Predicting Who Will Graduate | Mining Institutional Data Using Knowledge Discovery and Statistical Techniques

John Campbell

ISBN 10: 3639140230 ISBN 13: 9783639140231

Edit� par VDM Verlag Dr. Müller, 2009

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Titre : Predicting Who Will Graduate | Mining ...
�diteur : VDM Verlag Dr. M�ller
Date d'�dition : 2009
Reliure : Taschenbuch
Etat : Neu

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Predicting Who Will Graduate

John Campbell

Edit� par VDM Verlag Dr. M�ller, 2009

ISBN 10 : 3639140230 ISBN 13 : 9783639140231

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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Campbell JohnJohn is Director of Academic Computing at Northern ArizonanUniversity. He entered college, unknowingly, with risk factorsnagainst graduating but graduate he did, in math, thennengineering, and finally with an EdD in edu. N� de r�f. du vendeur 4961083

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Predicting Who Will Graduate : Mining Institutional Data Using Knowledge Discovery and Statistical Techniques

John Campbell

Edit� par VDM Verlag Dr. M�ller, 2009

ISBN 10 : 3639140230 ISBN 13 : 9783639140231

Neuf Taschenbuch

impression � la demande

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Predicting who will graduate from a university is adifficult challenge, especially for US publicuniversities whose missions serve diverse populationsunder relaxed admission criteria. Building predictivemodels for entering freshmen poses many problems:some students receive financial aid, others do not;some enter with SAT scores, others with ACT scores;some students stop out and then return. And, with theadvent of the modern data warehouse, a dizzying arrayof data exists, which might, or might not, help buildpredictive models. This doctoral study examines thework required to build four predictive models forentering freshmen: logistic regression, automaticcluster detection, neural network, and decision tree.Practical problems are addressed squarely: Cleaninginstitutional data, dealing with missing data,adjusting model parameters, recognizing model drift,grouping students into prediction bands, andevaluating disparate model types are just some of thepractical solutions shared in this work. N� de r�f. du vendeur 9783639140231

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