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）范围内的一项活动，为通过杰出的研究、优秀的教育以及在其组织使命的背景下将教育和研究结合起来，体现教师学者角色的初级教师提供奖励。统计和数据分析在现代社会中扮演着越来越重要的角色。如果没有处理数据的能力（例如组织、表示、总结和建模），就不可能充分理解并开始解决重大社会问题，也很难就个人健康、财务和政治选择做出重要决定。这个职业项目旨在开发框架，以理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，它还将产生用于本科统计教学的工具，以更好地支持本科统计教学。这项工作将为所有学生在统计学课堂上的教育策略提供信息。本项目以研究为基础，探讨本科统计学教学的新课程方法，广泛使用软件学习数据组织、表示、建模和仿真。该项目应加强和补充现有的研究型课程CATALST（教学和学习统计的变革推动者），该课程结合了促进学生学习的技术。该项目打算调查统计教育界关于利用技术从建模和模拟方法教授统计推断以及利用技术进行数据探测工作（组织、表示和总结数据）的优势的猜测。在课堂上收集丰富的数据，探索学生使用技术构建模型和运行模拟的方式，以回答统计问题，或使用技术组织、表示和解释数据集，这将使主要研究者能够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。首席研究员通过调查她所教的课程，并在整个项目中整合研究生作为统计教育研究人员的指导，将研究和教育结合起来，作为该职业奖的一部分。

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.