Project Lead: Añuli C. Uzozie (Assistant Professor of Teaching & Director BMLSc Program, Pathology and Laboratory Medicine)
Co-Leads: Amanda Bradley (Associate Professor of Teaching, Pathology & Laboratory Medicine); Michael Sutherland (Adjunct Professor, Pathology and Laboratory Medicine)
Project Description
This project aims to evaluate a new Senate-approved 4th-year course that explores the application of modern techniques including digital pathology, next-generation sequencing, metabolomics, artificial intelligence, and machine learning in clinical- and research-based settings. The course will be delivered using evidence-based practices centered on pedagogical content knowledge (PCK), case-based collaborative learning, problem-based learning, experiential learning, and reflective inquiry. PCK emphasizes the intersection of content and pedagogy, and involves adapting teaching methods to the content and the learner’s needs. Course delivery, assessment strategies and feedback will be evaluated to assess level of student engagement, achievement of learning outcomes, and overall, improve quality.
Research Questions
- Do experiential learning activities with digital tools and automated equipment promote practical skills that improve student competency in medical laboratory science?
- Will problem-based small group activities using case studies improve collaborative skills and student understanding of advanced techniques in pathology? How does this impact their ability to interpret and explain experimental data acquired with these techniques?
- How well do assessments designed in line with the PCK framework measure student’s understanding of the course content? Do they evaluate student understanding and ability to critically analyze and explain technique applications?