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Harnessing Decentralized Data to Improve
Advising and Student Success
NASPA 2016, Indianapolis, IN
Introductions and Learning Objectives
 Identify and prioritize a comprehensive picture of sources of data within
the inst...
Small Group Activity
In 5 minutes, identify as many sources of
student data that exist within your institution
and priorit...
Descriptive
Data
Student demographics - race, gender, ethnicity, first-generation status, citizenship, age, marital status...
More Data = More Effective?
Effectively Sharing Data with Campus Stakeholders
University of Notre Dame
Effectively Sharing Data with Campus Stakeholders
University of Notre Dame
Effectively Sharing Data with Campus Stakeholders
Data-Driven Advising
 Student Populations you may collect data on
 First-Generation
 Ethnic Minorities
 Transfer Stude...
Using Data Proactively and Reactively
 Reactive use of data
 Data is not collected until situations force the organizati...
From Descriptive to Prescriptive
Lessons Learned – What to Consider
Focus on people and culture, not data and systems
Start with the end in mind (and wid...
Q&A and Contact Information
 Emily Akil
Academic Advisor, Miami University
Emily.Akil@MiamiOH.edu
 Evan Baum, Ph.D.
Dire...
References
Dost, M., and Tannous, J. (2013). Adopting a campus wide student note system. Washington, DC: Educational Advis...
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Harnessing Decentralized Data to Improve Advising and Student Success - NASPA 2016

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

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Harnessing Decentralized Data to Improve Advising and Student Success - NASPA 2016

  1. 1. Harnessing Decentralized Data to Improve Advising and Student Success NASPA 2016, Indianapolis, IN
  2. 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. 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. 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. 5. More Data = More Effective?
  6. 6. Effectively Sharing Data with Campus Stakeholders University of Notre Dame
  7. 7. Effectively Sharing Data with Campus Stakeholders University of Notre Dame
  8. 8. Effectively Sharing Data with Campus Stakeholders
  9. 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. 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. 11. From Descriptive to Prescriptive
  12. 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. 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. 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

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