The impact of course enrollment sequences on student success

Ahmad Slim, Gregory L. Heileman, Wisam Al-Doroubi, Chaouki T. Abdallah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Many universities are working to improve their graduation rates. The factors that correlate to student success and hence graduation rates are many, varying from pre-institutional factors, including high school GPA and admissions scores, to institutional factors, including student support services and the quality of faculty. An essential institutional factor that is often overlooked is the structure of the curriculum. In this paper we consider the degree to which the underlying curriculum that a student must traverse in order to earn a degree impacts progress. Using data mining methods, complex network analysis and graph theory, this paper proposes a framework for analyzing university course enrollment networks at the program level. The analyses we provide are based on quantifying the importance of course enrollment sequences on a student's final GPA, a metric that is highly correlated to graduation rates. In particular, we investigate the orderings of courses enrollment sequences that best contribute to student performance and achievement. Experimental results, using data from the University of New Mexico, show that Electrical Engineering students who graduated with "high GPA" values tend to follow a common course enrollment sequence that is quite different than that of students who graduated with relatively "low GPA" values. This work may be useful to both students and decision makers at universities as it presents a robust framework for analyzing the ease of flow of students through curricula, which may lead to improvements that facilitate improved student success.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 30th International Conference on Advanced Information Networking and Applications, IEEE AINA 2016
EditorsLeonard Barolli, Tomoya Enokido, Makoto Takizawa, Antonio J. Jara, Yann Bocchi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages59-65
Number of pages7
ISBN (Electronic)9781509018574
DOIs
StatePublished - May 19 2016
Externally publishedYes
Event30th IEEE International Conference on Advanced Information Networking and Applications, AINA 2016 - Crans-Montana, Switzerland
Duration: Mar 23 2016Mar 25 2016

Publication series

NameProceedings - International Conference on Advanced Information Networking and Applications, AINA
Volume2016-May
ISSN (Print)1550-445X

Conference

Conference30th IEEE International Conference on Advanced Information Networking and Applications, AINA 2016
Country/TerritorySwitzerland
CityCrans-Montana
Period3/23/163/25/16

Keywords

  • Complex networks
  • Institutional analytics
  • Sequential pattern mining
  • University curricula

ASJC Scopus subject areas

  • General Engineering

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