CAREER: Transforming College Students' Statistical Thinking: Data, Technology & Modeling

教师早期职业发展（CAREER）计划是美国国家科学基金会（NSF）范围内的活动，提供奖励，以支持初级教师谁通过杰出的研究，优秀的教育，以及教育和研究的整合在其组织的使命的背景下，发挥教师学者的作用。统计和数据分析在现代社会中发挥着越来越重要的作用。如果没有处理数据的能力（例如组织、表示、总结和建模），就不可能充分理解和开始解决重大的社会问题，也很难做出有关个人健康、财务和政治选择的重要决定。这个CAREER项目旨在开发框架，以了解学生如何学习表示，建模和组织数据，作为他们对统计和数据分析的理解的一部分。此外，它将导致本科统计教学的工具，以更好地支持本科生的统计教学和学习。这项工作将告知教育策略在统计课堂上为所有student.The项目的特点是一个新的课程方法，本科统计教学和学习的研究为基础的调查，广泛使用的软件学习有关数据的组织，表示，建模和模拟。 该项目应增强和补充现有的基于研究的课程CATALST（统计教学变革推动者），该课程融入了学生学习技术。 该项目旨在调查统计教育界关于使用技术从建模和模拟方法教授统计推断以及使用技术进行数据检测工作（组织，表示和总结数据）的优势。在课堂上收集的丰富数据的收集，探索学生如何使用技术来构建模型和运行模拟来回答统计问题，或使用技术来组织，表示和解释数据集，将允许首席研究员构建学生统计学习和理解的模型。收集的数据包括课堂观察和视频，学生访谈和学生学习评估。首席研究员已经整合了研究和教育，作为这个职业奖的一部分，通过调查她所教授的课程，并在整个项目中整合研究生的指导作为统计教育研究人员。

The Faculty Early Career Development (CAREER) program is a National Science Foundation (NSF)-wide activity that offers awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research within the context of the mission of their organizations. Statistics and data analysis plays an increasingly important role in modern society. Without the ability to work with data (e.g. organize, represent, summarize and model) it is impossible to adequately understand and begin to solve major social issues and it is difficult to make important decisions regarding personal health, finances, and political choices. This CAREER project seeks to develop frameworks for understanding how students learn to represent, model, and organize data as part of their understanding of statistics and data analysis. In addition, it will result in tools for undergraduate statistics instruction to better support statistics teaching and learning for undergraduates. This work will inform educational strategies in the statistics classroom for all students.The project features a research-based investigation of new curricular approaches to undergraduate statistics teaching and learning with extensive use of software for learning about data organization, representation, modeling and simulation. This project should enhance and complement an existing research-based curriculum, CATALST (Change Agents for Teaching and Learning Statistics) that incorporates technology for students' learning. This project intends to investigate conjectures made within the statistics education community about the advantages of using technology to teach statistical inference from a modeling and simulation approach as well as using technology for data detective work (organizing, representing and summarizing data). The collection of rich data gathered in classrooms that explores the ways students use technology to construct models and run simulations to answer statistical questions or to use technology to organize, represent and interpret data sets will allow the principal investigator to construct models of students' statistical learning and understanding. The data to be collected includes classroom observations and video, student interviews, and assessments of student learning. The principal investigator has integrated research and education as part of this CAREER award by investigating classes she teaches and by integrating mentoring of graduate students as statistics education researchers throughout the project.