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2013 Conference Proceedings - University of Nevada, Las Vegas

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level <strong>of</strong> difficulty for each participant, (b) efficient use <strong>of</strong> time, and (c) reduction in test fatigue.Scores for number sense (elementary number concepts and operations) and algebraic thinking(elementary patterns, functions, and algebra) were reported in standard deviation units based onthe expected performance <strong>of</strong> the average K-8 inservice teacher, mean = 0, SD = 1. The MKTmeasures have enabled the documentation <strong>of</strong> growth in mathematical knowledge amongelementary teachers participating in pr<strong>of</strong>essional development and among elementary pre-serviceteachers following the completion <strong>of</strong> mathematics content courses designed for teachers. Whenthe MKT measures were used with pre-service teachers, the level <strong>of</strong> difficulty was adjusted aslow as -0.25 since pre-service teachers have had less time in the classroom working withmathematics curriculum (Welder, 2007).In order to evaluate the influence <strong>of</strong> participant’s prior knowledge <strong>of</strong> mathematics, andattitudes and beliefs towards mathematics, a background survey and an Attitudes and Beliefssurvey (henceforth known as AB survey) was adapted from the Student Assessment, Parts 1 & 3from the ME.ET project. The background survey gathered information on participantdemographics and prior knowledge <strong>of</strong> mathematics (e.g. mathematics courses taken, examscores, high school GPA). The background survey was administered once at the beginning <strong>of</strong>the study. Items on the AB survey were grouped into four factors: (a) usefulness <strong>of</strong> mathematics,(b) multiple ways <strong>of</strong> doing mathematics, (c) nature <strong>of</strong> mathematics (rigor and precision), and (d)processes <strong>of</strong> doing mathematics (enjoyment). The AB survey was administered to allparticipants at each <strong>of</strong> the five time-points along with the MKT measures.To evaluate the performance <strong>of</strong> the participants on the MKT measures and AB survey, as afunction <strong>of</strong> their progression through Methods and Student Teaching, the data was analyzedusing a piecewise growth model. MPlus statistical s<strong>of</strong>tware (Muthén & Muthén, 1998-2010)was used to model the longitudinal developmental trajectories <strong>of</strong> MKT (number sense andalgebraic thinking) and to identify possible influencing factors related to (a) demographics, and(b) prior knowledge <strong>of</strong> mathematics from the background survey, and (c) attitudes and beliefstowards mathematics from the AB survey. This confirmatory approach founded in structuralequation modeling, allowed for the analysis <strong>of</strong> data for inferential purposes, using longitudinaldata over multiple phases (Byrne, 2012; Johnson, 2001). A repeated measures ANOVA wasused to analyze changes in the AB factors over the nine months.<strong>Proceedings</strong> <strong>of</strong> the 40 th Annual Meeting <strong>of</strong> the Research Council on Mathematics Learning <strong>2013</strong> 36

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