Glancy, Aran2022-11-142022-11-142020-08https://hdl.handle.net/11299/243178University of Minnesota Ph.D. dissertation. 2020. Major: Education, Curriculum and Instruction. Advisors: Tamara Moore, Erin Baldinger. 1 computer file (PDF); 227 pages.Preparing students to use and consume data both inside and outside of school is an important goal in mathematics, science, and engineering education, but even basic data analysis tasks can quickly become complex. Planning and designing classroom data analysis tasks that support students’ learning of statistical principals requires an understanding of the ways that students engage with data as they work through authentic analysis tasks. In this study I investigate the obstacles and affordances that students encounter during such tasks, and I connect those obstacles and affordances to the characteristics of the task themselves. This study used a multiple, embedded case study approach (Yin, 2009). I observed eight groups of students from the classrooms of four different teachers as they worked through a variety of data analysis tasks in both engineering and scientific inquiry contexts. The teachers involved in this study were participants in the EngrTEAMS project, an MSP that supported teachers in the development and implementation of engineering-based STEM units. The two STEM units investigated in this study were developed by the teachers as part of the EngrTEAMS project. Audio and video recordings, student artifacts, and curriculum documents were the sources of data, and I analyzed these sources for themes among the obstacles and affordances encountered by the students. Students in this study encountered obstacles in all phases of the data analysis process. Measurement error, which came in many forms, was a primary obstacle for the students; however, the design of the tasks themselves also introduced challenges to the data analysis process. Students encountered difficulty when trying to coordinate or connect measurements, data representations, and the phenomenon they were investigating. Despite the obstacles they encountered, the hands-on experiences themselves, certain measurement instruments, and activities that encouraged students to make connections between representations helped support the students as they made sense of their data. The results suggest that engaging students in authentic data analysis tasks is complex work which presents both obstacles and affordance for students. Great care must be taken in planning these tasks to maximize the learning opportunities for students.enData Analysis and MeasurementEngineering EducationStatistics EducationSTEM IntegrationThesis or Dissertation