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using-big-data-scalable-visually-interpretable-methods-for-institutional-research-(ronca-and-vaade-air-2012)-.pdf

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In this study, researchers Justin Ronca and Beth Stransky Vaade propose using classification and regression tree methodology in conjunction with the NELS:88 database to build a coarse, data-driven policy framework that can then be enhanced by the findings in the academic literature. Using this method, they will establish guidelines for data-collection protocols that isolate and call for the collection of the appropriate data at the appropriate time so as to make clear which types of interventions work best for which types of students, and when. They will also compare this method with parametric models typically used in education data analysis Read more↗

Size1.10MB
Content typeapplication/pdf
Updated6 years ago
Checked3 months ago
Hostwiscape.wisc.edu
Keywordsusing big data scalable visually interpretable methods for institutional research ronca and vaade air pdf
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