Investigating the Relationship Between Teacher-Level and Student-Level Factors and NAEP Mathematics Test Performance by American Indian and Alaska Native Students

WestEd的研究人员进行的这项探索性研究正在研究教师层面因素（包括教师背景变量和教学实践）和学生层面因素（如自评数学兴趣和熟练程度）以及这些因素之间的相互作用，与美国印第安人/阿拉斯加原住民（AI/AN）学生在中年级数学方面的学业成绩相关。 最终目标是确定可塑性因素，如果改变，可以提高教师的做法和AI/AN学生的数学成绩。 第一阶段正在探索2007年全国印第安人教育研究（NIES）的数据，该研究汇编了关于NAEP评估学生表现的信息，并通过个人问卷从大样本AI/AN学生及其教师那里收集了信息。NIES的信息链接NAEP性能数据和NIES调查数据的方式，将有助于了解特定的教师层面的因素和学生层面的因素（以及这两个因素之间的相互作用）与学生的学习。分层线性模型（HLM）被用来研究学生/教师层面的因素和学生成绩之间的关系。 该研究的第二阶段涉及对阿拉斯加、亚利桑那州和新墨西哥州三个地点选定学校的数百次采访和课堂观察的数据进行定性分析，以更深入地了解AI/AN的教学背景以及为什么特定的教师层面和学生层面的因素与第一阶段分析的NAEP数学评估中的学生表现相关/不相关。研究数据源和多层次建模技术的应用将揭示特定文化背景下的教师如何应用标准，适应和实施课程，并以促进学生学习和成就的方式评估学生。这项工作的长期回报将是加强对提供文化上敏感的STEM教育的方式的理解，提高AI/AN学生在STEM职业和STEM影响的社会和个人决策中的表现和参与。

This exploratory study by researchers at WestEd is examining ways that teacher-level factors (including teacher background variables and instructional practices) and student-level factors (such as self-rated mathematics interest and proficiency), and interactions among these factors, are associated with American Indian/Alaska native (AI/AN) student academic achievement in middle grades mathematics. The ultimate goal is to identify malleable factors that, if changed, could improve teachers' practices and AI/AN student achievement in mathematics.The study has two main phases. Phase I is exploring data from the 2007 National Indian Education Study (NIES), which compiled information on student performance on NAEP assessments and collected information from a large sample of AI/AN students and their teachers through individual questionnaires. The NIES information links NAEP performance data and NIES survey data in ways that will assist understanding of how particular teacher-level factors and student-level factors (and the interactions between these two factors) relate to student learning. Hierarchical linear modeling (HLM) is being used to study the relationship between student/teacher-level factors and student performance. Phase II of the study involves qualitative analysis of data from several hundred interviews and classroom observations in selected schools at three sites in Alaska, Arizona, and New Mexico to enable deeper understanding of AI/AN contexts of teaching and learning and why particular teacher-level and student-level factors are/are not associated with student performance on the NAEP mathematics assessment as analyzed in Phase I. Study data sources and application of multi-level modeling techniques will shed light on how teachers in particular cultural settings apply standards, adapt and implement curriculum, and assess their students in ways that promote student learning and achievement. The long-term payoff from this work will be enhanced understanding of ways to provide culturally responsive STEM education and increased performance and participation of AI/AN students in STEM careers and STEM-affected social and personal decision-making.