Moving from Forecast to Prediction: How Honors Programs Can Use Easily Accessible Predictive Analytics to Improve Enrollment Management

Joseph A. Cazier; Leslie Sargent Jones; Jennifer Mcgee; Mark Jacobs; Daniel Paprocki; Rachel A. Sledge; Jennifer McGee

Moving from Forecast to Prediction: How Honors Programs Can Use Easily Accessible Predictive Analytics to Improve Enrollment Management

Joseph A. Cazier
, Leslie Sargent Jones
, Jennifer Mcgee
, Mark Jacobs
, Daniel Paprocki
, Rachel A. Sledge
, Jennifer McGee

Appalachian State University

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	The Journal of the National Collegiate Honors Council
Volume	18
State	Published - Sep 22 2017

Disciplines

Computer Sciences

Cite this

@article{9c82f964c90c4914b1b5c7640d6b9d14,

title = "Moving from Forecast to Prediction: How Honors Programs Can Use Easily Accessible Predictive Analytics to Improve Enrollment Management",

author = "Cazier, \{Joseph A.\} and Jones, \{Leslie Sargent\} and Jennifer Mcgee and Mark Jacobs and Daniel Paprocki and Sledge, \{Rachel A.\} and Jennifer McGee",

note = "INTRODUCTION Most enrollment management systems today use historical data to build rough forecasts of what percentage of students will likely accept an offer of enrollment based on historical acceptance rates. While this aggregate forecast method has its uses, we propose that building an enrollment model based on predicting an individual's likelihood of matriculation can be much more beneficial to an honors director than a historical aggregate forecast.",

year = "2017",

month = sep,

day = "22",

language = "American English",

volume = "18",

journal = "The Journal of the National Collegiate Honors Council",

}

TY - JOUR

T1 - Moving from Forecast to Prediction: How Honors Programs Can Use Easily Accessible Predictive Analytics to Improve Enrollment Management

AU - Cazier, Joseph A.

AU - Jones, Leslie Sargent

AU - Mcgee, Jennifer

AU - Jacobs, Mark

AU - Paprocki, Daniel

AU - Sledge, Rachel A.

AU - McGee, Jennifer

N1 - INTRODUCTION Most enrollment management systems today use historical data to build rough forecasts of what percentage of students will likely accept an offer of enrollment based on historical acceptance rates. While this aggregate forecast method has its uses, we propose that building an enrollment model based on predicting an individual's likelihood of matriculation can be much more beneficial to an honors director than a historical aggregate forecast.

PY - 2017/9/22

Y1 - 2017/9/22

UR - https://www.questia.com/library/journal/1G1-523276384/moving-from-forecast-to-prediction-how-honors-programs

M3 - Article

VL - 18

JO - The Journal of the National Collegiate Honors Council

JF - The Journal of the National Collegiate Honors Council

ER -