Assessing Implicit Computational Thinking In Zoombinis Gameplay

In this study we examine how playing Zoombinis can help upper elementary and middle school learners build implicit computational thinking (CT) skills. Building on prior methods used with the digital science learning games, Impulse and Quantum Spectre, we are combining video analysis and educational data mining to identify implicit computational thinking that emerges through gameplay [1]. This paper reports on the first phase of this process: developing a human labeling system for evidence of specific CT skills (e.g., problem decomposition, pattern recognition, algorithmic thinking, abstraction) in three Zoombinis puzzle by analyzing video data from a sample of elementary learners, middle school learners, game experts, and computer scientists. Future work will combine these human-labeled video data with game log data from these 70+ learners and computer scientists to create automated assessments of implicit computational thinking skills from gameplay behaviors in large player audiences. This poster with video examples will share results of this work-in-progress.