Theses and Dissertations

Date of Award


Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Education (EdD)


Abraham S. Fischler College of Education


Roberta Silfen

Committee Member

Charles A. Schlosser

Committee Member

Kimberly Durham


Academic institution leaders, education researchers, and policy makers have come together to restore America’s ranking among the top nations who are educating citizens to prepare for the technology jobs of the future. President Barack Obama tasked the nation to do so after alarming figures in student achievement were revealed from the 2009 PISA reports. The White House and Lumina Foundation called for a transformation, setting deadlines for 2020 and 2025, respectively, that 60% of Americans should attain degrees or credentials. This study centered on degree attainment by, assessing distance learning persistence for warning signs of impeding these goals. This study had two components in determining how a collection of disparate data could be assembled for further analysis: one, to collect sample graduation statistics from available primary education data sources. Secondary data sources were reviewed for relevance; and stored for potential future value. The second component was to review a large set of best-evidence research studies (100-200) and identify sample data for identifying or calculating effect size in best-evidence research reports on distance learning persistence to graduation. The two sample result sets were combined in a discovery process of synthesis with data education statistics. Wolf (1986) defined analyses: primary analysis is the original analysis; secondary analysis is revisiting or re-analysis of data to uncover “better statistical techniques or answering new questions”; meta-analysis is the analysis of analyses. This research investigated ways and means to synthesize disparate datasets, utilizing both new and existing data, to garner and/or record aggregate gaps for analyzing student attrition over the term of the goals. A knowledge base, deemed Best Evidence Statistics Synthesis (BESS1.0) was developed for long term research to capture the effort invested into meta collection studies and for sharing among research teams engaged in those efforts.

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