Andy Bryant

TL;DR: In this paper , the authors examine how comparison with other players' data influenced adaptation, a part of reflection, in the context of a game that teaches parallel programming and conclude that comparison with peers does significantly impact willingness to try a different approach, but suggest that there may also be other ways.

TL;DR: In this paper , the authors examine how players make meaning from process visualizations of other players' gameplay and identify two interpretation methods comprised of six techniques and discuss what these results mean for future use of player-facing process visualisations.

TL;DR: In this paper, the authors combine Interactive Behavior Analytics (IBA) and sequence analysis (SA) to create a human-in-the-loop data analysis methodology that uses behavioral labels and visualizations to allow analysts to examine player behavior in a way that is context sensitive, scalable, and generalizable.

TL;DR: A new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Wrapping and clustering algorithms to facilitate knowledge discovery from game data is presented.