Detail

Summary

In this Connect + Learn session, Durham College shared the outcomes and lessons learned from its ONCAT-funded Transfer Credit Database Expansion initiative. The project focused on maximizing its student information system (SIS) to develop sustainable and efficient processes for maintaining credit transfer data. In turn, they were able to shift toward a more proactive model in which students can get credits assessed for transfer earlier in their academic journey.

Key takeaways from the presentation 

• Proactive pre-assessment is a data and process problem before it is a technology problem. Durham's system only works because the team first built a reliable equivalency foundation, adding more than a thousand external equivalencies since August 2025. 
• The operating model is: automate, then validate. Automated matching handles repetitive transcript comparison against equivalencies recorded in the student information system, while staff retain ownership of program applicability, exceptions and new assessments. Automation does not remove academic expertise from the process. 
• Eligibility does not equal recognition: about 40% of the time, applicants either did not pursue the validation process or credits that the system had pre-assessed as eligible for transfer were ultimately not validated.
• Testing the new system shifted the central question from how much can be automated to what should be automated. Successful matching immediately exposed more complex challenges: applicability, elective complexity and the need for deliberate control points. 
• Maintenance must keep pace with proactive recognition: the fall 2026 practical nursing curriculum change required Durham to remove outdated equivalencies before they shaped future credit decisions, showing that proactive recognition and proactive maintenance are inseparable. 
• Durham removed the $16 credit transfer application fee because it is a barrier for students. 

View the recording of this Connect + Learn. You can also view the presentation deck below.