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With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses.
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This content will become publicly available on December 24, 2025
Teaching with shared data for learning qualitative data analysis: a multi-sited case study of instructor and student experiences
In this paper, we report findings from a multiple case study that examined how instructors used shared data when teaching qualitative data analysis. More specifically, we explored both instructor and student experiences in two graduate-level qualitative methods courses located at U.S. universities. Drawing upon thematic analysis and the theory of active learning, we identified two themes that centred the faciliatory role of shared data for teaching data analysis in an active way (i.e. doing qualitative data analysis). Both participating students and instructors identified shared data – conceptualized as both a noun and a verb (i.e. a thing and an action) – as contributing to learning how to do qualitative data analysis. Although conceptualizations of shared data varied, overarching considerations tended to emphasize this use of shared data as beneficial to the general pedagogical structure of qualitative methods courses by contributing to shared vulnerability and engendering supportive peer learning environments. We highlight how these findings offer important implications for using shared data more systematically when teaching qualitative data analysis in methods courses.
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- Award ID(s):
- 2116935
- PAR ID:
- 10621553
- Publisher / Repository:
- Taylor & Francis
- Date Published:
- Journal Name:
- International Journal of Research & Method in Education
- ISSN:
- 1743-727X
- Page Range / eLocation ID:
- 1 to 15
- Subject(s) / Keyword(s):
- active learning archived data qualitative data analysis shared data teaching qualitative methods
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses.more » « less
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