The Design and Implementation of a Virtual Statistical Computing Lab to Teach R Coding to Introductory Statistics Students

2026-09-08

AI Summary

研究设计并实施了一个虚拟统计计算实验室，通过两种R编码教学方式提高统计学入门学生的概念学习和数据科学准备度。

Abstract

Motivated by national calls for computationally enriched, data-centric instruction across the statistics curriculum, this study investigates the design, implementation, and impact of a Virtual Statistical Computing Lab (VSCL) integrated into an introductory statistics course at a medium-sized minority-serving university in the USA. The redesigned course embedded R-based coding through two virtual lab formats: Design I (a static Posit Cloud environment) and Design II (an interactive learnr-based interface). Using a quasi-experimental design across three instructional formats, traditional (no lab), Design I, and Design II, we evaluated students'conceptual learning gains, levels of data science (DS) readiness, and DS aspirations. The results indicated significant learning gains across all groups, with the highest gains observed in Design II. Students in both VSCL formats achieved greater gains in DS readiness than the traditional group, with Design II again yielding the largest gains across the demographic subgroups. Conversely, DS aspirations remained low or declined, suggesting a gap between skill acquisition and long-term interest. These findings highlight the value of structured, interactive computing environments in supporting statistical reasoning and building confidence in modern data tools. They also point to the need for intentional curricular bridges and career mentoring to help students translate early computing exposure into sustained academic and professional pathways in statistics and data science.

Problem

• Virtual Statistical Computing Lab
• R Coding
• Data Science Readiness
• Introductory Statistics
• Student Learning Gains

Innovation

• Virtual Statistical Computing Lab
• interactive learnr-based interface
• data science readiness