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