Author(s):
Need: Undergraduate students’ scientific reasoning skills are challenging to develop in undergraduate general-education science classes. Some research has shown that non-STEM students are more commonly “concrete operational or transitional reasoners in Piaget’s theory of cognitive development,” and thus need intentional instruction in scientific reasoning patterns to show gains on their scientific reasoning (Moore and Rubbo, 2012). Integral to scientific reasoning is the evaluation of evidence, which requires data literacy skills (McFadden et al., 2019). Earlier preliminary work by Browne (2014) revealed that scientific reasoning is difficult for undergraduate students, and even harder if they cannot or will not describe and analyze the data they are considering to draw conclusions about a natural phenomenon. This project is working to address gaps in the knowledge base that provides guidance about practical pedagogical approaches to develop students’ scientific reasoning, specifically by concurrently and systematically developing data literacy skills.
Guiding Questions: Our Engaged Learning Level I ISUE project seeks to explore the following research questions: First, how do scientific explanation frameworks influence the development of scientific explanations skills? We have been specifically focusing on the variations in depth and quality of scientific reasonings when the instructional focus utilizes no framework (our control) as compared to a framework that guides students to use a D-C-E-R framework. Second, what are the potential opportunities for learning as well as challenges that undergraduate students experience when constructing scientific explanations? We are exploring the influence of data literacy skills, scientific reasoning skills, and content knowledge on students’ learning. The work involves testing effectiveness of an instructional framework designed to help students link scientific reasoning with data literacy skills to improve their proficiency in composing evidence-based scientific explanations that includes: guiding students to routinely describe data (trends, patterns, ranges, outliers, similarities, differences, etc.) (D); from these pattern descriptions, asking students to make claims about the data and relevant phenomena (C), and support those conclusions with scientific reasoning that includes proper evidence (E) and that demonstrates the students’ understanding of relevant science concepts through their scientific reasoning (R) [the D-C-E-R framework].
Outcomes: In year 1, we piloted, revised and finalized the following in undergraduate courses at Rider University: pre and post concept inventories for scientific reasoning, data literacy, and oceanography concepts to align across control and intervention courses; four web-based interactive data visualizations with authentic data from the Ocean Observatories Initiative; instructional strategies to guide students to develop data literacy and scientific reasoning through their scaffolded interactions with the web-based data visualization tools; three exam essay questions which require scientific explanations of novel oceanographic data visualizations to be used across control and intervention course sections; and student interview protocols. These materials are being used in year 2 in both control and intervention oceanography sections and data collected will be analyzed summer 2022.
Broader Impacts: The framework proposed in this project is expected to be applicable in any undergraduate science class and provide needed guidance on pedagogical approaches to support the development of science reasoning skills in undergraduate learners.
Coauthors
Andrea Drewes & Gabriela Smalley, Rider University, Lawrenceville, NJ; Charles S. Lichtenwalner, Rutgers University, New Brunswick, NJ
