Improving Undergraduate Scientific Explanations: Exploring the Role of Data Literacy Skills in Scientific Reasoning

Rider University, Lawrenceville NJ

Investigators

Abstract

This project aims to serve the national interest by investigating how teaching undergraduate students to interpret data and apply scientific reasoning can improve their scientific explanations. To this end, the project aims to develop strategic, easy-to-implement approaches to enhance students’ data literacy skills. Students will be guided through a sequence of steps to help them recognize and describe patterns in data. Additional steps will guide them to use such data patterns as evidence for making conclusions about the phenomena under investigation. These teaching strategies will be tested in introductory oceanography courses, using data sets from the NSF-funded Ocean Observatories Initiative (OII). By leveraging the community of educators associated with OII, the results of this work will be directly shared with people who teach similar oceanography courses. As a result, the project anticipates wide adoption of the strategies it finds to be most effective for undergraduate oceanography students. The results of the project are also expected to be applicable across science disciplines and might be used to enhance a broad range of undergraduate courses. Consequently, the project plans to provide online access to the materials and data sets it develops, so that they can be easily adopted or adapted by other educators. This project is centered on improving undergraduate STEM education by building students’ data literacy and reasoning skills and, ultimately, enhancing their ability to construct scientifically accurate explanations. The project aims to investigate how a modified explanation framework could influence the development of undergraduate students' scientific explanation skills and seeks to identify the specific challenges undergraduate students experience when constructing scientific explanations. Specifically, this project will link two cognitive realms, scientific reasoning and data literacy, to develop more effective instructional strategies for improving undergraduate students’ science literacy. The project will follow a convergent, multiphase mixed methods research approach, which will include a pilot study to collect both quantitative and qualitative data that will inform the later stages of the project. Guided by their instructors, students will use pedagogically appropriate and adjustable data visualizations from the OOI Data Explorations project, together with complementary static (e.g., paper) graphical representations. The design and implementation of instructional strategies used in this project will be evaluated to consider the professional development needs of future instructors seeking to implement the interventions. Dissemination plans have been designed to ensure that effective strategies are shared broadly with science instructors, learning scientists, and other educational researchers. This proposal was submitted in response to the “Dear Colleague Letter: Stimulating Participation from Institutions New to the Improving Undergraduate STEM Education: Education and Human Resources Program" (NSF 20-034). The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, 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.