Author(s):
Need: Undergraduate science courses for non-STEM students can be the last opportunity of formal science education for many people. In these courses, students can improve their scientific reasoning if it is intentionally targeted and effectively integrated into learning experiences. This project seeks to fill a gap in the knowledge base by exploring the impact of instruction using an explanatory framework in introductory oceanography courses. Our focus has been on the interactions of data literacy and scientific reasoning skills to improve evidence-supported scientific explanations constructed by undergraduates. In K-12 settings, the use of scaffolds, such as CER, have been shown to enhance the quality and depth of scientific reasoning and explanations (e.g. McNeill et al., 2006; Songer & Gotwals, 2012) but such techniques have not been examined closely in undergraduate settings (Becker, 2014). Guiding Questions: Our study investigates how instruction that supports careful examination of data visualizations and use of relevant evidence influences the quality of written scientific explanations. Our hypothesis is that students who are guided to use a modified structure of “Data Description–Claim–Evidence– Reasoning” (DCER) will develop better explanations than if no explanatory structure is used. Additionally, we hypothesize that the use of the DCER framework for writing scientific explanations positively influences the development of students’ scientific reasoning and data literacy skills.Methods: Our study has included comparison of students receiving direct, repeated guidance to develop scientific explanations using the DCER framework with students who completed sections of the same course with no intervention nor modification of traditional, lecture-based instructional approaches. Students’ written scientific explanations, from both formative and summative assessments, were evaluated to determine the progression of their skills development throughout the semester. In our undergraduate oceanography classes, we specifically looked at their ability to read and make sense of time series and spatial data visualizations. Other research data sources include pre/post assessments and cognitive interviews with a subset of students from both groups.Outcomes: Results show that students in intervention classes scored significantly higher on exam essay questions, most notably on their data description, use of evidence, and scientific reasoning work when compared to students not exposed to the framework (t-tests, p<.001). In addition, intervention students had a significantly larger increase in their ocean concept knowledge than those in the comparison group (2-way mixed ANOVA for effect of interaction between group and time, p=.017). Our work indicates that incorporation of targeted activities that integrate data literacy and scientific reasoning skills to compose evidence-supported explanations can lead to significant gains not just in these targeted skills, but also in ocean content knowledge retention overall.Broader Impacts: All science disciplines rely on the identification of trends and patterns in data sets to make sense of phenomena. The framework used in this project is applicable in any undergraduate science class and our work will provide needed guidance on pedagogical approaches to support the development of science reasoning skills in undergraduate learners. The strategies could also be modified for different educational levels (e.g. middle and high school).
Coauthors
Kathleen Browne & Gabriela Smalley, Rider University, Department of Earth & Chemical Sciences; Sage Lichtenwalner, Rutgers University, Department of Marine and Coastal Sciences
