Players can build implicit understanding of challenging scientific concepts when playing digital science learning games. In this study, we examine implicit computational thinking (CT) skills of 72 upper elementary and middle school students and 10 computer scientists playing a game called Pizza Pass. We report on the process of creating automated detectors to identify four CT skills from gameplay: problem decomposition, pattern recognition, algorithmic thinking, and abstraction. This paper reports on hand-labeled playback data obtaining acceptable inter-rater reliability and 100 gameplay features distilled from digital log data. In future work, we will mine these features to automatically identify the CT skills previously labeled by humans. These automated detectors of CT will be used to analyze gameplay data at scale and provide actionable feedback to teachers in real-time.
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