Elizabeth Rowe
ABSTRACT
Players can build implicit understanding of challenging scientific concepts when playing digital science learning games [1]. In this study, we examine implicit computational thinking (CT) skills among upper elementary and middle school students during Zoombinis gameplay. We report on the development of a human labeling system for gameplay evidence of four CT skills: problem decomposition, pattern recognition, algorithmic thinking, and abstraction. We define labels that identify use of these skills in three Zoombinis puzzles, based on analysis of video data from both CT novices (upper elementary and middle school students) and CT experts (computer scientists and expert Zoombinis players). Future work will involve the construction of detectors for implicit CT skills based on these human labels, in order to analyze gamelog data at scale and give feedback to teachers.
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Index Terms
- Assessing Implicit Computational Thinking in Zoombinis Gameplay: Pizza Pass, Fleens & Bubblewonder Abyss
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