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Scholarly Works, Engineering Education

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https://hdl.handle.net/10919/24289
Research articles, presentations, and other scholarship

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Browsing Scholarly Works, Engineering Education by Author "Asamen, Dawit M."

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    Understanding Data Science Instruction in Multiple STEM Disciplines
    Snyder, Caitlin; Asamen, Dawit M.; Naseri, Mohammad Y.; Aryal, Niroj; Biswas, Gautam; Dubey, Abhishek; Henrick, Erin; Hotchkiss, Erin R.; Jha, Manoj K.; Jiang, Steven X.; Kern, Emily C.; Lohani, Vinod K.; Marston, Landon T.; Vanags, Christopher P.; Xia, Kang (2021-07)
    As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.