Science Seminar Speaker: Kevin Haudek, Michigan State University


Wednesday, November 14, 2018 - 12:00pm to 1:30pm


133F Erickson Hall

Title: Using Automated Analysis to Reveal Student Thinking in STEM

Abstract: In the Automated Analysis of Constructed Response (AACR) Research Group, we develop constructed response assessment items and associated predictive machine learning models that allow formative evaluation of student writing. Our items and computerized analyses are particularly informative for revealing prevalent student ideas in large-enrollment, introductory STEM courses. We employ a mixed-methods approach to develop these questions and analytic tools, which reveal a rich and complex picture of student thinking about important disciplinary ideas across biology, statistics and chemistry. Recently, our group launched projects that develop assessment items aligned with learning progressions in physiology for undergraduates and scientific argumentation for middle school students. We have also begun to explore for possible interactions between predictive scoring models and responses from English language learner middle school students.
This talk will provide a brief overview of the methodology we employ to develop automated predictive scoring models and some examples of developed items, which reveal that students have complex, mixed models of thinking about key STEM ideas. Also, this talk will discuss our attempts to summarize and report this rich information to college faculty. Additionally, this talk will report on recent projects aimed at adapting these approaches to learning progressions.

Bio: Kevin Haudek is an assistant professor in the Biochemistry and Molecular Biology department at Michigan State and engages in discipline-based education research (DBER). His research interests focus on uncovering student thinking about key concepts in biology using formative assessments. As part of this work, he investigates the application of computerized tools to help evaluate student writing. These tools enable both exploration of ideas in student writing, as well as applying machine learning models to predict expert scores of student responses. He is currently leading projects associated with the Automated Analysis of Constructed Response (AACR) group, which conducts this research and development of such tools. Kevin earned his Ph.D. in Biochemistry at MSU, then transitioned into undergraduate science education research for his postdoctoral studies and became engaged in DBER.