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Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn./
作者:	Whitlock, Joshua Lee.
面頁冊數:	1 online resource (187 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.
Contained By:	Dissertation Abstracts International79-11A(E).
標題:	Educational leadership. -
電子資源:	click for full text (PQDT)
ISBN:	9780438147515

Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn.
Whitlock, Joshua Lee.

Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn. - 1 online resource (187 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: A.

Thesis (Ed.D.)--East Tennessee State University, 2018.

Includes bibliographical references

The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A subset of 18 variables about these students in their first semester were used to perform principal component analysis and produce a set of 4 factors that were used in 5 predictive models. The 4 factors of students who transferred and graduated elsewhere were "Institutional Characteristics," "Institution's Focus on Academics," "Student Aptitude," and "Student Community." These 4 factors were combined with the additional demographic variables of gender, race, residency, and initial institution to form a final dataset used in predictive modeling. The predictive models used were a logistic regression, decision tree, random forest, artificial neural network, and support vector machine. All models had predictive power beyond that of random chance. The logistic regression and support vector machine models had the most predictive power, followed by the artificial neural network, random forest, and decision tree models respectively.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438147515Subjects--Topical Terms:

585508
Educational leadership.
Index Terms--Genre/Form:

554714
Electronic books.

Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn.
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504 $a Includes bibliographical references
520 $a The purpose of this study was to discover factors about first-time freshmen that began at one of the six 4-year universities in the former Tennessee Board of Regents (TBR) system, transferred to any other institution after their first year, and graduated with a degree or certificate. These factors would be used with predictive models to identify these students prior to their initial departure. Thirty-four variables about students and the institutions that they attended and graduated from were used to perform principal component analysis to examine the factors involved in their decisions. A subset of 18 variables about these students in their first semester were used to perform principal component analysis and produce a set of 4 factors that were used in 5 predictive models. The 4 factors of students who transferred and graduated elsewhere were "Institutional Characteristics," "Institution's Focus on Academics," "Student Aptitude," and "Student Community." These 4 factors were combined with the additional demographic variables of gender, race, residency, and initial institution to form a final dataset used in predictive modeling. The predictive models used were a logistic regression, decision tree, random forest, artificial neural network, and support vector machine. All models had predictive power beyond that of random chance. The logistic regression and support vector machine models had the most predictive power, followed by the artificial neural network, random forest, and decision tree models respectively.
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