Developing and Assessing a Conceptual Approach to an Algebra-based Second Course in Statistics

开发和评估基于代数的第二门统计学课程的概念方法

• 批准号: 1612201
• 负责人: Nathan Tintle
• 金额: $ 30万
• 依托单位: Dordt University, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612201&HistoricalAwards=false
• 关键词: Developing; Assessing; Conceptual; Approach; Algebra

Demands for a statistically literate society are increasing, and the algebra-based introductory statistics course remains the primary venue for learning statistics for the majority of high school and undergraduate students. However, the typical introductory statistics course does not give students experience with multivariable statistical methods, which are the primary methods used in the workforce today. The primary barrier to students' progressing beyond their first statistics course is that they have not yet taken (or do not intend to take) prerequisite courses in probability, calculus, and/or linear algebra. Toward increasing the number of students who take a statistics course that focuses on multivariable methods, this project will develop, pilot-test, and study the use of materials for an algebra-based second course. These materials will (1) emphasize the overarching statistical process in the context of multivariable hypotheses, (2) start with straightforward multivariable study design and exploratory data analysis concepts to build on student intuition and understanding, (3) utilize a writing style and pedagogical approach designed for the typical undergraduate student, (4) develop and integrate technology tools for facilitating student exploration and discovery, and (5) be informed by assessment results. The proposed project will provide college faculty with a fully integrated set of curriculum materials to teach a substantially different curriculum for a second course in statistics, aimed at important multivariable statistical concepts. Such concepts include minimizing unexplained variability, planning and controlling for confounding variables, and exploring and modelling interactions between variables. These materials will leverage prior support that produced materials for introductory statistics centered on simulation-based inference. The project's accompanying assessment activities, development of a new assessment tool (a Multivariable Statistics Concept Inventory), and examination of student assessment results, will address research questions such as (1) In which areas of multivariable statistical thinking are there significant gains in conceptual understanding using this new curriculum? and (2) What are the characteristics of students' learning trajectories for key inferential and descriptive statistics concepts?

对统计识字社会的需求正在增加，基于代数的统计学课程仍然是大多数高中和本科生学习统计数据的主要场所。但是，典型的介绍性统计课程并不能为学生提供多变量统计方法的经验，这是当今劳动力中使用的主要方法。超出学生第一个统计学课程的进步的主要障碍是，他们尚未在概率，微积分和/或线性代数方面（或不打算参加）前提课程（或不打算参加）。为了增加参加统计学课程的学生人数，该课程专注于多变量方法，该项目将开发，试点测试和研究用于基于代数的第二课程的材料的使用。这些材料（1）将在多变量假设的背景下强调总体统计过程，（2）从直接的多变量研究设计和探索性数据分析概念开始，以基于学生的直觉和理解，（（3）使用典型的学生和探索学生的探索和整合（4）探索学生，（4）（4）（4）（4），（4）利用学生的写作方式和教学方法，（4） 结果。拟议的项目将为大学教职员工提供一组完全集成的课程材料，以教授针对重要的多变量统计概念的统计学第二课程的实质性不同的课程。这些概念包括最大程度地减少无法解释的可变性，对混杂变量的计划和控制，以及探索和建模变量之间的相互作用。这些材料将利用以基于模拟推断为中心的入门统计量产生材料的先验支持。该项目随附的评估活动，开发新的评估工具（多变量统计概念清单）以及对学生评估结果的检查，将解决（1）诸如多变量统计思维领域在哪些新的课程中具有概念性理解的显着增长？ （2）学生学习轨迹的特征是关键推理和描述性统计概念的什么特征？