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Bendinelli, Anthony J.; Marder, M. – Physical Review Special Topics - Physics Education Research, 2012
We use visualization to find patterns in educational data. We represent student scores from high-stakes exams as flow vectors in fluids, define two types of streamlines and trajectories, and show that differences between streamlines and trajectories are due to regression to the mean. This issue is significant because it determines how quickly…
Descriptors: Visual Aids, Longitudinal Studies, Test Results, Data Analysis
Özek, Umut – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2014
In this paper, we present a closer look at the student achievement trends in the District of Columbia between 2006-07 and 2012-13. We have three main conclusions. First, we find that overall, math scores in the District have improved. The improvements in reading scores during this time frame, however, were primarily limited to the first year after…
Descriptors: Academic Achievement, Educational Trends, Urban Schools, Achievement Gains
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Carmichael, Dana L.; Martens, Rita Penney – Journal of Staff Development, 2012
"This is the piece that's been missing." This reaction is common among educators engaged in AIW Iowa, an initiative that engages teachers and administrators in professional learning communities that are improving student achievement, increasing student engagement, and building a schoolwide professional culture focused on improving…
Descriptors: Academic Achievement, Learner Engagement, Measurement, Elementary Secondary Education