The Design and Implementation of a Virtual Statistical Computing Lab to Teach R Coding to Introductory Statistics Students
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.
Related
- AI-Guided Assignments in Practice: Students' Experiences in a Large Introductory Statistics Course
- Statistical Leadership of What? Statistics After AI
- Computing statistical solutions of a Mach 2000 astrophysical jet
- Exploring continuous beta-ensembles: A Python implementation for random matrix spectral statistics
- Soft Computing Optimization of Industrial Robot Inverse Kinematics for Intelligent Manufacturing: A Statistical Benchmark of Metaheuristic Algorithms
BibTeX
@misc{mostafa2026design,
title = {{The Design and Implementation of a Virtual Statistical Computing Lab to Teach R Coding to Introductory Statistics Students}},
author = {Sayed A Mostafa and Tamer M Elbayoumi and Seongtae Kim and Oluwatobi Akinbode},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.08868},
eprint = {2609.08868},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.08868},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Design and Implementation of a Virtual Statistical Computing Lab to Teach R Coding to Introductory Statistics Students
AU - Sayed A Mostafa
AU - Tamer M Elbayoumi
AU - Seongtae Kim
AU - Oluwatobi Akinbode
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2609.08868
UR - https://arxiv.org/abs/2609.08868
AB - 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.
ER -