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1. Real Data and Application-based Interactive Modules for Data Science Education in Engineering

   Suthar, K.; Mitchell, T.; Hartwig, A. C.; Wang, J.; Mao, S.; Parson, L.; Zeng, P.; Liu, B.; He, P. (July 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   It has been recognized that jobs across different domains is becoming more data driven, and many aspects of the economy, society, and daily life depend more and more on data. Undergraduate education offers a critical link in providing more data science and engineering (DSE) exposure to students and expanding the supply of DSE talent. The National Academies have identified that effective DSE education requires both appropriate classwork and hands-on experience with real data and real applications. Currently significant progress has been made in classwork, while progress in hands-on research experience has been lacking. To fill this gap, we have proposed to create data-enabled engineering project (DEEP) modules based on real data and applications, which is currently funded by the National Science Foundation (NSF) under the Improving Undergraduate STEM Education (IUSE) program. To achieve project goal, we have developed two internet-of-things (IoT) enabled laboratory engineering testbeds (LETs) and generated real data under various application scenarios. In addition, we have designed and developed several sample DEEP modules in interactive Jupyter Notebook using the generated data. These sample DEEP modules will also be ported to other interactive DSE learning environments, including Matlab Live Script and R Markdown, for wide and easy adoption. Finally, we have conducted metacognitive awareness gain (MAG) assessments to establish a baseline for assessing the effectiveness of DEEP modules in enhancing students’ reflection and metacognition. The DEEP modules that are currently being developed target students in Chemical Engineering, Electrical Engineering, Computer Science, and MS program in Data Science at xxx University. The modules will be deployed in the Spring of 2021, and we expect to have immediate impact to the targeted classes and students. We also anticipate that the DEEP modules can be adopted without modification to other disciplines in Engineering such as Mechanical, Industrial and Aerospace Engineering. They can also be easily extended to other disciplines in other colleges such as Liberal Arts by incorporating real data and applications from the respective disciplines. In this work, we will share our ideas, the rationale behind the proposed approach, the planned tasks for the project, the demonstration of modules developed, and potential dissemination venues. 

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2. Enhancing STEM Retention and Graduation Rate by Incorporating Innovative Teaching Strategies in Selected STEM Introductory Courses

   https://doi.org/10.18260/1-2--34574  

   Swain, Nikunja; Biswal, Biswajit; Kennedy, Eugene (June 2020, ASEE Conferences)

   Gate-keeping courses provide students with their first and formal exposure to a deep understanding of science. Such courses influence students' decision to pursue STEM education and continue their college experience. Our records indicate that the many STEM students perform poorly or marginally in the introductory required courses and decide to change their major to non-STEM degree programs. One way to address this is using active learning techniques. The objective of this paper is to describe our experiences with the use of few of the active learning techniques in introductory computer programming courses offered in our Computer Science Program. One of these programming courses are required of all computer science majors and other course is usually taken by engineering, technology and science majors. The findings presented in this paper may be used by interested parties involved in STEM curriculum. 

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3. X+CS: A Computing Pathway for Non-Computer Science Majors

   Mitchell, S; Cole, K; Joshi, A (March 2020, ASEE Mid Atlantic Section Spring Conference, 2020)

   With computing impacting most every professional field, it has become essential to provide pathways for students other than those majoring in computer science to acquire computing knowledge and skills. Virtually all employers and graduate and professional schools seek these skills in their employees or students, regardless of discipline. Academia currently leans towards approaches such as double majors or combined majors between computer science and other non-CS disciplines, commonly referred to as “CS+X” programs. These programs tend to require rigorous courses gleaned from the institutions’ courses for computer science majors. Thus, they may not meet the needs of majors in disciplines such as the social and biological sciences, humanities, and others. The University of Maryland, Baltimore County (UMBC) is taking an approach more suitably termed “X+CS” to fulfill the computing needs of non-CS majors. As part of a National Science Foundation (NSF) grant, we are developing a “computing” minor specifically to meet their needs. To date, we have piloted the first two of the minor’s approximately six courses. The first is a variation on the existing Computer Science I course required for majors but restricted to nonmajors. Both versions of the course use the Python language and cover the same programming content, but with the non-majors assigned projects with relevance to non-CS disciplines. We use the same student assessment measures of homework, projects, and examinations for both courses. After four semesters, results show that non-CS majors perform comparably to majors. Students also express increased interest in computing and satisfaction with being part of a non- CS major cohort. The second course was piloted in fall 2019. It is a new course intended to enhance and hone programming skills and introduce topics such as web scraping, HTML and CSS, web application development, data formats, and database use. Students again express increased interest in computing and were already beginning to apply the computing skills that they were learning to their non-CS courses. As a welcome side effect, we experienced a significant increase in the number of women and under-represented minorities (URMs) in these two courses when compared with CS-major specific courses. Overall, women comprised 52% of the population, with URMs following a similar upward trend. We are currently developing the third course in the computing minor and exploring options for the remaining three. Possibilities include electives from our Information Systems major. We will also be working with our science, social science, and humanities departments to utilize existing courses in those disciplines that apply computing. The student response that we have received thus far provides us with evidence that our computing minor will be popular among UMBC’s non-CS population, providing them with a more suitable and positive computing education than existing CS+X efforts. 

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4. Understanding Data Science Instruction in Multiple STEM Domains

   Snyder, Caitlin (July 2021, 2021 ASEE Virtual Annual Conference)

   null (Ed.)

   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 education research and consulting groups. 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 have 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. 

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5. Is Computer Science for Me?: Understanding the Barriers of Enrollment and Engagement in Computer Science Courses in High Schools.

   Greenwood, J. (January 2023, American Sociological Association)

   This qualitative research study is part of a larger NSF-funded project entitled "Cogenerative Development of Culturally Relevant Pedagogical Guidelines for Computer Science and Computational Thinking in High Schools." During Year 1 of the project, qualitative semi- structured interviews were conducted with 26 high school students to better understand the challenges and barriers to enrollment in and/or engagement/success in Computer Science courses in high school. A grounded theory approach was used, given the exploratory nature of the study. Students were selected from three regional high schools and had to meet at least one of the criteria associated with underrepresented students in Computer Science: identifying as female; low socioeconomic status; and/or racial/ethnic minority (Black/African American; Hispanic/Latinx/Chicanx; Native American/Alaskan, and Native Hawaiian/Pacific Islander, or multi-racial). Common themes that emerged included the following: Challenges (Financial Factors, Role of Gender (i.e. identifying as female), and Race/Ethnicity Issues); Positive Influences (Role of Teacher, Role of Family); Other Interesting Insights (Wanting to be challenged in Computer Science Classes, Problems with the Marketing of Computer Science as a discipline). Social Identity Theory is used to better understand the experiences of high school students, especially what practices or beliefs keep underrepresented students of Computer Science from enrolling in Computer Science courses in the first place and/or persisting in the Computer Science field. Limitations of the current study are discussed as well as directions for future research and implications for a more culturally relevant pedagogical approach to teaching Computer Science and Computational Thinking in high schools. This project is funded by the National Science Foundation CS for All: Research and RPPs program, Award No. 2122367. 

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