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Predicting Who Will Graduate

  • Kartonierter Einband
  • 152 Seiten
Predicting who will graduate from a university is a difficult challenge, especially for US public universities whose missions serv... Weiterlesen
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Beschreibung

Predicting who will graduate from a university is a difficult challenge, especially for US public universities whose missions serve diverse populations under relaxed admission criteria. Building predictive models 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 the advent of the modern data warehouse, a dizzying array of data exists, which might, or might not, help build predictive models. This doctoral study examines the work required to build four predictive models for entering freshmen: logistic regression, automatic cluster detection, neural network, and decision tree. Practical problems are addressed squarely: Cleaning institutional data, dealing with missing data, adjusting model parameters, recognizing model drift, grouping students into prediction bands, and evaluating disparate model types are just some of the practical solutions shared in this work.

Autorentext

John is Director of Academic Computing at Northern ArizonaUniversity. He entered college, unknowingly, with risk factorsagainst graduating; but graduate he did, in math, thenengineering, and finally with an EdD in educational leadership.Here he combines his interests in education and open sourcesoftware to build graduation prediction models.



Klappentext

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.

Produktinformationen

Titel: Predicting Who Will Graduate
Untertitel: Mining Institutional Data Using Knowledge Discovery and Statistical Techniques
Autor:
EAN: 9783639140231
ISBN: 978-3-639-14023-1
Format: Kartonierter Einband
Genre: Sprach- und Literaturwissenschaften
Anzahl Seiten: 152
Gewicht: 215g
Größe: H220mm x B220mm
Jahr: 2013
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