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A data mining study of the matriculation of Covenant college applicants

Published: 28 March 2008 Publication History

Abstract

Data mining is transforming the way colleges and universities use their data to make informed decisions. Particularly in the area of new student recruitment, college admissions offices are using data mining techniques to better manage their recruitment efforts. Admissions offices use these techniques to determine how likely a particular student is to matriculate based on their geographic location, interests, or even scholastic ability. Predicting who will matriculate allows the admissions department to focus their resources on those populations of applicants most likely to attend.

References

[1]
Allen, J. (2006). Covenant climbs in u.s. news rankings. Retrieved April 7, 2007, from http://www.covenant.edu/news/archive/2006/08.18.06.php.
[2]
Farrell, E. F. (2006). The power and peril of admissions data. The Chronicle of Higher Education: Students, 53(8). Retrieved February 26, 2007 from http://chronicle.com/weekly/v53/i08/08a04601.htm.
[3]
Hannon, C. (2006). Mining meaning from information. The Chronicle of Higher Education: The Chronicle Review, 52(49). Retrieved February 26, 2007 from http://chronicle.com/weekly/v52/i49/49b01601.htm.
[4]
Luan, J. (2002a). Data mining and knowledge management in higher education -- potential applications. Paper presented at the Annual Forum for the Association for Institutional Research.
[5]
Luan, J. (2002b). Data mining and its applications in higher education. New Directions for Institutional Research, 113, 17--3

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  • (2023)Predictive Analytics for University Student Admission: A Literature ReviewBlended Learning : Lessons Learned and Ways Forward 10.1007/978-3-031-35731-2_22(250-259)Online publication date: 17-Jul-2023

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  1. A data mining study of the matriculation of Covenant college applicants

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    cover image ACM Other conferences
    ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference
    March 2008
    548 pages
    ISBN:9781605581057
    DOI:10.1145/1593105
    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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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 March 2008

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    Author Tags

    1. admissions
    2. data mining
    3. knowledge management
    4. predictive modeling

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    ACM SE08
    ACM SE08: ACM Southeast Regional Conference
    March 28 - 29, 2008
    Alabama, Auburn

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    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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    • (2023)Predictive Analytics for University Student Admission: A Literature ReviewBlended Learning : Lessons Learned and Ways Forward 10.1007/978-3-031-35731-2_22(250-259)Online publication date: 17-Jul-2023

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