CFEED at the Transfer Success Partnership Conference: Insights into Transfer Success

2/14/2025 – The CFEED team recently took the stage at the Transfer Success Partnership Conference to present groundbreaking insights into transfer student success. Led by Diana Pienaar (CFEED Director, Valencia College), Ashton Terry (Senior Manager, School District of Osceola County), and Sabrina Gonzalez Blohm (Senior Data Analyst, Valencia College), the presentation explored the key challenges transfer students face, CFEED’s role as a strategic partner, and how advanced machine learning models—specifically the Transfer Readiness Model—are driving better outcomes for students and institutions.

The Challenges of Transfer Success

For many students, the transition from a two-year institution to a four-year university presents significant hurdles that can impact retention and degree completion. During the session, CFEED highlighted some of the most common challenges institutions must address, including:

Credit Loss & Misalignment – Students frequently lose credits during transfer due to course incompatibilities, delaying graduation and increasing student debt.
Enrollment & Persistence Gaps – Many transfer students drop out before completing their degrees, often due to financial constraints, lack of academic advising, or unclear pathways.
Unclear Academic Pathways – Without structured course sequencing and degree planning, students risk taking unnecessary or non-applicable courses, extending their time to degree completion.
Transfer Shock & Readiness – Many students experience a decline in GPA and academic performance post-transfer, often due to gaps in preparation and institutional differences in curriculum expectations.

Without a data-driven strategy, institutions often lack visibility into these risks, making it difficult to intervene in time and provide targeted support. CFEED’s presentation emphasized the importance of early identification of student challenges and the role of predictive modeling in guiding interventions.

CFEED as a Strategic Partner in Transfer Success

As a trusted data and AI partner for higher education, CFEED empowers institutions to proactively address student challenges using real-time analytics and machine learning models.

By integrating institutional data, student progress tracking, and AI-driven insights, CFEED enables colleges and universities to:

Pinpoint where students struggle in the transfer process and implement interventions before they disengage.
Enhance advising and academic planning by predicting student outcomes based on course completion patterns.
Improve retention and graduation rates by aligning student pathways with their intended degree programs.
Enable faculty and administrators to make informed decisions with dynamic dashboards.

A strong component of CFEED’s work is the Transfer Readiness Model, a tool designed to assess how prepared students are for success at their transfer institution.

The Transfer Readiness Model: A Data-Driven Approach to Student Success

At the core of CFEED’s approach is the Transfer Readiness Model, which assesses students’ preparedness for their intended program based on the number of Relevant Courses they have completed at Valencia College.

Relevant Courses is a CFEED-defined term referring to key courses that, according to a student’s major, significantly influence their academic success after transferring to UCF. These courses provide a foundation in the discipline and align with program prerequisites to ensure students transition smoothly without unnecessary credit loss.

The model categorizes students into three readiness levels:

Developing: Students at this stage have completed few, if any, of the courses essential for their major. They may require additional academic support and guidance to stay on track for transfer.
On-Track: Students in this category have completed a moderate number of their major’s key courses and are making steady progress toward transfer readiness.
Prepared: These students have taken all the recommended Relevant Courses for their major, positioning them for a seamless transition and higher success rates post-transfer.

By leveraging machine learning models, institutions can predict which students are likely to struggle post-transfer and take early action to close achievement gaps.

For a deeper dive into the Dimensions of Transfer Readiness, visit CFEED’s official page.

The Power of AI and Predictive Analytics in Higher Education

During the session, CFEED demonstrated how AI and predictive modeling can be used to support student advising, academic planning, and institutional decision-making.

Some of the key takeaways from CFEED’s analytics-driven approach to student success include:

Predictive Analytics for Transfer Success – Institutions can use historical student data, course performance, and engagement patterns to predict which students will persist or struggle post-transfer.
Proactive Interventions for At-Risk Students – Instead of waiting for students to fail or drop out, institutions can use AI models to identify at-risk students early and offer tailored support services.
Institutional Decision-Making Powered by Data – Universities can optimize advising, course scheduling, and resource allocation based on real-time student progress tracking.

By leveraging CFEED’s data-driven tools, institutions gain the ability to make smarter, evidence-based decisions that enhance student success.

Looking Ahead: The Future of Transfer Student Success

CFEED’s work in data analytics, AI-driven interventions, and student success modeling continues to evolve, helping institutions create more efficient and equitable transfer pathways.

By collaborating with institutions, CFEED empowers educators, advisors, and administrators to:

Improve transfer student retention and degree completion rates.
Ensure students take the right courses for their intended majors, minimizing wasted credits.
Provide real-time insights for decision-makers to create policies that promote student success.

CFEED’s presentation at the Transfer Success Partnership Conference reinforced the transformational role of data and AI in higher education, ensuring that transfer students receive the support they need to thrive.

As more institutions embrace AI-powered analytics, the ability to proactively support transfer students will become a critical component of student success strategies in higher education.

Learn More & Connect

To see how CFEED’s initiatives can benefit your institution or to get involved, visit CFEED’s Homepage and follow us on LinkedIn for updates and discussions about enhancing student success across Central Florida. For more information, please contact Diana Pienaar, CFEED Director at Valencia College, via email at dpienaar@valenciacollege.edu.

About CFEED

Central Florida Education Ecosystem Database (CFEED) is a partnership, comprised of the Helios Education Foundation, the School District of Osceola County (SDOC), Orange County Public Schools (OCPS), Valencia College (Valencia), and the University of Central Florida (UCF), along with Midtown Consulting Group, that is engaged in a collaborative program to develop insights and create impacts for students in Central Florida.