dc.contributor.author	Ours, Alan R.
dc.coverage.spatial	United States	en_US
dc.coverage.temporal	2018-2020	en_US
dc.date.accessioned	2020-07-15T15:21:34Z
dc.date.available	2020-07-15T15:21:34Z
dc.date.issued	2020-04
dc.identifier.other	D7F20414-3778-60BE-42BB-0D8028332031	en_US
dc.identifier.uri	https://hdl.handle.net/10428/4267
dc.description.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;	en_US
dc.description.tableofcontents	Chapter I: INTRODUCTION 1 -- Problem Statement 7 -- Questions 8 -- Organization 9 -- Chapter II: LITERATURE REVIEW 10 -- Theoretical Basis 10 -- The Student 11 -- The Classroom Experience 12 -- The College Environment 15 -- Student Engagement Frameworks 16 -- Measuring Student Engagement 20 -- Prior Research 24 -- Technology 24 -- Predictive Data 25 -- Policy Implications 27 -- Hypotheses 30 -- Chapter III: METHODOLOGY 32 -- Definitions 32 -- Success 33 -- At-Risk 35 -- Sources 36 -- Population 36 -- Analysis 37 -- Research Data 39 -- Chapter IV: RESULTS 44 -- Population Description 44 -- Descriptive Analysis 48 -- Correlation Analysis 56 -- Regression Analysis 60 -- Analysis of Hypotheses 70 -- Chapter V: DISCUSSION 73 -- Findings 73 -- Study Limitations 75 -- Further Research 76 -- Verification and Expansion 77 -- Deeper Analysis 77 -- Wireless Tracking 78 -- The Next Step: Intervention Strategies 79 -- Conclusion 79 -- REFERENCES 82 -- APPENDIX A: Descriptive Analysis Tables 91 -- Spring 2019 Semester (15 weeks) 92 -- Summer 2019 Semester (8 weeks) 102 -- Fall 2019 Semester (15 weeks) 112 -- APPENDIX B: Regression Analysis Tables 122 -- Spring 2019 Semester 123 -- Summer 2019 Semester 133 -- Fall 2019 Semester 143 -- APPENDIX C: Institutional Review Board Exemptions 153	en_US
dc.format.extent	1 electronic document, 172 pages	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en_US	en_US
dc.rights	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.	en_US
dc.subject	Dissertations, Academic--United States	en_US
dc.subject	Big data	en_US
dc.subject	College dropouts--Prevention	en_US
dc.subject	Education, Higher	en_US
dc.subject	Service learning	en_US
dc.subject	Technology	en_US
dc.title	Assessment of Technology Use Data Contribution to Early Alert Efforts at an Access Based Institution	en_US
dc.type	Dissertation	en_US
dc.contributor.department	Department of Political Science of the College of Humanities and Social Sciences	en_US
dc.description.advisor	Watson, Todd
dc.description.committee	Kuck, Sarah
dc.description.committee	Dunn, Scott
dc.description.degree	D.P.A.	en_US
dc.description.major	Public Administration	en_US