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Abstract: "The rich interaction space of man y educational games presents a challenge for designers and researchers who strive to help players achieve specific learning outcomes. Giving players a large amount of freedom over how they perform a complex game task makes it difficult to anticipate what t hey will do. In order to ad dress this issue designers must ask : what are students do ing in my game? And does it embody what I intended them to learn? To answer these questions, designers need methods to expose the details of student play. We describe our a pproach for automatic extract ion of conceptu al features from logs of student play sessions within an open educational game utilizing a two - dimensional context - free grammar. We demonstrate how these features can be used to clu s- ter student solutions in the e ducational game RumbleBlocks . U s- ing these clusters , we explore the range of solutions and measure how many students use the designers' envisioned solution . Equipped with this information, designers and researchers can focus redesign efforts to areas in the game where discrepancies exist between the designer s' intention s and player experience s."