(549d) Interactive Modules for Teaching Hands-on Data Science in Engineering

Data science is emerging as a field that is revolutionizing the world. A 2018 National Academies report – Data Science for Undergraduates: Opportunities and Options¹ states that “Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data.” In 2019, LinkedIn ranked “data scientist” the No. 1 most promising job in the U.S. based on job openings, salary, and career advancement opportunities and reported a 56% rise in job openings for data scientists over the previous year. The Bureau of Labor Statistics considers data science in the top 20 fastest growing occupations and has projected 31% growth between 2019-2029. Due to COVID-19, new job postings in data science and analytics have declined overall, however, they appear to be declining at a slower rate than that of most other occupations. And within the finance and insurance industry, new job postings in the analytics and data science space have actually increased.² 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, the 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. Specifically, it is insufficient for undergraduate students to be handed a “canned” data set and be told to analyze it using the methods that they are studying. Such an approach will not prepare them to solve more realistic and complex problems, especially those involving large, unstructured data. Instead, students need repeated practice with the entire DSE cycle beginning with ill-posed questions and “messy” data ¹. To help fill this gap, we propose to develop data-enabled engineering project (DEEP) modules guided by the latest research on experiential learning theory (ELT). Experiential learning (EL) is the process of learning through experience, and is more specifically defined as “learning through reflection on doing”^3,4. Kolb helped to develop the modern theory of experiential learning, which focus on the learning process of individual³. In addition, course-based undergraduate research experience (CURE) is a form of experiential learning that promotes all EL components in a positive cyclical and spiral learning process. As most DSE applications are open-ended research problems and learning an entire DSE lifecycle is really a research experience, the latest research on CURE provides excellent guidance for assembling DEEP modules into research projects. In particular, a 2019 study found that short CURE modules are an excellent alternative to more complex and costly whole-course CUREs and provide measurable metacognitive benefits to students⁵. Another benefit of short CURE modules is that they can be flexibly insert into existing curricula^5,6. Therefore, we further propose to adapt the short CURE module mechanism to assemble DEEP modules into short DEEP-CUREs. The DEEP modules are developed in the forms of interactive Jupyter Notebooks using Python, R and SAS programing languages. We hypothesize that this web-based interactive development and learning environment (IDLE) will enable easy and wide adopted of the DEEP modules by other educators and institutions. The DEEP modules can also be easily ported to other platforms such as Matlab Live Scripts. In this work, we will present our ideas, the rationale behind the proposed approach, the work in progress, and the future plans for the project.

References:

1. National Academies of Sciences and Medicine, E. Data science for undergraduates: opportunities and options. (National Academies Press, 2018).

2. Camm, J., Bowers, M. & Thomas, T. The Recession’s Impact on Analytics and Data Science. MIT Sloan Management Review 1 (2020).

3. Kolb, D. Towards an applied theory of experiential learning. Theor. Gr. Process. 33–56 (1975).

4. Stonehouse, P., Allison, P. & Carr, D. Aristotle, Plato, and Socrates: Ancient Greek perspectives on experiential learning. Sourceb. Exp. Educ. Key thinkers their Contrib. 18–25 (2011).

5. Dahlberg, C. L. et al. A Short, Course-Based Research Module Provides Metacognitive Benefits in the Form of More Sophisticated Problem Solving. J. Coll. Sci. Teach. 48, (2019).

6. Howard, D. R. & Miskowski, J. A. Using a module-based laboratory to incorporate inquiry into a large cell biology course. Cell Biol. Educ. 4, 249–260 (2005).