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The mathematics placement process at the University of Northern Colorado includes brief faculty-student interviews, during which faculty members suggest mathematics courses for students in their first year at the university. Data from a sample of 1466 students admitted in the fall of 2007 were analyzed to assess the effectiveness of the placement interview. Logistic regression was used to model student success in the first university mathematics course as a function of variables available from university records or self-reported by students. The analysis indicated that, among the available variables, by far the most important factor in modeling success was high school grade point average. Whether or not a student followed the recommendation advice, and a student's most recent (usually high school) mathematics course and grade were also useful variables for modeling success.

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This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator -not just a descriptive statistic for the variation (spread) in data -is related to teachers' instruction regarding standard deviation, particularly around the issue of division by n-1. In this regard, the study contributes to our understanding about how knowledge of mathematics beyond the current instructional level, what we refer to as nonlocal mathematics, becomes important for teaching. The findings indicate that acquired knowledge of nonlocal mathematics can play a role in altering teachers' planned instructional approaches in terms of student activity and cognitive demand in their instruction.

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