Modeling the growth of students' covariational reasoning during an introductory statistics course

Annual Meeting of the American Educational Research Association, April
Zieffler, A., & Garfield, J.
Chicago, Ill

This students examined students' development of reasoning about quantitative bivariate data during a one-semester university-level introductory statistics course. There were three research questions of interest: (1) What is the nature, or pattern of change in students' development in reasoning about bivariate data?; (2) Is the sequencing of bivariate data within a course associated with changes in the pattern of change in students' reasoning about bivariate data?; and (3) Are changes in students' reasoning about the foundational concepts of distribution associated with changes in the pattern of development of students' reasoning about bivariate data?<br>Students' covariational and distributional reasoning were measured four times during four sections of an introductory statistics course using instruments developed by the NSF-funded ARTIST project. Two instructors were used as blocks to randomly assign each of four sections of the course to one of two different instructional sequences.<br>Data were analyzed using linear mixed-effects model (LMM) methodology. The results of the analyses suggest that students tend to exibit both linear and quadratic rates of change in their development of covariational reasoning. The results also suggest that the instructional sequence did not have a statistically significant effect of development of reasoning. There was some evidence that students' development of reasoning about univariate distribution was significantly positively related to the quadratic rate of development of their reasoning about bivariate data.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education