Higher Education Analytics: Key Star Schemas for Institutional Research

by Parul Singh

The need for robust reporting and a structured enterprise data warehouse for educational institutions is becoming a necessity.  This type of modern architecture empowers academic institutions with best-in-class reporting and analytic capabilities to increase faculty, staff and student productivity; streamline operations and institutional advancement; and ensure student success.

There are a few key star schemas that can aid universities and their stakeholders to make informed decisions and deliver a better student experience:

1. Central Campus (CFO, OPA, etc.)
2. Deans & Chairs
3. Department Curriculum Coordinators
4. Department Curriculum Coordinators
5. Department Schedulers, Advisors, Faculty

Course & Student Enrollments:

Decision support helps to inform and transform the way educational institutions analyzes, supports, communicates, and plans their curriculum.  A university would better support students by integrating information on enrollment patterns, current course availability, degree requirements, and student course history.

Sample Course & Student Enrollment Analysis:

1. Which courses have increase in demand over the years, or the demand is decreasing i.e., Is this course filling slowly than it usually does?
2. Which course, department, division or college has the highest average course grade points?
3. Which classes in a semester were below the minimum standard enrollments?
4. Enrollment counts compared over weekly basis to determine drop outs, waitlists etc
5. Have we offered enough sections to meet the demand for this course?
6. Before instruction starts, which classes are at >=95% of their seat limit?
7. What students are taking the courses we are offering? (ethnicity, gender, hometown, residency status, etc.)
8. What % of students taking our courses are in the major, from a different major, undeclared?

Teaching Activity:

Analysis of teaching activity at a university can be designed to support curriculum-related analytics and optimize faculty workloads.

Sample Teaching Activity Analysis:

1. Who is teaching courses and how has that changed over time?
2. What is the faculty workload/teaching load?
3. What % of faculty are teaching UG, LD, UD courses?
4. What % of UGs are taught by faculty, GSIs, lecturers?

Student Applications:

Data analyzed from the application process allows universities to better understand the applicant pool, how is it changing over time, and how they gauge the strategies and success in recruiting a strong and diverse student body.

It also allows them to view statistics on SAT scores, ACT scores, high school GPAs, and transfer GPAs, for potential students who apply, are admitted or submit statements of intent to register.

Sample Student Application Analysis:

1. Which courses have the highest headcount of applicants?
2. Year wise analysis of headcount for applicants, admits, and statements of intent to register
3. Compare the headcount of applicant, admits etc with other dimension entities ie residency status, gender etc..
4. What was the admit and yield rates by college/school compared over the years?
5. Percentage of admit students over GPA , SAT and ACT scores

Student Financials:

It will bring together student financial data including costs to students, financial aid, and other student income so we can understand trends, opportunities, and impacts of external events and make the best decisions possible regarding access to affordable education and financial planning. We could also analyze the balances due and the aging period for them.

Sample Student Financial Analysis:

1. What are the balances due aging more than 90 days?
2. Comparative Analysis of Financial aid provided by federal over the years
3. Percentage of Expenses incurred by the student i.e. tuition fees, library fees etc
4. Fees collected by university for various types

These are just high level representations of the star schemas that can provide or help universities take an informed decisions.  The scope can be grown wider in scale or be made more specific by bringing in other dimensions or metrics.

Parul Singh is a Senior Manager at KPI Partners and works with the expert team within the KPI Partners Offshore Technology Center. She is a Data Warehouse Specialist whose areas of professional expertise also include Oracle Business Intelligence Enterprise Edition and the Oracle BI Applications.  Check out Parul’s blog at KPIPartners.com.