Assessing the association between pre-course metrics of student preparation and student performance in introductory statistics: Results from early data on simulation-based inference vs. nonsimulation based inference

TL;DR

This study investigates how simulation-based inference (SBI) in introductory statistics affects student performance across different preparation levels, showing SBI improves conceptual understanding broadly regardless of initial student preparedness.

Contribution

It provides empirical evidence that SBI enhances conceptual understanding in statistics for students across all preparation levels, regardless of initial performance or math scores.

Findings

Students improved in all preparation levels with SBI.

SBI students showed greater improvement than traditional methods.

Gains were similar regardless of initial student preparation.

Abstract

The recent simulation-based inference (SBI) movement in algebra-based introductory statistics courses (Stat 101) has provided preliminary evidence of improved student conceptual understanding and retention. However, little is known about whether these positive effects are preferentially distributed across types of students entering the course. We consider how two metrics of Stat 101 student preparation (pre-course performance on concept inventory and math ACT score) may or may not be associated with end of course student performance on conceptual inventories. Students across all preparation levels tended to show improvement in Stat 101, but more improvement was observed across all student preparation levels in early versions of a SBI course. Furthermore, students' gains tended to be similar regardless of whether students entered the course with more preparation or less. Recent data on a sample of students using a current version of an SBI course showed similar results, though direct comparison with non-SBI students was not possible. Overall, our analysis provides additional evidence that SBI curricula are effective at improving students' conceptual understanding of statistical ideas post-course regardless student preparation. Further work is needed to better understand nuances of student improvement based on other student demographics, prior coursework, as well as instructor and institutional variables.