Join us on Monday, March 19 from 12:00 p.m. to 1:30 p.m. at North Quad Space 2435 (105 South State Street.) for AIM Analytics as we welcome Timothy J. Nokes-Malach, an Associate Professor of Psychology and a Research Scientist at the Learning Research and Development Center at the University of Pittsburgh.
AIM Analytics is a bi-weekly seminar series for researchers across U-M who are interested in learning analytics. The field of learning analytics is a multi and interdisciplinary field that brings together researchers from education, learning sciences, computational sciences and statistics, and all discipline-specific forms of educational inquiry.
Using Big Data to Understand Factors that Affect Student Success in STEM
Understanding both the barriers as well as the pathways to student success is critical to support all students to learn and achieve in STEM. In this talk, I will describe a research project that brings together an interdisciplinary team of learning scientists to understand the factors that affect student success in STEM disciplines. We are particularly concerned with questions about for whom educational innovations are effective, and their longitudinal outcomes. I will describe the overarching project and how we bring together different types of data and expertise to answer these questions. I will then describe one strand of the project in depth that has focused on understanding underrepresentation of women in physics. As a first step, we have begun to examine student motivational and performance patterns across multiple large introductory physics courses. The findings have implications for the development and implementation of pedagogies and learning tools to help all students learn.
Timothy J. Nokes- Malach received his Bachelors degree from the University of Wisconsin Whitewater, PhD from the University of Illinois at Chicago, and Postdoctoral training at the BeckmanInstitute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. His research examines human learning, problem solving, and motivation with an aim to understand, predict, and promote knowledge transfer. Specific topics include: identifying the cognitive and metacognitive processes underlying transfer success and failure, exploring the relations between instruction, motivation, cognition, and transfer, and examining social and ecological processes that support or inhibit transfer. An overarching goal is to develop instructional theories to promote learning and transfer in mathematics and science. His work has been supported with grants from the Pittsburgh Science of Learning Center, the National Science Foundation, the Department of Education’s Institute for Education Sciences, and the James S. McDonnell Foundation.
To register for this event, please RSVP below. Lunch will be provided.