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Authentic science experiences with STEM datasets: post-secondary results and potential gender influences

Andria C. Schwortz, Andrea C. Burrows

2020Research in Science & Technological Education13 citationsDOIOpen Access PDF

Abstract

Background: Dataset skills are used in STEM fields from astronomy to zoology. 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 of this work is to understand authentic science learning of STEM dataset skills within an astronomy context. Specifically, when participants work with a 200-entry Google Sheets dataset of astronomical data, what are they learning, how are they learning it, and who is doing the learning?Sample: The authors studied a total of 82 post-secondary participants, including a matched set of 54 pre/post-test (34 males, 18 females), 26 video recorded (22 males, 2 females), and 3 interviewed (2 males, 1 female) participants.Design and methods: In this mixed-methods study, 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 the cognitive load of questions spanning from recognition of terms through synthesizing multiple ideas. Pre/post-test scores were compared and ANOVA performed for subsamples by gender. Select examples of qualitative data are shown, including written answers to questions, video recordings, and interviews.Results: This project expands existing literature on authentic science experiences into the domain of dataset education in astronomy. Participants exhibited learning in both recall and synthesis questions. Females exhibited lower levels of learning than males which could be connected to gender influence. Conversations of both males and females included gendered topics.Conclusions: Implications of the study include a stronger dataset focus in post-secondary STEM education, and the need for further investigation into how instructors can ameliorate the challenges faced by female post-secondary students.

Topics & Concepts

Science educationSet (abstract data type)RecallPsychologyMathematics educationFocus groupInformal learningCognitionFocus (optics)Authentic learningDomain (mathematical analysis)Cognitive skillPedagogyQualitative propertyHigher education21st century skillsQualitative researchMultimethodologyScience learningTeaching methodStudy skillsActive learning (machine learning)Science communicationPhysics educationScientific literacySocial psychologyGender gapWork (physics)Statistics Education and MethodologiesCareer Development and DiversityGenetics, Bioinformatics, and Biomedical Research
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