TY - JOUR
AB - In recent years we have developed a data analytics pipeline using artificial neural networks to predict prospective student matriculation for university admissions using very limited demographic data. Predictions are generated at the earliest stages of the admissions process and successfully inform recruiting and admissions staff about the likelihood of matriculation. Results over numerous years of matriculation predictions are highly predictive and reliably consistent. We provide a detailed account of data collection, formatting, and transformation processes used, enabling others to replicate the process and results.
AU - David M. Hansen
CY - Washington, DC
DA - 
EP - 
IS - 1
J1 - Strategic Enrollment Management Quarterly
J2 - SEMQ
JA - SEM Quarterly
JF - Strategic Enrollment Management Quarterly
JO - Strategic Enrollment Management Quarterly
L1 - 
LA - EN
SP - 
T1 - Using Artificial Neural Networks to Predict Matriculation of University Prospects
UR - using-artificial-neural-networks-to-predict-matriculation-of-university-prospects
VL - 
Y1 - 2020/4/20
Y2 - 2024/4/24
ER -