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Student Progress Dashboard Versus Legacy Academic Monitoring for Medical Student Support: A Consolidated Framework
Karina R Clemmons1, Lindsey Sward1, John Spollen1
1College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA.
The Student Progress Dashboard (SPD) aids medical students by identifying those at risk and enabling timely interventions. This tool centralizes data for proactive support, improving academic advising and stakeholder efficiency in the evolving USMLE landscape.
Area of Science:
- Medical Education
- Educational Technology
- Student Support Services
Background:
- The USMLE Step 1 transition to pass/fail and declining pass rates increase pressure on medical students.
- Traditional monitoring systems are siloed, hindering effective student support.
- There is a need for precision education approaches to identify and support at-risk medical students.
Purpose of the Study:
- To develop and evaluate an innovative Student Progress Dashboard (SPD).
- To apply precision education principles for identifying at-risk students.
- To facilitate holistic and timely interventions for medical students.
Main Methods:
- A mixed-methods evaluation using the Consolidated Framework for Implementation Research (CFIR).
- Integration of retrospective-prospective system comparison, descriptive analytics of adoption, and stakeholder experience assessment.
- Surveys and task analyses were used to gather qualitative and quantitative data.
Main Results:
- The SPD was successfully adopted by faculty advisors, course directors, and learning specialists within one year.
- The dashboard provided a significant Relative Advantage over legacy systems, improving efficiency in identifying students near performance thresholds.
- Stakeholders reported clearer performance views and adjusted support strategies based on dashboard insights.
Conclusions:
- The SPD is a feasible and scalable tool for the current medical licensing environment.
- Centralizing data in the SPD enables proactive, individualized student support and enhances stakeholder productivity.
- While resource-intensive, the SPD's benefits in intervention timeliness and efficiency justify the investment, with future plans for machine learning integration.
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