Integrating lean six sigma and data analytics to improve student retention

Joel Patterson, Esther Dagne, Carol Putman, Faisal Aqlan

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

Abstract

Higher education institutions are expected to deliver a high-quality education while competing for enrollment. Each institution has unique features that attract students to apply, but most share the same challenge of retaining students after enrollment. The challenge does not seem to have an ideal solution in most higher education institutions. This study integrates Lean Six Sigma and data analytics to establish a standardized process for improving student retention rates. Data sets pertaining to admission, enrollment, transfer and dropout were analyzed to identify opportunities for improving the retention rate. Descriptive and predictive analytics models were developed to find trends in the data and predict future retention outcomes. Finally, recommendations are made to help higher education institutions to better understand and improve student retention.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IISE Annual Conference
EditorsL. Cromarty, R. Shirwaiker, P. Wang
PublisherInstitute of Industrial and Systems Engineers, IISE
Pages849-854
Number of pages6
ISBN (Electronic)9781713827818
StatePublished - 2020
Event2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020 - Virtual, Online, United States
Duration: Nov 1 2020Nov 3 2020

Publication series

NameProceedings of the 2020 IISE Annual Conference

Conference

Conference2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020
Country/TerritoryUnited States
CityVirtual, Online
Period11/1/2011/3/20

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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