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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. 

This work-in-progress research paper presents our experiences with a NSF-sponsored International Research Experiences for Undergraduates (IRES) program hosted by an a large land grant university in the United States. A major component of the program is implemented at an international laboratory in China. The lab has been established in 2010 as a collaborative research platform, for a large land-grant university in the U.S. and a public comprehensive university, to study the biosonar sensing and flight of bats from an engineering perspective. The lab has pioneered work on the diversity and dynamics of the structures (noseleaves and ears) that bats use to emit and receive ultrasonic pulses In addition, the lab has hosted research experiences for international (primarily U.S.) students and collaborative BS/MS and MS/PhD educational programs. In the last two years, two cohorts of IRES scholars completed the 10-week IRES program. In addition to research and technical experiences, the IRES students participated in several field trips and seminars and were exposed to Chinese culture. To better understand the impact of the program, students were invited to complete pre- and post-program surveys and a post-program interview. The surveys included cultural intelligence assessment, Global Competency Activity, and Sojourn Readiness Assessment. In addition, students answered a few open-ended questions about their technical and cultural experiences. In this paper, we first explain the history of the lab and its research and educational contribution to date. Then, we describe the IRES program and program evaluation measures. Finally, we focus on the influence of the program on students’ intercultural skills and present the assessment results. 

Engineering work is becoming increasingly global in nature, making it essential that engineering students develop global competence [1], [2]. However, traditional global programs (e.g., study abroad) present challenges for engineering students who often have to fit such experiences within a highly structured curricular schedule. Further, study abroad can be a financial burden for many students who are already paying significant amounts to attend college [3], [4]. One type of global engineering program that has the potential to address these challenges are international research experiences, which typically take place during the summer and provide students with a salary. Research has suggested that such experiences can meaningfully influence students’ global competence [5], but few studies have explored how components of the experience may influence learning. This study compares two NSF-sponsored international research experiences for students (IRES) programs that send students to two different countries to identify differences in learning outcomes between the program participants. This work represents a collaborative effort among faculty members and graduate students from three engineering departments with the goal of creating research opportunities for students at various international sites using research-based educational practices. By understanding how context influences students’ learning opportunities, faculty developing such programs may select research locations more intentionally or offer supplemental programming for students to ensure they achieve all of the program’s intended learning outcomes.