Scholar User Group’s Updates

Critical Clinical Thinking in Scholar

Complex Problem Solving and Critical Thinking: Online Tools and Computational Approaches

  • Join us for a Workshop:  11.00-12.30, Monday, November 9, followed by lunch, Education Building, Room 166, University of Illinois, Urbana-Campaign

The Challenge: Complex problem solving and critical thinking are required in today’s medical, design, and engineering professions. Knowledge in these domains must frequently be presented in the form of an argument, particularly alternative application scenarios in context-specific cases. However, much of our teaching and assessment is still focused on empirically definite facts, and procedures that produce single, apparently ‘correct’ answers.

The Project: The general problem addressed by this project is how to teach and assess ‘complex epistemic performance’ such as critical thinking that weighs up alternatives, and problem solving that is context- and case-sensitive. Our solution uses the Scholar platform developed by U of I researchers to support multimodal knowledge representation and structured peer feedback, focusing on critical disciplinary practices and metacognitive strategies. We are also exploring computational possibilities, both around structured peer and instructor data and computational approaches that mine unstructured or semi-structured data emerging through all stages of the learning process.

The Intervention: With the support of the Illinois Learning Science Design Initiative (ILSDI), these possibilities are now being explored in the area of critical clinical thinking. Experiments are underway in first year medical curricula on campus: the Vet Cases Scholar community is home to Clinical Correlations cases in the College of Veterinary Medicine and the Cardiovascular Physiology community houses case analyses on this subject in the College of Medicine.

Join the project team for a presentation on this project, with a discussion of these educational challenges, as well as the emerging computational and learning-analytic approaches.

Project Team:

PI: Duncan C. Ferguson, V.M.D., Ph.D., Dept. of Comparative Biosciences, College of Veterinary Medicine

CIs: ChengXiang Zhai, Ph.D., Dept. of Computer Sciences, College of Engineering

William Cope, Ph.D., Dept. of Education Policy, Organization and Leadership, College of Education

Willem Els, Ph.D., Molecular and Integrative Physiology, College of Liberal Arts and Sciences, and College of Medicine

Chase Geigle, graduate student, Dept. of Computer Sciences, College of Engineering