harnessing decentralized data to improve advising and student success - naspa 2016
TRANSCRIPT
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 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
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.
Descriptive Data
Student demographics - race, gender, ethnicity, first-generation status, citizenship, age, marital status, veteran statusPre-college variables - HS GPA, placement tests, application data, dual enrollment, AP/IB/CLEP, work history, prior degreesFinancial 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/waiversProgram level - major, minor, specialization, credits earned/attempted/remaining, program and cumulative GPARegistration data - term enrollment status, waitlisted or overloads, demand forecasts
Instructional & Classroom
Data
LMS data – gradebook averages, assignment grades/submission dates, discussion board activity, login activityAttendance and participation – absences, tardiness, excused, clicker data, interactive/adapter learning dataCourse 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 dataAttitudinal data – self-efficacy, motivation, resilience, social integration, sense of belongingOther – 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 commitmentsCard swipe/barcode reader data - housing, dining, library, campus recreation, event attendanceCommunication data – email open rates, social media engagement, website content analytics
Predictive & Prescriptive
Data
Risk data (student) - persistence probabilities, velocity indicators, propensity score matchingRisk data (course) - obstacle or milestone courses, gateway requirementsRisk data (intervention) – which interventions matter, when, and how much is needed, for which students?
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 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
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?
From Descriptive to Prescriptive
Lessons Learned – What to Consider
Focus on people and culture, not data and systemsStart with the end in mind (and widely share it)Remember that you may not always end up with the
initially desired outcome/results.
Q&A and Contact Information
Emily AkilAcademic Advisor, Miami [email protected]
Evan Baum, Ph.D.Director, Student Success and Advising, [email protected]
Arnel BulaoroAssistant Director, University of Notre [email protected]
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