How Students Learn Introductory Statistics: Modeling Motivation, Engagement, and Performance Pathways (SDSS conference, Milwaukee, WI)

Authors: Icy(Yunyi) Zhang, Claudia Sutter, Yinqiu He, Ji Son

Summary: This study addresses three persistent barriers in the literature: contextual limitations of laboratory-based studies, measurement limitations in assessing engagement, and insufficient attention to individual differences that shape motivational processes. Leveraging a shared research infrastructure embedded within the CourseKata statistics platform, we integrate repeated motivation surveys, behavioral log data capturing student engagement, and embedded performance assessments across authentic high school and college statistics courses. This infrastructure enables longitudinal modeling of motivational beliefs, engagement behaviors, and learning outcomes as they occur in real instructional settings. Preliminary analyses show that behavioral engagement consistently predicts performance, but the motivational pathways leading to engagement differ depending on students’ initial mindset beliefs. For students with stronger fixed mindset beliefs, perceived cost disrupts engagement and performance, whereas students with lower fixed mindset beliefs show more consistent links between motivation, engagement, and achievement. These findings suggest that motivation does not uniformly translate into learning behaviors or outcomes, but instead depends on students’ beliefs about their ability.