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

教师早期职业发展(CALEAR)计划是美国国家科学基金会(NSF)范围内的一项活动，旨在为那些通过杰出的研究、出色的教育以及在其组织使命范围内整合教育和研究而体现教师学者作用的初级教师提供奖励。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、汇总和建模)，就不可能充分理解和开始解决主要的社会问题，也很难做出关于个人健康、财务和政治选择的重要决策。该职业项目旨在开发框架，帮助学生了解如何表示、建模和组织数据，以此作为他们理解统计和数据分析的一部分。此外，它还将产生本科生统计教学工具，以更好地支持本科生的统计教学。这项工作将为所有学生在统计课堂上的教育策略提供信息。该项目以研究性调查为特色，通过广泛使用软件学习数据组织、表示、建模和模拟，对本科统计教学的新课程方法进行调查。该项目应加强和补充现有的以研究为基础的课程--CATALST(教学和学习统计变革促进剂)，该课程纳入了促进学生学习的技术。本项目旨在调查统计教育界对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检测工作(组织、表示和汇总数据)的好处的猜测。在课堂上收集的丰富数据探索了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，或者使用技术来组织、表示和解释数据集，这将使首席研究者能够构建学生统计学习和理解的模型。要收集的数据包括课堂观察和视频、学生访谈和对学生学习的评估。这位首席研究员通过调查她教授的课程，并在整个项目中将研究生的指导作为统计教育研究人员的一部分，将研究和教育作为这一职业奖项的一部分进行了整合。

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.