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1. Data-Enabled Engineering Projects (DEEPs) Modules for Data Science Education in Engineering

   He, Q.P. ; Wang, J. ; Mao, S. ; Parson, L. ; Liu, B. ; Zeng, P. ; Smith, A. ; Henry, D. ( March 2000 , Proceedings of 2020 ASEE-SE Conference)

   The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requiresmore »both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education.« less
2. 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)

   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 proposedmore »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.« less
3. Enabling data science education in STEM disciplines through supervised undergraduate research experiences

   Yaser Banadaki ( July 2022 , American Society of Electrical Engineers (ASEE) Annual Conference Proceeding)

   Data Science plays a vital role in sciences and engineering disciplines to discover meaningful information and predict the outcome of real-world problems. Despite the significance of this field and high demand, knowledge of how to effectively provide data science research experience to STEM students is scarce. This paper focuses on the role of data science and analytics education to improve the students' computing and analytical skills across a range of domain-specific problems. The paper studies four examples of data-intensive STEM projects for supervised undergraduate research experiences (SURE) in Mechanical Engineering, Biomedical science, Quantum Physics, and Cybersecurity. The developed projects includemore »the applications of data science for improving additive manufacturing, automating microscopy image analysis, identifying the quantum optical modes, and detecting network intrusion. The paper aims to provide some guidelines to effectively educate the next generation of STEM undergraduate and graduate students and prepare STEM professionals with interdisciplinary knowledge, skills, and competencies in data science. The paper includes a summary of activities and outcomes from our research and education in the field of data science and machine learning. We will evaluate the student learning outcomes in solving big data interdisciplinary projects to confront the new challenges in a computationally-driven world.« less
4. Long-Term Outcomes of RET Programs on Female and Minority Student High School Graduation Rates and Undergraduate STEM Major Rates (Fundamental)

   Nichol, C. ; Crawford, C. A. ; Barr, C. ; Cerda, I. ( January 2021 , ASEE Annual Conference proceedings)

   Research Experience for Teachers (RET) programs are National Science Foundation (NSF) funded programs designed to provide K- 12 Science, Technology, Engineering, and Mathematics (STEM) teachers with immersive, hands-on research experiences at Universities around the country. The NSF RET in nanotechnology encourages teachers to translate cutting-edge research into culturally relevant Project-Based Learning (PjBL) and engineering curriculum. Traditionally, the evaluation of RET programs focuses on the growth and development of teacher self-efficacy, engineering content knowledge gains, or classroom implementation of developed curriculum materials. However, reported methods for evaluating the impact of RETs on their female, minority student populations' high school graduation andmore »undergraduate STEM major rates are limited. This study's objective was to compare RET high school student graduation rates and undergraduate STEM major rates across gender, race, and ethnicity to a comparison sample to determine the RET program's long-term impact on students' likelihood of pursuing STEM careers. The approach of collecting and analyzing the Texas Education Research Center Database (EdRC) data is a novel methodology for assessing RET programs' effectiveness on students. The EdRC is a repository of K-12 student data from the Texas Education Agency (TEA) and Higher Education data from the Texas Higher Education Coordinating Board (THECB). This joint database contains demographic, course registration, graduation, standardized testing, and college major, among others, for all students that attended a K-12 public school in Texas and any college in Texas, public or private. The RET program participants at Rice University (2010 – 2018) taught numerous students, a sample size of 11,240 students. A propensity score matching generated the student comparison group within the database. Students' school campus, gender, race/ethnic status, and English proficiency status were applied to produce a graduation comparison sample size of 11,240 students of Non-RET participants. Linking the TEA database to the THECB database resulted in college STEM participants and comparison sample sizes of 4,029 students. The project team conducted a logistic regression using RET status to predict high school graduation rates as a whole and by individual variables: gender, Asian American, Black, Caucasian, and Latinx students. All models were significant at p less than 0.05, with models in favor of students RET teachers. The project team conducted a logistic regression using RET status to predict student STEM undergraduate major rates as a whole and by individual variables: Gender, Asian American, Black, Caucasian, and Latinx students. African American and Caucasian models were significant at p less than 0.05; Gender, Asian American, and Latinx models were marginally significant (0.05 less than p greater than 0.1), where RET students had higher STEM major rates than matched controls. The findings demonstrate that RET programs have a long-term positive impact on the students' high school graduation rates and undergraduate STEM major rates. As teachers who participate in the RET programs are more likely to conduct courses using PjBL strategies and incorporate real-world engineering practices, female and minority students are more likely to benefit from these practices and seek careers utilizing these skills.« less
5. Bringing Real-World Data And Visualizations Into Data Structures Courses Using BRIDGES

   https://doi.org/10.1145/2839509.2850512  

   Subramanian, Kalpathi ; Payton, Jamie ; Burlinson, David ; Mehedint, Mihai ( January 2016 , ACM SIGCSE 2016)

   This demo introduces participants to the concepts and application of BRIDGES, a software infrastructure designed to facilitate hands-on experience for solving traditional problems in introductory computer science courses using data from real-world systems that are of interest to students, such as Facebook, Twitter, and Google Maps. BRIDGES provides access to real-world data sets for use in traditional data structures programming assignments, without requiring students to work with complex and varied APIs to acquire such data. BRIDGES also helps the students to explore and understand the use of data structures by providing each student with a visualization of operations performed onmore »the student's own implementation of a data structure. BRIDGES visualizations can be easily shared (via a weblink) with peers, friends, and family. Demo attendees will see (and possibly engage in) hands-on experience with BRIDGES and will have the opportunity to discuss how BRIDGES can be used to support various introductory computer science courses. Additionally, the demo will complement our oral presentation of our work at SIGCSE, by providing hands-on demonstrations of BRIDGES.« less