Alex Rudniy, Ph.D. Raymond Calluori, Ph.D. Perry Deess, Ph. D. Big Data Analytics for Institutional...
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- Alex Rudniy, Ph.D. Raymond Calluori, Ph.D. Perry Deess, Ph. D.
Big Data Analytics for Institutional Effectiveness Presented at
NJAIR 20th Annual Conference, Institutional Effectiveness: IE and
IR. St. Peters University, Jersey City, NJ,4/4/2014.
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- Goals Simplify and automate IR activities Produce graphical and
tabular reports in a user-friendly way For users with fewer
technical skills within IR Self-serve report generation for other
departments Simpler than Cognos The big picture for senior
management Audit operational database data Overcome restrictions
FERPA protects student data Many stakeholders dont have access
privileges Dynamic report generation overcomes complexity of large
static reports
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- Technology Backend Microsoft SQL Server database Frontend
designed in Microsoft Visual Studio Hosted on Windows Server
Accessible from: Any platform via an internet browser Standalone
desktop application
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- NJIT IRP Factbook
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- Past Reports as Adobe PDF
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- Current Reports as Excel Pivot Tables
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- Big Data Analytics In high demand by the industry and academia
Scale differs by industry, e.g. bioinformatics vs. academics
Features: Large scale of data Powerful servers are required for
processing Components of a dashboard: Backend database Tabular
representation Graphic representation
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- ETL Complications ETL = Extract, Transform, Load process ETL is
needed to build a backend database Historical data is spread among
multiple databases Data specifications lost/unknown Data need to be
unified Attributes missing Attributes coded differently Attributes
spread among multiple tables within the same database
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- The Dashboard More than 30 years of data Accurate: from 1982
Partial: 1957-1981 Multiple dimensions Tabular & graphical
representation Overcomes FERPA restrictions by aggregating data
Impossible to identify a person Privacy concerns Does not contain:
names, SSNs,emails, etc. Access allowed for secured user
accounts
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- Dashboard Main Screen
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- Dashboard Structure Consists of multiple tabs on the top Each
tab contains a pivot table and a linked chart Pivot table has
several areas: filter area, column headers, row headers, and data
area Attributes can be moved between areas
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- Enrollment Tab, 1983-2013 This view of enrollment contains
Student IDs in the data area Semester type and year in the row
header area
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- Enrollment Tab, 1983-2013 (cont.) Added student level
(U/G)
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- Enrollment Tab (continued) Past 5 years enrollment by level and
attendance status
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- Bachelors Retention, 1988-2012 Cohorts Total retention by
year
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- Bachelors Retention, 1988-2012 (cont.) Female vs. male
retention
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- Bachelors Graduation, 1988-2007 cohorts Six-year full-time
first-time undergraduates graduation rates By Ethnicity
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- Dashboard Screens Main dashboard (static) Dynamic: Applications
Enrollment Bachelors Retention Masters Retention Bachelors
Graduation Masters Graduation Awarded Degrees Ph.D. Retention Ph.D.
Graduation
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