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Article Abstract

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Entrance awards are financial incentives offered to admitted students at or near the time of admission. These awards are a frequently used tool to enhance yield rates among post-secondary institutions, sometimes among specifically-targeted populations. The impacts of these awards have been studied in many different contexts, using a variety of methods (Bartik, Hershbein and Lachowska 2021, Cornwell, Mustard and Sridhar 2006, Crowne 2022, Gurantz and Odle 2022). The most effective way to quantify the effect of entrance awards is to assign them randomly among qualified applicants, and then compare the outcomes with the outcome of the group that was not assigned any awards. This method was used in (Firoozi 2022) but is not advisable in the authors’ regulatory environment. Therefore, this research compared the observed yield rate among students to the yield rate that was predicted using a machine learning model that was trained on data collected before the introduction of entrance awards. The predicted yield rates can be thought of as the behaviour of a “synthetic control group” that behaves as would be expected if no entrance awards were offered, allowing the researchers to measure the impact of the awards.

Author(s)

Alexander A. Ondrus, Ph.D., is a Senior Research Specialist in the Education
Insights, Data and Research department at the Northern Alberta Institute of
Technology (NAIT). He is responsible for the Enrollment Planning and Reporting
unit in NAIT’s Education Insights department. His primary interest is the
application of statistical techniques, including machine learning, to better
understand the student lifecycle. Ondrus received his Ph.D. in Mathematics
from the University of Alberta.

Ashmita De, Ph.D., is a Research Analyst in the Education Insights, Data and
Research department at the Northern Alberta Institute of Technology (NAIT).
She is part of the Enrollment Planning and Reporting unit, specializing in
enrollment projections through advanced statistical and machine learning-based
methods. De received her Ph.D. in Biomedical Engineering from the University
of Alberta.

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