Need: Data science is emerging as a field that is revolutionizing the world. The ever-increasing computing power, the exponential growth of data, and the desires of various industries and institutions to better use the data for informed decisions and optimal business outcomes, have been widely considered the reasons for the increasing demand of talents in data science and data analytics. To meet this increasing demand of data scientists and engineers, a National Academies report has recognized that undergraduate education offers a critical link in providing more data science and engineering (DSE) exposure to students and expanding the supply of DSE talents. 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.
Guiding Questions: Since there is a gap in “providing hands-on experience with real data and real applications” in DSE, while experiential learning theory (ELT) promoting “learning through experience”, how can ELT guide the design and development of such learning materials?
Outcomes: We have been developing data-enabled engineering project (DEEP) modules guided by the latest research on experiential learning theory (ELT). In addition, course-based undergraduate research experience (CURE) is a form of experiential learning, and the latest research on CURE provides excellent guidance for assembling DEEP modules into research projects. Currently, the DEEP modules are developed in the forms of interactive Matlab Live Scripts and Jupyter Notebooks. We hypothesize that these interactive development and learning environments (IDLE) will enable easy and wide adopted of the DEEP modules by other educators and institutions. We have started testing some of the DEEP modules in two courses in Chemical Engineering and Electrical and Computer Engineering at Auburn University in the Spring of 2022. We plan to expand the test to four courses to be offered by four engineering disciplines in the Fall of 2022. The effectiveness of these modules in students learning will be assessed using the Metacognition Awareness Inventory (MAI) questionnaire to quantify students’ metacognition awareness gains. The scores will be analyzed and compared categorically and holistically. After testing, we will make DEEP modules publicly available through different channels.
Broader Impacts: This project helps address a very important national need – preparing workforce talents with required data-skills to meet the demand of the current and future job market, which contribute to the NSF goal of “development of a diverse, globally competitive STEM workforce” and “increase economic competitiveness of the U.S.”. If successful, this project will serve as a model for other researchers to contribute to DEEPs development based on real data and applications from their lab experiments, research projects, and/or industrial projects. This future nationwide effort will truly amplify the impact of DEEPs in DSE education.
Anna Hartwig, Shiwen Mao, Jin Wang, Peter He, Auburn University, Auburn, AL