Stability of Motivation in Introductory Statistics: A Random-Intercept Latent Transition Analysis (Poster 17)

• In Event: Sunday Poster Session 11:25 am
  In Poster Session: Mathematics Education Poster Session

Abstract

Introductory statistics course research often overlooks diverse and developmental aspects of student motivation, focusing on a variable-oriented approach. We analyzed 1,531 college students' motivational beliefs in such courses using a person-oriented analysis and the Situated Expectancy-Value Theory framework. Capturing combinations of expectancies, values, and costs related to statistics, four motivational profiles emerged at the beginning, middle, and end of the course. While most students maintained consistent motivation, some underwent transitions between motivational states, especially in the first half of the semester. Certain profiles, like High expectancies, showed greater stability than others. Gender predicted profile memberships and transitions, but race/ethnicity did not. Implications for practice and future research will be discussed.

Authors

• John Keane, University of Michigan

• Garam A. Lee, Michigan State University

• Utku Caybas, Michigan State University

• Goun Choi, Michigan State University

• Lisa Linnenbrink-Garcia, Michigan State University