| Summary: | A central criticism of the assessment-based evaluation policies now in vogue in American public education is reduction of student learning time. Likewise, many see the current crop of year-end, summative assessments as only serving the data needs of politicians and higher-level school administrators. Stemming from these criticisms and a combination of technological and cognitive psychological curiosity, the computer-science community has offered a unique alternative the traditional assessment form. Intelligent Tutoring Systems (ITS) offer the hope of just-in-time assessment with no time away from instruction. That is, ITS are purported to both test and teach at the same time. However, inherent to ITS are the inference of learning. While the inference of learning, and ITS's themselves, are placed within the context of education the analytic logic employed for justification are grounded in data mining and artificial intelligence traditions. This proposed dissertation seeks to bridge the analytic traditions of educational measurement and data mining. The proposed study, carried out in three steps, will apply measurement strategies to a form of Intelligent Tutoring to compare the determination of learning between the two different analytic traditions.
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