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Big Data Analytics for Institutional Effectiveness

Big Data Analytics for Institutional Effectiveness. Alex Rudniy, Ph.D. Raymond Calluori , Ph.D. Perry Deess, Ph. D. Presented at NJAIR 20th Annual Conference, Institutional Effectiveness: IE and IR. St. Peter’s University, Jersey City, NJ,4/4/2014. Goals.

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Big Data Analytics for Institutional Effectiveness

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  1. Big Data Analytics for Institutional Effectiveness Alex Rudniy, Ph.D. Raymond Calluori, Ph.D. Perry Deess, Ph. D. Presented at NJAIR 20th Annual Conference, Institutional Effectiveness: IE and IR. St. Peter’s University, Jersey City, NJ,4/4/2014.

  2. 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 don’t have access privileges • Dynamic report generation overcomes complexity of large static reports

  3. 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

  4. NJIT IRP Factbook

  5. Past Reports as Adobe PDF

  6. Current Reports as Excel Pivot Tables

  7. 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

  8. 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

  9. 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

  10. Dashboard Main Screen

  11. 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

  12. Enrollment Tab, 1983-2013 • This view of enrollment contains • Student IDs in the data area • Semester type and year in the row header area

  13. Enrollment Tab, 1983-2013 (cont.) • Added student level (U/G)

  14. Enrollment Tab (continued) • Past 5 years enrollment by level and attendance status

  15. Bachelors Retention, 1988-2012 Cohorts • Total retention by year

  16. Bachelors Retention, 1988-2012 (cont.) • Female vs. male retention

  17. Bachelors Graduation, 1988-2007 cohorts • Six-year full-time first-time undergraduates’ graduation rates • By Ethnicity

  18. 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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