Kenneth Frank, Michigan State University
Guanglei Hong, University of Chicago
Stephen Raudenbush, University of Chicago
Yanyan Sheng, University of Chicago
Kaitlin Torphy, Michigan State University
Date: Thursday, February 25, 2021; 1:00pm - 3:30pm
This workshop seeks to inform participants of research questions, data structures, and advanced analytic techniques in the context of theory-driven and data-informed rigorous empirical investigations of STEM education, especially concerning under-represented groups. Cutting-edge methods are essential to study student and teacher experiences with STEM education programs developed, implemented, and evaluated in complex environments that outstrip what can be rendered by conventional statistical techniques. To illustrate major methodological considerations, instructors will use a stylized case that evaluates the potentially differential impacts of curricular innovations representing the Next Generation Scientific Standards on instructional practices, student engagement, and science achievement. Key methodological issues will be discussed:
(1) How to select the sample of schools and teachers and whether to adopt an experimental or a quasi-experimental design suitable for causal inference of the effects of the curricular innovation.
(2) How to construct theoretically grounded instruments with strong psychometric properties to measure student engagement, student learning, teacher practices, etc.
(3) How to represent and model teachers’ interactions with one another as they adapt and implement the new curriculum.
(4) ] How to represent and model the student, teacher, and school level factors that affect the implementation and outcomes of the curriculum.
(5) How to examine instructional practices as a mediator of the effects of the curriculum on student outcomes.
(6) How to account for teachers’ and students’ engagement with one another and educational resources on-line.