The development of tools that promote active learning in engineering disciplines is critical. It is widely understood that students engaged in active learning environments outperform those taught using passive methods. Previously, we reported on the development and implementation of hands-on Low-Cost Desktop Learning Modules (LCDLMs) that replicate real-world industrial equipment which serves to create active learning environments. Thus far, miniaturized venturi meter, hydraulic loss, and double-pipe and shell & tube heat exchanger DLMs have been utilized by hundreds of students across the country. It was demonstrated that the use of DLMs in face-to-face classrooms results in statistically significant improvements inmore »
This content will become publicly available on July 26, 2022
Real Data and Application-based Interactive Modules for Data Science Education in Engineering
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, more »
- Award ID(s):
- 1933873
- Publication Date:
- NSF-PAR ID:
- 10288339
- Journal Name:
- 2021 ASEE Virtual Annual Conference
- Sponsoring Org:
- National Science Foundation
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