Authentic Science Experiences with STEM Datasets: Post-secondary Results and Potential Gender Influences

Abstract: Background: Dataset skills are used in STEM fields from healthcare work to astronomy research. Few fields explicitly teach students the skills to analyze datasets, and yet the increasing push for authentic science implies these skills should be taught. Purpose: The overarching motivation is to understand learning of dataset skills within an astronomy context. Specifically, when participants work with a 200-entry Google Sheets dataset of astronomical data about quasars, what are they learning, how are they learning it, and who is doing the learning? Sample: The authors studied a matched set of participants (n=87) consisting of 54 university undergraduate students (34 male, 18 female), and 33 science educators (16 male, 17 female). Design and methods: Participants explored a three-phase dataset activity and were given an eight-question multiple-choice pre/post-test covering skills of analyzing datasets and astronomy content, with questions spanning Bloom's Taxonomy. Pre/post-test scores were compared and a t-test performed for subsamples by population. Results: Participants exhibited learning of both dataset skills and astronomy content, indicating that dataset skills can be learned through this astronomy activity. Participants exhibited gains in both recall and synthesis questions, indicating learning is non-sequential. Female undergraduate students exhibited lower levels of learning than other populations. Conclusions: Implications of the study include a stronger dataset focus in post-secondary STEM education and among science educators, and the need for further investigation into how instructors can ameliorate the challenges faced by female undergraduate students.