The Wieman group’s research falls into the following main categories:
HHMI (Howard Hughes Medical Institute) – The goal of this project is to develop problems to measure how well courses and undergraduate major programs are accomplishing the goal of training students (of various levels and disciplines) to transfer knowledge across scientific disciplines, effectively use newly learned information, and apply knowledge and skills in new contexts. This work is done in collaboration with a team of higher education researchers, data analytics experts, and faculty in medicine and science disciplines.
We have approached this project by studying how experts across science, engineering, and medical disciplines solve authentic problems in their work, identifying a set of decisions-to-be-made that were consistent across all the experts and fields we examined. We also found that the process of making those decisions relies heavily on domain specific predictive models that embody the relevant disciplinary knowledge and standards. We are then using this detailed characterization of the problem-solving process to guide efforts to improve the measurement and teaching of problem-solving in science, engineering, and medicine. We and our collaborators are developing problems to measure how well courses and departmental programs are training students to become expert-like problem solvers in a variety of disciplines. Current measurements developed or in development include: medicine (clinical reasoning and oncology), engineering (mechanical and chemical), and science (earth science, biochemistry, ecology, and physics). We are also working with instructors to improve teaching based on this framework.
(Argenta Price and Candice Kim)
Identifying student inquiry skills
We are examining the interplay between content knowledge and problem solving strategies, and how this relation is mediated by technology and socioemotional factors. Problem solving strategies are too often studied separately from social and emotional contexts and are evaluated too universally regardless of the accompanying content knowledge. The goals of this research are, primarily, to identify which problem solving strategies pave the way to expertise and which strategies paralyze the experts; and, secondarily, how socioemotional factors and technology use can positively or negatively contribute to these processes.
Physics Quantitative Literacy – Mathematics is often called the “language of physics,” and the ability to infer physical meaning from mathematical expressions—Quantitative Literacy—is essential for students to develop. Previous research in cognition has identified empirical models for how students interpret mathematical expressions, but we lack an understanding of how students develop these skills. Through think-aloud interviews with students who have varying levels of experience in physics, we are identifying the specific skills that students need to be able to interpret the physical meaning of mathematical expressions, and how those skills develop.
Cognitive principles for instructional design
Although current “active learning” efforts have been shown to provide better learning outcomes than traditional instructional methods, there is currently little guidance on how to design such materials to best support learning. We are designing, implementing, and studying instructional materials that take into account findings on human cognition, such as the benefits of inventing from a series of contrasting cases (e.g. Schwartz et al., 2011). By studying the efficacy of these materials, we hope to provide instructors, curriculum developers, and researchers with new principles for designing effective instructional materials for typical classroom instruction.
Effective and Inclusive Teaching
The Wieman group studies various factors influencing student performance in introductory STEM courses, including demographics, prior preparation, social-psychological factors, and variables related to students’ study habits and backgrounds. Our research has revealed that differences in high school preparation lead to differences in performance in introductory STEM courses, and the same research reveals that this is inherently discriminatory. We use a combination of qualitative and quantitative methods to determine what other factors are important for student success in these courses, and leverage this research to redesign introductory STEM courses to be more effective and more inclusive.
Understanding Student Performance in Introductory Physics Courses – Prior research in science education has shown substantial improvements in average student performance when shifting classrooms from a traditional lecture format to an active learning format. However, there are still large variations in performance between individual students in these courses. Using both quantitative and qualitative methods, we are investigating what factors explain the variability in student performance in introductory physics courses. One of our most consistent findings is that students’ high-school preparation explains a substantial portion of this variation, while other factors such as demographic variables are not significant predictors of students’ performance in physics. We are particularly interested in identifying factors that (i) help students with weak levels of high-school preparation and (ii) that are under instructors’ control.