Exploring Students' Learning of Data Analysis in a Physics for Life Sciences Laboratory Environment

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This project aims to serve the national interest by studying how undergraduate students learn data analysis concepts and skills. Specifically, it will study student learning in an introductory physics for life sciences course for health-science majors. To promote student learning, the course integrates scientific practices, disciplinary core ideas, and crosscutting concepts. Limited research currently exists about how students learn data analysis concepts and skills. To help fill this gap, this project seeks to understand how students analyze and interpret data, a scientific practice that is crucial for STEM-related careers. For this project, the process of data analysis will involve data collection, cleaning, manipulation, treatment, and interpretation. The project’s research component will provide insight into students' learning about and engagement with the data analysis process. This project aims to advance new knowledge about the nature of data analysis, how students engage in data analysis, and how to successfully implement data analysis education in other undergraduate courses. In addition, it will develop a theory of how students learn data science, and thus contribute to theoretical understanding of data science education. The overall goal of this project is to advance understanding of how undergraduate students learn and develop data analysis skills while reasoning about complex biological and physical systems. To do so, it will build on complementary scholarship in mathematics education, computational thinking, and undergraduate laboratory instruction. Analyzing and interpreting data can be a primary practice for knowledge building in these settings, where the intention is to build a conceptual understanding through iterations of data collection, cleaning, manipulation, mathematics, and interpretation. This project will use class observations and interviews to develop a new theoretical understanding of students' learning and enactment of data analysis. That new understanding will guide the design and validation of a task-based assessment tool and a student attitudes and perceptions survey. Information from the assessment and survey will in turn be used to gain insight into students' conceptual shifts. The results of this project are expected to: 1) build a new theoretical understanding of how students learn about analyzing and interpreting data; 2) advance knowledge about how to conduct research on analyzing and interpreting data through the building of a new assessment tool and survey; 3) contribute new knowledge about the implementation of instructional techniques that emphasize analyzing and interpreting data in undergraduate labs; and 4) promote new learning opportunities for all students in these settings. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources, which supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.