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• W27573774 abstract "Our analysis shows how a longitudinal data mart can provide a simple and effective way to analyze student test performance over time. Our data mart in this case is a mega-table compiled from several years of archival student-level test data, where we have modified all of the fields so that they have a common meaning over time. Using this longitudinal data base we then compared the performance statistics and effect sizes of test results in math and reading on the Connecticut Mastery Test (CMT) series for grades 4, 6 and 8 and on the Connecticut Academic Performance Test (CAPT) in grade 10, from 2000 to 2007. We found that students tested sequentially in grades 4, 6, 8 and 10 achieve better performance in mathematics and reading at the State, ERG and school district levels, as compared to new incoming students who began the testing sequence sometime after grade 4. This suggests that mobility relates to lower student performance on our tests, a finding that others have reported (Bourque, Mary D., 2008, Rumsberger, 2002). We conclude that student mobility should be monitored and that academic and/or social interventions may be warranted. We also conclude that a longitudinal data mart may provide a practical way to look at student test performance over time particularly when vertical scaling or vertical modulation are not available. A data mart could also serve as a simple low-tech way to cross-validate results from these techniques. Introduction The focus of this report is to investigate the impact of student mobility on academic test performance using a longitudinal data mart. We have developed vertical scales in Connecticut using IRT and we think that this will provide a very useful way to examine student performance over time in the future. However, it will take several years to develop sufficient test results on vertically equated forms to be able to look at long-term student performance using this model. Meantime, we have many years of archival data that may also be useful for looking at student performance over time. Therefore, we wanted to see if data mart technology might offer a good alternative for evaluation of the longitudinal performance on Connecticut’s student assessments using this existing archival data. Finally, it is often useful to have more than one way to examine data. Comparing how different models converge and diverge might provide us with a better way to evaluate the real effects and also a way to appreciate the contribution of each of them. Therefore, we also thought that _______________________________________________________________________ A paper submitted to the Northeastern Education Research Association (NERA) 39 th Annual Conference, October 22-24, 2008" @default.
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• W27573774 date "2008-01-01" @default.
• W27573774 modified "2023-09-24" @default.
• W27573774 title "Using a Longitudinal Data Mart to Examine the Effects of Student Mobility on Test Performance Over Time" @default.
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