In many intelligent tutoring systems, a detailed model of the task domain is constructed and used to provide students with assistance and direction. Reciprocal tutoring systems, however, can be constructed without needing to codify a full-blown model for each new domain. This provides various advantages: these systems can be developed rapidly and can be applied to complex domains for which detailed models are not yet known. In systems built on the reciprocal tutoring model, detailed validation is needed to ensure that learning indeed occurs. Here, we provide such validation for SpellBEE, a reciprocal tutoring system for the complex task domain of American-English spelling. Using a granular definition of response accuracy, we present a statistical study designed to assess and characterize student learning from collected data. We find that students using this reciprocal tutoring system exhibit learning at the word, syllable, and grapheme levels of task granularity.
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