Electronic Testing to Enhance Learning in the School of Medicine

Dean Rosenthal (Biochemistry and Molecular and Cellular Biology) · Round 1

Ultimately, the proposal was focused on student learning but the faculty benefitted as well. By tagging these questions, it really was a learning opportunity for the faculty.

Dean Rosenthal

For medical students and those in related health sciences, life is defined by licensure examinations. To help students prepare for the examinations, Dean Rosenthal and a team of School of Medicine faculty and staff utilized the Global Evaluation Management System (GEMS), a comprehensive, integrated online platform for creating, managing, and delivering normed and other examinations.

Implementing GEMS allowed School of Medicine faculty to standardize the testing experience and enhance learning through assessments. Secure access control in this reliable, proven tool allowed course directors and faculty to build a collaborative item bank, tag items by custom categories, and share or modify items to build assessments with the desired balance of discipline-specific content and level of complexity. Faculty embedded immediate and specific feedback on difficult concepts to learners once they submitted their exams. Faculty then used the robust analytics of GEMS to 1) identify consistent areas of student deficit, 2) monitor individual student performance longitudinally, 3) modify teaching sessions, 4) customize remediation, and 5) conduct program evaluation.

Faculty participating in the project benefitted by creating challenging questions for GEMS. By tagging individual questions by discipline and intellectual difficulty, they became more aware and adept at writing questions.

They also became more aware of how individual questions could be edited to better assess student learning, covering the range of basic facts by application of concepts and taking advantage of opportunities for critical thinking. Students who participated in this study shared that they found the software a benefit to their learning.