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Educational games offer an opportunity to engage and inspire students to take interest in science, technology, engineering, and mathematical (STEM) subjects. Unobtrusive learning assessment techniques coupled with machine learning algorithms can be utilized to record students' in-game actions and formulate a model of the students' knowledge without interrupting the students' play. This paper introduces “Experiment Centered Assessment Design” (XCD), a framework for structuring a learning assessment feedback loop. XCD builds on the “Evidence Centered Assessment Design” (ECD) approach, which uses tasks to elicit evidence about students and their learning. XCD defines every task as an experiment in the scientific method, where an experiment maps a test of factors to observable outcomes. This XCD framework was applied to prototype quests in a massively multiplayer online (MMO) educational game. Future work would build upon the XCD framework and use machine learning techniques to provide feedback to students, teachers, and researchers

A Framework for Structuring Learning Assessment in a Massively Multiplayer Online Educational Game