Presentation by Emily Akil, Evan Baum, and Arnel Bulaoro at the NASPA Conference 2016.

1. Harnessing Decentralized Data to Improve
Advising and Student Success
NASPA 2016, Indianapolis, IN

2. Introductions and Learning Objectives
 Identify and prioritize a comprehensive picture of sources of data within
the institution that, if shared across silos, could improve holistic advising
among units and providers;
 Develop strategies for collaborating with information technology,
institutional research, and other campus partners who own data sources
that could benefit from being shared;
 Differentiate between methods and reasons for utilizing data to drive
proactive outreach and interventions compared to reactive outreach and
interventions

3. Small Group Activity
In 5 minutes, identify as many sources of
student data that exist within your institution
and prioritize their importance for informing
student success and advising efforts.

4. Descriptive
Data
Student demographics - race, gender, ethnicity, first-generation status, citizenship, age, marital status, veteran status
Pre-college variables - HS GPA, placement tests, application data, dual enrollment, AP/IB/CLEP, work history, prior
degrees
Financial information (EFC, unmet need, scholarship, work study, Pell/TRIO, payment plans, financial transactions)
Enrollment &
Academic
Program Data
Course level - developmental, withdrawals, incompletes, repeats, midterm and final grades, substitutions/waivers
Program level - major, minor, specialization, credits earned/attempted/remaining, program and cumulative GPA
Registration data - term enrollment status, waitlisted or overloads, demand forecasts
Instructional
& Classroom
Data
LMS data – gradebook averages, assignment grades/submission dates, discussion board activity, login activity
Attendance and participation – absences, tardiness, excused, clicker data, interactive/adapter learning data
Course and instructor type – traditional, hybrid, online only, experiential, full-time/part-time, TAs, instructor
modalities
Noncognitive
& Behavioral
Data
Entry surveys – college readiness assessments, intake surveys, orientation data
Attitudinal data – self-efficacy, motivation, resilience, social integration, sense of belonging
Other – service utilization, co-curricular learning assessments, satisfaction data, career and personality inventories
Engagement
Data
Non-curricular - involvement data, special programs or cohorts, conduct status, work/family commitments
Card swipe/barcode reader data - housing, dining, library, campus recreation, event attendance
Communication data – email open rates, social media engagement, website content analytics
Predictive &
Prescriptive
Data
Risk data (student) - persistence probabilities, velocity indicators, propensity score matching
Risk data (course) - obstacle or milestone courses, gateway requirements
Risk data (intervention) – which interventions matter, when, and how much is needed, for which students?

5. More Data = More Effective?

6. Effectively Sharing Data with Campus Stakeholders
University of Notre Dame

7. Effectively Sharing Data with Campus Stakeholders
University of Notre Dame

8. Effectively Sharing Data with Campus Stakeholders

9. Data-Driven Advising
 Student Populations you may collect data on
 First-Generation
 Ethnic Minorities
 Transfer Students
 Do these students need different types of support
(or challenges) when it comes to advising?
 Utilizing decentralized sources of data can help
those advising students to ensure a variety of
actions are possible:
 Keep students on track academically
 Students eligible for graduation know they are
eligible and improving completion rates
(Straumsheim, 2015).
 LGBTQIQ Students
 At-risk Students
 Non-traditional Students

10. Using Data Proactively and Reactively
 Reactive use of data
 Data is not collected until situations force the organization to act.
 This reaction could be in response to another organization and attempting to keep up on trends in the field
or it could be in response to an issue on campus that needs to be addressed
 Proactive use of data
 Data is collected in order to continually analyze the environment for patterns that would allow organizations
to improve their performance.
 This reaction allows organizations to be a bit ahead of the game; however, it’s not always possible to know
what data to collect in order to do so continuously.
 Moving from theory-based models (proactive) to on-the-ground realities (reactive)
 Lots of our interventions are based on theory and proactive intentions
 Are we well equipped to be effectively react?

11. From Descriptive to Prescriptive

12. Lessons Learned – What to Consider
Focus on people and culture, not data and systems
Start with the end in mind (and widely share it)
Remember that you may not always end up with the initially
desired outcome/results.

13. Q&A and Contact Information
 Emily Akil
Academic Advisor, Miami University
Emily.Akil@MiamiOH.edu
 Evan Baum, Ph.D.
Director, Student Success and Advising, Hobsons
evan.baum@hobsons.com
 Arnel Bulaoro
Assistant Director, University of Notre Dame
Arnel.A.Bulaoro.2@nd.edu

14. References
Dost, M., and Tannous, J. (2013). Adopting a campus wide student note system. Washington, DC: Educational Advisory Board.
Eduventures. (2013). Predictive analytics in higher education: Data-driven decision-making for the student lifecycle. Boston, MA:
Author.
Hickman, C., and Koproske, C. (2014). A student-centered approach to advising: Redeploying academic advisors to create
accountability and scale personalized interventions. Washington, DC: Educational Advisory Board.
Lee, J.M. and Keys, S.W. (2013). High tech, high touch: Campus based strategies for student success. (APLU Office of Access and
Success Report 2013-01). Washington, DC: Association of Public and Land-grant Universities.
Light, R. (2004). Making the most out of college: Students speak their minds. Cambridge, MA: Harvard University Press.
McAleese, V. and Taylor, L. (2012). Beyond retention: Early identification and intervention with first year students. Proceedings of the
Eighth Annual National Symposium on Student Retention, Charleston, SC.
Straumsheim, C. (2015, December 9). Using data-driven advising, colleges find more students eligible to graduate. Retrieved
February 24, 2016, from https://www.insidehighered.com/news/2015/12/09/using-data-driven-advising-colleges-find-more-students-
eligible-graduate