Predictive Modeling to Forecast Student Outcomes and Drive Effective Interventions in Online Community College Courses

Authors

  • Vernon C. Smith MyCollege Foundation
  • Adam Lange Ellucian
  • Daniel R. Huston Rio Salado College

DOI:

https://doi.org/10.24059/olj.v16i3.275

Keywords:

Online Learning, Learning Analytics, Predictive Modeling, Community Colleges, Risk Levels for Online Students, Faculty

Abstract

Community colleges continue to experience tremendous growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging the huge volumes of existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables.

Author Biographies

Vernon C. Smith, MyCollege Foundation

Vernon C. Smith is Provost at MyCollege Foundation, a non-profit (501c3) organization seeking to help low-income youth in America gain high-quality college credentials more affordably. Vernon is former faculty and Vice President of Academic Affairs at Rio Salado College.

Adam Lange, Ellucian

Adam Lange is Analyst/Developer at Ellucian Inc. where he builds reporting and analytics solutions for higher education institutions. Adam is former Institutional Research Programmer at Rio Salado College.

Daniel R. Huston, Rio Salado College

Dan Huston is Coordinator of Strategic Systems at Rio Salado College.

Published

2012-06-18

Issue

Vol. 16 No. 3 (2012)

Section

Learning Analytics: Special Issue

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