Student Engagement in Statistics Using Technology: Making Data Based Decisions

学生利用技术参与统计：做出基于数据的决策

• 批准号: 1712475
• 负责人: Shonda Kuiper
• 金额: $ 30万
• 依托单位: Grinnell College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712475&HistoricalAwards=false
• 关键词: Student; Engagement; Statistics; Using; Technology

There are ongoing national needs on two important fronts for improvements in undergraduate statistics and data science education. There is a need for United States residents to establish a deeper understanding of these arenas in order to make informed decisions in an increasingly diverse and complex society. Likewise, there is a fundamental need for the country to produce college graduates in science, technology, engineering, and mathematics (STEM) areas who can apply statistics and data science to help provide the Nation with a globally competitive STEM workforce. The investigators on this project will address these needs by designing, developing and evaluating inquiry-based online technology that will simulate current real-world scenarios in statistics and data science to connect students to the importance of the investigative process of problem-solving and data-based decision making and to the skills needed for these activities. In connection with this, the project will take advantage of large, publically available datasets which are now easily accessible to engage students with research-like experiences and technologically interactive educational materials to foster students' abilities in understanding and applying statistics and data science. In addition to materials for students, resources to be developed will be specifically designed to help instructors incorporate key ideas typically not taught in traditional textbooks, such as interactive visualizations, working with messy data, bias, data relevance, reliability, and full research-like experiences involving real-world data. A key aim of the project is to advance STEM learning through the creation, implementation, and testing of inquiry-based, interactive, online investigative labs that will simulate data-based decision making. Goals of the project include: (1) incorporating research-like experiences for students into their studies; (2) addressing the increased importance of data science and challenging statistical concepts not easily addressed in current courses and textbooks; (3) developing full story line models of real-world scenarios using game-like simulations; and (4) creating and vetting materials that can be incorporated into a variety of traditional introductory and advanced undergraduate courses. The project team's theory of action is built on developing, implementing and assessing each simulation lab and other components of the project according to these goals. An important motivation is to allow students to develop their own research questions, generate and use their own unique data to make decisions, and then observe and learn from the choices made through their interactions with the technology. To evaluate success of the approaches taken, the project will employ a mixed methods approach to compare learning gains from more traditional materials to the gains made with these new materials, evaluate student attitudes and engagement, use data analytics to assess the effectiveness of the components embedded in each lab, and determine best practices for incorporating the resources into a variety of traditional introductory and advanced undergraduate courses. These fully immersive online game-like labs will be significantly different from other current textbook and online sets of educational materials as each lab will include inquiry-based case studies that contain real-world data analysis complexities, thereby providing a solid introduction to the intellectual content and broad applicability of statistics and data science. In concert with the software lab development, the team will create and vet a new student assessment tool that will be implemented to evaluate students' abilities related to understanding of conceptual connections, communication skills, critical thinking, and problem-solving, as well as their ability to understand and work through challenges emanating from real-world, unstructured datasets. Overall, this project will have significant impact in STEM education by stimulating the power of innovation, creativity, and excitement that occurs within STEM research and applications.

目前，国家在两个重要方面需要改进本科统计和数据科学教育。美国居民需要对这些领域有更深入的了解，以便在日益多样化和复杂的社会中作出知情的决定。 同样，国家也需要培养科学，技术，工程和数学（STEM）领域的大学毕业生，他们可以应用统计和数据科学来帮助国家提供具有全球竞争力的STEM劳动力。该项目的研究人员将通过设计，开发和评估基于调查的在线技术来满足这些需求，该技术将模拟统计和数据科学中的当前现实世界场景，将学生与解决问题和基于数据的决策的调查过程的重要性以及这些活动所需的技能联系起来。在这方面，该项目将利用现有的大型数据集，让学生获得类似研究的经验和技术互动教育材料，以培养学生理解和应用统计和数据科学的能力。除了为学生提供的材料外，还将专门设计开发资源，以帮助教师结合传统教科书中通常没有教授的关键思想，例如交互式可视化，处理混乱的数据，偏见，数据相关性，可靠性以及涉及真实世界数据的完整研究体验。该项目的一个主要目标是通过创建，实施和测试基于调查的，互动的在线调查实验室来推进STEM学习，这些实验室将模拟基于数据的决策。该项目的目标包括：（1）将学生的研究经验融入他们的学习中;（2）解决数据科学日益重要的问题，并挑战当前课程和教科书中不易解决的统计概念;（3）使用类似游戏的模拟开发真实世界场景的完整故事线模型;以及（4）创建和审查可纳入各种传统入门和高级本科课程的材料。项目团队的行动理论是建立在根据这些目标开发，实施和评估每个模拟实验室和项目的其他组成部分。一个重要的动机是让学生开发自己的研究问题，生成和使用自己独特的数据来做出决策，然后通过与技术的互动来观察和学习所做的选择。为了评估所采取的方法的成功，该项目将采用混合方法的方法来比较从更传统的材料中获得的学习收益与这些新材料的收益，评估学生的态度和参与度，使用数据分析来评估每个实验室中嵌入的组件的有效性，并确定将资源纳入各种传统入门和高级本科课程的最佳实践。 这些完全沉浸式的在线游戏式实验室将与其他现有的教科书和在线教育材料明显不同，因为每个实验室都将包括基于调查的案例研究，这些案例研究包含真实世界的数据分析复杂性，从而为统计和数据科学的知识内容和广泛适用性提供坚实的介绍。为了配合软件实验室的开发，该团队将创建和审查一个新的学生评估工具，该工具将用于评估学生对概念联系、沟通技巧、批判性思维和解决问题的理解能力，以及他们理解和应对现实世界非结构化数据集挑战的能力。总的来说，该项目将通过激发STEM研究和应用中的创新，创造力和兴奋的力量，对STEM教育产生重大影响。