Day:
Wednesday, October 28,2020
Time:
3:15 PM - 5:00 PM
First Learning Outcome: Understand potential logic models for rethinking SEM based data collection, assessment, and modeling
Second Learning Outcome: Identify lessons learned regarding the use of logic models applied to SEM
Third Learning Outcome: Envision mitigation efforts and potential paths forward likely to provide sustainable SEM processes and student outcomes
Core Competencies: Holistic and Systemic Thinking, Technological Knowledge
Proficiencies: Enrollment Management: SEM Assessment, Enrollment Management: SEM Leadership
Intended Audience: Some experience in the profession, Faculty
Post Pandemic SEM Planning: Rethinking Data Collection and Predictive Analytics to Recalibrate Recruitment and Retention Activities
Category
Workshop
Description
The most effective SEM programs have been highly regimented with the use of active market data, consistent student assessment measures, and predictive modeling throughout the student lifecycle. The 2020 Pandemic has seriously limited most of the engagement and data collection activities that previously supplied reliable information and performance milestones. Now that fall enrollments have been registered and the preliminary impacts can be measured, most colleges and universities are focusing on rebuilding their projection models and developing responsive recruitment and retention programs. This session is designed to address potential logic models for rethinking SEM based data collection, assessment, and modeling in light of the changes caused by the COVID-19 Pandemic. The speakers will address specific “lessons learned”, mitigation efforts, and protentional paths forward that are likely to provide a more dependable and sustainable re-set of key enrollment management processes and student outcomes.
Submission ID:
14473
Presenter(s):
Jay Goff George Washington University