Next Article in Journal
Lateral Root and Nodule Transcriptomes of Soybean
Previous Article in Journal
Semantics in the Deep: Semantic Analytics for Big Data
Article Menu

Export Article

Open AccessArticle

Predictive Models of Student College Commitment Decisions Using Machine Learning

1,†, 2,*,†, 2,† and 2
1
SnackNation, 3534 Hayden Avenue, Culver City, CA 90232, USA
2
Occidental College, 1600 Campus Road, Los Angeles, CA 90041, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 3 March 2019 / Revised: 3 May 2019 / Accepted: 3 May 2019 / Published: 8 May 2019
  |  
PDF [307 KB, uploaded 10 May 2019]
  |  

Abstract

Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue. View Full-Text
Keywords: educational data mining; supervised machine learning; binary classification; accuracy; F-measure; class imbalance; college admission; mathematical modeling; applied mathematics educational data mining; supervised machine learning; binary classification; accuracy; F-measure; class imbalance; college admission; mathematical modeling; applied mathematics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Basu, K.; Basu, T.; Buckmire, R.; Lal, N. Predictive Models of Student College Commitment Decisions Using Machine Learning. Data 2019, 4, 65.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Data EISSN 2306-5729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top