An Assessment Data–Driven Decision Model for Identifying At-Risk Medical Students Utilizing a Holistic View of Pre-Matriculation and Post-Matriculation Variables

• In Event: Marriott Roundtable Session Eight
  In Roundtable Session: Issues in Assessment Design and Use (Table 3)

Abstract

Academic progress interruptions are problematic for medical schools in terms of medical education operation planning and resource allocation and for medical students in terms of success, retention and personal progress. Identifying at-risk medical students as early as possible has become increasingly important as remediation could be beneficial to medical students. The purpose of this study is to identify significant predictors influencing medical student performance on medical licensing examination to design an assessment data driven decision model to identify at-risk medical students as early as possible. In this study we will investigate factors influencing medical student performance utilizing both pre-matriculation and post matriculation data longitudinally in order to design a systemic approach to identify at-risk medical students as soon as possible.

Authors

• Mostafa Hosseinzadeh, Methodist University
  Presenting Author

• Chris S. Candler, University of Oklahoma
  Non-Presenting Author