Student Progress Dashboard Versus Legacy Academic Monitoring for Medical Student Support: A Consolidated Framework...

The 2022 transition of United States Medical Licensure Examination (USMLE) Step 1 to a pass/fail system, coupled with declining national pass rates, has intensified pressure on medical students to succeed on their first attempt and heightened the stakes for Step 2 CK (clinical knowledge) performance. In response to these evolving assessment demands, we developed an innovative Student Progress Dashboard (SPD) to move beyond siloed legacy monitoring. The goal was to apply the tenets of precision education to identify at-risk students and facilitate holistic, timely interventions.

A mixed-methods evaluation was conducted using the Consolidated Framework for Implementation Research (CFIR). The study integrated: (1) a retrospective-prospective comparison of system functionality, (2) descriptive analytics of system adoption and workflow fidelity among faculty and staff, and (3) a CFIR-guided assessment of stakeholder experiences through surveys and task analyses.

One year post-implementation, usage metrics and survey data indicated that the SPD has been successfully adopted by faculty advisors, course directors, and learning specialists. The dashboard demonstrated significant Relative Advantage (CFIR) over legacy spreadsheets by providing a comprehensive view of the student lifecycle. Results show improved efficiency in identifying students nearing performance thresholds. Course directors reported a clearer view of performance shifts across the curriculum, while learning specialists utilized specific content-area insights to adjust support strategies. Usage averaged 11.5 sessions per month across 25 unique users, with high fidelity to intended workflows.

The SPD is a feasible, scalable tool that addresses the increased pressures of the current medical licensing landscape. By centralizing disparate data into a unified platform, the dashboard enables proactive, individualized support. These findings suggest that while developing such tools is resource-intensive, the benefits, including improved intervention timeliness and enhanced stakeholder productivity, outweigh the investment. Future steps include integrating machine learning for predictive modeling to further refine career and academic advising.