Neural Network Classification with Uneven Misclassification Costs and Imbalanced Group Sizes

Lan, Jyhshyan; Hu, Michael Y.; Patuwo, Eddy; Zhang, G. Peter

doi:10.1201/b15783-9

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…A student retention study conducted at The University of Alabama for entering freshmen from 1999-2001 showed distances from university to home as one of the important indicators to dropout students. The study further found English course grade, and math course grade as other significant variables (Davis, Hardin, Bohannon, & Oglesby, 2007). The highest level of mathematics completed in high school is a very strong factor in student degree completion (Adelman, 1999).…”

Section: Retention and Graduationmentioning

confidence: 91%

Exploring Student Characteristics of Retention that Lead to Graduation in Higher Education Using Data Mining Models

Raju

Schumacker

2015

Journal of College Student Retention: Research, Theory & Pr

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The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree and logistic regression models indicated first semester GPA, earned credit hours after end of first semester, status (full/part time) at the end of semester, and high school GPA as the most important variables. Of the 22,099 students who were full-time, first time freshmen from 1995-2005, 7,293 did not graduate (33%). Out of the 7,293 who did not graduate, 2,845 students (39%) had first semester GPA < 2.25 with less than 12 earned credit hours. Characteristics of student retention leading to graduation can be predicted as early as end first semester instead of waiting until the end of the first year of school.

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Section: Retention and Graduationmentioning

confidence: 91%

Exploring Student Characteristics of Retention that Lead to Graduation in Higher Education Using Data Mining Models

Raju

Schumacker

2015

Journal of College Student Retention: Research, Theory & Pr

View full text Add to dashboard Cite

show abstract

“…Many of these models utilized logistic regression methods. The University of Alabama (Davis, Hardin, Bohannon, & Oglesby, 2007) was one of the pioneers in predictive models in higher education. Graduate students at the institution were given access to the data of enrolled freshmen from 1999, 2000 and 2001, and asked to develop predictive models of at-risk students.…”

Section: Previous Research Methods To Predict Academic Outcomesmentioning

confidence: 99%

The impact of predictive analytics based advising on the selection and change of major among first-year, first-term students in engineering

Upah

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Neural Network Classification with Uneven Misclassification Costs and Imbalanced Group Sizes

Cited by 2 publications

References 8 publications

Exploring Student Characteristics of Retention that Lead to Graduation in Higher Education Using Data Mining Models

Exploring Student Characteristics of Retention that Lead to Graduation in Higher Education Using Data Mining Models

The impact of predictive analytics based advising on the selection and change of major among first-year, first-term students in engineering

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