Authentic Science Experiences with STEM Datasets: Post-Secondary Results and Potential Gender Influences
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| Title: | Authentic Science Experiences with STEM Datasets: Post-Secondary Results and Potential Gender Influences |
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| Language: | English |
| Authors: | Schwortz, Andria C. (ORCID |
| Source: | Research in Science & Technological Education. 2021 39(3):347-367. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| Publication Date: | 2021 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Undergraduate Education (DUE) |
| Contract Number: | 1339853 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Authentic Learning, STEM Education, Science Activities, Data Analysis, Gender Differences, Skill Development, Knowledge Level, Astronomy, College Science, College Students, Nonmajors, Introductory Courses |
| DOI: | 10.1080/02635143.2020.1761783 |
| ISSN: | 0263-5143 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2021 |
| Accession Number: | EJ1306693 |
| Database: | ERIC |
| 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. |
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| ISSN: | 0263-5143 |
| DOI: | 10.1080/02635143.2020.1761783 |
