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1. Real Data and Application based 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 requires 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. 

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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)

   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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3. An empirical analysis of high school students' practices of modelling with unstructured data

   https://doi.org/10.1111/bjet.13253  

   Jiang, Shiyan ; Nocera, Amato ; Tatar, Cansu ; Yoder, Michael Miller ; Chao, Jie ; Wiedemann, Kenia ; Finzer, William ; Rosé, Carolyn P. ( July 2022 , British Journal of Educational Technology)

   To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.

   What is already known about this topic

   Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

   While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

   There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

   What this paper adds

   Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

   Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

   Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

   Implications for practice and/or policy

   It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

   Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

   To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

    

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4. An Introductory Aeronautics Course for Pre-Engineering Students to Understand How Drones (Flying Robots) Work

   He, S. ( June 2023 , The 130th ASEE Annual Conference and Exposition, June 25-28, 2023, Baltimore, MD)

   Robotics has emerged as one of the most popular subjects in STEM (Science, Technology, Engineering, and Mathematics) education for students in elementary, middle, and high schools, providing them with an opportunity to gain knowledge of engineering and technology. In recent years, flying robots (or drones) have also gained popularity as teaching tool to impart the fundamentals of computer programming to high school students. However, despite completing the programming course, students may still lack an understanding of the working principle of drones. This paper proposes an approach to teach students the basic principles of drone aeronautics through laboratory programming. This course was designed by professors from Vaughn College of Aeronautics and Technology for high school students who work on after-school and weekend programs during the school year or summer. In early 2021, the college applied for and was approved to offer a certificate program in UAS (Unmanned Aerial Systems) Designs, Applications, and Operations to college students by the Education Department of New York State. Later that year, the college also received a grant from the Federal Aviation Administration (FAA) to provide tuition-free early higher education for high school students, allowing them to complete the majority of the credits in the UAS certificate program while still enrolled in high school. The program aims to equip students with the hands-on skills necessary for successful careers as versatile engineers and technicians. Most of the courses in the certificate program are introductory or application-oriented, such as Introduction to Drones, Drone Law, Part 107 License, or Fundamentals of Land Surveying and Photogrammetry. However, one of the courses, Introduction to Drone Aeronautics, is more focused on the theory of drone flight and control. Organizing the lectures and laboratory of the course for high school students who are interested in pursuing the certificate can be a challenge. To create the Introduction to Drone Aeronautics course, a variety of school courses and online resources were examined. After careful consideration, the Robolink Co-drone [1] was chosen as the experimental platform for students to study drone flight, and control and stabilize a drone. However, developing a set of comprehensible lectures proved to be a difficult task. Based on the requirements of the certificate program, the lectures were designed to cover the following topics: (a) an overview of fundamentals of drone flight principles, including the forces acting on a drone such as lift, weight, drag, and thrust, as well as the selection of on-board components and trade-offs for proper payload and force balance; (b) an introduction to the proportional-integral-directive (PID) controller and its role in stabilizing a drone and reducing steady-state errors; (c) an explanation of the forces acting on a drone in different coordinates, along with coordinate transformations; and (d) an opportunity for students to examine the dynamic model of a 3D quadcopter with control parameters, but do not require them to derive the 3D drone dynamic equations. In the future, the course can be improved to cater to the diverse learning needs of the students. More interactive and accessible tools can be developed to help different types of students understand drone aeronautics. For instance, some students may prefer to apply mathematical skills to derive results, while others may find it easier to comprehend the stable flight of a drone by visualizing the continuous changes in forces and balances resulting from the control of DC motor speeds. Despite the differences in students’ mathematical abilities, the course has helped high school students appreciate that mathematics is a powerful tool for solving complex problems in the real world, rather than just a subject of abstract numbers. 

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5. Promoting Open-source Hardware and Software Platforms in Mechatronics and Robotics Engineering Education

   https://doi.org/10.18260/1-2--35107  

   Lotfi, N. ; Mbanisi, C. ; Auslander, D. ; Berry, C. ; Rodriguez, L. ; Molki, M. ( June 2020 , ASEE annual conference exposition proceedings)

   The evolution of Mechatronics and Robotics Engineering (MRE) has enabled numerous technological advancements since the early 20th century. Professionals in this field are reshaping the world by designing smart and autonomous systems aiming to improve human well-being. Recognizing the need for preparing highly-educated MRE professionals, many universities and colleges are adopting MRE as a distinct degree program. One of the cornerstones of MRE education is laboratory- and project-based learning to provide a hands-on and engaging experience for the students. To this end, numerous software and hardware platforms have been developed and utilized in MRE courses and laboratories. Commercial products can provide a rich hands-on experience for the students, but they can be cost-prohibitive. On the other hand, open-source platforms are low-cost alternatives to their commercial counterparts and are being increasingly used in industry. Developing open-source laboratory platforms will be a more feasible option for a wider range of institutions and would enable familiarizing the students with recent technological trends in industry and exposing them to the development details of a real-world system. However, adoption of open-source platforms in MRE courses can be lengthy and time consuming. Educators who wish to utilize such systems typically lack the expertise in all aspects of their implementation which can make them difficult to troubleshoot. Debugging open-source systems can also be challenging because most of the troubleshooting is done through forum discussions which appear to be very noisy and unfocused. The flip side of this chaotic nature of the open-source world is that there is a vast amount of information available, including tutorials, examples, and commentary and, with some focused searching, debugging and usage questions can often get answered. There is also a disconnect between the forum participants, typically computer scientists and hobbyists, and MRE educators and students. Finally, the available resources and documentation for utilizing open-source platforms in MRE education are insufficient and incomprehensive. Therefore, the main goal of this paper is to increase awareness and familiarity with the use of open-source software and hardware packages in MRE education and practice towards accelerating their adoption. To this end, open-source software packages such as Python, GNU Octave, OpenFOAM, Java, Modelica, Gazebo, SPICE, Scilab, and Gnuplot, which have the potential to be useful in the modeling and analysis of MRE systems are introduced. Furthermore, low-cost and powerful open-source hardware packages such as Arduino, Raspberry Pi, and BeagleBone which can be used as the main processing unit for data acquisition and control implementation in a wide range of MRE systems are reviewed and their limitations and potentials are investigated. This paper provides a valuable resource for MRE students and faculty who would like to utilize open-source hardware and software platforms in their education and research. 

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