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

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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.