stay connected through our multiple channels