MedU Research Grant Recipients Announced
Congratulations to the 2014-2015 MedU research grant recipients! There were a record number of submissions this year and it was a challenging review process. The two projects below were selected as the grant recipients. We look forward to working with them and seeing results of their research. For more information on MedU Grant Opportunities.
Using Learning Analytics to Assess Clinical Reasoning in an Online Learning Enviroment
Principle Investigator: Susanne P. Lajoie, PhD (McGill University)
The project will assess medical student performance in MedU based on both responses to embedded multiple-choice questions and written case summaries. The outcomes of the investigation will provide a better understanding of learners’ strengths and weaknesses, which ultimately can lead to the design and implementation of personalized feedback to benefit future learners. This research will also demonstrate to educators, administrators, and researchers the strengths and limitations of data mining techniques for making informed decisions related to institutional and pedagogical resources.
The Impacts of Cognitive Load on Diagnostic and Therapeutic Reasoning during the Medical Encounter
Principle Investigator: Katherine Picho, PhD (Uniformed Services University)
The purpose of this study is to gather validity evidence for correlates of cognitive load in medical students and to explore the impact of cognitive load on students’ clinical reasoning performance. Both intrinsic and extrinsic cognitive load represent a potential means of assessing both learning and performance in medical education.
The proposed project has two specific aims: (1) to examine correlates of cognitive load and clinical reasoning performance, and (2) to gather validity evidence for the use of various cognitive load measures for learning and performance.
Improving our understanding of the role of cognitive load on diagnostic and therapeutic reasoning is needed and this study has the potential to inform efforts geared towards optimizing student learning and test performance, under a variety of conditions while solving virtual patient cases.