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Abstract Title: What Multi-Level Models Can Tell Us About Learning Assistants and Equity
Abstract: The Learning Assistant (LA) Student Supported Outcomes (LASSO) project collects multi-disciplinary data from LA-using institutions across the country. The data falls into three categories: (1) student data (e.g. pre & post test scores, gender, and ethnicity), (2) course data (e.g. discipline, LA-uses, & instructor), and (3) institution data (e.g. institution type & term type). In this investigation, we develop a theoretical framework that is based in Critical Race Theory and Cultural Historical Activity Theory. To examine the dynamic interactions between varying components of the activity system, we develop Multi-Level Models that nest data within other sets of data. In our models student level data is nested within course level data, which is nested within institution level data. This allows us to measure and control for the complex interactions between various classroom and institutional contexts when analyzing student outcomes. In this sessions we will examine the impacts of LAs on diverse students and contexts.
Abstract Type: Symposium Talk
Parallel Session: Investigating the Impact of Learning Assistant Model Adoption on Students and Learning Assistants

Author/Organizer Information

Primary Contact: Ben Van Dusen
Department of Science Education, California State University, Chico
and Co-Presenter(s)
Jada-Simone White, Department of Science Education, California State University, Chico

Invited Presentation

Invited Presentation: Download the Invited Presentation