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Applications of supervised learning techniques on undergraduate admissions data

Published: 16 May 2016 Publication History

Abstract

In making undergraduate admissions decisions, colleges and universities must take a large amount of data into consideration for each applicant. Surprisingly, there is almost no work reported in the literature for a systematic, automated use of the wealth of data gathered by an institution over the years; such a system could guide admissions offices in targeting applicants so that their yield (the applicants who enroll) is maximized by effectively distributing resources (counselors' time and energy) across applicants.
We discuss the use of supervised learning techniques, namely perceptrons and support vector machines, in predicting admission decisions and enrollment based on historical applicant data. We show through experimental results that a classifier, trained and validated on previous years' data, can identify with reasonable accuracy (1) those applicants that the admissions office is likely to accept (based on historical decisions made by the admissions office), and (2) of the accepted applicants, those ones that are likely to enroll at the institution. Additionally, the results from our feature selection experiments can inform admissions offices of the significance of applicant features relative to acceptance and enrollment, thus aiding the office in future data collection and decision making.

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cover image ACM Conferences
CF '16: Proceedings of the ACM International Conference on Computing Frontiers
May 2016
487 pages
ISBN:9781450341288
DOI:10.1145/2903150
  • General Chairs:
  • Gianluca Palermo,
  • John Feo,
  • Program Chairs:
  • Antonino Tumeo,
  • Hubertus Franke
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 May 2016

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Author Tags

  1. SVM
  2. classification
  3. machine learning
  4. neural network
  5. prediction
  6. undergraduate admissions

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CF'16
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CF'16: Computing Frontiers Conference
May 16 - 19, 2016
Como, Italy

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CF '16 Paper Acceptance Rate 30 of 94 submissions, 32%;
Overall Acceptance Rate 273 of 785 submissions, 35%

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  • (2023)Evaluating a Learned Admission-Prediction Model as a Replacement for Standardized Tests in College AdmissionsProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3593382(195-203)Online publication date: 20-Jul-2023
  • (2023)Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach2023 Fifth International Conference on Transdisciplinary AI (TransAI)10.1109/TransAI60598.2023.00040(135-140)Online publication date: 25-Sep-2023
  • (2023)Predictive Analytics for University Student Admission: A Literature ReviewBlended Learning : Lessons Learned and Ways Forward10.1007/978-3-031-35731-2_22(250-259)Online publication date: 9-Jul-2023
  • (2022)Performance Analysis of Supervised Learning Algorithms on Different ApplicationsComputer Science & Technology Trends10.5121/csit.2022.121903(29-35)Online publication date: 12-Nov-2022
  • (2022)Feasibility of Machine Learning Support for Holistic Review of Undergraduate Applications2022 International Conference on Applied Artificial Intelligence (ICAPAI)10.1109/ICAPAI55158.2022.9801571(1-6)Online publication date: 5-May-2022
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  • (2020)Interpolation of sparse high-dimensional dataNumerical Algorithms10.1007/s11075-020-01040-2Online publication date: 13-Nov-2020
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