Algebra Project Mathematics Content and Pedagogy Initiative

代数项目数学内容和教学法倡议

• 批准号: 1621416
• 负责人: Oliver Hill
• 金额: $ 300万
• 依托单位: Virginia State University Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2017-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1621416&HistoricalAwards=false
• 关键词: Algebra; Project; Mathematics; Content; Pedagogy

Algebra continues to serve as a gatekeeper and potential barrier for high school students. The Algebra Project Mathematics Content and Pedagogy Initiative (APMCPI) will scale up, implement and assess the efficacy of interventions in K-12 mathematics education based on the well-established Algebra Project (AP) pedagogical framework. The APMCPI project team is comprised of four HBCUs (Virginia State University, Dillard University, Xavier University, Lincoln University), the Southern Initiative Algebra Project (SIAP), and four school districts that are closely aligned with partner universities. The purpose of the Algebra Project is to improve performance and participation in mathematics by members of students in distressed school districts, particularly those with a large population of low-income students from underserved populations including African American and Hispanics. The project will provide professional development and implement the Algebra Project in four districts and study the impact on student learning. The research results will inform the nation's learning how to improve mathematics achievement for all children, particularly those in distressed inner-city school districts. The study builds on a prior pilot project with a 74% increase in students who passed the state exam. In the early stages of this project, teachers in four districts closely associated with the four universities will receive Algebra Project professional development in Summer Teacher Institutes with ongoing support during the academic year, including a community development plan. The professional development is designed to help teachers combine mathematical problem solving with context-rich lessons, which both strengthen and integrate teachers' understanding of key concepts in mathematics so that they better engage their students. The project also will focus on helping teachers establish a framework for mathematically substantive, conceptually-rich and experientially-grounded conversations with students. The first year of the study will begin a longitudinal quasi-experimental, explanatory, mixed-method design. Over the course of the project, researchers will follow cohorts who are in grade-levels 5 through 12 in Year 1 to allow analyses across crucial transition periods - grades 5 to 6; grades 8 to 9; and grades 12 to college/workforce. Student and teacher data will be collected in September of Project Year 1, and in May of each project year, providing five data points for each student and teacher participant. Student data will include student attitude, belief, anxiety, and relationship to mathematics and science, in addition to student learning outcome measures. Teacher data will include content knowledge, attitudes and beliefs, and practices. Qualitative data will provide information on the implementation in both the experimental and control conditions. Analysis will include hierarchical linear modeling and multivariate analysis of covariance.

代数仍然是高中生的看门人和潜在的障碍。代数项目数学内容和教育学倡议(APMCPI)将根据公认的代数项目(AP)教学框架，扩大、实施和评估K-12数学教育干预措施的效果。APMCPI项目小组由四所HBCU(弗吉尼亚州立大学、迪拉德大学、泽维尔大学、林肯大学)、南方倡议代数项目(亚太统计所)和与伙伴大学密切合作的四个学区组成。代数项目的目的是提高贫困学区学生的成绩和数学参与度，特别是那些有大量低收入学生的学生，这些学生来自服务不足的人群，包括非裔美国人和西班牙裔美国人。该项目将提供专业发展，并在四个学区实施代数项目，并研究对学生学习的影响。这项研究结果将为全国学习如何提高所有孩子的数学成绩提供参考，特别是那些生活在贫困市中心学区的孩子。这项研究建立在之前的一个试点项目的基础上，通过州考试的学生人数增加了74%。在这一项目的早期阶段，与四所大学关系密切的四个地区的教师将在暑期师范学院接受代数项目专业发展，并在学年期间得到持续的支持，包括社区发展计划。专业发展旨在帮助教师将数学问题解决与情景丰富的课程结合起来，加强和整合教师对数学关键概念的理解，以便更好地吸引学生。该项目还将侧重于帮助教师建立一个框架，与学生进行数学上实质性的、概念丰富的和以经验为基础的对话。研究的第一年将开始纵向准实验、解释性、混合方法设计。在该项目的整个过程中，研究人员将跟踪一年级5到12年级的队列，以便能够在关键的过渡期进行分析-5到6年级；8到9年级；以及12年级到大学/劳动力。学生和教师的数据将在项目第一年的9月收集，并在每个项目年的5月收集，为每位学生和教师参与者提供五个数据点。学生数据将包括学生的态度、信念、焦虑、与数学和科学的关系，以及学生学习结果的衡量标准。教师数据将包括内容知识、态度和信念以及实践。定性数据将提供有关在实验和控制条件下实施情况的信息。分析将包括分层线性建模和多元协方差分析。