发表机构
Department of Mathematics & Statistics, North Carolina A&T State University（北卡罗来纳农业州立大学数学与统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究设计并评估了两种虚拟统计计算实验室（VSCL）形式，发现交互式learnr界面在提升入门统计学学生的概念学习和数据科学准备方面效果最佳，但学生长期DS志向仍低，需课程与职业支持。
AI 中文摘要
受全国范围内对统计学课程中计算强化、数据驱动教学呼声的推动，本研究调查了在美国一所中等规模的少数族裔服务大学中，将虚拟统计计算实验室（VSCL）整合到入门统计学课程中的设计、实施及影响。重新设计的课程通过两种虚拟实验室形式嵌入基于R的编码：设计一（静态Posit Cloud环境）和设计二（基于交互式learnr的界面）。采用跨三种教学形式的准实验设计——传统（无实验室）、设计一和设计二——我们评估了学生的概念学习收益、数据科学（DS）准备水平及DS志向。结果表明，所有组的学习收益均显著，其中设计二的收益最高。两种VSCL形式的学生在DS准备方面的收益均高于传统组，且设计二在各人口统计子群体中再次产生最大收益。相反，DS志向保持低位或有所下降，表明技能获取与长期兴趣之间存在差距。这些发现凸显了结构化、交互式计算环境在支持统计推理和建立对现代数据工具信心方面的价值。它们还指出了需要有意的课程桥梁和职业指导，以帮助学生将早期的计算接触转化为统计学和数据科学领域的持续学术与职业路径。
英文摘要
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.
