Assessment of Technology Use Data Contribution to Early Alert Efforts at an Access Based Institution
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Authors
Ours, Alan R.
Issue Date
2020-04
Type
Dissertation
Language
en_US
Keywords
Dissertations, Academic--United States , Big data , College dropouts--Prevention , Education, Higher , Service learning , Technology
Alternative Title
Abstract
The need for institutions of higher education to be more responsive and results oriented has become more acute with each passing year. Erratic enrollment trends, a shrinking base from which to draw potential students, the external pressures of performance-based funding, increasing amounts of student debt, and increasing costs are all prompting colleges and universities to action. Institutions of higher education are closing their doors as the cost of attending college rises and the number of degrees awarded decreases (National Center for Education Statistics, 2018).
Researching why so many students do not complete college and the ways to effectively intervene to help students be successful has become an important field of study in higher education. The advent of massive amounts of electronic data and the lower costs of storage have given rise to an era of big data analytics. Companies the world over are using big data analysis in an effort to intervene with customers and alter behaviors. Why not begin to do the same in higher education, especially if it can help students be successful?
This research study was performed for the analysis of basic technology engagement data at the individual student level in hopes of applying it to the development of early alert efforts for students who appear to be struggling with their academic work.
Wireless logins, campus portal logins and learning management system logins were studied over three semesters at one access-based institution. When added to traditional academic predictors, results suggest that technology engagement data significantly strengthens the accuracy of models intended to flag students who may be at risk.
Keywords: higher education; student engagement; technology; big data;
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This dissertation is protected by the Copyright Laws of the United States (Public Law 94-553, revised in 1976). Consistent with fair use as defined in the Copyright Laws, brief quotations from this material are allowed with proper acknowledgement. Use of the materials for financial gain with the author's expressed written permissions is not allowed.